Econstudentlog

Links and random stuff

i. Pulmonary Aspects of Exercise and Sports.

“Although the lungs are a critical component of exercise performance, their response to exercise and other environmental stresses is often overlooked when evaluating pulmonary performance during high workloads. Exercise can produce capillary leakage, particularly when left atrial pressure increases related to left ventricular (LV) systolic or diastolic failure. Diastolic LV dysfunction that results in elevated left atrial pressure during exercise is particularly likely to result in pulmonary edema and capillary hemorrhage. Data from race horses, endurance athletes, and triathletes support the concept that the lungs can react to exercise and immersion stress with pulmonary edema and pulmonary hemorrhage. Immersion in water by swimmers and divers can also increase stress on pulmonary capillaries and result in pulmonary edema.”

“Zavorsksy et al. studied individuals under several different workloads and performed lung imaging to document the presence or absence of lung edema. Radiographic image readers were blinded to the exposures and reported visual evidence of lung fluid. In individuals undergoing a diagnostic graded exercise test, no evidence of lung edema was noted. However, 15% of individuals who ran on a treadmill at 70% of maximum capacity for 2 hours demonstrated evidence of pulmonary edema, as did 65% of those who ran at maximum capacity for 7 minutes. Similar findings were noted in female athletes. Pingitore et al. examined 48 athletes before and after completing an iron man triathlon. They used ultrasound to detect lung edema and reported the incidence of ultrasound lung comets. None of the athletes had evidence of lung edema before the event, while 75% showed evidence of pulmonary edema immediately post-race, and 42% had persistent findings of pulmonary edema 12 hours post-race. Their data and several case reports have demonstrated that extreme exercise can result in pulmonary edema”

Conclusions

Sports and recreational participation can result in lung injury caused by high pulmonary pressures and increased blood volume that raises intracapillary pressure and results in capillary rupture with subsequent pulmonary edema and hemorrhage. High-intensity exercise can result in accumulation of pulmonary fluid and evidence of pulmonary edema. Competitive swimming can result in both pulmonary edema related to fluid shifts into the thorax from immersion and elevated LV end diastolic pressure related to diastolic dysfunction, particularly in the presence of high-intensity exercise. […] The most important approach to many of these disorders is prevention. […] Prevention strategies include avoiding extreme exercise, avoiding over hydration, and assuring that inspiratory resistance is minimized.”

ii. Some interesting thoughts on journalism and journalists from a recent SSC Open Thread by user ‘Well’ (quotes from multiple comments). His/her thoughts seem to line up well with my own views on these topics, and one of the reasons why I don’t follow the news is that my own answer to the first question posed below is quite briefly that, ‘…well, I don’t’:

“I think a more fundamental problem is the irrational expectation that newsmedia are supposed to be a reliable source of information in the first place. Why do we grant them this make-believe power?

The English and Acting majors who got together to put on the shows in which they pose as disinterested arbiters of truth use lots of smoke and mirror techniques to appear authoritative: they open their programs with regal fanfare, they wear fancy suits, they make sure to talk or write in a way that mimics the disinterestedness of scholarly expertise, they appear with spinning globes or dozens of screens behind them as if they’re omniscient, they adorn their publications in fancy black-letter typefaces and give them names like “Sentinel” and “Observer” and “Inquirer” and “Plain Dealer”, they invented for themselves the title of “journalists” as if they take part in some kind of peer review process… But why do these silly tricks work? […] what makes the press “the press” is the little game of make-believe we play where an English or Acting major puts on a suit, talks with a funny cadence in his voice, sits in a movie set that looks like God’s Control Room, or writes in a certain format, using pseudo-academic language and symbols, and calls himself a “journalist” and we all pretend this person is somehow qualified to tell us what is going on in the world.

Even when the “journalist” is saying things we agree with, why do we participate in this ridiculous charade? […] I’m not against punditry or people putting together a platform to talk about things that happen. I’m against people with few skills other than “good storyteller” or “good writer” doing this while painting themselves as “can be trusted to tell you everything you need to know about anything”. […] Inasumuch as what I’m doing can be called “defending” them, I’d “defend” them not because they are providing us with valuable facts (ha!) but because they don’t owe us facts, or anything coherent, in the first place. It’s not like they’re some kind of official facts-providing service. They just put on clothes to look like one.”

iii. Chatham house rule.

iv. Sex Determination: Why So Many Ways of Doing It?

“Sexual reproduction is an ancient feature of life on earth, and the familiar X and Y chromosomes in humans and other model species have led to the impression that sex determination mechanisms are old and conserved. In fact, males and females are determined by diverse mechanisms that evolve rapidly in many taxa. Yet this diversity in primary sex-determining signals is coupled with conserved molecular pathways that trigger male or female development. Conflicting selection on different parts of the genome and on the two sexes may drive many of these transitions, but few systems with rapid turnover of sex determination mechanisms have been rigorously studied. Here we survey our current understanding of how and why sex determination evolves in animals and plants and identify important gaps in our knowledge that present exciting research opportunities to characterize the evolutionary forces and molecular pathways underlying the evolution of sex determination.”

v. So Good They Can’t Ignore You.

“Cal Newport’s 2012 book So Good They Can’t Ignore You is a career strategy book designed around four ideas.

The first idea is that ‘follow your passion’ is terrible career advice, and people who say this should be shot don’t know what they’re talking about. […] The second idea is that instead of believing in the passion hypothesis, you should adopt what Newport calls the ‘craftsman mindset’. The craftsman mindset is that you should focus on gaining rare and valuable skills, since this is what leads to good career outcomes.

The third idea is that autonomy is the most important component of a ‘dream’ job. Newport argues that when choosing between two jobs, there are compelling reasons to ‘always’ pick the one with higher autonomy over the one with lower autonomy.

The fourth idea is that having a ‘mission’ or a ‘higher purpose’ in your job is probably a good idea, and is really nice if you can find it. […] the book structure is basically: ‘following your passion is bad, instead go for Mastery[,] Autonomy and Purpose — the trio of things that have been proven to motivate knowledge workers’.” […]

“Newport argues that applying deliberate practice to your chosen skill market is your best shot at becoming ‘so good they can’t ignore you’. The key is to stretch — you want to practice skills that are just above your current skill level, so that you experience discomfort — but not too much discomfort that you’ll give up.” […]

“Newport thinks that if your job has one or more of the following qualities, you should leave your job in favour of another where you can build career capital:

  • Your job presents few opportunities to distinguish yourself by developing relevant skills that are rare and valuable.
  • Your job focuses on something you think is useless or perhaps even actively bad for the world.
  • Your job forces you to work with people you really dislike.

If you’re in a job with any of these traits, your ability to gain rare and valuable skills would be hampered. So it’s best to get out.”

vi. Structural brain imaging correlates of general intelligence in UK Biobank.

“The association between brain volume and intelligence has been one of the most regularly-studied—though still controversial—questions in cognitive neuroscience research. The conclusion of multiple previous meta-analyses is that the relation between these two quantities is positive and highly replicable, though modest (Gignac & Bates, 2017; McDaniel, 2005; Pietschnig, Penke, Wicherts, Zeiler, & Voracek, 2015), yet its magnitude remains the subject of debate. The most recent meta-analysis, which included a total sample size of 8036 participants with measures of both brain volume and intelligence, estimated the correlation at r = 0.24 (Pietschnig et al., 2015). A more recent re-analysis of the meta-analytic data, only including healthy adult samples (N = 1758), found a correlation of r = 0.31 (Gignac & Bates, 2017). Furthermore, the correlation increased as a function of intelligence measurement quality: studies with better-quality intelligence tests—for instance, those including multiple measures and a longer testing time—tended to produce even higher correlations with brain volume (up to 0.39). […] Here, we report an analysis of data from a large, single sample with high-quality MRI measurements and four diverse cognitive tests. […] We judge that the large N, study homogeneity, and diversity of cognitive tests relative to previous large scale analyses provides important new evidence on the size of the brain structure-intelligence correlation. By investigating the relations between general intelligence and characteristics of many specific regions and subregions of the brain in this large single sample, we substantially exceed the scope of previous meta-analytic work in this area. […]

“We used a large sample from UK Biobank (N = 29,004, age range = 44–81 years). […] This preregistered study provides a large single sample analysis of the global and regional brain correlates of a latent factor of general intelligence. Our study design avoids issues of publication bias and inconsistent cognitive measurement to which meta-analyses are susceptible, and also provides a latent measure of intelligence which compares favourably with previous single-indicator studies of this type. We estimate the correlation between total brain volume and intelligence to be r = 0.276, which applies to both males and females. Multiple global tissue measures account for around double the variance in g in older participants, relative to those in middle age. Finally, we find that associations with intelligence were strongest in frontal, insula, anterior and medial temporal, lateral occipital and paracingulate cortices, alongside subcortical volumes (especially the thalamus) and the microstructure of the thalamic radiations, association pathways and forceps minor.”

vii. Another IQ study: Low IQ as a predictor of unsuccessful educational and occupational achievement: A register-based study of 1,098,742 men in Denmark 1968–2016.

“Intelligence test score is a well-established predictor of educational and occupational achievement worldwide […]. Longitudinal studies typically report cor-relation coefficients of 0.5–0.6 between intelligence and educational achievement as assessed by educational level or school grades […], correlation coefficients of 0.4–0.5 between intelligence and occupational level […] and cor-relation coefficients of 0.2–0.4 between intelligence and income […]. Although the above-mentioned associations are well-established, low intelligence still seems to be an overlooked problem among young people struggling to complete an education or gain a foothold in the labour market […] Due to contextual differences with regard to educational system and flexibility and security on the labour market as well as educational and labour market policies, the role of intelligence in predicting unsuccessful educational and occupational courses may vary among countries. As Denmark has free admittance to education at all levels, state financed student grants for all students, and a relatively high support of students with special educational needs, intelligence might be expected to play a larger role – as socioeconomic factors might be of less importance – with regard to educational and occupational achievement compared with countries outside Scandinavia. The aim of this study was therefore to investigate the role of IQ in predicting a wide range of indicators of unsuccessful educational and occupational achievement among young people born across five decades in Denmark.”

“Individuals who differed in IQ score were found to differ with regard to all indicators of unsuccessful educational and occupational achievement such that low IQ was associated with a higher proportion of unsuccessful educational and occupational achievement. For example, among the 12.1% of our study population who left lower secondary school without receiving a certificate, 39.7% had an IQ < 80 and 23.1% had an IQ of 80–89, although these individuals only accounted for 7.8% and 13.1% of the total study population. The main analyses showed that IQ was inversely associated with all indicators of unsuccessful educational and occupational achievement in young adulthood after adjustment for covariates […] With regard to unsuccessful educational achievement, […] the probabilities of no school leaving certificate, no youth education at age 25, and no vocational qualification at age 30 decreased with increasing IQ in a cubic relation, suggesting essentially no or only weak associations at superior IQ levels. IQ had the strongest influence on the probability of no school leaving certificate. Although the probabilities of the three outcome indicators were almost the same among individuals with extremely low IQ, the probability of no school leaving certificate approached zero among individuals with an IQ of 100 or above whereas the probabilities of no youth education at age 25 and no vocational qualification at age 30 remained notably higher. […] individuals with an IQ of 70 had a median gross income of 301,347 DKK, individuals with an IQ of 100 had a median gross income of 331,854, and individuals with an IQ of 130 had a median gross income of 363,089 DKK – in the beginning of June 2018 corresponding to about 47,856 USD, 52,701 USD, and 57,662 USD, respectively. […] The results showed that among individuals undergoing education, low IQ was associated with a higher hazard rate of passing to employment, unemployment, sickness benefits receipt and welfare benefits receipt […]. This indicates that individuals with low IQ tend to leave the educational system to find employment at a younger age than individuals with high IQ, but that this early leave from the educational system often is associated with a transition into unemployment, sickness benefits receipt and welfare benefits receipt.”

Fig 1

Conclusions
This study of 1,098,742 Danish men followed in national registers from 1968 to 2016 found that low IQ was a strong and consistent predictor of 10 indicators of unsuccessful educational and occupational achievement in young adulthood. Overall, it seemed that IQ had the strongest influence on the risk of unsuccessful educational achievement and on the risk of disability pension, and that the influence of IQ on educational achievement was strongest in the early educational career and decreased over time. At the community level our findings suggest that intelligence should be considered when planning interventions to reduce the rates of early school leaving and the unemployment rates and at the individual level our findings suggest that assessment of intelligence may provide crucial information for the counselling of poor-functioning schoolchildren and adolescents with regard to both the immediate educational goals and the more distant work-related future.”

September 15, 2019 Posted by | Biology, IQ, Medicine, Psychology, Studies | Leave a comment

Learning Phylogeny Through Simple Statistical Genetics

From a brief skim I concluded that a lot of the stuff Patterson talks about in this lecture, particularly in terms of the concepts and methods part (…which, as he also alludes to in his introduction, makes up a substantial proportion of the talk), is included/covered in this Ancient Admixture in Human History paper he coauthored, so if you’re either curious to know more, or perhaps just wondering what the talk might be about, it’s probably worth checking it out. In the latter case I would also recommend perhaps just watching the first few minutes of the talk; he provides a very informative outline of the talk in the first four and a half minutes of the video.

A few other links of relevance:

Martingale (probability theory).
GitHub – DReichLab/AdmixTools.
Human Genome Diversity Project.
Jackknife resampling.
Ancient North Eurasian.
Upper Palaeolithic Siberian genome reveals dual ancestry of Native Americans (Raghavan et al, 2014).
General theory for stochastic admixture graphs and F-statistics. This one is only very slightly related to the talk; I came across it while looking for stuff about admixture graphs, a topic he does briefly discuss in the lecture.

July 29, 2019 Posted by | Archaeology, Biology, Genetics, Lectures, Molecular biology, Statistics | Leave a comment

Viruses

This book is not great, but it’s also not bad – I ended up giving it three stars on goodreads, being much closer to 2 stars than 4. It’s a decent introduction to the field of virology, but not more than that. Below some quotes and links related to the book’s coverage.

“[I]t was not until the invention of the electron microscope in 1939 that viruses were first visualized and their structure elucidated, showing them to be a unique class of microbes. Viruses are not cells but particles. They consist of a protein coat which surrounds and protects their genetic material, or, as the famous immunologist Sir Peter Medawar (1915–87) termed it, ‘a piece of bad news wrapped up in protein’. The whole structure is called a virion and the outer coat is called the capsid. Capsids come in various shapes and sizes, each characteristic of the virus family to which it belongs. They are built up of protein subunits called capsomeres and it is the arrangement of these around the central genetic material that determines the shape of the virion. For example, pox viruses are brick-shaped, herpes viruses are icosahedral (twenty-sided spheres), the rabies virus is bullet-shaped, and the tobacco mosaic virus is long and thin like a rod […]. Some viruses have an outer layer surrounding the capsid called an envelope. […] Most viruses are too small to be seen under a light microscope. In general, they are around 100 to 500 times smaller than bacteria, varying in size from 20 to 300 nanometres in diameter […] Inside the virus capsid is its genetic material, or genome, which is either RNA or DNA depending on the type of virus […] Viruses usually have between 4 and 200 genes […] Cells of free-living organisms, including bacteria, contain a variety of organelles essential for life such as ribosomes that manufacture proteins, mitochondria, or other structures that generate energy, and complex membranes for transporting molecules within the cell, and also across the cell wall. Viruses, not being cells, have none of these and are therefore inert until they infect a living cell. Then they hijack a cell’s organelles and use what they need, often killing the cell in the process. Thus viruses are obliged to obtain essential components from other living things to complete their life cycle and are therefore called obligate parasites.”

“Plant viruses either enter cells through a break in the cell wall or are injected by a sap-sucking insect vector like aphids. They then spread very efficiently from cell to cell via plasmodesmata, pores that transport molecules between cells. In contrast, animal viruses infect cells by binding to specific cell surface receptor molecules. […] Once a virus has bound to its cellular receptor, the capsid penetrates the cell and its genome (DNA or RNA) is released into the cell cytoplasm. The main ‘aim’ of a virus is to reproduce successfully, and to do this its genetic material must download the information it carries. Mostly, this will take place in the cell’s nucleus where the virus can access the molecules it needs to begin manufacturing its own proteins. Some large viruses, like pox viruses, carry genes for the enzymes they need to make their proteins and so are more self-sufficient and can complete the whole life cycle in the cytoplasm. Once inside a cell, DNA viruses simply masquerade as pieces of cellular DNA, and their genes are transcribed and translated using as much of the cell’s machinery as they require. […] Because viruses have a high mutation rate, significant evolutionary change, estimated at around 1 per cent per year for HIV, can be measured over a short timescale. […] RNA viruses have no proof-reading system so they have a higher mutation rate than DNA viruses. […] By constantly evolving, […] viruses appear to have honed their skills for spreading from one host to another to reach an amazing degree of sophistication. For instance, the common cold virus (rhinovirus), while infecting cells lining the nasal cavities, tickles nerve endings to cause sneezing. During these ‘explosions’, huge clouds of virus-carrying mucus droplets are forcefully ejected, then float in the air until inhaled by other susceptible hosts. Similarly, by wiping out sheets of cells lining the intestine, rotavirus prevents the absorption of fluids from the gut cavity. This causes severe diarrhea and vomiting that effectively extrudes the virus’s offspring back into the environment to reach new hosts. Other highly successful viruses hitch a ride from one host to another with insects. […] As a virus’s generation time is so much shorter than ours, the evolution of genetic resistance to a new human virus is painfully slow, and constantly leaves viruses with the advantage.”

“The phytoplankton is a group of organisms that uses solar energy and carbon dioxide to generate energy by photosynthesis. As a by-product of this reaction, they produce almost half of the world’s oxygen and are therefore of vital importance to the chemical stability of the planet. Phytoplankton forms the base of the whole marine food-web, being grazed upon by zooplankton and young marine animals which in turn fall prey to fish and higher marine carnivores. By infecting and killing plankton microbes, marine viruses control the dynamics of all these essential populations and their interactions. For example, the common and rather beautiful phytoplankton Emiliania huxleyi regularly undergoes blooms that turn the ocean surface an opaque blue over areas so vast that they can be detected from space by satellites. These blooms disappear as quickly as they arise, and this boom-and-bust cycle is orchestrated by the viruses in the community that specifically infect E. huxleyi. Because they can produce thousands of offspring from every infected cell, virus numbers amplify in a matter of hours and so act as a rapid-response team, killing most of the bloom microbes in just a few days. […] Overall, marine viruses kill an estimated 20-40 per cent of marine bacteria every day, and as the major killer of marine microbes, they profoundly affect the carbon cycle by the so-called ‘viral shunt‘.”

“By the end of 2015 WHO reported 36.7 million people living with HIV globally, 70 per cent of whom are in sub-Saharan Africa. Since the first identification of HIV-induced acquired immunodeficiency syndrome (AIDS) approximately 78 million people have been infected with HIV, causing around 35 million deaths […] Antiviral drugs are key in curtailing HIV spread and are being rolled out worldwide, with present coverage of around 46 per cent of those in need. […] The HIVs are most closely related to primate retroviruses called simian immunodeficiency viruses (SIVs) and it is now clear that these HIV-like viruses have jumped from primates to humans in central Africa on several occasions in the past giving rise to human infections with HIV-1 types M, N, O, and P as well as HIV-2. Yet only one of these viruses, HIV-1 type M, has succeeded in spreading globally. The ancestor of this virus has been traced to a subspecies of chimpanzees (Pan troglodytes troglodytes), among whom it can cause an AIDS-like disease. Since these animals are hunted for bush meat, it is most likely that human infection occurred by blood contamination during the killing and butchering process. This event probably took place in south-east Cameroon where chimpanzees carrying an SIV most closely related to HIV-1 type M live.”

Flu viruses are paramyxoviruses with an RNA genome with eight genes that are segmented, meaning that instead of being a continuous RNA chain, each gene forms a separate strand. The H (haemaglutinin) and N (neuraminidase) genes are the most important in stimulating protective host immunity. There are sixteen different H and nine different N genes, all of which can be found in all combinations in bird flu viruses. Because these genes are separate RNA strands, on occasions they become mixed up, or recombined. So if two flu A viruses with different H and/or N genes infect a single cell, the offspring will carry varying combinations of genes from the two parent viruses. Most of these viruses will be unable to infect humans, but occasionally a new virus strain is produced that can jump directly to humans and cause a pandemic. […] The emergence of almost all recent novel flu viruses has been traced to China where they circulate freely among animals kept in cramped conditions in farms and live bird markets. […] once established in humans their spread has been much enhanced by travel, particularly air travel that can take a virus inside a traveller across the globe before they even realize they are infected. […] With over a billion people worldwide boarding international flights every year, novel viruses have an efficient mechanism for rapid spread.”

“Once an acute emerging virus such as a new strain of flu is successfully established in a population, it generally settles into a mode of cyclical epidemics during which many susceptible people are infected and become immune to further attack. When most are immune, the virus moves on, only returning when a new susceptible population has emerged, which generally consists of those born since the last epidemic. Before vaccination programmes became widespread, young children suffered from a series of well-recognized infectious diseases called the ‘childhood infections’. These included measles, mumps, rubella, and chickenpox, all caused by viruses […] following the introduction of vaccine programmes these have become a rarity, particularly in the developed world. […] Of the three viruses, measles is the most infectious and produces the severest disease. It killed millions of children each year before vaccination was introduced in the mid-20th century. Even today, this virus kills over 70,000 children annually in countries with low vaccine coverage. […] In developing countries, measles kills 1-5 per cent of those it infects”.

Smallpox virus is in a class of its own as the world’s worst killer virus. It first infected humans at least 5,000 years ago and killed around 300 million in the 20th century alone. The virus killed up to 30 per cent of those it infected, scarring and blinding many of the survivors. […] Worldwide, eradication of smallpox was declared in 1980.”

“Viruses spread between hosts in many different ways, but those that cause acute epidemics generally utilize fast and efficient methods, such as the airborne or faecal-oral routes. […] Broadly speaking, virus infections are distinguished by the organs they affect, with airborne viruses mainly causing respiratory illnesses, […] and those transmitted by faecal-oral contamination causing intestinal upsets, with nausea, vomiting, and diarrhoea. There are literally thousands of viruses capable of causing human epidemics […] worldwide, acute respiratory infections, mostly viral, cause an estimated four million deaths a year in children under 5. […] Most people get two or three colds a year, suggesting that the immune system, which is so good at protecting us against a second attack of measles, mumps, or rubella, is defeated by the common cold virus. But this is not the case. In fact, there are so many viruses that cause the typical symptoms of blocked nose, headache, malaise, sore throat, sneezing, coughing, and sometimes fever, that even if we live for a hundred years, we will not experience them all. The common cold virus, or rhinovirus, alone has over one hundred different types, and there are many other viruses that infect the cells lining the nose and throat and cause similar symptoms, often with subtle variations. […] Viruses that target the gut are just as diverse as respiratory viruses […] Rotaviruses are a major cause of gastroenteritis globally, particularly targeting children under 5. The disease varies in severity […] rotaviruses cause over 600,000 infant deaths a year worldwide […] Noroviruses are the second most common cause of viral gastroenteritis after rotaviruses, producing a milder disease of shorter duration. These viruses account for around 23 million cases of gastroenteritis every year […] Many virus families such as rotaviruses that rely on faecal-oral transmission and cause gastroenteritis in humans produce the same symptoms in animals, resulting in great economic loss to the farming industry. […] over the centuries, Rinderpest virus, the cause of cattle plague, has probably been responsible for more loss and hardship than any other. […] Rinderpest is classically described by the three Ds: discharge, diarrhoea, and death, the latter being caused by fluid loss with rapid dehydration. The disease kills around 90 per cent of animals infected. Rinderpest used to be a major problem in Europe and Asia, and when it was introduced into Africa in the late 19th century it killed over 90 per cent of cattle, with devastating economic loss. The Global Rinderpest Eradication Programme was set up in the 1980s aiming to use the effective vaccine to rid the world of the virus by 2010. This was successful, and in October 2010 the disease was officially declared eradicated, the first animal disease and second infectious disease ever to be eliminated.”

“At present, 1.8 million virus-associated cancers are diagnosed worldwide annually. This accounts for 18 per cent of all cancers, but since these human tumour viruses were only identified fairly recently, it is probable that there are several more out there waiting to be discovered. […] Primary liver cancer is a major global health problem, being one of the ten most common cancers worldwide, with over 250,000 cases diagnosed every year and only 5 per cent of sufferers surviving five years. The tumour is more common in men than women and is most prevalent in sub-Saharan Africa and South East Asia where the incidence reaches over 30 per 100,000 population per year, compared to fewer than 5 per 100,000 in the USA and Europe. Up to 80 per cent of these tumours are caused by a hepatitis virus, the remainder being related to liver damage from toxic agents such as alcohol. […] hepatitis B and C viruses cause liver cancer. […] a large study carried out on 22,000 men in Taiwan in the 1990s showed that those persistently infected with HBV were over 200 times more likely than non-carriers to develop liver cancer, and that over half the deaths in this group were due to liver cancer or cirrhosis. […] A vaccine against HBV is available, and its use has already caused a decline in HBV-related liver cancer in Taiwan, where a vaccination programme was implemented in the 1980s”.

“Most persistent viruses have evolved to cause mild or even asymptomatic infections, since a life-threatening disease would not only be detrimental to the host but also deprive the virus of its home. Indeed, some viruses apparently cause no ill effects at all, and have been discovered only by chance. One example is TTV, a tiny DNA virus found in 1997 during the search for the cause of hepatitis and named after the initials (TT) of the patient from whom it was first isolated. We now know that TTV, and its relative TTV-like mini virus, represent a whole spectrum of similar viruses that are carried by almost all humans, non-human primates, and a variety of other vertebrates, but so far they have not been associated with any disease. With modern, highly sensitive molecular techniques for identifying non-pathogenic viruses, we can expect to find more of these silent passengers in the future. […] Historically, diagnosis and treatment of virus infections have lagged far behind those of bacterial diseases and are only now catching up. […] Diagnostic laboratories are still unable to find a culprit virus in many so-called ‘viral’ meningitis, encephalitis, and respiratory infections. This strongly suggests that there are many pathogenic viruses waiting to be discovered”.

“There is no doubt that although vaccines are expensive to prepare and test, they are the safest, easiest, and most cost-effective way of controlling infectious diseases worldwide.”

Virology. Virus. RNA virus. DNA virus. Retrovirus. Reverse transcriptase. Integrase. Provirus.
Germ theory of disease.
Antonie van Leeuwenhoek. Louis Pasteur. Robert Koch. Adolf Mayer. Dmitri Ivanovsky. Martinus Beijerinck.
Tobacco mosaic virus.
Mimivirus.
Viral evolution – origins.
White spot syndrome.
Fibropapillomatosis.
Acyrthosiphon pisum.
Vibrio_cholerae#Genome (Vibrio cholerae are bacteria, but viruses play a very important role here regarding the toxin-producing genes – “Only cholera bacteria infected with the toxigenic phage are pathogenic to humans”).
Yellow fever.
Dengue fever.
CCR5.
Immune system. Cytokine. Interferon. Macrophage. Lymphocyte. Antigen. CD4++ T cells. CD8+ T-cell. Antibody. Regulatory T cell. Autoimmunity.
Zoonoses.
Arbovirus. Coronavirus. SARS-CoV. MERS-CoV. Ebolavirus. Henipavirus. Influenza virus. H5N1. HPAI. H7N9. Foot-and-mouth disease. Monkeypox virus. Chikungunya virus. Schmallenberg virus. Zika virus. Rift valley fever. Bluetongue disease. Arthrogryposis. West Nile fever. Chickenpox. Polio. Bocavirus.
Sylvatic cycle.
Nosocomial infections.
Subacute sclerosing panencephalitis.
Herpesviridae. CMV. Herpes simplex virus. Epstein–Barr virus. Human herpesvirus 6. Human betaherpesvirus 7. Kaposi’s sarcoma-associated herpesvirus (KSHV). Varicella-zoster virus (VZV). Infectious mononucleosis. Hepatitis. Rous sarcoma virus. Human T-lymphotropic virus. Adult t cell leukemia. HPV. Cervical cancer.
Oncovirus. Myc.
Variolation. Edward Jenner. Mary Wortley Montagu. Benjamin Jesty. James Phipps. Joseph Meister. Jonas Salk. Albert Sabin.
Marek’s disease. Rabies. Post-exposure prophylaxis.
Vaccine.
Aciclovir. Oseltamivir.
PCR.

 

June 10, 2019 Posted by | Biology, Books, Cancer/oncology, Immunology, Infectious disease, Medicine, Microbiology, Molecular biology | Leave a comment

Random stuff

i. Your Care Home in 120 Seconds. Some quotes:

“In order to get an overall estimate of mental power, psychologists have chosen a series of tasks to represent some of the basic elements of problem solving. The selection is based on looking at the sorts of problems people have to solve in everyday life, with particular attention to learning at school and then taking up occupations with varying intellectual demands. Those tasks vary somewhat, though they have a core in common.

Most tests include Vocabulary, examples: either asking for the definition of words of increasing rarity; or the names of pictured objects or activities; or the synonyms or antonyms of words.

Most tests include Reasoning, examples: either determining which pattern best completes the missing cell in a matrix (like Raven’s Matrices); or putting in the word which completes a sequence; or finding the odd word out in a series.

Most tests include visualization of shapes, examples: determining the correspondence between a 3-D figure and alternative 2-D figures; determining the pattern of holes that would result from a sequence of folds and a punch through folded paper; determining which combinations of shapes are needed to fill a larger shape.

Most tests include episodic memory, examples: number of idea units recalled across two or three stories; number of words recalled from across 1 to 4 trials of a repeated word list; number of words recalled when presented with a stimulus term in a paired-associate learning task.

Most tests include a rather simple set of basic tasks called Processing Skills. They are rather humdrum activities, like checking for errors, applying simple codes, and checking for similarities or differences in word strings or line patterns. They may seem low grade, but they are necessary when we try to organise ourselves to carry out planned activities. They tend to decline with age, leading to patchy, unreliable performance, and a tendency to muddled and even harmful errors. […]

A brain scan, for all its apparent precision, is not a direct measure of actual performance. Currently, scans are not as accurate in predicting behaviour as is a simple test of behaviour. This is a simple but crucial point: so long as you are willing to conduct actual tests, you can get a good understanding of a person’s capacities even on a very brief examination of their performance. […] There are several tests which have the benefit of being quick to administer and powerful in their predictions.[..] All these tests are good at picking up illness related cognitive changes, as in diabetes. (Intelligence testing is rarely criticized when used in medical settings). Delayed memory and working memory are both affected during diabetic crises. Digit Symbol is reduced during hypoglycaemia, as are Digits Backwards. Digit Symbol is very good at showing general cognitive changes from age 70 to 76. Again, although this is a limited time period in the elderly, the decline in speed is a notable feature. […]

The most robust and consistent predictor of cognitive change within old age, even after control for all the other variables, was the presence of the APOE e4 allele. APOE e4 carriers showed over half a standard deviation more general cognitive decline compared to noncarriers, with particularly pronounced decline in their Speed and numerically smaller, but still significant, declines in their verbal memory.

It is rare to have a big effect from one gene. Few people carry it, and it is not good to have.

ii. What are common mistakes junior data scientists make?

Apparently the OP had second thoughts about this query so s/he deleted the question and marked the thread nsfw (??? …nothing remotely nsfw in that thread…). Fortunately the replies are all still there, there are quite a few good responses in the thread. I added some examples below:

“I think underestimating the domain/business side of things and focusing too much on tools and methodology. As a fairly new data scientist myself, I found myself humbled during this one project where I had I spent a lot of time tweaking parameters and making sure the numbers worked just right. After going into a meeting about it became clear pretty quickly that my little micro-optimizations were hardly important, and instead there were X Y Z big picture considerations I was missing in my analysis.”

[…]

  • Forgetting to check how actionable the model (or features) are. It doesn’t matter if you have amazing model for cancer prediction, if it’s based on features from tests performed as part of the post-mortem. Similarly, predicting account fraud after the money has been transferred is not going to be very useful.

  • Emphasis on lack of understanding of the business/domain.

  • Lack of communication and presentation of the impact. If improving your model (which is a quarter of the overall pipeline) by 10% in reducing customer churn is worth just ~100K a year, then it may not be worth putting into production in a large company.

  • Underestimating how hard it is to productionize models. This includes acting on the models outputs, it’s not just “run model, get score out per sample”.

  • Forgetting about model and feature decay over time, concept drift.

  • Underestimating the amount of time for data cleaning.

  • Thinking that data cleaning errors will be complicated.

  • Thinking that data cleaning will be simple to automate.

  • Thinking that automation is always better than heuristics from domain experts.

  • Focusing on modelling at the expense of [everything] else”

“unhealthy attachments to tools. It really doesn’t matter if you use R, Python, SAS or Excel, did you solve the problem?”

“Starting with actual modelling way too soon: you’ll end up with a model that’s really good at answering the wrong question.
First, make sure that you’re trying to answer the right question, with the right considerations. This is typically not what the client initially told you. It’s (mainly) a data scientist’s job to help the client with formulating the right question.”

iii. Some random wikipedia links: Ottoman–Habsburg wars. Planetshine. Anticipation (genetics). Cloze test. Loop quantum gravity. Implicature. Starfish Prime. Stall (fluid dynamics). White Australia policy. Apostatic selection. Deimatic behaviour. Anti-predator adaptation. Lefschetz fixed-point theorem. Hairy ball theorem. Macedonia naming dispute. Holevo’s theorem. Holmström’s theorem. Sparse matrix. Binary search algorithm. Battle of the Bismarck Sea.

iv. 5-HTTLPR: A Pointed Review. This one is hard to quote, you should read all of it. I did however decide to add a few quotes from the post, as well as a few quotes from the comments:

“…what bothers me isn’t just that people said 5-HTTLPR mattered and it didn’t. It’s that we built whole imaginary edifices, whole castles in the air on top of this idea of 5-HTTLPR mattering. We “figured out” how 5-HTTLPR exerted its effects, what parts of the brain it was active in, what sorts of things it interacted with, how its effects were enhanced or suppressed by the effects of other imaginary depression genes. This isn’t just an explorer coming back from the Orient and claiming there are unicorns there. It’s the explorer describing the life cycle of unicorns, what unicorns eat, all the different subspecies of unicorn, which cuts of unicorn meat are tastiest, and a blow-by-blow account of a wrestling match between unicorns and Bigfoot.

This is why I start worrying when people talk about how maybe the replication crisis is overblown because sometimes experiments will go differently in different contexts. The problem isn’t just that sometimes an effect exists in a cold room but not in a hot room. The problem is more like “you can get an entire field with hundreds of studies analyzing the behavior of something that doesn’t exist”. There is no amount of context-sensitivity that can help this. […] The problem is that the studies came out positive when they shouldn’t have. This was a perfectly fine thing to study before we understood genetics well, but the whole point of studying is that, once you have done 450 studies on something, you should end up with more knowledge than you started with. In this case we ended up with less. […] I think we should take a second to remember that yes, this is really bad. That this is a rare case where methodological improvements allowed a conclusive test of a popular hypothesis, and it failed badly. How many other cases like this are there, where there’s no geneticist with a 600,000 person sample size to check if it’s true or not? How many of our scientific edifices are built on air? How many useless products are out there under the guise of good science? We still don’t know.”

A few more quotes from the comment section of the post:

“most things that are obviously advantageous or deleterious in a major way aren’t gonna hover at 10%/50%/70% allele frequency.

Population variance where they claim some gene found in > [non trivial]% of the population does something big… I’ll mostly tend to roll to disbelieve.

But if someone claims a family/village with a load of weirdly depressed people (or almost any other disorder affecting anything related to the human condition in any horrifying way you can imagine) are depressed because of a genetic quirk… believable but still make sure they’ve confirmed it segregates with the condition or they’ve got decent backing.

And a large fraction of people have some kind of rare disorder […]. Long tail. Lots of disorders so quite a lot of people with something odd.

It’s not that single variants can’t have a big effect. It’s that really big effects either win and spread to everyone or lose and end up carried by a tiny minority of families where it hasn’t had time to die out yet.

Very few variants with big effect sizes are going to be half way through that process at any given time.

Exceptions are

1: mutations that confer resistance to some disease as a tradeoff for something else […] 2: Genes that confer a big advantage against something that’s only a very recent issue.”

“I think the summary could be something like:
A single gene determining 50% of the variance in any complex trait is inherently atypical, because variance depends on the population plus environment and the selection for such a gene would be strong, rapidly reducing that variance.
However, if the environment has recently changed or is highly variable, or there is a trade-off against adverse effects it is more likely.
Furthermore – if the test population is specifically engineered to target an observed trait following an apparently Mendelian inheritance pattern – such as a family group or a small genetically isolated population plus controls – 50% of the variance could easily be due to a single gene.”

v. Less research is needed.

“The most over-used and under-analyzed statement in the academic vocabulary is surely “more research is needed”. These four words, occasionally justified when they appear as the last sentence in a Masters dissertation, are as often to be found as the coda for a mega-trial that consumed the lion’s share of a national research budget, or that of a Cochrane review which began with dozens or even hundreds of primary studies and progressively excluded most of them on the grounds that they were “methodologically flawed”. Yet however large the trial or however comprehensive the review, the answer always seems to lie just around the next empirical corner.

With due respect to all those who have used “more research is needed” to sum up months or years of their own work on a topic, this ultimate academic cliché is usually an indicator that serious scholarly thinking on the topic has ceased. It is almost never the only logical conclusion that can be drawn from a set of negative, ambiguous, incomplete or contradictory data.” […]

“Here is a quote from a typical genome-wide association study:

“Genome-wide association (GWA) studies on coronary artery disease (CAD) have been very successful, identifying a total of 32 susceptibility loci so far. Although these loci have provided valuable insights into the etiology of CAD, their cumulative effect explains surprisingly little of the total CAD heritability.”  [1]

The authors conclude that not only is more research needed into the genomic loci putatively linked to coronary artery disease, but that – precisely because the model they developed was so weak – further sets of variables (“genetic, epigenetic, transcriptomic, proteomic, metabolic and intermediate outcome variables”) should be added to it. By adding in more and more sets of variables, the authors suggest, we will progressively and substantially reduce the uncertainty about the multiple and complex gene-environment interactions that lead to coronary artery disease. […] We predict tomorrow’s weather, more or less accurately, by measuring dynamic trends in today’s air temperature, wind speed, humidity, barometric pressure and a host of other meteorological variables. But when we try to predict what the weather will be next month, the accuracy of our prediction falls to little better than random. Perhaps we should spend huge sums of money on a more sophisticated weather-prediction model, incorporating the tides on the seas of Mars and the flutter of butterflies’ wings? Of course we shouldn’t. Not only would such a hyper-inclusive model fail to improve the accuracy of our predictive modeling, there are good statistical and operational reasons why it could well make it less accurate.”

vi. Why software projects take longer than you think – a statistical model.

Anyone who built software for a while knows that estimating how long something is going to take is hard. It’s hard to come up with an unbiased estimate of how long something will take, when fundamentally the work in itself is about solving something. One pet theory I’ve had for a really long time, is that some of this is really just a statistical artifact.

Let’s say you estimate a project to take 1 week. Let’s say there are three equally likely outcomes: either it takes 1/2 week, or 1 week, or 2 weeks. The median outcome is actually the same as the estimate: 1 week, but the mean (aka average, aka expected value) is 7/6 = 1.17 weeks. The estimate is actually calibrated (unbiased) for the median (which is 1), but not for the the mean.

A reasonable model for the “blowup factor” (actual time divided by estimated time) would be something like a log-normal distribution. If the estimate is one week, then let’s model the real outcome as a random variable distributed according to the log-normal distribution around one week. This has the property that the median of the distribution is exactly one week, but the mean is much larger […] Intuitively the reason the mean is so large is that tasks that complete faster than estimated have no way to compensate for the tasks that take much longer than estimated. We’re bounded by 0, but unbounded in the other direction.”

I like this way to conceptually frame the problem, and I definitely do not think it only applies to software development.

“I filed this in my brain under “curious toy models” for a long time, occasionally thinking that it’s a neat illustration of a real world phenomenon I’ve observed. But surfing around on the interwebs one day, I encountered an interesting dataset of project estimation and actual times. Fantastic! […] The median blowup factor turns out to be exactly 1x for this dataset, whereas the mean blowup factor is 1.81x. Again, this confirms the hunch that developers estimate the median well, but the mean ends up being much higher. […]

If my model is right (a big if) then here’s what we can learn:

  • People estimate the median completion time well, but not the mean.
  • The mean turns out to be substantially worse than the median, due to the distribution being skewed (log-normally).
  • When you add up the estimates for n tasks, things get even worse.
  • Tasks with the most uncertainty (rather the biggest size) can often dominate the mean time it takes to complete all tasks.”

vii. Attraction inequality and the dating economy.

“…the relentless focus on inequality among politicians is usually quite narrow: they tend to consider inequality only in monetary terms, and to treat “inequality” as basically synonymous with “income inequality.” There are so many other types of inequality that get air time less often or not at all: inequality of talent, height, number of friends, longevity, inner peace, health, charm, gumption, intelligence, and fortitude. And finally, there is a type of inequality that everyone thinks about occasionally and that young single people obsess over almost constantly: inequality of sexual attractiveness. […] One of the useful tools that economists use to study inequality is the Gini coefficient. This is simply a number between zero and one that is meant to represent the degree of income inequality in any given nation or group. An egalitarian group in which each individual has the same income would have a Gini coefficient of zero, while an unequal group in which one individual had all the income and the rest had none would have a Gini coefficient close to one. […] Some enterprising data nerds have taken on the challenge of estimating Gini coefficients for the dating “economy.” […] The Gini coefficient for [heterosexual] men collectively is determined by [-ll-] women’s collective preferences, and vice versa. If women all find every man equally attractive, the male dating economy will have a Gini coefficient of zero. If men all find the same one woman attractive and consider all other women unattractive, the female dating economy will have a Gini coefficient close to one.”

“A data scientist representing the popular dating app “Hinge” reported on the Gini coefficients he had found in his company’s abundant data, treating “likes” as the equivalent of income. He reported that heterosexual females faced a Gini coefficient of 0.324, while heterosexual males faced a much higher Gini coefficient of 0.542. So neither sex has complete equality: in both cases, there are some “wealthy” people with access to more romantic experiences and some “poor” who have access to few or none. But while the situation for women is something like an economy with some poor, some middle class, and some millionaires, the situation for men is closer to a world with a small number of super-billionaires surrounded by huge masses who possess almost nothing. According to the Hinge analyst:

On a list of 149 countries’ Gini indices provided by the CIA World Factbook, this would place the female dating economy as 75th most unequal (average—think Western Europe) and the male dating economy as the 8th most unequal (kleptocracy, apartheid, perpetual civil war—think South Africa).”

Btw., I’m reasonably certain “Western Europe” as most people think of it is not average in terms of Gini, and that half-way down the list should rather be represented by some other region or country type, like, say Mongolia or Bulgaria. A brief look at Gini lists seemed to support this impression.

Quartz reported on this finding, and also cited another article about an experiment with Tinder that claimed that that “the bottom 80% of men (in terms of attractiveness) are competing for the bottom 22% of women and the top 78% of women are competing for the top 20% of men.” These studies examined “likes” and “swipes” on Hinge and Tinder, respectively, which are required if there is to be any contact (via messages) between prospective matches. […] Yet another study, run by OkCupid on their huge datasets, found that women rate 80 percent of men as “worse-looking than medium,” and that this 80 percent “below-average” block received replies to messages only about 30 percent of the time or less. By contrast, men rate women as worse-looking than medium only about 50 percent of the time, and this 50 percent below-average block received message replies closer to 40 percent of the time or higher.

If these findings are to be believed, the great majority of women are only willing to communicate romantically with a small minority of men while most men are willing to communicate romantically with most women. […] It seems hard to avoid a basic conclusion: that the majority of women find the majority of men unattractive and not worth engaging with romantically, while the reverse is not true. Stated in another way, it seems that men collectively create a “dating economy” for women with relatively low inequality, while women collectively create a “dating economy” for men with very high inequality.”

I think the author goes a bit off the rails later in the post, but the data is interesting. It’s however important keeping in mind in contexts like these that sexual selection pressures apply at multiple levels, not just one, and that partner preferences can be non-trivial to model satisfactorily; for example as many women have learned the hard way, males may have very different standards for whom to a) ‘engage with romantically’ and b) ‘consider a long-term partner’.

viii. Flipping the Metabolic Switch: Understanding and Applying Health Benefits of Fasting.

“Intermittent fasting (IF) is a term used to describe a variety of eating patterns in which no or few calories are consumed for time periods that can range from 12 hours to several days, on a recurring basis. Here we focus on the physiological responses of major organ systems, including the musculoskeletal system, to the onset of the metabolic switch – the point of negative energy balance at which liver glycogen stores are depleted and fatty acids are mobilized (typically beyond 12 hours after cessation of food intake). Emerging findings suggest the metabolic switch from glucose to fatty acid-derived ketones represents an evolutionarily conserved trigger point that shifts metabolism from lipid/cholesterol synthesis and fat storage to mobilization of fat through fatty acid oxidation and fatty-acid derived ketones, which serve to preserve muscle mass and function. Thus, IF regimens that induce the metabolic switch have the potential to improve body composition in overweight individuals. […] many experts have suggested IF regimens may have potential in the treatment of obesity and related metabolic conditions, including metabolic syndrome and type 2 diabetes.()”

“In most studies, IF regimens have been shown to reduce overall fat mass and visceral fat both of which have been linked to increased diabetes risk.() IF regimens ranging in duration from 8 to 24 weeks have consistently been found to decrease insulin resistance.(, , , , , , , , , ) In line with this, many, but not all,() large-scale observational studies have also shown a reduced risk of diabetes in participants following an IF eating pattern.”

“…we suggest that future randomized controlled IF trials should use biomarkers of the metabolic switch (e.g., plasma ketone levels) as a measure of compliance and the magnitude of negative energy balance during the fasting period. It is critical for this switch to occur in order to shift metabolism from lipidogenesis (fat storage) to fat mobilization for energy through fatty acid β-oxidation. […] As the health benefits and therapeutic efficacies of IF in different disease conditions emerge from RCTs, it is important to understand the current barriers to widespread use of IF by the medical and nutrition community and to develop strategies for broad implementation. One argument against IF is that, despite the plethora of animal data, some human studies have failed to show such significant benefits of IF over CR [Calorie Restriction].() Adherence to fasting interventions has been variable, some short-term studies have reported over 90% adherence,() whereas in a one year ADMF study the dropout rate was 38% vs 29% in the standard caloric restriction group.()”

ix. Self-repairing cells: How single cells heal membrane ruptures and restore lost structures.

June 2, 2019 Posted by | Astronomy, Biology, Data, Diabetes, Economics, Evolutionary biology, Genetics, Geography, History, Mathematics, Medicine, Physics, Psychology, Statistics, Wikipedia | Leave a comment

Artificial intelligence (I?)

This book was okay, but nothing all that special. In my opinion there’s too much philosophy and similar stuff in there (‘what does intelligence really mean anyway?’), and the coverage isn’t nearly as focused on technological aspects as e.g. Winfield’s (…in my opinion better…) book from the same series on robotics (which I covered here) was; I am certain I’d have liked this book better if it’d provided a similar type of coverage as did Winfield, but it didn’t. However it’s far from terrible and I liked the authors skeptical approach to e.g. singularitarianism. Below I have added some quotes and links, as usual.

“Artificial intelligence (AI) seeks to make computers do the sorts of things that minds can do. Some of these (e.g. reasoning) are normally described as ‘intelligent’. Others (e.g. vision) aren’t. But all involve psychological skills — such as perception, association, prediction, planning, motor control — that enable humans and animals to attain their goals. Intelligence isn’t a single dimension, but a richly structured space of diverse information-processing capacities. Accordingly, AI uses many different techniques, addressing many different tasks. […] although AI needs physical machines (i.e. computers), it’s best thought of as using what computer scientists call virtual machines. A virtual machine isn’t a machine depicted in virtual reality, nor something like a simulated car engine used to train mechanics. Rather, it’s the information-processing system that the programmer has in mind when writing a program, and that people have in mind when using it. […] Virtual machines in general are comprised of patterns of activity (information processing) that exist at various levels. […] the human mind can be understood as the virtual machine – or rather, the set of mutually interacting virtual machines, running in parallel […] – that is implemented in the brain. Progress in AI requires progress in defining interesting/useful virtual machines. […] How the information is processed depends on the virtual machine involved. [There are many different approaches.] […] In brief, all the main types of AI were being thought about, and even implemented, by the late 1960s – and in some cases, much earlier than that. […] Neural networks are helpful for modelling aspects of the brain, and for doing pattern recognition and learning. Classical AI (especially when combined with statistics) can model learning too, and also planning and reasoning. Evolutionary programming throws light on biological evolution and brain development. Cellular automata and dynamical systems can be used to model development in living organisms. Some methodologies are closer to biology than to psychology, and some are closer to non-reflective behaviour than to deliberative thought. To understand the full range of mentality, all of them will be needed […]. Many AI researchers [however] don’t care about how minds work: they seek technological efficiency, not scientific understanding. […] In the 21st century, […] it has become clear that different questions require different types of answers”.

“State-of-the-art AI is a many-splendoured thing. It offers a profusion of virtual machines, doing many different kinds of information processing. There’s no key secret here, no core technique unifying the field: AI practitioners work in highly diverse areas, sharing little in terms of goals and methods. […] A host of AI applications exist, designed for countless specific tasks and used in almost every area of life, by laymen and professionals alike. Many outperform even the most expert humans. In that sense, progress has been spectacular. But the AI pioneers weren’t aiming only for specialist systems. They were also hoping for systems with general intelligence. Each human-like capacity they modelled — vision, reasoning, language, learning, and so on — would cover its entire range of challenges. Moreover, these capacities would be integrated when appropriate. Judged by those criteria, progress has been far less impressive. […] General intelligence is still a major challenge, still highly elusive. […] problems can’t always be solved merely by increasing computer power. New problem-solving methods are often needed. Moreover, even if a particular method must succeed in principle, it may need too much time and/or memory to succeed in practice. […] Efficiency is important, too: the fewer the number of computations, the better. In short, problems must be made tractable. There are several basic strategies for doing that. All were pioneered by classical symbolic AI, or GOFAI, and all are still essential today. One is to direct attention to only a part of the search space (the computer’s representation of the problem, within which the solution is assumed to be located). Another is to construct a smaller search space by making simplifying assumptions. A third is to order the search efficiently. Yet another is to construct a different search space, by representing the problem in a new way. These approaches involve heuristics, planning, mathematical simplification, and knowledge representation, respectively. […] Often, the hardest part of AI problem solving is presenting the problem to the system in the first place. […] the information (‘knowledge’) concerned must be presented to the system in a fashion that the machine can understand – in other words, that it can deal with. […] AI’s way of doing this are highly diverse.”

“The rule-baed form of knowledge representation enables programs to be built gradually, as the programmer – or perhaps an AGI system itself – learns more about the domain. A new rule can be added at any time. There’s no need to rewrite the program from scratch. However, there’s a catch. If the new rule isn’t logically consistent with the existing ones, the system won’t always do what it’s supposed to do. It may not even approximate what it’s supposed to do. When dealing with a small set of rules, such logical conflicts are easily avoided, but larger systems are less transparent. […] An alternative form of knowledge representation for concepts is semantic networks […] A semantic network links concepts by semantic relations […] semantic networks aren’t the same thing as neural networks. […] distributed neural networks represent knowledge in a very different way. There, individual concepts are represented not by a single node in a carefully defined associative net, but by the changing patterns of activity across an entire network. Such systems can tolerate conflicting evidence, so aren’t bedevilled by the problems of maintaining logical consistency […] Even a single mind involves distributed cognition, for it integrates many cognitive, motivational, and emotional subsystems […] Clearly, human-level AGI would involve distributed cognition.”

“In short, most human visual achievements surpass today’s AI. Often, AI researchers aren’t clear about what questions to ask. For instance, think about folding a slippery satin dress neatly. No robot can do this (although some can be instructed, step by step, how to fold an oblong terry towel). Or consider putting on a T-shirt: the head must go in first, and not via a sleeve — but why? Such topological problems hardly feature in AI. None of this implies that human-level computer vision is impossible. But achieving it is much more difficult than most people believe. So this is a special case of the fact noted in Chapter 1: that AI has taught us that human minds are hugely richer, and more subtle, than psychologists previously imagined. Indeed, that is the main lesson to be learned from AI. […] Difficult though it is to build a high-performing AI specialist, building an AI generalist is orders of magnitude harder. (Deep learning isn’t the answer: its aficionados admit that ‘new paradigms are needed’ to combine it with complex reasoning — scholarly code for ‘we haven’t got a clue’.) That’s why most AI researchers abandoned that early hope, turning instead to multifarious narrowly defined tasks—often with spectacular success.”

“Some machine learning uses neural networks. But much relies on symbolic AI, supplemented by powerful statistical algorithms. In fact, the statistics really do the work, the GOFAI merely guiding the worker to the workplace. Accordingly, some professionals regard machine learning as computer science and/or statistics —not AI. However, there’s no clear boundary here. Machine learning has three broad types: supervised, unsupervised, and reinforcement learning. […] In supervised learning, the programmer ‘trains’ the system by defining a set of desired outcomes for a range of inputs […], and providing continual feedback about whether it has achieved them. The learning system generates hypotheses about the relevant features. Whenever it classifies incorrectly, it amends its hypothesis accordingly. […] In unsupervised learning, the user provides no desired outcomes or error messages. Learning is driven by the principle that co-occurring features engender expectations that they will co-occur in future. Unsupervised learning can be used to discover knowledge. The programmers needn’t know what patterns/clusters exist in the data: the system finds them for itself […but even though Boden does not mention this fact, caution is most definitely warranted when applying such systems/methods to data (..it remains true that “Truth and true models are not statistically identifiable from data” – as usual, the go-to reference here is Burnham & Anderson)]. Finally, reinforcement learning is driven by analogues of reward and punishment: feedback messages telling the system that what it just did was good or bad. Often, reinforcement isn’t simply binary […] Given various theories of probability, there are many different algorithms suitable for distinct types of learning and different data sets.”

“Countless AI applications use natural language processing (NLP). Most focus on the computer’s ‘understanding’ of language that is presented to it, not on its own linguistic production. That’s because NLP generation is even more difficult than NLP acceptance [I had a suspicion this might be the case before reading the book, but I didn’t know – US]. […] It’s now clear that handling fancy syntax isn’t necessary for summarizing, questioning, or translating a natural-language text. Today’s NLP relies more on brawn (computational power) than on brain (grammatical analysis). Mathematics — specifically, statistics — has overtaken logic, and machine learning (including, but not restricted to, deep learning) has displaced syntactic analysis. […] In modern-day NLP, powerful computers do statistical searches of huge collections (‘corpora’) of texts […] to find word patterns both commonplace and unexpected. […] In general […], the focus is on words and phrases, not syntax. […] Machine-matching of languages from different language groups is usually difficult. […] Human judgements of relevance are often […] much too subtle for today’s NLP. Indeed, relevance is a linguistic/conceptual version of the unforgiving ‘frame problem‘ in robotics […]. Many people would argue that it will never be wholly mastered by a non-human system.”

“[M]any AI research groups are now addressing emotion. Most (not quite all) of this research is theoretically shallow. And most is potentially lucrative, being aimed at developing ‘computer companions’. These are AI systems — some screen-based, some ambulatory robots — designed to interact with people in ways that (besides being practically helpful) are affectively comfortable, even satisfying, for the user. Most are aimed at the elderly and/or disabled, including people with incipient dementia. Some are targeted on babies or infants. Others are interactive ‘adult toys’. […] AI systems can already recognize human emotions in various ways. Some are physiological: monitoring the person’s breathing rate and galvanic skin response. Some are verbal: noting the speaker’s speed and intonation, as well as their vocabulary. And some are visual: analysing their facial expressions. At present, all these methods are relatively crude. The user’s emotions are both easily missed and easily misinterpreted. […] [An] point [point], here [in the development and evaluation of AI], is that emotions aren’t merely feelings. They involve functional, as well as phenomenal, consciousness […]. Specifically, they are computational mechanisms that enable us to schedule competing motives – and without which we couldn’t function. […] If we are ever to achieve AGI, emotions such as anxiety will have to be included – and used.”

[The point made in the book is better made in Aureli et al.‘s book, especially the last chapters to which the coverage in the linked post refer. The point is that emotions enable us to make better decisions, or perhaps even to make a decision in the first place; the emotions we feel in specific contexts will tend not to be even remotely random, rather they will tend to a significant extent to be Nature’s (…and Mr. Darwin’s) attempt to tell us how to handle a specific conflict of interest in the ‘best’ manner. You don’t need to do the math, your forebears did it for you, which is why you’re now …angry, worried, anxious, etc. If you had to do the math every time before you made a decision, you’d be in trouble, and emotions provide a great shortcut in many contexts. The potential for such short-cuts seems really important if you want an agent to act intelligently, regardless of whether said agent is ‘artificial’ or not. The book very briefly mentions a few of Minsky’s thoughts on these topics, and people who are curious could probably do worse than read some of his stuff. This book seems like a place to start.]

Links:

GOFAI (“Good Old-Fashioned Artificial Intelligence”).
Ada Lovelace. Charles Babbage. Alan Turing. Turing machine. Turing test. Norbert WienerJohn von Neumann. W. Ross Ashby. William Grey Walter. Oliver SelfridgeKenneth Craik. Gregory Bateson. Frank Rosenblatt. Marvin Minsky. Seymour Papert.
A logical calculus of the ideas immanent in nervous activity (McCulloch & Pitts, 1943).
Propositional logic. Logic gate.
Arthur Samuel’s checkers player. Logic Theorist. General Problem Solver. The Homeostat. Pandemonium architecture. Perceptron. Cyc.
Fault-tolerant computer system.
Cybernetics.
Programmed Data Processor (PDP).
Artificial life.
Forward chaining. Backward chaining.
Rule-based programming. MYCIN. Dendral.
Semantic network.
Non-monotonic logic. Fuzzy logic.
Facial recognition system. Computer vision.
Bayesian statistics.
Helmholtz machine.
DQN algorithm.
AlphaGo. AlphaZero.
Human Problem Solving (Newell & Simon, 1970).
ACT-R.
NELL (Never-Ending Language Learning).
SHRDLU.
ALPAC.
Google translate.
Data mining. Sentiment analysis. Siri. Watson (computer).
Paro (robot).
Uncanny valley.
CogAff architecture.
Connectionism.
Constraint satisfaction.
Content-addressable memory.
Graceful degradation.
Physical symbol system hypothesis.

January 10, 2019 Posted by | Biology, Books, Computer science, Engineering, Language, Mathematics, Papers, Psychology, Statistics | Leave a comment

Perception (I)

Here’s my short goodreads review of the book. In this post I’ll include some observations and links related to the first half of the book’s coverage.

“Since the 1960s, there have been many attempts to model the perceptual processes using computer algorithms, and the most influential figure of the last forty years has been David Marr, working at MIT. […] Marr and his colleagues were responsible for developing detailed algorithms for extracting (i) low-level information about the location of contours in the visual image, (ii) the motion of those contours, and (iii) the 3-D structure of objects in the world from binocular disparities and optic flow. In addition, one of his lasting achievements was to encourage researchers to be more rigorous in the way that perceptual tasks are described, analysed, and formulated and to use computer models to test the predictions of those models against human performance. […] Over the past fifteen years, many researchers in the field of perception have characterized perception as a Bayesian process […] According to Bayesian theory, what we perceive is a consequence of probabilistic processes that depend on the likelihood of certain events occurring in the particular world we live in. Moreover, most Bayesian models of perceptual processes assume that there is noise in the sensory signals and the amount of noise affects the reliability of those signals – the more noise, the less reliable the signal. Over the past fifteen years, Bayes theory has been used extensively to model the interaction between different discrepant cues, such as binocular disparity and texture gradients to specify the slant of an inclined surface.”

“All surfaces have the property of reflectance — that is, the extent to which they reflect (rather than absorb) the incident illumination — and those reflectances can vary between 0 per cent and 100 per cent. Surfaces can also be selective in the particular wavelengths they reflect or absorb. Our colour vision depends on these selective reflectance properties […]. Reflectance characteristics describe the physical properties of surfaces. The lightness of a surface refers to a perceptual judgement of a surface’s reflectance characteristic — whether it appears as black or white or some grey level in between. Note that we are talking about the perception of lightness — rather than brightness — which refers to our estimate of how much light is coming from a particular surface or is emitted by a source of illumination. The perception of surface lightness is one of the most fundamental perceptual abilities because it allows us not only to differentiate one surface from another but also to identify the real-world properties of a particular surface. Many textbooks start with the observation that lightness perception is a difficult task because the amount of light reflected from a particular surface depends on both the reflectance characteristic of the surface and the intensity of the incident illumination. For example, a piece of black paper under high illumination will reflect back more light to the eye than a piece of white paper under dim illumination. As a consequence, lightness constancy — the ability to correctly judge the lightness of a surface under different illumination conditions — is often considered to be an ‘achievement’ of the perceptual system. […] The alternative starting point for understanding lightness perception is to ask whether there is something that remains constant or invariant in the patterns of light reaching the eye with changes of illumination. In this case, it is the relative amount of light reflected off different surfaces. Consider two surfaces that have different reflectances—two shades of grey. The actual amount of light reflected off each of the surfaces will vary with changes in the illumination but the relative amount of light reflected off the two surfaces remains the same. This shows that lightness perception is necessarily a spatial task and hence a task that cannot be solved by considering one particular surface alone. Note that the relative amount of light reflected off different surfaces does not tell us about the absolute lightnesses of different surfaces—only their relative lightnesses […] Can our perception of lightness be fooled? Yes, of course it can and the ways in which we make mistakes in our perception of the lightnesses of surfaces can tell us much about the characteristics of the underlying processes.”

“From a survival point of view, the ability to differentiate objects and surfaces in the world by their ‘colours’ (spectral reflectance characteristics) can be extremely useful […] Most species of mammals, birds, fish, and insects possess several different types of receptor, each of which has a a different spectral sensitivity function […] having two types of receptor with different spectral sensitivities is the minimum necessary for colour vision. This is referred to as dicromacy and the majority of mammals are dichromats with the exception of the old world monkeys and humans. […] The only difference between lightness and colour perception is that in the latter case we have to consider the way a surface selectively reflects (and absorbs) different wavelengths, rather than just a surface’s average reflectance over all wavelengths. […] The similarities between the tasks of extracting lightness and colour information mean that we can ask a similar question about colour perception [as we did about lightness perception] – what is the invariant information that could specify the reflectance characteristic of a surface? […] The information that is invariant under changes of spectral illumination is the relative amounts of long, medium, and short wavelength light reaching our eyes from different surfaces in the scene. […] the successful identification and discrimination of coloured surfaces is dependent on making spatial comparisons between the amounts of short, medium, and long wavelength light reaching our eyes from different surfaces. As with lightness perception, colour perception is necessarily a spatial task. It follows that if a scene is illuminated by the light of just a single wavelength, the appropriate spatial comparisons cannot be made. This can be demonstrated by illuminating a real-world scene containing many different coloured objects with yellow, sodium light that contains only a single wavelength. All objects, whatever their ‘colours’, will only reflect back to the eye different intensities of that sodium light and hence there will only be absolute but no relative differences between the short, medium, and long wavelength lightness records. There is a similar, but less dramatic, effect on our perception of colour when the spectral characteristics of the illumination are restricted to just a few wavelengths, as is the case with fluorescent lighting.”

“Consider a single receptor mechanism, such as a rod receptor in the human visual system, that responds to a limited range of wavelengths—referred to as the receptor’s spectral sensitivity function […]. This hypothetical receptor is more sensitive to some wavelengths (around 550 nm) than others and we might be tempted to think that a single type of receptor could provide information about the wavelength of the light reaching the receptor. This is not the case, however, because an increase or decrease in the response of that receptor could be due to either a change in the wavelength or an increase or decrease in the amount of light reaching the receptor. In other words, the output of a given receptor or receptor type perfectly confounds changes in wavelength with changes in intensity because it has only one way of responding — that is, more or less. This is Rushton’s Principle of Univariance — there is only one way of varying or one degree of freedom. […] On the other hand, if we consider a visual system with two different receptor types, one more sensitive to longer wavelengths (L) and the other more sensitive to shorter wavelengths (S), there are two degrees of freedom in the system and thus the possibility of signalling our two independent variables — wavelength and intensity […] it is quite possible to have a colour visual system that is based on just two receptor types. Such a colour visual system is referred to as dichromatic.”

“So why is the human visual system trichromatic? The answer can be found in a phenomenon known as metamerism. So far, we have restricted our discussion to the effect of a single wavelength on our dichromatic visual system: for example, a single wavelength of around 550 nm that stimulated both the long and short receptor types about equally […]. But what would happen if we stimulated our dichromatic system with light of two different wavelengths at the same time — one long wavelength and one short wavelength? With a suitable choice of wavelengths, this combination of wavelengths would also have the effect of stimulating the two receptor types about equally […] As a consequence, the output of the system […] with this particular mixture of wavelengths would be indistinguishable from that created by the single wavelength of 550 nm. These two indistinguishable stimulus situations are referred to as metamers and a little thought shows that there would be many thousands of combinations of wavelength that produce the same activity […] in a dichromatic visual system. As a consequence, all these different combinations of wavelengths would be indistinguishable to a dichromatic observer, even though they were produced by very different combinations of wavelengths. […] Is there any way of avoiding the problem of metamerism? The answer is no but we can make things better. If a visual system had three receptor types rather than two, then many of the combinations of wavelengths that produce an identical pattern of activity in two of the mechanisms (L and S) would create a different amount of activity in our third receptor type (M) that is maximally sensitive to medium wavelengths. Hence the number of indistinguishable metameric matches would be significantly reduced but they would never be eliminated. Using the same logic, it follows that a further increase in the number of receptor types (beyond three) would reduce the problem of metamerism even more […]. There would, however, also be a cost. Having more distinct receptor types in a finite-sized retina would increase the average spacing between the receptors of the same type and thus make our acuity for fine detail significantly poorer. There are many species, such as dragonflies, with more than three receptor types in their eyes but the larger number of receptor types typically serves to increase the range of wavelengths to which the animal is sensitive into the infra-red or ultra-violet parts of the spectrum, rather than to reduce the number of metamers. […] the sensitivity of the short wavelength receptors in the human eye only extends to ~540 nm — the S receptors are insensitive to longer wavelengths. This means that human colour vision is effectively dichromatic for combinations of wavelengths above 540 nm. In addition, there are no short wavelength cones in the central fovea of the human retina, which means that we are also dichromatic in the central part of our visual field. The fact that we are unaware of this lack of colour vision is probably due to the fact that our eyes are constantly moving. […] It is […] important to appreciate that the description of the human colour visual system as trichromatic is not a description of the number of different receptor types in the retina – it is a property of the whole visual system.”

“Recent research has shown that although the majority of humans are trichromatic there can be significant differences in the precise matches that individuals make when matching colour patches […] the absence of one receptor type will result in a greater number of colour confusions than normal and this does have a significant effect on an observer’s colour vision. Protanopia is the absence of long wavelength receptors, deuteranopia the absence of medium wavelength receptors, and tritanopia the absence of short wavelength receptors. These three conditions are often described as ‘colour blindness’ but this is a misnomer. We are all colour blind to some extent because we all suffer from colour metamerism and fail to make discriminations that would be very apparent to any biological or machine vision system with a greater number of receptor types. For example, most stomatopod crustaceans (mantis shrimps) have twelve different visual pigments and they also have the ability to detect both linear and circularly polarized light. What I find interesting is that we believe, as trichromats, that we have the ability to discriminate all the possible shades of colour (reflectance characteristics) that exist in our world. […] we are typically unaware of the limitations of our visual systems because we have no way of comparing what we see normally with what would be seen by a ‘better’ visual system.”

“We take it for granted that we are able to segregate the visual input into separate objects and distinguish objects from their backgrounds and we rarely make mistakes except under impoverished conditions. How is this possible? In many cases, the boundaries of objects are defined by changes of luminance and colour and these changes allow us to separate or segregate an object from its background. But luminance and colour changes are also present in the textured surfaces of many objects and therefore we need to ask how it is that our visual system does not mistake these luminance and colour changes for the boundaries of objects. One answer is that object boundaries have special characteristics. In our world, most objects and surfaces are opaque and hence they occlude (cover) the surface of the background. As a consequence, the contours of the background surface typically end—they are ‘terminated’—at the boundary of the occluding object or surface. Quite often, the occluded contours of the background are also revealed at the opposite side of the occluding surface because they are physically continuous. […] The impression of occlusion is enhanced if the occluded contours contain a range of different lengths, widths, and orientations. In the natural world, many animals use colour and texture to camouflage their boundaries as well as to fool potential predators about their identity. […] There is an additional source of information — relative motion — that can be used to segregate a visual scene into objects and their backgrounds and to break any camouflage that might exist in a static view. A moving, opaque object will progressively occlude and dis-occlude (reveal) the background surface so that even a well-camouflaged, moving animal will give away its location. Hence it is not surprising that a very common and successful strategy of many animals is to freeze in order not to be seen. Unless the predator has a sophisticated visual system to break the pattern or colour camouflage, the prey will remain invisible.”

Some links:

Perception.
Ames room. Inverse problem in optics.
Hermann von Helmholtz. Richard Gregory. Irvin Rock. James Gibson. David Marr. Ewald Hering.
Optical flow.
La dioptrique.
Necker cube. Rubin’s vase.
Perceptual constancy. Texture gradient.
Ambient optic array.
Affordance.
Luminance.
Checker shadow illusion.
Shape from shading/Photometric stereo.
Colour vision. Colour constancy. Retinex model.
Cognitive neuroscience of visual object recognition.
Motion perception.
Horace Barlow. Bernhard Hassenstein. Werner E. Reichardt. Sigmund Exner. Jan Evangelista Purkyně.
Phi phenomenon.
Motion aftereffect.
Induced motion.

October 14, 2018 Posted by | Biology, Books, Ophthalmology, Physics, Psychology | Leave a comment

Oncology (I)

I really disliked the ‘Pocket…’ part of this book, so I’ll sort of pretend to overlook this aspect also in my coverage of the book here. This’ll be a hard thing to do, given the way the book is written – I refer to my goodreads review for details, I’ll include only one illustrative quote from that review here:

“In terms of content, the book probably compares favourably with many significantly longer oncology texts (mainly, but certainly not only, because of the publication date). In terms of readability it compares unfavourably to an Egyptian translation of Alan Sokal’s 1996 article in Social Text, if it were translated by a 12-year old dyslexic girl.”

I don’t yet know in how much detail I’ll blog the book; this may end up being the only post about the book, or I may decide to post a longer sequence of posts. The book is hard to blog, which is an argument against covering it in detail – and also the reason why I haven’t already blogged it – but some of the content included in the book is really, really nice stuff to know, which is a strong argument in favour of covering at least some of the material here. The book has a lot of stuff, so regardless of the level of detail of my future coverage a lot of interesting stuff will of necessity have been left out.

My coverage below includes some observations and links related to the first 100 pages of the book.

“Understanding Radiation Response: The 4 Rs of Radiobiology
Repair of sublethal damage
Reassortment of cells w/in the cell cycle
Repopulation of cells during the course of radiotherapy
Reoxygenation of hypoxic cells […]

*Oxygen enhances DNA damage induced by free radicals, thereby facilitating the indirect action of IR [ionizing radiation, US] *Biologically equivalent dose can vary by a factor of 2–3 depending upon the presence or absence of oxygen (referred to as the oxygen enhancement ratio) *Poorly oxygenated postoperative beds frequently require higher doses of RT than preoperative RT [radiation therapy] […] Chemotherapy is frequently used sequentially or concurrently w/radiotherapy to maximize therapeutic benefit. This has improved pt outcomes although also a/w ↑ overall tox. […] [Many chemotherapeutic agents] show significant synergy with RT […] Mechanisms for synergy vary widely: Include cell cycle effects, hypoxic cell sensitization, & modulation of the DNA damage response”.

“Specific dose–volume relationships have been linked to the risk of late organ tox. […] *Dose, volume, underlying genetics, and age of the pt at the time of RT are critical determinants of the risk for 2° malignancy *The likelihood of 2° CA is correlated w/dose, but there is no threshold dose below which there is no additional risk of 2° malignancy *Latent period for radiation-induced solid tumors is generally between 10 and 60 y […]. Latent period for leukemias […] is shorter — peak between 5 & 7 y.”

“The immune system plays an important role in CA surveillance; Rx’s that modulate & amplify the immune system are referred to as immunotherapies […] tumors escape the immune system via loss of molecules on tumor cells important for immune activation […]; tumors can secrete immunosuppressing cytokines (IL-10 & TGF-β) & downregulate IFN-γ; in addition, tumors often express nonmutated self-Ag, w/c the immune system will, by definition, not react against; tumors can express molecules that inhibit T-cell function […] Ubiquitous CD47 (Don’t eat me signal) with ↑ expression on tumor cells mediates escape from phagocytosis. *Tumor microenvironment — immune cells are found in tumors, the exact composition of these cells has been a/w [associated with, US] pt outcomes; eg, high concentration of tumor-infiltrating lymphocytes (CD8+ cells) are a/w better outcomes & ↑ response to chemotherapy, Tregs & myeloid-derived suppressor cells are a/w worse outcomes, the exact role of Th17 in tumors is still being elucidated; the milieu of cytokines & chemokines also plays a role in outcome; some cytokines (VEGF, IL-1, IL-8) lead to endothelial cell proliferation, migration, & activation […] Expression of PD-L1 in tumor microenvironment can be indicator of improved likelihood of response to immune checkpoint blockade. […] Tumor mutational load correlates w/increased response to immunotherapy (NEJM; 2014;371:2189.).”

“Over 200 hereditary CA susceptibility syndromes, most are rare […]. Inherited CAs arise from highly penetrant germline mts [mutations, US]; “familial” CAss may be caused by interaction of low-penetrance genes, gene–environment interactions, or both. […] Genetic testing should be done based on individual’s probability of being a mt carrier & after careful discussion & informed consent”.

Pharmacogenetics: Effect of heritable genes on response to drugs. Study of single genes & interindividual differences in drug metabolizing enzymes. Pharmacogenomics: Effect of inherited & acquired genetic variation on drug response. Study of the functions & interactions of all genes in the genome & how the overall variability of drug response may be used to predict the right tx in individual pts & to design new drugs. Polymorphisms: Common variations in a DNA sequence that may lead to ↓ or ↑ activity of the encoded gene (SNP, micro- & minisatellites). SNPs: Single nucleotide polymorphisms that may cause an amino acid exchange in the encoded protein, account for >90% of genetic variation in the human genome.”

Tumor lysis syndrome [TLS] is an oncologic emergency caused by electrolyte abnormalities a/w spontaneous and/or tx-induced cell death that can be potentially fatal. […] 4 key electrolyte abnormalities 2° to excessive tumor/cell lysis: *Hyperkalemia *Hyperphosphatemia *Hypocalcemia *Hyperuricemia (2° to catabolism of nucleic acids) […] Common Malignancies Associated with a High Risk of Developing TLS in Adult Patients [include] *Acute leukemias [and] *High-grade lymphomas such as Burkitt lymphoma & DLBCL […] [Disease] characteristics a/w TLS risk: Rapidly progressive, chemosensitive, myelo- or lymphoproliferative [disease] […] [Patient] characteristics a/w TLS risk: *Baseline impaired renal function, oliguria, exposure to nephrotoxins, hyperuricemia *Volume depletion/inadequate hydration, acidic urine”.

Hypercalcemia [affects] ~10–30% of all pts w/malignancy […] Symptoms: Polyuria/polydipsia, intravascular volume depletion, AKI, lethargy, AMS [Altered Mental Status, US], rarely coma/seizures; N/V [nausea/vomiting, US] […] Osteolytic Bone Lesions [are seen in] ~20% of all hyperCa of malignancy […] [Treat] underlying malignancy, only way to effectively treat, all other tx are temporizing”.

“National Consensus Project definition: Palliative care means patient and family-centered care that optimizes quality of life by anticipating, preventing, and treating suffering. Palliative care throughout the continuum of illness involves addressing physical, intellectual, emotional, social, and spiritual needs to facilitate patient autonomy, access to information, and choice.” […] *Several RCTs have supported the integration of palliative care w/oncologic care, but specific interventions & models of care have varied. Expert panels at NCCN & ASCO recently reviewed the data to release evidence-based guidelines. *NCCN guidelines (2016): “Palliative care should be initiated by the primary oncology team and then augmented by collaboration with an interdisciplinary team of palliative care experts… All cancer patients should be screened for palliative care needs at their initial visit, at appropriate intervals, and as clinically indicated.” *ASCO guideline update (2016): “Inpatients and outpatients with advanced cancer should receive dedicated palliative care services, early in the disease course, concurrent with active tx. Referral of patients to interdisciplinary palliative care teams is optimal […] Essential Components of Palliative Care (ASCO) *Rapport & relationship building w/pts & family caregivers *Symptom, distress, & functional status mgmt (eg, pain, dyspnea, fatigue, sleep disturbance, mood, nausea, or constipation) *Exploration of understanding & education about illness & prognosis *Clarification of tx goals *Assessment & support of coping needs (eg, provision of dignity therapy) *Assistance w/medical decision making *Coordination w/other care providers *Provision of referrals to other care providers as indicated […] Useful Communication Tips *Use open-ended questions to elicit pt concerns *Clarify how much information the pt would like to know […] Focus on what can be done (not just what can’t be done) […] Remove the phrase “do everything” from your medical vocabulary […] Redefine hope by supporting realistic & achievable goals […] make empathy explicit”.

Some links:

Radiation therapy.
Brachytherapy.
External beam radiotherapy.
Image-guided radiation therapy.
Stereotactic Radiosurgery.
Total body irradiation.
Cancer stem cell.
Cell cycle.
Carcinogenesis. Oncogene. Tumor suppressor gene. Principles of Cancer Therapy: Oncogene and Non-oncogene Addiction.
Cowden syndrome. Peutz–Jeghers syndrome. Familial Atypical Multiple Mole Melanoma Syndrome. Li–Fraumeni syndrome. Lynch syndrome. Turcot syndrome. Muir–Torre syndrome. Von Hippel–Lindau disease. Gorlin syndrome. Werner syndrome. Birt–Hogg–Dubé syndrome. Neurofibromatosis type I. -ll- type 2.
Knudson hypothesis.
DNA sequencing.
Cytogenetics.
Fluorescence in situ hybridization.
CAR T Cell therapy.
Antimetabolite. Alkylating antineoplastic agentAntimicrotubule agents/mitotic inhibitors. Chemotherapeutic agentsTopoisomerase inhibitorMonoclonal antibodiesBisphosphonatesProteasome inhibitors. [The book covers all of these agents, and others I for one reason or another decided not to include, in great detail, listing many different types of agents and including notes on dosing, pharmacokinetics & pharmacodynamics, associated adverse events and drug interactions etc. These parts of the book were very interesting, but they are impossible to blog – US).
Syndrome of inappropriate antidiuretic hormone secretion.
Acute lactic acidosis (“Often seen w/liver mets or rapidly dividing heme malignancies […] High mortality despite aggressive tx [treatment]”).
Superior vena cava syndrome.

October 12, 2018 Posted by | Biology, Books, Cancer/oncology, Genetics, Immunology, Medicine, Pharmacology | Leave a comment

Principles of memory (II)

I have added a few more quotes from the book below:

Watkins and Watkins (1975, p. 443) noted that cue overload is “emerging as a general principle of memory” and defined it as follows: “The efficiency of a functional retrieval cue in effecting recall of an item declines as the number of items it subsumes increases.” As an analogy, think of a person’s name as a cue. If you know only one person named Katherine, the name by itself is an excellent cue when asked how Katherine is doing. However, if you also know Cathryn, Catherine, and Kathryn, then it is less useful in specifying which person is the focus of the question. More formally, a number of studies have shown experimentally that memory performance systematically decreases as the number of items associated with a particular retrieval cue increases […] In many situations, a decrease in memory performance can be attributed to cue overload. This may not be the ultimate explanation, as cue overload itself needs an explanation, but it does serve to link a variety of otherwise disparate findings together.”

Memory, like all other cognitive processes, is inherently constructive. Information from encoding and cues from retrieval, as well as generic information, are all exploited to construct a response to a cue. Work in several areas has long established that people will use whatever information is available to help reconstruct or build up a coherent memory of a story or an event […]. However, although these strategies can lead to successful and accurate remembering in some circumstances, the same processes can lead to distortion or even confabulation in others […]. There are a great many studies demonstrating the constructive and reconstructive nature of memory, and the literature is quite well known. […] it is clear that recall of events is deeply influenced by a tendency to reconstruct them using whatever information is relevant and to repair holes or fill in the gaps that are present in memory with likely substitutes. […] Given that memory is a reconstructive process, it should not be surprising to find that there is a large literature showing that people have difficulty distinguishing between memories of events that happened and memories of events that did not happen […]. In a typical reality monitoring experiment […], subjects are shown pictures of common objects. Every so often, instead of a picture, the subjects are shown the name of an object and are asked to create a mental image of the object. The test involves presenting a list of object names, and the subject is asked to judge whether they saw the item (i.e., judge the memory as “real”) or whether they saw the name of the object and only imagined seeing it (i.e., judge the memory as “imagined”). People are more likely to judge imagined events as real than real events as imagined. The likelihood that a memory will be judged as real rather than imagined depends upon the vividness of the memory in terms of its sensory quality, detail, plausibility, and coherence […]. What this means is that there is not a firm line between memories for real and imagined events: if an imagined event has enough qualitative features of a real event it is likely to be judged as real.”

“One hallmark of reconstructive processes is that in many circumstances they aid in memory retrieval because they rely on regularities in the world. If we know what usually happens in a given circumstance, we can use that information to fill in gaps that may be present in our memory for that episode. This will lead to a facilitation effect in some cases but will lead to errors in cases in which the most probable response is not the correct one. However, if we take this standpoint, we must predict that the errors that are made when using reconstructive processes will not be random; in fact, they will display a bias toward the most likely event. This sort of mechanism has been demonstrated many times in studies of schema-based representations […], and language production errors […] but less so in immediate recall. […] Each time an event is recalled, the memory is slightly different. Because of the interaction between encoding and retrieval, and because of the variations that occur between two different retrieval attempts, the resulting memories will always differ, even if only slightly.”

In this chapter we discuss the idea that a task or a process can be a “pure” measure of memory, without contamination from other hypothetical memory stores or structures, and without contributions from other processes. Our impurity principle states that tasks and processes are not pure, and therefore one cannot separate out the contributions of different memory stores by using tasks thought to tap only one system; one cannot count on subjects using only one process for a particular task […]. Our principle follows from previous arguments articulated by Kolers and Roediger (1984) and Crowder (1993), among others, that because every event recruits slightly different encoding and retrieval processes, there is no such thing as “pure” memory. […] The fundamental issue is the extent to which one can determine the contribution of a particular memory system or structure or process to performance on a particular memory task. There are numerous ways of assessing memory, and many different ways of classifying tasks. […] For example, if you are given a word fragment and asked to complete it with the first word that pops in your head, you are free to try a variety of strategies. […] Very different types of processing can be used by subjects even when given the same type of test or cue. People will use any and all processes to help them answer a question.”

“A free recall test typically provides little environmental support. A list of items is presented, and the subject is asked to recall which items were on the list. […] The experimenter simply says, “Recall the words that were on the list,” […] A typical recognition test provides more environmental support. Although a comparable list of items might have been presented, and although the subject is asked again about memory for an item in context, the subject is provided with a more specific cue, and knows exactly how many items to respond to. Some tests, such as word fragment completion and general knowledge questions, offer more environmental support. These tests provide more targeted cues, and often the cues are unique […] One common processing distinction involves the aspects of the stimulus that are focused on or are salient at encoding and retrieval: Subjects can focus more on an item’s physical appearance (data driven processing) or on an item’s meaning (conceptually driven processing […]). In general, performance on tasks such as free recall that offer little environmental support is better if the rememberer uses conceptual rather than perceptual processing at encoding. Although there is perceptual information available at encoding, there is no perceptual information provided at test so data-driven processes tend not to be appropriate. Typical recognition and cued-recall tests provide more specific cues, and as such, data-driven processing becomes more appropriate, but these tasks still require the subject to discriminate which items were presented in a particular specific context; this is often better accomplished using conceptually driven processing. […] In addition to distinctions between data driven and conceptually driven processing, another common distinction is between an automatic retrieval process, which is usually referred to as familiarity, and a nonautomatic process, usually called recollection […]. Additional distinctions abound. Our point is that very different types of processing can be used by subjects on a particular task, and that tasks can differ from one another on a variety of different dimensions. In short, people can potentially use almost any combination of processes on any particular task.”

Immediate serial recall is basically synonymous with memory span. In one the first reviews of this topic, Blankenship (1938, p. 2) noted that “memory span refers to the ability of an individual to reproduce immediately, after one presentation, a series of discrete stimuli in their original order.”3 The primary use of memory span was not so much to measure the capacity of a short-term memory system, but rather as a measure of intellectual abilities […]. Early on, however, it was recognized that memory span, whatever it was, varied as function of a large number of variables […], and could even be increased substantially by practice […]. Nonetheless, memory span became increasingly seen as a measure of the capacity of a short-term memory system that was distinct from long-term memory. Generally, most individuals can recall about 7 ± 2 items (Miller, 1956) or the number of items that can be pronounced in about 2 s (Baddeley, 1986) without making any mistakes. Does immediate serial recall (or memory span) measure the capacity of short-term (or working) memory? The currently available evidence suggests that it does not. […] The main difficulty in attempting to construct a “pure” measure of immediate memory capacity is that […] the influence of previously acquired knowledge is impossible to avoid. There are numerous contributions of long-term knowledge not only to memory span and immediate serial recall […] but to other short-term tasks as well […] Our impurity principle predicts that when distinctions are made between types of processing (e.g., conceptually driven versus data driven; familiarity versus recollection; automatic versus conceptual; item specific versus relational), each of those individual processes will not be pure measures of memory.”

“Over the past 20 years great strides have been made in noninvasive techniques for measuring brain activity. In particular, PET and fMRI studies have allowed us to obtain an on-line glimpse into the hemodynamic changes that occur in the brain as stimuli are being processed, memorized, manipulated, and recalled. However, many of these studies rely on subtractive logic that explicitly assumes that (a) there are different brain areas (structures) subserving different cognitive processes and (b) we can subtract out background or baseline activity and determine which areas are responsible for performing a particular task (or process) by itself. There have been some serious challenges to these underlying assumptions […]. A basic assumption is that there is some baseline activation that is present all of the time and that the baseline is built upon by adding more activation. Thus, when the baseline is subtracted out, what is left is a relatively pure measure of the brain areas that are active in completing the higher-level task. One assumption of this method is that adding a second component to the task does not affect the simple task. However, this assumption does not always hold true. […] Even if the additive factors logic were correct, these studies often assume that a task is a pure measure of one process or another. […] Again, the point is that humans will utilize whatever resources they can recruit in order to perform a task. Individuals using different retrieval strategies (e.g., visualization, verbalization, lax or strict decision criteria, etc.) show very different patterns of brain activation even when performing the same memory task (Miller & Van Horn, 2007). This makes it extremely dangerous to assume that any task is made up of purely one process. Even though many researchers involved in neuroimaging do not make task purity assumptions, these examples “illustrate the widespread practice in functional neuroimaging of interpreting activations only in terms of the particular cognitive function being investigated (Cabeza et al., 2003, p. 390).” […] We do not mean to suggest that these studies have no value — they clearly do add to our knowledge of how cognitive functioning works — but, instead, would like to urge more caution in the interpretation of localization studies, which are sometimes taken as showing that an activated area is where some unique process takes place.”

October 6, 2018 Posted by | Biology, Books, Psychology | Leave a comment

Circadian Rhythms (I)

“Circadian rhythms are found in nearly every living thing on earth. They help organisms time their daily and seasonal activities so that they are synchronized to the external world and the predictable changes in the environment. These biological clocks provide a cross-cutting theme in biology and they are incredibly important. They influence everything, from the way growing sunflowers track the sun from east to west, to the migration timing of monarch butterflies, to the morning peaks in cardiac arrest in humans. […] Years of work underlie most scientific discoveries. Explaining these discoveries in a way that can be understood is not always easy. We have tried to keep the general reader in mind but in places perseverance on the part of the reader may be required. In the end we were guided by one of our reviewers, who said: ‘If you want to understand calculus you have to show the equations.’”

The above quote is from the book‘s foreword. I really liked this book and I was close to giving it five stars on goodreads. Below I have added some observations and links related to the first few chapters of the book’s coverage (as noted in my review on goodreads the second half of the book is somewhat technical, and I’ve not yet decided if I’ll be blogging that part of the book in much detail, if at all).

“There have been over a trillion dawns and dusks since life began some 3.8 billion years ago. […] This predictable daily solar cycle results in regular and profound changes in environmental light, temperature, and food availability as day follows night. Almost all life on earth, including humans, employs an internal biological timer to anticipate these daily changes. The possession of some form of clock permits organisms to optimize physiology and behaviour in advance of the varied demands of the day/night cycle. Organisms effectively ‘know’ the time of day. Such internally generated daily rhythms are called ‘circadian rhythms’ […] Circadian rhythms are embedded within the genomes of just about every plant, animal, fungus, algae, and even cyanobacteria […] Organisms that use circadian rhythms to anticipate the rotation of the earth are thought to have a major advantage over both their competitors and predators. For example, it takes about 20–30 minutes for the eyes of fish living among coral reefs to switch vision from the night to daytime state. A fish whose eyes are prepared in advance for the coming dawn can exploit the new environment immediately. The alternative would be to wait for the visual system to adapt and miss out on valuable activity time, or emerge into a world where it would be more difficult to avoid predators or catch prey until the eyes have adapted. Efficient use of time to maximize survival almost certainly provides a large selective advantage, and consequently all organisms seem to be led by such anticipation. A circadian clock also stops everything happening within an organism at the same time, ensuring that biological processes occur in the appropriate sequence or ‘temporal framework’. For cells to function properly they need the right materials in the right place at the right time. Thousands of genes have to be switched on and off in order and in harmony. […] All of these processes, and many others, take energy and all have to be timed to best effect by the millisecond, second, minute, day, and time of year. Without this internal temporal compartmentalization and its synchronization to the external environment our biology would be in chaos. […] However, to be biologically useful, these rhythms must be synchronized or entrained to the external environment, predominantly by the patterns of light produced by the earth’s rotation, but also by other rhythmic changes within the environment such as temperature, food availability, rainfall, and even predation. These entraining signals, or time-givers, are known as zeitgebers. The key point is that circadian rhythms are not driven by an external cycle but are generated internally, and then entrained so that they are synchronized to the external cycle.”

“It is worth emphasizing that the concept of an internal clock, as developed by Richter and Bünning, has been enormously powerful in furthering our understanding of biological processes in general, providing a link between our physiological understanding of homeostatic mechanisms, which try to maintain a constant internal environment despite unpredictable fluctuations in the external environment […], versus the circadian system which enables organisms to anticipate periodic changes in the external environment. The circadian system provides a predictive 24-hour baseline in physiological parameters, which is then either defended or temporarily overridden by homeostatic mechanisms that accommodate an acute environmental challenge. […] Zeitgebers and the entrainment pathway synchronize the internal day to the astronomical day, usually via the light/dark cycle, and multiple output rhythms in physiology and behaviour allow appropriately timed activity. The multitude of clocks within a multicellular organism can all potentially tick with a different phase angle […], but usually they are synchronized to each other and by a central pacemaker which is in turn entrained to the external world via appropriate zeitgebers. […] Most biological reactions vary greatly with temperature and show a Q10 temperature coefficient of about 2 […]. This means that the biological process or reaction rate doubles as a consequence of increasing the temperature by 10°C up to a maximum temperature at which the biological reaction stops. […] a 10°C temperature increase doubles muscle performance. By contrast, circadian rhythms exhibit a Q10 close to 1 […] Clocks without temperature compensation are useless. […] Although we know that circadian clocks show temperature compensation, and that this phenomenon is a conserved feature across all circadian rhythms, we have little idea how this is achieved.”

“The systematic study of circadian rhythms only really started in the 1950s, and the pioneering studies of Colin Pittendrigh brought coherence to this emerging new discipline. […] From [a] mass of emerging data, Pittendrigh had key insights and defined the essential properties of circadian rhythms across all life. Namely that: all circadian rhythms are endogenous and show near 24-hour rhythms in a biological process (biochemistry, physiology, or behaviour); they persist under constant conditions for several cycles; they are entrained to the astronomical day via synchronizing zeitgebers; and they show temperature compensation such that the period of the oscillation does not alter appreciably with changes in environmental temperature. Much of the research since the 1950s has been the translation of these formalisms into biological structures and processes, addressing such questions as: What is the clock and where is it located within the intracellular processes of the cell? How can a set of biochemical reactions produce a regular self-sustaining rhythm that persists under constant conditions and has a period of about 24 hours? How is this internal oscillation synchronized by zeitgebers such as light to the astronomical day? Why is the clock not altered by temperature, speeding up when the environment gets hotter and slowing down in the cold? How is the information of the near 24-hour rhythm communicated to the rest of the organism?”

“There have been hundreds of studies showing that a broad range of activities, both physical and cognitive, vary across the 24-hour day: tooth pain is lowest in the morning; proofreading is best performed in the evening; labour pains usually begin at night and most natural births occur in the early morning hours. The accuracy of short and long badminton serves is higher in the afternoon than in the morning and evening. Accuracy of first serves in tennis is better in the morning and afternoon than in the evening, although speed is higher in the evening than in the morning. Swimming velocity over 50 metres is higher in the evening than in the morning and afternoon. […] The majority of studies report that performance increases from morning to afternoon or evening. […] Typical ‘optimal’ times of day for physical or cognitive activity are gathered routinely from population studies […]. However, there is considerable individual variation. Peak performance will depend upon age, chronotype, time zone, and for behavioural tasks how many hours the participant has been awake when conducting the task, and even the nature of the task itself. As a general rule, the circadian modulation of cognitive functioning results in an improved performance over the day for younger adults, while in older subjects it deteriorates. […] On average the circadian rhythms of an individual in their late teens will be delayed by around two hours compared with an individual in their fifties. As a result the average teenager experiences considerable social jet lag, and asking a teenager to get up at 07.00 in the morning is the equivalent of asking a 50-year-old to get up at 05.00 in the morning.”

“Day versus night variations in blood pressure and heart rate are among the best-known circadian rhythms of physiology. In humans, there is a 24-hour variation in blood pressure with a sharp rise before awakening […]. Many cardiovascular events, such as sudden cardiac death, myocardial infarction, and stroke, display diurnal variations with an increased incidence between 06.00 and 12.00 in the morning. Both atrial and ventricular arrhythmias appear to exhibit circadian patterning as well, with a higher frequency during the day than at night. […] Myocardial infarction (MI) is two to three times more frequent in the morning than at night. In the early morning, the increased systolic blood pressure and heart rate results in an increased energy and oxygen demand by the heart, while the vascular tone of the coronary artery rises in the morning, resulting in a decreased coronary blood flow and oxygen supply. This mismatch between supply and demand underpins the high frequency of onset of MI. Plaque blockages are more likely to occur in the morning as platelet surface activation markers have a circadian pattern producing a peak of thrombus formation and platelet aggregation. The resulting hypercoagulability partially underlies the morning onset of MI.”

“A critical area where time of day matters to the individual is the optimum time to take medication, a branch of medicine that has been termed ‘chronotherapy’. Statins are a family of cholesterol-lowering drugs which inhibit HMGCR-reductase […] HMGCR is under circadian control and is highest at night. Hence those statins with a short half-life, such as simvastatin and lovastatin, are most effective when taken before bedtime. In another clinical domain entirely, recent studies have shown that anti-flu vaccinations given in the morning provoke a stronger immune response than those given in the afternoon. The idea of using chronotherapy to improve the efficacy of anti-cancer drugs has been around for the best part of 30 years. […] In experimental models more than thirty anti-cancer drugs have been found to vary in toxicity and efficacy by as much as 50 per cent as a function of time of administration. Although Lévi and others have shown the advantages to treating individual patients by different timing regimes, few hospitals have taken it up. One reason is that the best time to apply many of these treatments is late in the day or during the night, precisely when most hospitals lack the infrastructure and personnel to deliver such treatments.”

“Flying across multiple time zones and shift work has significant economic benefits, but the costs in terms of ill health are only now becoming clear. Sleep and circadian rhythm disruption (SCRD) is almost always associated with poor health. […] The impact of jet lag has long been known by elite athletes […] even when superbly fit individuals fly across time zones there is a very prolonged disturbance of circadian-driven rhythmic physiology. […] Horses also suffer from jet lag. […] Even bees can get jet lag. […] The misalignments that occur as a result of the occasional transmeridian flight are transient. Shift working represents a chronic misalignment. […] Nurses are one of the best-studied groups of night shift workers. Years of shift work in these individuals has been associated with a broad range of health problems including type II diabetes, gastrointestinal disorders, and even breast and colorectal cancers. Cancer risk increases with the number of years of shift work, the frequency of rotating work schedules, and the number of hours per week working at night [For people who are interested to know more about this, I previously covered a text devoted exclusively to these topics here and here.]. The correlations are so strong that shift work is now officially classified as ‘probably carcinogenic [Group 2A]’ by the World Health Organization. […] the partners and families of night shift workers need to be aware that mood swings, loss of empathy, and irritability are common features of working at night.”

“There are some seventy sleep disorders recognized by the medical community, of which four have been labelled as ‘circadian rhythm sleep disorders’ […] (1) Advanced sleep phase disorder (ASPD) […] is characterized by difficulty staying awake in the evening and difficulty staying asleep in the morning. Typically individuals go to bed and rise about three or more hours earlier than the societal norm. […] (2) Delayed sleep phase disorder (DSPD) is a far more frequent condition and is characterized by a 3-hour delay or more in sleep onset and offset and is a sleep pattern often found in some adolescents and young adults. […] ASPD and DSPD can be considered as pathological extremes of morning or evening preferences […] (3) Freerunning or non-24-hour sleep/wake rhythms occur in blind individuals who have either had their eyes completely removed or who have no neural connection from the retina to the brain. These people are not only visually blind but are also circadian blind. Because they have no means of detecting the synchronizing light signals they cannot reset their circadian rhythms, which freerun with a period of about 24 hours and 10 minutes. So, after six days, internal time is on average 1 hour behind environmental time. (4) Irregular sleep timing has been observed in individuals who lack a circadian clock as a result of a tumour in their anterior hypothalamus […]. Irregular sleep timing is [also] commonly found in older people suffering from dementia. It is an extremely important condition because one of the major factors in caring for those with dementia is the exhaustion of the carers which is often a consequence of the poor sleep patterns of those for whom they are caring. Various protocols have been attempted in nursing homes using increased light in the day areas and darkness in the bedrooms to try and consolidate sleep. Such approaches have been very successful in some individuals […] Although insomnia is the commonly used term to describe sleep disruption, technically insomnia is not a ‘circadian rhythm sleep disorder’ but rather a general term used to describe irregular or disrupted sleep. […] Insomnia is described as a ‘psychophysiological’ condition, in which mental and behavioural factors play predisposing, precipitating, and perpetuating roles. The factors include anxiety about sleep, maladaptive sleep habits, and the possibility of an underlying vulnerability in the sleep-regulating mechanism. […] Even normal ‘healthy ageing’ is associated with both circadian rhythm sleep disorders and insomnia. Both the generation and regulation of circadian rhythms have been shown to become less robust with age, with blunted amplitudes and abnormal phasing of key physiological processes such as core body temperature, metabolic processes, and hormone release. Part of the explanation may relate to a reduced light signal to the clock […]. In the elderly, the photoreceptors of the eye are often exposed to less light because of the development of cataracts and other age-related eye disease. Both these factors have been correlated with increased SCRD.”

“Circadian rhythm research has mushroomed in the past twenty years, and has provided a much greater understanding of the impact of both imposed and illness-related SCRD. We now appreciate that our increasingly 24/7 society and social disregard for biological time is having a major impact upon our health. Understanding has also been gained about the relationship between SCRD and a spectrum of different illnesses. SCRD in illness is not simply the inconvenience of being unable to sleep at an appropriate time but is an agent that exacerbates or causes serious health problems.”

Links:

Circadian rhythm.
Acrophase.
Phase (waves). Phase angle.
Jean-Jacques d’Ortous de Mairan.
Heliotropism.
Kymograph.
John Harrison.
Munich Chronotype Questionnaire.
Chronotype.
Seasonal affective disorder. Light therapy.
Parkinson’s disease. Multiple sclerosis.
Melatonin.

August 25, 2018 Posted by | Biology, Books, Cancer/oncology, Cardiology, Medicine | Leave a comment

Developmental Biology (II)

Below I have included some quotes from the middle chapters of the book and some links related to the topic coverage. As I already pointed out earlier, this is an excellent book on these topics.

Germ cells have three key functions: the preservation of the genetic integrity of the germline; the generation of genetic diversity; and the transmission of genetic information to the next generation. In all but the simplest animals, the cells of the germline are the only cells that can give rise to a new organism. So, unlike body cells, which eventually all die, germ cells in a sense outlive the bodies that produced them. They are, therefore, very special cells […] In order that the number of chromosomes is kept constant from generation to generation, germ cells are produced by a specialized type of cell division, called meiosis, which halves the chromosome number. Unless this reduction by meiosis occurred, the number of chromosomes would double each time the egg was fertilized. Germ cells thus contain a single copy of each chromosome and are called haploid, whereas germ-cell precursor cells and the other somatic cells of the body contain two copies and are called diploid. The halving of chromosome number at meiosis means that when egg and sperm come together at fertilization, the diploid number of chromosomes is restored. […] An important property of germ cells is that they remain pluripotent—able to give rise to all the different types of cells in the body. Nevertheless, eggs and sperm in mammals have certain genes differentially switched off during germ-cell development by a process known as genomic imprinting […] Certain genes in eggs and sperm are imprinted, so that the activity of the same gene is different depending on whether it is of maternal or paternal origin. Improper imprinting can lead to developmental abnormalities in humans. At least 80 imprinted genes have been identified in mammals, and some are involved in growth control. […] A number of developmental disorders in humans are associated with imprinted genes. Infants with Prader-Willi syndrome fail to thrive and later can become extremely obese; they also show mental retardation and mental disturbances […] Angelman syndrome results in severe motor and mental retardation. Beckwith-Wiedemann syndrome is due to a generalized disruption of imprinting on a region of chromosome 7 and leads to excessive foetal overgrowth and an increased predisposition to cancer.”

“Sperm are motile cells, typically designed for activating the egg and delivering their nucleus into the egg cytoplasm. They essentially consist of a nucleus, mitochondria to provide an energy source, and a flagellum for movement. The sperm contributes virtually nothing to the organism other than its chromosomes. In mammals, sperm mitochondria are destroyed following fertilization, and so all mitochondria in the animal are of maternal origin. […] Different organisms have different ways of ensuring fertilization by only one sperm. […] Early development is similar in both male and female mammalian embryos, with sexual differences only appearing at later stages. The development of the individual as either male or female is genetically fixed at fertilization by the chromosomal content of the egg and sperm that fuse to form the fertilized egg. […] Each sperm carries either an X or Y chromosome, while the egg has an X. The genetic sex of a mammal is thus established at the moment of conception, when the sperm introduces either an X or a Y chromosome into the egg. […] In the absence of a Y chromosome, the default development of tissues is along the female pathway. […] Unlike animals, plants do not set aside germ cells in the embryo and germ cells are only specified when a flower develops. Any meristem cell can, in principle, give rise to a germ cell of either sex, and there are no sex chromosomes. The great majority of flowering plants give rise to flowers that contain both male and female sexual organs, in which meiosis occurs. The male sexual organs are the stamens; these produce pollen, which contains the male gamete nuclei corresponding to the sperm of animals. At the centre of the flower are the female sex organs, which consist of an ovary of two carpels, which contain the ovules. Each ovule contains an egg cell.”

“The character of specialized cells such as nerve, muscle, or skin is the result of a particular pattern of gene activity that determines which proteins are synthesized. There are more than 200 clearly recognizable differentiated cell types in mammals. How these particular patterns of gene activity develop is a central question in cell differentiation. Gene expression is under a complex set of controls that include the actions of transcription factors, and chemical modification of DNA. External signals play a key role in differentiation by triggering intracellular signalling pathways that affect gene expression. […] the central feature of cell differentiation is a change in gene expression, which brings about a change in the proteins in the cells. The genes expressed in a differentiated cell include not only those for a wide range of ‘housekeeping’ proteins, such as the enzymes involved in energy metabolism, but also genes encoding cell-specific proteins that characterize a fully differentiated cell: hemoglobin in red blood cells, keratin in skin epidermal cells, and muscle-specific actin and myosin protein filaments in muscle. […] several thousand different genes are active in any given cell in the embryo at any one time, though only a small number of these may be involved in specifying cell fate or differentiation. […] Cell differentiation is known to be controlled by a wide range of external signals but it is important to remember that, while these external signals are often referred to as being ‘instructive’, they are ‘selective’, in the sense that the number of developmental options open to a cell at any given time is limited. These options are set by the cell’s internal state which, in turn, reflects its developmental history. External signals cannot, for example, convert an endodermal cell into a muscle or nerve cell. Most of the molecules that act as developmentally important signals between cells during development are proteins or peptides, and their effect is usually to induce a change in gene expression. […] The same external signals can be used again and again with different effects because the cells’ histories are different. […] At least 1,000 different transcription factors are encoded in the genomes of the fly and the nematode, and as many as 3,000 in the human genome. On average, around five different transcription factors act together at a control region […] In general, it can be assumed that activation of each gene involves a unique combination of transcription factors.”

“Stem cells involve some special features in relation to differentiation. A single stem cell can divide to produce two daughter cells, one of which remains a stem cell while the other gives rise to a lineage of differentiating cells. This occurs in our skin and gut all the time and also in the production of blood cells. It also occurs in the embryo. […] Embryonic stem (ES) cells from the inner cell mass of the early mammalian embryo when the primitive streak forms, can, in culture, differentiate into a wide variety of cell types, and have potential uses in regenerative medicine. […] it is now possible to make adult body cells into stem cells, which has important implications for regenerative medicine. […] The goal of regenerative medicine is to restore the structure and function of damaged or diseased tissues. As stem cells can proliferate and differentiate into a wide range of cell types, they are strong candidates for use in cell-replacement therapy, the restoration of tissue function by the introduction of new healthy cells. […] The generation of insulin-producing pancreatic β cells from ES cells to replace those destroyed in type 1 diabetes is a prime medical target. Treatments that direct the differentiation of ES cells towards making endoderm derivatives such as pancreatic cells have been particularly difficult to find. […] The neurodegenerative Parkinson disease is another medical target. […] To generate […] stem cells of the patient’s own tissue type would be a great advantage, and the recent development of induced pluripotent stem cells (iPS cells) offers […] exciting new opportunities. […] There is [however] risk of tumour induction in patients undergoing cell-replacement therapy with ES cells or iPS cells; undifferentiated pluripotent cells introduced into the patient could cause tumours. Only stringent selection procedures that ensure no undifferentiated cells are present in the transplanted cell population will overcome this problem. And it is not yet clear how stable differentiated ES cells and iPS cells will be in the long term.”

“In general, the success rate of cloning by body-cell nuclear transfer in mammals is low, and the reasons for this are not yet well understood. […] Most cloned mammals derived from nuclear transplantation are usually abnormal in some way. The cause of failure is incomplete reprogramming of the donor nucleus to remove all the earlier modifications. A related cause of abnormality may be that the reprogrammed genes have not gone through the normal imprinting process that occurs during germ-cell development, where different genes are silenced in the male and female parents. The abnormalities in adults that do develop from cloned embryos include early death, limb deformities and hypertension in cattle, and immune impairment in mice. All these defects are thought to be due to abnormalities of gene expression that arise from the cloning process. Studies have shown that some 5% of the genes in cloned mice are not correctly expressed and that almost half of the imprinted genes are incorrectly expressed.”

“Organ development involves large numbers of genes and, because of this complexity, general principles can be quite difficult to distinguish. Nevertheless, many of the mechanisms used in organogenesis are similar to those of earlier development, and certain signals are used again and again. Pattern formation in development in a variety of organs can be specified by position information, which is specified by a gradient in some property. […] Not surprisingly, the vascular system, including blood vessels and blood cells, is among the first organ systems to develop in vertebrate embryos, so that oxygen and nutrients can be delivered to the rapidly developing tissues. The defining cell type of the vascular system is the endothelial cell, which forms the lining of the entire circulatory system, including the heart, veins, and arteries. Blood vessels are formed by endothelial cells and these vessels are then covered by connective tissue and smooth muscle cells. Arteries and veins are defined by the direction of blood flow as well as by structural and functional differences; the cells are specified as arterial or venous before they form blood vessels but they can switch identity. […] Differentiation of the vascular cells requires the growth factor VEGF (vascular endothelial growth factor) and its receptors, and VEGF stimulates their proliferation. Expression of the Vegf gene is induced by lack of oxygen and thus an active organ using up oxygen promotes its own vascularization. New blood capillaries are formed by sprouting from pre-existing blood vessels and proliferation of cells at the tip of the sprout. […] During their development, blood vessels navigate along specific paths towards their targets […]. Many solid tumours produce VEGF and other growth factors that stimulate vascular development and so promote the tumour’s growth, and blocking new vessel formation is thus a means of reducing tumour growth. […] In humans, about 1 in 100 live-born infants has some congenital heart malformation, while in utero, heart malformation leading to death of the embryo occurs in between 5 and 10% of conceptions.”

“Separation of the digits […] is due to the programmed cell death of the cells between these digits’ cartilaginous elements. The webbed feet of ducks and other waterfowl are simply the result of less cell death between the digits. […] the death of cells between the digits is essential for separating the digits. The development of the vertebrate nervous system also involves the death of large numbers of neurons.”

Links:

Budding.
Gonad.
Down Syndrome.
Fertilization. In vitro fertilisation. Preimplantation genetic diagnosis.
SRY gene.
X-inactivation. Dosage compensation.
Cellular differentiation.
MyoD.
Signal transduction. Enhancer (genetics).
Epigenetics.
Hematopoiesis. Hematopoietic stem cell transplantation. Hemoglobin. Sickle cell anemia.
Skin. Dermis. Fibroblast. Epidermis.
Skeletal muscle. Myogenesis. Myoblast.
Cloning. Dolly.
Organogenesis.
Limb development. Limb bud. Progress zone model. Apical ectodermal ridge. Polarizing region/Zone of polarizing activity. Sonic hedgehog.
Imaginal disc. Pax6. Aniridia. Neural tube.
Branching morphogenesis.
Pistil.
ABC model of flower development.

July 16, 2018 Posted by | Biology, Books, Botany, Cancer/oncology, Diabetes, Genetics, Medicine, Molecular biology, Ophthalmology | Leave a comment

Oceans (II)

In this post I have added some more observations from the book and some more links related to the book‘s coverage.

“Almost all the surface waves we observe are generated by wind stress, acting either locally or far out to sea. Although the wave crests appear to move forwards with the wind, this does not occur. Mechanical energy, created by the original disturbance that caused the wave, travels through the ocean at the speed of the wave, whereas water does not. Individual molecules of water simply move back and forth, up and down, in a generally circular motion. […] The greater the wind force, the bigger the wave, the more energy stored within its bulk, and the more energy released when it eventually breaks. The amount of energy is enormous. Over long periods of time, whole coastlines retreat before the pounding waves – cliffs topple, rocks are worn to pebbles, pebbles to sand, and so on. Individual storm waves can exert instantaneous pressures of up to 30,000 kilograms […] per square metre. […] The rate at which energy is transferred across the ocean is the same as the velocity of the wave. […] waves typically travel at speeds of 30-40 kilometres per hour, and […] waves with a greater wavelength will travel faster than those with a shorter wavelength. […] With increasing wind speed and duration over which the wind blows, the wave height, period, and length all increase. The distance over which the wind blows is known as fetch, and is critical in influencing the growth of waves — the greater the area of ocean over which a storm blows, then the larger and more powerful the waves generated. The three stages in wave development are known as sea, swell, and surf. […] The ocean is highly efficient at transmitting energy. Water offers so little resistance to the small orbital motion of water particles in waves that individual wave trains may continue for thousands of kilometres. […] When the wave train encounters shallow water — say 50 metres for a 100-metre wavelength — the waves first feel the bottom and begin to slow down in response to frictional resistance. Wavelength decreases, the crests bunch closer together, and wave height increases until the wave becomes unstable and topples forwards as surf. […] Very often, waves approach obliquely to the coast and set up a significant transfer of water and sediment along the shoreline. The long-shore currents so developed can be very powerful, removing beach sand and building out spits and bars across the mouths of estuaries.” (People who’re interested in knowing more about these topics will probably enjoy Fredric Raichlen’s book on these topics – I did, US.)

“Wind is the principal force that drives surface currents, but the pattern of circulation results from a more complex interaction of wind drag, pressure gradients, and Coriolis deflection. Wind drag is a very inefficient process by which the momentum of moving air molecules is transmitted to water molecules at the ocean surface setting them in motion. The speed of water molecules (the current), initially in the direction of the wind, is only about 3–4 per cent of the wind speed. This means that a wind blowing constantly over a period of time at 50 kilometres per hour will produce a water current of about 1 knot (2 kilometres per hour). […] Although the movement of wind may seem random, changing from one day to the next, surface winds actually blow in a very regular pattern on a planetary scale. The subtropics are known for the trade winds with their strong easterly component, and the mid-latitudes for persistent westerlies. Wind drag by such large-scale wind systems sets the ocean waters in motion. The trade winds produce a pair of equatorial currents moving to the west in each ocean, while the westerlies drive a belt of currents that flow to the east at mid-latitudes in both hemispheres. […] Deflection by the Coriolis force and ultimately by the position of the continents creates very large oval-shaped gyres in each ocean.”

“The control exerted by the oceans is an integral and essential part of the global climate system. […] The oceans are one of the principal long-term stores on Earth for carbon and carbon dioxide […] The oceans are like a gigantic sponge holding fifty times more carbon dioxide than the atmosphere […] the sea surface acts as a two-way control valve for gas transfer, which opens and closes in response to two key properties – gas concentration and ocean stirring. First, the difference in gas concentration between the air and sea controls the direction and rate of gas exchange. Gas concentration in water depends on temperature—cold water dissolves more carbon dioxide than warm water, and on biological processes—such as photosynthesis and respiration by microscopic plants, animals, and bacteria that make up the plankton. These transfer processes affect all gases […]. Second, the strength of the ocean-stirring process, caused by wind and foaming waves, affects the ease with which gases are absorbed at the surface. More gas is absorbed during stormy weather and, once dissolved, is quickly mixed downwards by water turbulence. […] The transfer of heat, moisture, and other gases between the ocean and atmosphere drives small-scale oscillations in climate. The El Niño Southern Oscillation (ENSO) is the best known, causing 3–7-year climate cycles driven by the interaction of sea-surface temperature and trade winds along the equatorial Pacific. The effects are worldwide in their impact through a process of atmospheric teleconnection — causing floods in Europe and North America, monsoon failure and severe drought in India, South East Asia, and Australia, as well as decimation of the anchovy fishing industry off Peru.”

“Earth’s climate has not always been as it is today […] About 100 million years ago, for example, palm trees and crocodiles lived as far north as 80°N – the equivalent of Arctic Canada or northern Greenland today. […] Most of the geological past has enjoyed warm conditions. These have been interrupted at irregular intervals by cold and glacial climates of altogether shorter duration […][,] the last [of them] beginning around 3 million years ago. We are still in the grip of this last icehouse state, although in one of its relatively brief interglacial phases. […] Sea level has varied in the past in close consort with climate change […]. Around twenty-five thousand years ago, at the height of the last Ice Age, the global sea level was 120 metres lower than today. Huge tracts of the continental shelves that rim today’s landmasses were exposed. […] Further back in time, 80 million years ago, the sea level was around 250–350 metres higher than today, so that 82 per cent of the planet was ocean and only 18 per cent remained as dry land. Such changes have been the norm throughout geological history and entirely the result of natural causes.”

“Most of the solar energy absorbed by seawater is converted directly to heat, and water temperature is vital for the distribution and activity of life in the oceans. Whereas mean temperature ranges from 0 to 40 degrees Celsius, 90 per cent of the oceans are permanently below 5°C. Most marine animals are ectotherms (cold-blooded), which means that they obtain their body heat from their surroundings. They generally have narrow tolerance limits and are restricted to particular latitudinal belts or water depths. Marine mammals and birds are endotherms (warm-blooded), which means that their metabolism generates heat internally thereby allowing the organism to maintain constant body temperature. They can tolerate a much wider range of external conditions. Coping with the extreme (hydrostatic) pressure exerted at depth within the ocean is a challenge. For every 30 metres of water, the pressure increases by 3 atmospheres – roughly equivalent to the weight of an elephant.”

“There are at least 6000 different species of diatom. […] An average litre of surface water from the ocean contains over half a million diatoms and other unicellular phytoplankton and many thousands of zooplankton.”

“Several different styles of movement are used by marine organisms. These include floating, swimming, jet propulsion, creeping, crawling, and burrowing. […] The particular physical properties of water that most affect movement are density, viscosity, and buoyancy. Seawater is about 800 times denser than air and nearly 100 times more viscous. Consequently there is much more resistance on movement than on land […] Most large marine animals, including all fishes and mammals, have adopted some form of active swimming […]. Swimming efficiency in fishes has been achieved by minimizing the three types of drag resistance created by friction, turbulence, and body form. To reduce surface friction, the body must be smooth and rounded like a sphere. The scales of most fish are also covered with slime as further lubrication. To reduce form drag, the cross-sectional area of the body should be minimal — a pencil shape is ideal. To reduce the turbulent drag as water flows around the moving body, a rounded front end and tapered rear is required. […] Fins play a versatile role in the movement of a fish. There are several types including dorsal fins along the back, caudal or tail fins, and anal fins on the belly just behind the anus. Operating together, the beating fins provide stability and steering, forwards and reverse propulsion, and braking. They also help determine whether the motion is up or down, forwards or backwards.”

Links:

Rip current.
Rogue wave. Agulhas Current. Kuroshio Current.
Tsunami.
Tide. Tidal range.
Geostrophic current.
Ekman Spiral. Ekman transport. Upwelling.
Global thermohaline circulation system. Antarctic bottom water. North Atlantic Deep Water.
Rio Grande Rise.
Denmark Strait. Denmark Strait cataract (/waterfall?).
Atmospheric circulation. Jet streams.
Monsoon.
Cyclone. Tropical cyclone.
Ozone layer. Ozone depletion.
Milankovitch cycles.
Little Ice Age.
Oxygen Isotope Stratigraphy of the Oceans.
Contourite.
Earliest known life forms. Cyanobacteria. Prokaryote. Eukaryote. Multicellular organism. Microbial mat. Ediacaran. Cambrian explosion. Pikaia. Vertebrate. Major extinction events. Permian–Triassic extinction event. (The author seems to disagree with the authors of this article about potential causes, in particular in so far as they relate to the formation of Pangaea – as I felt uncertain about the accuracy of the claims made in the book I decided against covering this topic in this post, even though I find it interesting).
Tethys Ocean.
Plesiosauria. Pliosauroidea. Ichthyosaur. Ammonoidea. Belemnites. Pachyaena. Cetacea.
Pelagic zone. Nekton. Benthic zone. Neritic zone. Oceanic zone. Bathyal zone. Hadal zone.
Phytoplankton. Silicoflagellates. Coccolithophore. Dinoflagellate. Zooplankton. Protozoa. Tintinnid. Radiolaria. Copepods. Krill. Bivalves.
Elasmobranchii.
Ampullae of Lorenzini. Lateral line.
Baleen whale. Humpback whale.
Coral reef.
Box jellyfish. Stonefish.
Horseshoe crab.
Greenland shark. Giant squid.
Hydrothermal vent. Pompeii worms.
Atlantis II Deep. Aragonite. Phosphorite. Deep sea mining. Oil platform. Methane clathrate.
Ocean thermal energy conversion. Tidal barrage.
Mariculture.
Exxon Valdez oil spill.
Bottom trawling.

June 24, 2018 Posted by | Biology, Books, Engineering, Geology, Paleontology, Physics | Leave a comment

Developmental Biology (I)

On goodreads I called the book “[a]n excellent introduction to the field of developmental biology” and I gave it five stars.

Below I have included some sample observations from the first third of the book or so, as well as some supplementary links.

“The major processes involved in development are: pattern formation; morphogenesis or change in form; cell differentiation by which different types of cell develop; and growth. These processes involve cell activities, which are determined by the proteins present in the cells. Genes control cell behaviour by controlling where and when proteins are synthesized, and cell behaviour provides the link between gene action and developmental processes. What a cell does is determined very largely by the proteins it contains. The hemoglobin in red blood cells enables them to transport oxygen; the cells lining the vertebrate gut secrete specialized digestive enzymes. These activities require specialized proteins […] In development we are concerned primarily with those proteins that make cells different from one another and make them carry out the activities required for development of the embryo. Developmental genes typically code for proteins involved in the regulation of cell behaviour. […] An intriguing question is how many genes out of the total genome are developmental genes – that is, genes specifically required for embryonic development. This is not easy to estimate. […] Some studies suggest that in an organism with 20,000 genes, about 10% of the genes may be directly involved in development.”

“The fate of a group of cells in the early embryo can be determined by signals from other cells. Few signals actually enter the cells. Most signals are transmitted through the space outside of cells (the extracellular space) in the form of proteins secreted by one cell and detected by another. Cells may interact directly with each other by means of molecules located on their surfaces. In both these cases, the signal is generally received by receptor proteins in the cell membrane and is subsequently relayed through other signalling proteins inside the cell to produce the cellular response, usually by turning genes on or off. This process is known as signal transduction. These pathways can be very complex. […] The complexity of the signal transduction pathway means that it can be altered as the cell develops so the same signal can have a different effect on different cells. How a cell responds to a particular signal depends on its internal state and this state can reflect the cell’s developmental history — cells have good memories. Thus, different cells can respond to the same signal in very different ways. So the same signal can be used again and again in the developing embryo. There are thus rather few signalling proteins.”

“All vertebrates, despite their many outward differences, have a similar basic body plan — the segmented backbone or vertebral column surrounding the spinal cord, with the brain at the head end enclosed in a bony or cartilaginous skull. These prominent structures mark the antero-posterior axis with the head at the anterior end. The vertebrate body also has a distinct dorso-ventral axis running from the back to the belly, with the spinal cord running along the dorsal side and the mouth defining the ventral side. The antero-posterior and dorso-ventral axes together define the left and right sides of the animal. Vertebrates have a general bilateral symmetry around the dorsal midline so that outwardly the right and left sides are mirror images of each other though some internal organs such as the heart and liver are arranged asymmetrically. How these axes are specified in the embryo is a key issue. All vertebrate embryos pass through a broadly similar set of developmental stages and the differences are partly related to how and when the axes are set up, and how the embryo is nourished. […] A quite rare but nevertheless important event before gastrulation in mammalian embryos, including humans, is the splitting of the embryo into two, and identical twins can then develop. This shows the remarkable ability of the early embryo to regulate [in this context, regulation refers to ‘the ability of an embryo to restore normal development even if some portions are removed or rearranged very early in development’ – US] and develop normally when half the normal size […] In mammals, there is no sign of axes or polarity in the fertilized egg or during early development, and it only occurs later by an as yet unknown mechanism.”

“How is left–right established? Vertebrates are bilaterally symmetric about the midline of the body for many structures, such as eyes, ears, and limbs, but most internal organs are asymmetric. In mice and humans, for example, the heart is on the left side, the right lung has more lobes than the left, the stomach and spleen lie towards the left, and the bulk of the liver is towards the right. This handedness of organs is remarkably consistent […] Specification of left and right is fundamentally different from specifying the other axes of the embryo, as left and right have meaning only after the antero-posterior and dorso-ventral axes have been established. If one of these axes were reversed, then so too would be the left–right axis and this is the reason that handedness is reversed when you look in a mirror—your dorsoventral axis is reversed, and so left becomes right and vice versa. The mechanisms by which left–right symmetry is initially broken are still not fully understood, but the subsequent cascade of events that leads to organ asymmetry is better understood. The ‘leftward’ flow of extracellular fluid across the embryonic midline by a population of ciliated cells has been shown to be critical in mouse embryos in inducing asymmetric expression of genes involved in establishing left versus right. The antero-posterior patterning of the mesoderm is most clearly seen in the differences in the somites that form vertebrae: each individual vertebra has well defined anatomical characteristics depending on its location along the axis. Patterning of the skeleton along the body axis is based on the somite cells acquiring a positional value that reflects their position along the axis and so determines their subsequent development. […] It is the Hox genes that define positional identity along the antero-posterior axis […]. The Hox genes are members of the large family of homeobox genes that are involved in many aspects of development and are the most striking example of a widespread conservation of developmental genes in animals. The name homeobox comes from their ability to bring about a homeotic transformation, converting one region into another. Most vertebrates have clusters of Hox genes on four different chromosomes. A very special feature of Hox gene expression in both insects and vertebrates is that the genes in the clusters are expressed in the developing embryo in a temporal and spatial order that reflects their order on the chromosome. Genes at one end of the cluster are expressed in the head region, while those at the other end are expressed in the tail region. This is a unique feature in development, as it is the only known case where a spatial arrangement of genes on a chromosome corresponds to a spatial pattern in the embryo. The Hox genes provide the somites and adjacent mesoderm with positional values that determine their subsequent development.”

“Many of the genes that control the development of flies are similar to those controlling development in vertebrates, and indeed in many other animals. it seems that once evolution finds a satisfactory way of developing animal bodies, it tends to use the same mechanisms and molecules over and over again with, of course, some important modifications. […] The insect body is bilaterally symmetrical and has two distinct and largely independent axes: the antero-posterior and dorso-ventral axes, which are at right angles to each other. These axes are already partly set up in the fly egg, and become fully established and patterned in the very early embryo. Along the antero-posterior axis the embryo becomes divided into a number of segments, which will become the head, thorax, and abdomen of the larva. A series of evenly spaced grooves forms more or less simultaneously and these demarcate parasegments, which later give rise to the segments of the larva and adult. Of the fourteen larval parasegments, three contribute to mouthparts of the head, three to the thoracic region, and eight to the abdomen. […] Development is initiated by a gradient of the protein Bicoid, along the axis running from anterior to posterior in the egg; this provides the positional information required for further patterning along this axis. Bicoid is a transcription factor and acts as a morphogen—a graded concentration of a molecule that switches on particular genes at different threshold concentrations, thereby initiating a new pattern of gene expression along the axis. Bicoid activates anterior expression of the gene hunchback […]. The hunchback gene is switched on only when Bicoid is present above a certain threshold concentration. The protein of the hunchback gene, in turn, is instrumental in switching on the expression of the other genes, along the antero-posterior axis. […] The dorso-ventral axis is specified by a different set of maternal genes from those that specify the anterior-posterior axis, but by a similar mechanism. […] Once each parasegment is delimited, it behaves as an independent developmental unit, under the control of a particular set of genes. The parasegments are initially similar but each will soon acquire its own unique identity mainly due to Hox genes.”

“Because plant cells have rigid cell walls and, unlike animal cells, cannot move, a plant’s development is very much the result of patterns of oriented cell divisions and increase in cell size. Despite this difference, cell fate in plant development is largely determined by similar means as in animals – by a combination of positional signals and intercellular communication. […] The logic behind the spatial layouts of gene expression that pattern a developing flower is similar to that of Hox gene action in patterning the body axis in animals, but the genes involved are completely different. One general difference between plant and animal development is that most of the development occurs not in the embryo but in the growing plant. Unlike an animal embryo, the mature plant embryo inside a seed is not simply a smaller version of the organism it will become. All the ‘adult’ structures of the plant – shoots, roots, stalks, leaves, and flowers – are produced in the adult plant from localized groups of undifferentiated cells known as meristems. […] Another important difference between plant and animal cells is that a complete, fertile plant can develop from a single differentiated somatic cell and not just from a fertilized egg. This suggests that, unlike the differentiated cells of adult animals, some differentiated cells of the adult plant may retain totipotency and so behave like animal embryonic stem cells. […] The small organic molecule auxin is one of the most important and ubiquitous chemical signals in plant development and plant growth.”

“All animal embryos undergo a dramatic change in shape during their early development. This occurs primarily during gastrulation, the process that transforms a two-dimensional sheet of cells into the complex three-dimensional animal body, and involves extensive rearrangements of cell layers and the directed movement of cells from one location to another. […] Change in form is largely a problem in cell mechanics and requires forces to bring about changes in cell shape and cell migration. Two key cellular properties involved in changes in animal embryonic form are cell contraction and cell adhesiveness. Contraction in one part of a cell can change the cell’s shape. Changes in cell shape are generated by forces produced by the cytoskeleton, an internal protein framework of filaments. Animal cells stick to one another, and to the external support tissue that surrounds them (the extracellular matrix), through interactions involving cell-surface proteins. Changes in the adhesion proteins at the cell surface can therefore determine the strength of cell–cell adhesion and its specificity. These adhesive interactions affect the surface tension at the cell membrane, a property that contributes to the mechanics of the cell behaviour. Cells can also migrate, with contraction again playing a key role. An additional force that operates during morphogenesis, particularly in plants but also in a few aspects of animal embryogenesis, is hydrostatic pressure, which causes cells to expand. In plants there is no cell movement or change in shape, and changes in form are generated by oriented cell division and cell expansion. […] Localized contraction can change the shape of the cells as well as the sheet they are in. For example, folding of a cell sheet—a very common feature in embryonic development—is caused by localized changes in cell shape […]. Contraction on one side of a cell results in it acquiring a wedge-like form; when this occurs among a few cells locally in a sheet, a bend occurs at the site, deforming the sheet.”

“The integrity of tissues in the embryo is maintained by adhesive interactions between cells and between cells and the extracellular matrix; differences in cell adhesiveness also help maintain the boundaries between different tissues and structures. Cells stick to each other by means of cell adhesion molecules, such as cadherins, which are proteins on the cell surface that can bind strongly to proteins on other cell surfaces. About 30 different types of cadherins have been identified in vertebrates. […] Adhesion of a cell to the extracellular matrix, which contains proteins such as collagen, is by the binding of integrins in the cell membrane to these matrix molecules. […] Convergent extension plays a key role in gastrulation of [some] animals and […] morphogenetic processes. It is a mechanism for elongating a sheet of cells in one direction while narrowing its width, and occurs by rearrangement of cells within the sheet, rather than by cell migration or cell division. […] For convergent extension to take place, the axes along which the cells will intercalate and extend must already have been defined. […] Gastrulation in vertebrates involves a much more dramatic and complex rearrangement of tissues than in sea urchins […] But the outcome is the same: the transformation of a two-dimensional sheet of cells into a three-dimensional embryo, with ectoderm, mesoderm, and endoderm in the correct positions for further development of body structure. […] Directed dilation is an important force in plants, and results from an increase in hydrostatic pressure inside a cell. Cell enlargement is a major process in plant growth and morphogenesis, providing up to a fiftyfold increase in the volume of a tissue. The driving force for expansion is the hydrostatic pressure exerted on the cell wall as a result of the entry of water into cell vacuoles by osmosis. Plant-cell expansion involves synthesis and deposition of new cell-wall material, and is an example of directed dilation. The direction of cell growth is determined by the orientation of the cellulose fibrils in the cell wall.”

Links:

Developmental biology.
August Weismann. Hans Driesch. Hans Spemann. Hilde Mangold. Spemann-Mangold organizer.
Induction. Cleavage.
Developmental model organisms.
Blastula. Embryo. Ectoderm. Mesoderm. Endoderm.
Gastrulation.
Xenopus laevis.
Notochord.
Neurulation.
Organogenesis.
DNA. Gene. Protein. Transcription factor. RNA polymerase.
Epiblast. Trophoblast/trophectoderm. Inner cell mass.
Pluripotency.
Polarity in embryogenesis/animal-vegetal axis.
Primitive streak.
Hensen’s node.
Neural tube. Neural fold. Neural crest cells.
Situs inversus.
Gene silencing. Morpholino.
Drosophila embryogenesis.
Pair-rule gene.
Cell polarity.
Mosaic vs regulative development.
Caenorhabditis elegans.
Fate mapping.
Plasmodesmata.
Arabidopsis thaliana.
Apical-basal axis.
Hypocotyl.
Phyllotaxis.
Primordium.
Quiescent centre.
Filopodia.
Radial cleavage. Spiral cleavage.

June 11, 2018 Posted by | Biology, Books, Botany, Genetics, Molecular biology | Leave a comment

Blood (II)

Below I have added some quotes from the chapters of the book I did not cover in my first post, as well as some supplementary links.

Haemoglobin is of crucial biological importance; it is also easy to obtain safely in large quantities from donated blood. These properties have resulted in its becoming the most studied protein in human history. Haemoglobin played a key role in the history of our understanding of all proteins, and indeed the science of biochemistry itself. […] Oxygen transport defines the primary biological function of blood. […] Oxygen gas consists of two atoms of oxygen bound together to form a symmetrical molecule. However, oxygen cannot be transported in the plasma alone. This is because water is very poor at dissolving oxygen. Haemoglobin’s primary function is to increase this solubility; it does this by binding the oxygen gas on to the iron in its haem group. Every haem can bind one oxygen molecule, increasing the amount of oxygen able to dissolve in the blood.”

“An iron atom can exist in a number of different forms depending on how many electrons it has in its atomic orbitals. In its ferrous (iron II) state iron can bind oxygen readily. The haemoglobin protein has therefore evolved to stabilize its haem iron cofactor in this ferrous state. The result is that over fifty times as much oxygen is stored inside the confines of the red blood cell compared to outside in the watery plasma. However, using iron to bind oxygen comes at a cost. Iron (II) can readily lose one of its electrons to the bound oxygen, a process called ‘oxidation’. So the same form of iron that can bind oxygen avidly (ferrous) also readily reacts with that same oxygen forming an unreactive iron III state, called ‘ferric’. […] The complex structure of the protein haemoglobin is required to protect the ferrous iron from oxidizing. The haem iron is held in a precise configuration within the protein. Specific amino acids are ideally positioned to stabilize the iron–oxygen bond and prevent it from oxidizing. […] the iron stays ferrous despite the presence of the nearby oxygen. Having evolved over many hundreds of millions of years, this stability is very difficult for chemists to mimic in the laboratory. This is one reason why, desirable as it might be in terms of cost and convenience, it is not currently possible to replace blood transfusions with a simple small chemical iron oxygen carrier.”

“Given the success of the haem iron and globin combination in haemoglobin, it is no surprise that organisms have used this basic biochemical architecture for a variety of purposes throughout evolution, not just oxygen transport in blood. One example is the protein myoglobin. This protein resides inside animal cells; in the human it is found in the heart and skeletal muscle. […] Myoglobin has multiple functions. Its primary role is as an aid to oxygen diffusion. Whereas haemoglobin transports oxygen from the lung to the cell, myoglobin transports it once it is inside the cell. As oxygen is so poorly soluble in water, having a chain of molecules inside the cell that can bind and release oxygen rapidly significantly decreases the time it takes the gas to get from the blood capillary to the part of the cell—the mitochondria—where it is needed. […] Myoglobin can also act as an emergency oxygen backup store. In humans this is trivial and of questionable importance. Not so in diving mammals such as whales and dolphins that have as much as thirty times the myoglobin content of the terrestrial equivalent; indeed those mammals that dive for the longest duration have the most myoglobin. […] The third known function of myoglobin is to protect the muscle cells from damage by nitric oxide gas.”

“The heart is the organ that pumps blood around the body. If the heart stops functioning, blood does not flow. The driving force for this flow is the pressure difference between the arterial blood leaving the heart and the returning venous blood. The decreasing pressure in the venous side explains the need for unidirectional valves within veins to prevent the blood flowing in the wrong direction. Without them the return of the blood through the veins to the heart would be too slow, especially when standing up, when the venous pressure struggles to overcome gravity. […] normal [blood pressure] ranges rise slowly with age. […] high resistance in the arterial circulation at higher blood pressures [places] additional strain on the left ventricle. If the heart is weak, it may fail to achieve the extra force required to pump against this resistance, resulting in heart failure. […] in everyday life, a low blood pressure is rarely of concern. Indeed, it can be a sign of fitness as elite athletes have a much lower resting blood pressure than the rest of the population. […] the effect of exercise training is to thicken the muscles in the walls of the heart and enlarge the chambers. This enables more blood to be pumped per beat during intense exercise. The consequence of this extra efficiency is that when an athlete is resting—and therefore needs no more oxygen than a more sedentary person—the heart rate and blood pressure are lower than average. Most people’s experience of hypotension will be reflected by dizzy spells and lack of balance, especially when moving quickly to an upright position. This is because more blood pools in the legs when you stand up, meaning there is less blood for the heart to pump. The immediate effect should be for the heart to beat faster to restore the pressure. If there is a delay, the decrease in pressure can decrease the blood flow to the brain and cause dizziness; in extreme cases this can lead to fainting.”

“If hypertension is persistent, patients are most likely to be treated with drugs that target specific pathways that the body uses to control blood pressure. For example angiotensin is a protein that can trigger secretion of the hormone aldosterone from the adrenal gland. In its active form angiotensin can directly constrict blood vessels, while aldosterone enhances salt and water retention, so raising blood volume. Both these effects increase blood pressure. Angiotensin is converted into its active form by an enzyme called ‘Angiotensin Converting Enzyme’ (ACE). An ACE inhibitor drug prevents this activity, keeping angiotensin in its inactive form; this will therefore drop the patient’s blood pressure. […] The metal calcium controls many processes in the body. Its entry into muscle cells triggers muscle contraction. Preventing this entry can therefore reduce the force of contraction of the heart and the ability of arteries to constrict. Both of these will have the effect of decreasing blood pressure. Calcium enters muscle cells via specific protein-based channels. Drugs that block these channels (calcium channel blockers) are therefore highly effective at treating hypertension.”

Autoregulation is a homeostatic process designed to ensure that blood flow remains constant [in settings where constancy is desirable]. However, there are many occasions when an organism actively requires a change in blood flow. It is relatively easy to imagine what these are. In the short term, blood supplies oxygen and nutrients. When these are used up rapidly, or their supply becomes limited, the response will be to increase blood flow. The most obvious example is the twenty-fold increase in oxygen and glucose consumption that occurs in skeletal muscle during exercise when compared to rest. If there were no accompanying increase in blood flow to the muscle the oxygen supply would soon run out. […] There are hundreds of molecules known that have the ability to increase or decrease blood flow […] The surface of all blood vessels is lined by a thin layer of cells, the ‘endothelium’. Endothelial cells form a barrier between the blood and the surrounding tissue, controlling access of materials into and out of the blood. For example white blood cells can enter or leave the circulation via interacting with the endothelium; this is the route by which neutrophils migrate from the blood to the site of tissue damage or bacterial/viral attack as part of the innate immune response. However, the endothelium is not just a selective barrier. It also plays an active role in blood physiology and biochemistry.”

“Two major issues [related to blood transfusions] remained at the end of the 19th century: the problem of clotting, which all were aware of; and the problem of blood group incompatbility, which no one had the slightest idea even existed. […] For blood transfusions to ever make a recovery the key issues of blood clotting and adverse side effects needed to be resolved. In 1875 the Swedish biochemist Olof Hammarsten showed that adding calcium accelerated the rate of blood clotting (we now know the mechanism for this is that key enzymes in blood platelets that catalyse fibrin formation require calcium for their function). It therefore made sense to use chemicals that bind calcium to try to prevent clotting. Calcium ions are positively charged; adding negatively charged ions such as oxalate and citrate neutralized the calcium, preventing its clot-promoting action. […] At the same time as anticoagulants were being discovered, the reason why some blood transfusions failed even when there were no clots was becoming clear. It had been shown that animal blood given to humans tended to clump together or agglutinate, eventually bursting and releasing free haemoglobin and causing kidney damage. In the early 1900s, working in Vienna, Karl Landsteiner showed the same effect could occur with human-to-human transfusion. The trick was the ability to separate blood cells from serum. This enabled mixing blood cells from a variety of donors with plasma from a variety of participants. Using his laboratory staff as subjects, Landsteiner showed that only some combinations caused the agglutination reaction. Some donor cells (now known as type O) never clumped. Others clumped depending on the nature of the plasma in a reproducible manner. A careful study of Landsteiner’s results revealed the ABO blood type distinctions […]. Versions of these agglutination tests still form the basis of checking transfused blood today.”

“No blood product can be made completely sterile, no matter how carefully it is processed. The best that can be done is to ensure that no new bacteria or viruses are added during the purification, storage, and transportation processes. Nothing can be done to inactivate any viruses that are already present in the donor’s blood, for the harsh treatments necessary to do this would inevitably damage the viability of the product or be prohibitively expensive to implement on the industrial scale that the blood market has become. […] In the 1980s over half the US haemophiliac population was HIV positive.”

“Three fundamentally different ways have been attempted to replace red blood cell transfusions. The first uses a completely chemical approach and makes use of perfluorocarbons, inert chemicals that, in liquid form, can dissolve gasses without reacting with them. […] Perfluorocarbons can dissolve oxygen much more effectively than water. […] The problem with their use as a blood substitute is that the amount of oxygen dissolved in these solutions is linear with increasing pressure. This means that the solution lacks the advantages of the sigmoidal binding curve of haemoglobin, which has evolved to maximize the amount of oxygen captured from the limited fraction found in air (20 per cent oxygen). However, to deliver the same amount of oxygen as haemoglobin, patients using the less efficient perfluorocarbons in their blood need to breathe gas that is almost 100 per cent pure oxygen […]; this restricts the use of these compounds. […] The second type of blood substitute makes use of haemoglobin biology. Initial attempts used purified haemoglobin itself. […] there is no haemoglobin-based blood substitute in general use today […] The problem for the lack of uptake is not that blood substitutes cannot replace red blood cell function. A variety of products have been shown to stay in the vasculature for several days, provide volume support, and deliver oxygen. However, they have suffered due to adverse side effects, most notably cardiac complications. […] In nature the plasma proteins haptoglobin and haemopexin bind and detoxify any free haemoglobin and haem released from red blood cells. The challenge for blood substitute research is to mimic these effects in a product that can still deliver oxygen. […] Despite ongoing research, these problems may prove to be insurmountable. There is therefore interest in a third approach. This is to grow artificial red blood cells using stem cell technology.”

Links:

Porphyrin. Globin.
Felix Hoppe-Seyler. Jacques Monod. Jeffries Wyman. Jean-Pierre Changeux.
Allosteric regulation. Monod-Wyman-Changeux model.
Structural Biochemistry/Hemoglobin (wikibooks). (Many of the topics covered in this link – e.g. comments on affinity, T/R-states, oxygen binding curves, the Bohr effect, etc. – are also covered in the book, so although I do link to some of the other topics also covered in this link below it should be noted that I did in fact leave out quite a few potentially relevant links on account of those topics being covered in the above link).
1,3-Bisphosphoglycerate.
Erythrocruorin.
Haemerythrin.
Hemocyanin.
Cytoglobin.
Neuroglobin.
Sickle cell anemia. Thalassaemia. Hemoglobinopathy. Porphyria.
Pulse oximetry.
Daniel Bernoulli. Hydrodynamica. Stephen Hales. Karl von Vierordt.
Arterial line.
Sphygmomanometer. Korotkoff sounds. Systole. Diastole. Blood pressure. Mean arterial pressure. Hypertension. Antihypertensive drugs. Atherosclerosis Pathology. Beta blocker. Diuretic.
Autoregulation.
Guanylate cyclase. Glyceryl trinitrate.
Blood transfusion. Richard Lower. Jean-Baptiste Denys. James Blundell.
Parabiosis.
Penrose Inquiry.
ABLE (Age of Transfused Blood in Critically Ill Adults) trial.
RECESS trial.

June 7, 2018 Posted by | Biology, Books, Cardiology, Chemistry, History, Medicine, Molecular biology, Pharmacology, Studies | Leave a comment

Molecular biology (III)

Below I have added a few quotes and links related to the last few chapters of the book‘s coverage.

“Normal ageing results in part from exhaustion of stem cells, the cells that reside in most organs to replenish damaged tissue. As we age DNA damage accumulates and this eventually causes the cells to enter a permanent non-dividing state called senescence. This protective ploy however has its downside as it limits our lifespan. When too many stem cells are senescent the body is compromised in its capacity to renew worn-out tissue, causing the effects of ageing. This has a knock-on effect of poor intercellular communication, mitochondrial dysfunction, and loss of protein balance (proteostasis). Low levels of chronic inflammation also increase with ageing and could be the trigger for changes associated with many age-related disorders.”

“There has been a dramatic increase in ageing research using yeast and invertebrates, leading to the discovery of more ‘ageing genes’ and their pathways. These findings can be extrapolated to humans since longevity pathways are conserved between species. The major pathways known to influence ageing have a common theme, that of sensing and metabolizing nutrients. […] The field was advanced by identification of the mammalian Target Of Rapamycin, aptly named mTOR. mTOR acts as a molecular sensor that integrates growth stimuli with nutrient and oxygen availability. Small molecules such as rapamycin that reduce mTOR signalling act in a similar way to severe dietary restriction in slowing the ageing process in organisms such as yeast and worms. […] Rapamycin and its derivatives (rapalogs) have been involved in clinical trials on reducing age-related pathologies […] Another major ageing pathway is telomere maintenance. […] Telomere attrition is a hallmark of ageing and studies have established an association between shorter telomere length (TL) and the risk of various common age-related ailments […] Telomere loss is accelerated by known determinants of ill health […] The relationship between TL and cancer appears complex.”

“Cancer is not a single disease but a range of diseases caused by abnormal growth and survival of cells that have the capacity to spread. […] One of the early stages in the acquisition of an invasive phenotype is epithelial-mesenchymal transition (EMT). Epithelial cells form skin and membranes and for this they have a strict polarity (a top and a bottom) and are bound in position by close connections with adjacent cells. Mesenchymal cells on the other hand are loosely associated, have motility, and lack polarization. The transition between epithelial and mesenchymal cells is a normal process during embryogenesis and wound healing but is deregulated in cancer cells. EMT involves transcriptional reprogramming in which epithelial structural proteins are lost and mesenchymal ones acquired. This facilitates invasion of a tumour into surrounding tissues. […] Cancer is a genetic disease but mostly not inherited from the parents. Normal cells evolve to become cancer cells by acquiring successive mutations in cancer-related genes. There are two main classes of cancer genes, the proto-oncogenes and the tumour suppressor genes. The proto-oncogenes code for protein products that promote cell proliferation. […] A mutation in a proto-oncogene changes it to an ‘oncogene’ […] One gene above all others is associated with cancer suppression and that is TP53. […] approximately half of all human cancers carry a mutated TP53 and in many more, p53 is deregulated. […] p53 plays a key role in eliminating cells that have either acquired activating oncogenes or excessive genomic damage. Thus mutations in the TP53 gene allows cancer cells to survive and divide further by escaping cell death […] A mutant p53 not only lacks the tumour suppressor functions of the normal or wild type protein but in many cases it also takes on the role of an oncogene. […] Overall 5-10 per cent of cancers occur due to inherited or germ line mutations that are passed from parents to offspring. Many of these genes code for DNA repair enzymes […] The vast majority of cancer mutations are not inherited; instead they are sporadic with mutations arising in somatic cells. […] At least 15 per cent of cancers are attributable to infectious agents, examples being HPV and cervical cancer, H. pylori and gastric cancer, and also hepatitis B or C and liver cancer.”

“There are about 10 million different sites at which people can vary in their DNA sequence withing the 3 billion bases in our DNA. […] A few, but highly variable sequences or minisatellites are chosen for DNA profiling. These give a highly sensitive procedure suitable for use with small amounts of body fluids […] even shorter sequences called microsatellite repeats [are also] used. Each marker or microsatellite is a short tandem repeat (STR) of two to five base pairs of DNA sequence. A single STR will be shared by up to 20 per cent of the population but by using a dozen or so identification markers in profile, the error is miniscule. […] Microsatellites are extremely useful for analysing low-quality or degraded DNA left at a crime scene as their short sequences are usually preserved. However, DNA in specimens that have not been optimally preserved persists in exceedingly small amounts and is also highly fragmented. It is probably also riddled by contamination and chemical damage. Such sources of DNA sources of DNA are too degraded to obtain a profile using genomic STRs and in these cases mitochondrial DNA, being more abundant, is more useful than nuclear DNA for DNA profiling. […]  Mitochondrial DNA profiling is the method of choice for determining the identities of missing or unknown people when a maternally linked relative can be found. Molecular biologists can amplify hypervariable regions of mitochondrial DNA by PCR to obtain enough material for analysis. The DNA products are sequenced and single nucleotide differences are sought with a reference DNA from a maternal relative. […] It has now become possible for […] ancient DNA to reveal much more than genotype matches. […] Pigmentation characteristics can now be determined from ancient DNA since skin, hair, and eye colour are some of the easiest characteristics to predict. This is due to the limited number of base differences or SNPs required to explain most of the variability.”

“A broad range of debilitating and fatal conditions, non of which can be cured, are associated with mitochondrial DNA mutations. […] [M]itochondrial DNA mutates ten to thirty times faster than nuclear DNA […] Mitochondrial DNA mutates at a higher rate than nuclear DNA due to higher numbers of DNA molecules and reduced efficiency in controlling DNA replication errors. […] Over 100,000 copies of mitochondrial DNA are present in the cytoplasm of the human egg or oocyte. After fertilization, only maternal mitochondria survive; the small numbers of the father’s mitochondria in the zygote are targeted for destruction. Thus all mitochondrial DNA for all cell types in the resulting embryo is maternal-derived. […] Patients affected by mitochondrial disease usually have a mixture of wild type (normal) and mutant mitochondrial DNA and the disease severity depends on the ratio of the two. Importantly the actual level of mutant DNA in a mother’s heteroplas[m]y […curiously the authors throughout the coverage insist on spelling this ‘heteroplasty’, which according to google is something quite different – I decided to correct the spelling error (?) here – US] is not inherited and offspring can be better or worse off than the mother. This also causes uncertainty since the ratio of wild type to mutant mitochondria may change during development. […] Over 700 mutations in mitochondrial DNA have been found leading to myopathies, neurodegeneration, diabetes, cancer, and infertility.”

Links:

Dementia. Alzheimer’s disease. Amyloid hypothesis. Tau protein. Proteopathy. Parkinson’s disease. TP53-inducible glycolysis and apoptosis regulator (TIGAR).
Progeria. Progerin. Werner’s syndrome. Xeroderma pigmentosum. Cockayne syndrome.
Shelterin.
Telomerase.
Alternative lengthening of telomeres: models, mechanisms and implications (Nature).
Coats plus syndrome.
Neoplasia. Tumor angiogenesis. Inhibitor protein MDM2.
Li–Fraumeni syndrome.
Non-coding RNA networks in cancer (Nature).
Cancer stem cell. (“The reason why current cancer therapies often fail to eradicate the disease is that the CSCs survive current DNA damaging treatments and repopulate the tumour.” See also this IAS lecture which covers closely related topics – US.)
Imatinib.
Restriction fragment length polymorphism (RFLP).
CODIS.
MC1R.
Archaic human admixture with modern humans.
El Tor strain.
DNA barcoding.
Hybrid breakdown/-inviability.
Trastuzumab.
Digital PCR.
Pearson’s syndrome.
Mitochondrial replacement therapy.
Synthetic biology.
Artemisinin.
Craig Venter.
Genome editing.
Indel.
CRISPR.
Tyrosinemia.

June 3, 2018 Posted by | Biology, Books, Cancer/oncology, Genetics, Medicine, Molecular biology | Leave a comment

Blood (I)

As I also mentioned on goodreads I was far from impressed with the first few pages of this book – but I read on, and the book actually turned out to include a decent amount of very reasonable coverage. Taking into consideration the way the author started out the three star rating should be considered a high rating, and in some parts of the book the author covers very complicated stuff in a really very decent manner, considering the format of the book and its target group.

Below I have added some quotes and some links to topics/people/ideas/etc. covered in the first half of the book.

“[Clotting] makes it difficult to study the components of blood. It also [made] it impossible to store blood for transfusion [in the past]. So there was a need to find a way to prevent clotting. Fortunately the discovery that the metal calcium accelerated the rate of clotting enabled the development of a range of compounds that bound calcium and therefore prevented this process. One of them, citrate, is still in common use today [here’s a relevant link, US] when blood is being prepared for storage, or to stop blood from clotting while it is being pumped through kidney dialysis machines and other extracorporeal circuits. Adding citrate to blood, and leaving it alone, will result in gravity gradually separating the blood into three layers; the process can be accelerated by rapid spinning in a centrifuge […]. The top layer is clear and pale yellow or straw-coloured in appearance. This is the plasma, and it contains no cells. The bottom layer is bright red and contains the dense pellet of red cells that have sunk to the bottom of the tube. In-between these two layers is a very narrow layer, called the ‘buffy coat’ because of its pale yellow-brown appearance. This contains white blood cells and platelets. […] red cells, white cells, and platelets […] define the primary functions of blood: oxygen transport, immune defence, and coagulation.”

“The average human has about five trillion red blood cells per litre of blood or thirty trillion […] in total, making up a quarter of the total number of cells in the body. […] It is clear that the red cell has primarily evolved to perform a single function, oxygen transportation. Lacking a nucleus, and the requisite machinery to control the synthesis of new proteins, there is a limited ability for reprogramming or repair. […] each cell [makes] a complete traverse of the body’s circulation about once a minute. In its three- to four-month lifetime, this means every cell will do the equivalent of 150,000 laps around the body. […] Red cells lack mitochondria; they get their energy by fermenting glucose. […] A prosaic explanation for their lack of mitochondria is that it prevents the loss of any oxygen picked up from the lungs on the cells’ journey to the tissues that need it. The shape of the red cell is both deformable and elastic. In the bloodstream each cell is exposed to large shear forces. Yet, due to the properties of the membrane, they are able to constrict to enter blood vessels smaller in diameter than their normal size, bouncing back to their original shape on exiting the vessel the other side. This ability to safely enter very small openings allows capillaries to be very small. This in turn enables every cell in the body to be close to a capillary. Oxygen consequently only needs to diffuse a short distance from the blood to the surrounding tissue; this is vital as oxygen diffusion outside the bloodstream is very slow. Various pathologies, such as diabetes, peripheral vascular disease, and septic shock disturb this deformability of red blood cells, with deleterious consequences.”

“Over thirty different substances, proteins and carbohydrates, contribute to an individual’s blood group. By far the best known are the ABO and Rhesus systems. This is not because the proteins and carbohydrates that comprise these particular blood group types are vitally important for red cell function, but rather because a failure to account for these types during a blood transfusion can have catastrophic consequences. The ABO blood group is sugar-based […] blood from an O person can be safely given to anyone (with no sugar antigens this person is a ‘universal’ donor). […] As all that is needed to convert A and B to O is to remove a sugar, there is commercial and medical interest in devising ways to do this […] the Rh system […] is protein-based rather than sugar based. […] Rh proteins sit in the lipid membrane of the cell and control the transport of molecules into and out of the cell, most probably carbon dioxide and ammonia. The situation is complex, with over thirty different subgroups relating to subtle differences in the protein structure.”

“Unlike the red cells, all white cell subtypes contain nuclei. Some also contain on their surface a set of molecules called the ‘major histocompatibility complex’ (MHC). In humans, these receptors are also called ‘human leucocyte antigens’ (HLA). Their role is to recognize fragments of protein from pathogens and trigger the immune response that will ultimately destroy the invaders. Crudely, white blood cells can be divided into those that attack ‘on sight’ any foreign material — whether it be a fragment of inanimate material such as a splinter or an invading microorganism — and those that form part of a defence mechanism that recognizes specific biomolecules and marshals a slower, but equally devastating response. […] cells of the non-specific (or innate) immune system […] are divided into those that have nuclei with multiple lobed shapes (polymorphonuclear leukocytes or PMN) and those that have a single lobe nucleus ([…] ‘mononuclear leucocytes‘ or ‘MN’). PMN contain granules inside them and so are sometimes called ‘granulocytes‘.”

“Neutrophils are by far the most abundant PMN, making up over half of the total white blood cell count. The primary role of a neutrophil is to engulf a foreign object such as an invading microorganism. […] Eosinophils and basophils are the least abundant PMN cell type, each making up less than 2 per cent of white blood cells. The role of basophils is to respond to tissue injury by triggering an inflammatory response. […] When activated, basophils and mast cells degranulate, releasing molecules such as histamine, leukotrienes, and cytokines. Some of these molecules trigger an increase in blood flow causing redness and heat in the damaged site, others sensitize the area to pain. Greater permeability of the blood vessels results in plasma leaking out of the vessels and into the surrounding tissue at an increased rate, causing swelling. […] This is probably an evolutionary adaption to prevent overuse of a damaged part of the body but also helps to bring white cells and proteins to the damaged, inflamed area. […] The main function of eosinophils is to tackle invaders too large to be engulfed by neutrophils, such as the multicellular parasitic tapeworms and nematodes. […] Monocytes are a type of mononuclear leucocyte (MN) making up about 5 per cent of white blood cells. They spend even less tiem in the circulation than neutrophils, generally less than ten hours, but their time in the blood circulation does not end in death. Instead, they are converted into a cell called a ‘macrophage‘ […] Their role is similar to the neutrophil, […] the ultimate fate of both the red blood cell and the neutrophil is to be engulfed by a macrophage. An excess of monocytes in a blood count (monocytosis) is an indicator of chronic inflammation”.

“Blood has to flow freely. Therefore, the red cells, white cells, and platelets are all suspended in a watery solution called ‘plasma’. But plasma is more than just water. In fact if it were only water all the cells would burst. Plasma has to have a very similar concentration of molecules and ions as the cells. This is because cells are permeable to water. So if the concentration of dissolved substances in the plasma was significantly higher than that in the cells, water would flow from the cells to the plasma in an attempt to equalize this gradient by diluting the plasma; this would result in cell shrinkage. Even worse, if the concentration in the plasma was lower than in the cells, water would flow into the cells from the plasma, and the resulting pressure increase would burst the cells, releasing all their contents into the plasma in the process. […] Plasma contains much more than just the ions required to prevent cells bursting or shrinking. It also contains key components designed to assist in cellular function. The protein clotting factors that are part of the coagulation cascade are always present in low concentrations […] Low levels of antibodies, produced by the lymphocytes, circulate […] In addition to antibodies, the plasma contains C-reactive proteins, Mannose-binding lectin and complement proteins that function as ‘opsonins‘ […] A host of other proteins perform roles independent of oxygen delivery or immune defence. By far the most abundant protein in serum is albumin. […] Blood is the transport infrastructure for any molecule that needs to be moved around the body. Some, such as the water-soluble fuel glucose, and small hormones like insulin, dissolve freely in the plasma. Others that are less soluble hitch a ride on proteins [….] Dangerous reactive molecules, such as iron, are also bound to proteins, in this case transferrin.”

Immunoglobulins are produced by B lymphocytes and either remain bound on the surface of the cell (as part of the B cell receptor) or circulate freely in the plasma (as antibodies). Whatever their location, their purpose is the same – to bind to and capture foreign molecules (antigens). […] To perform the twin role of binding the antigen and the phagocytosing cell, immunoglobulins need to have two distinct parts to their structure — one that recognizes the foreign antigen and one that can be recognized — and destroyed — by the host defence system. The host defence system does not vary; a specific type of immunoglobulin will be recognized by one of the relatively few types of immune cells or proteins. Therefore this part of the immunoglobulin structure is not variable. But the nature of the foreign antigen will vary greatly; so the antigen-recognizing part of the structure must be highly variable. It is this that leads to the great variety of immunoglobulins. […] within the blood there is an army of potential binding sites that can recognize and bind to almost any conceivable chemical structure. Such variety is why the body is able to adapt and kill even organisms it has never encountered before. Indeed the ability to make an immunoglobulin recognize almost any structure has resulted in antibody binding assays being used historically in diagnostic tests ranging from pregnancy to drugs testing.”

“[I]mmunoglobulins consist of two different proteins — a heavy chain and a light chain. In the human heavy chain there are about forty different V (variable) segments, twenty-five different D (Diversity) segments, and six J (Joining) segments. The light chain also contains variable V and J segments. A completed immunoglobulin has a heavy chain with only one V, D, and J segment, and a light chain with only one V and D segment. It is the shuffling of these segments during development of the mature B lymphocyte that creates the diversity required […] the hypervariable regions are particularly susceptible to mutation during development. […] A separate class of immunoglobulin-like molecules also provide the key to cell-to-cell communication in the immune system. In humans, with the exception of the egg and sperm cells, all cells that possess a nucleus also have a protein on their surface called ‘Human Leucocyte Antigen (HLA) Class I’. The function of HLA Class I is to display fragments (antigens) of all the proteins currently being made inside the cell. It therefore acts like a billboard displaying the current highlights of cellular activity. Any proteins recognized as non-self by cytotoxic T cell lymphocytes will result in the whole cell being targeted for destruction […]. Another form of HLA, Class II, is only present on the surface of specialized cells of the immune system termed antigen presenting cells. In contrast to HLA Class I, the surface of HLA Class II cells displays antigens that originate from outside of the cell.”

Galen.
Bloodletting.
Marcello Malpighi.
William Harvey. De Motu Cordis.
Andreas Vesalius. De humani corporis fabrica.
Ibn al-Nafis. Michael Servetus. Realdo Colombo. Andrea Cesalpino.
Pulmonary circulation.
Hematopoietic stem cell. Bone marrow. Erythropoietin.
Hemoglobin.
Anemia.
Peroxidase.
Lymphocytes. NK cells. Granzyme. B lymphocytes. T lymphocytes. Antibody/Immunoglobulin. Lymphoblast.
Platelet. Coagulation cascade. Fibrinogen. Fibrin. Thrombin. Haemophilia. Hirudin. Von Willebrand disease. Haemophilia A. -ll- B.
Tonicity. Colloid osmotic pressure.
Adaptive immune system. Vaccination. VariolationAntiserum. Agostino Bassi. Muscardine. Louis Pasteur. Élie Metchnikoff. Paul Ehrlich.
Humoral immunity. Membrane attack complex.
Niels Kaj Jerne. David Talmage. Frank Burnet. Clonal selection theory. Peter Medawar.
Susumu Tonegawa.

June 2, 2018 Posted by | Biology, Books, Immunology, Medicine, Molecular biology | Leave a comment

Molecular biology (II)

Below I have added some more quotes and links related to the book’s coverage:

“[P]roteins are the most abundant molecules in the body except for water. […] Proteins make up half the dry weight of a cell whereas DNA and RNA make up only 3 per cent and 20 per cent respectively. […] The approximately 20,000 protein-coding genes in the human genome can, by alternative splicing, multiple translation starts, and post-translational modifications, produce over 1,000,000 different proteins, collectively called ‘the proteome‘. It is the size of the proteome and not the genome that defines the complexity of an organism. […] For simple organisms, such as viruses, all the proteins coded by their genome can be deduced from its sequence and these comprise the viral proteome. However for higher organisms the complete proteome is far larger than the genome […] For these organisms not all the proteins coded by the genome are found in any one tissue at any one time and therefore a partial proteome is usually studied. What are of interest are those proteins that are expressed in specific cell types under defined conditions.”

“Enzymes are proteins that catalyze or alter the rate of chemical reactions […] Enzymes can speed up reactions […] but they can also slow some reactions down. Proteins play a number of other critical roles. They are involved in maintaining cell shape and providing structural support to connective tissues like cartilage and bone. Specialized proteins such as actin and myosin are required [for] muscular movement. Other proteins act as ‘messengers’ relaying signals to regulate and coordinate various cell processes, e.g. the hormone insulin. Yet another class of protein is the antibodies, produced in response to foreign agents such as bacteria, fungi, and viruses.”

“Proteins are composed of amino acids. Amino acids are organic compounds with […] an amino group […] and a carboxyl group […] In addition, amino acids carry various side chains that give them their individual functions. The twenty-two amino acids found in proteins are called proteinogenic […] but other amino acids exist that are non-protein functioning. […] A peptide bond is formed between two amino acids by the removal of a water molecule. […] each individual unit in a peptide or protein is known as an amino acid residue. […] Chains of less than 50-70 amino acid residues are known as peptides or polypeptides and >50-70 as proteins, although many proteins are composed of more than one polypeptide chain. […] Proteins are macromolecules consisting of one or more strings of amino acids folded into highly specific 3D-structures. Each amino acid has a different size and carries a different side group. It is the nature of the different side groups that facilitates the correct folding of a polypeptide chain into a functional tertiary protein structure.”

“Atoms scatter the waves of X-rays mainly through their electrons, thus forming secondary or reflected waves. The pattern of X-rays diffracted by the atoms in the protein can be captured on a photographic plate or an image sensor such as a charge coupled device placed behind the crystal. The pattern and relative intensity of the spots on the diffraction image are then used to calculate the arrangement of atoms in the original protein. Complex data processing is required to convert the series of 2D diffraction or scatter patterns into a 3D image of the protein. […] The continued success and significance of this technique for molecular biology is witnessed by the fact that almost 100,000 structures of biological molecules have been determined this way, of which most are proteins.”

“The number of proteins in higher organisms far exceeds the number of known coding genes. The fact that many proteins carry out multiple functions but in a regulated manner is one way a complex proteome arises without increasing the number of genes. Proteins that performed a single role in the ancestral organism have acquired extra and often disparate functions through evolution. […] The active site of an enzyme employed in catalysis is only a small part of the protein, leaving spare capacity for acquiring a second function. […] The glycolytic pathway is involved in the breakdown of sugars such as glucose to release energy. Many of the highly conserved and ancient enzymes from this pathway have developed secondary or ‘moonlighting’ functions. Proteins often change their location in the cell in order to perform a ‘second job’. […] The limited size of the genome may not be the only evolutionary pressure for proteins to moonlight. Combining two functions in one protein can have the advantage of coordinating multiple activities in a cell, enabling it to respond quickly to changes in the environment without the need for lengthy transcription and translational processes.”

Post-translational modifications (PTMs) […] is [a] process that can modify the role of a protein by addition of chemical groups to amino acids in the peptide chain after translation. Addition of phosphate groups (phosphorylation), for example, is a common mechanism for activating or deactivating an enzyme. Other common PTMs include addition of acetyl groups (acetylation), glucose (glucosylation), or methyl groups (methylation). […] Some additions are reversible, facilitating the switching between active and inactive states, and others are irreversible such as marking a protein for destruction by ubiquitin. [The difference between reversible and irreversible modifications can be quite important in pharmacology, and if you’re curious to know more about these topics Coleman’s drug metabolism text provide great coverage of related topics – US.] Diseases caused by malfunction of these modifications highlight the importance of PTMs. […] in diabetes [h]igh blood glucose lead to unwanted glocosylation of proteins. At the high glucose concentrations associated with diabetes, an unwanted irreversible chemical reaction binds the gllucose to amino acid residues such as lysines exposed on the protein surface. The glucosylated proteins then behave badly, cross-linking themselves to the extracellular matrix. This is particularly dangerous in the kidney where it decreases function and can lead to renal failure.”

“Twenty thousand protein-coding genes make up the human genome but for any given cell only about half of these are expressed. […] Many genes get switched off during differentiation and a major mechanism for this is epigenetics. […] an epigenetic trait […] is ‘a stably heritable phenotype resulting from changes in the chromosome without alterations in the DNA sequence’. Epigenetics involves the chemical alteration of DNA by methyl or other small molecular groups to affect the accessibility of a gene by the transcription machinery […] Epigenetics can […] act on gene expression without affecting the stability of the genetic code by modifying the DNA, the histones in chromatin, or a whole chromosome. […] Epigenetic signatures are not only passed on to somatic daughter cells but they can also be transferred through the germline to the offspring. […] At first the evidence appeared circumstantial but more recent studies have provided direct proof of epigenetic changes involving gene methylation being inherited. Rodent models have provided mechanistic evidence. […] the importance of epigenetics in development is highlighted by the fact that low dietary folate, a nutrient essential for methylation, has been linked to higher risk of birth defects in the offspring.” […on the other hand, well…]

The cell cycle is divided into phases […] Transition from G1 into S phase commits the cell to division and is therefore a very tightly controlled restriction point. Withdrawal of growth factors, insufficient nucleotides, or energy to complete DNA replication, or even a damaged template DNA, would compromise the process. Problems are therefore detected and the cell cycle halted by cell cycle inhibitors before the cell has committed to DNA duplication. […] The cell cycle inhibitors inactive the kinases that promote transition through the phases, thus halting the cell cycle. […] The cell cycle can also be paused in S phase to allow time for DNA repairs to be carried out before cell division. The consequences of uncontrolled cell division are so catastrophic that evolution has provided complex checks and balances to maintain fidelity. The price of failure is apoptosis […] 50 to 70 billion cells die every day in a human adult by the controlled molecular process of apoptosis.”

“There are many diseases that arise because a particular protein is either absent or a faulty protein is produced. Administering a correct version of that protein can treat these patients. The first commercially available recombinant protein to be produced for medical use was human insulin to treat diabetes mellitus. […] (FDA) approved the recombinant insulin for clinical use in 1982. Since then over 300 protein-based recombinant pharmaceuticals have been licensed by the FDA and the European Medicines Agency (EMA) […], and many more are undergoing clinical trials. Therapeutic proteins can be produced in bacterial cells but more often mammalian cells such as the Chinese hamster ovary cell line and human fibroblasts are used as these hosts are better able to produce fully functional human protein. However, using mammalian cells is extremely expensive and an alternative is to use live animals or plants. This is called molecular pharming and is an innovative way of producing large amounts of protein relatively cheaply. […] In plant pharming, tobacco, rice, maize, potato, carrots, and tomatoes have all been used to produce therapeutic proteins. […] [One] class of proteins that can be engineered using gene-cloning technology is therapeutic antibodies. […] Therapeutic antibodies are designed to be monoclonal, that is, they are engineered so that they are specific for a particular antigen to which they bind, to block the antigen’s harmful effects. […] Monoclonal antibodies are at the forefront of biological therapeutics as they are highly specific and tend not to induce major side effects.”

“In gene therapy the aim is to restore the function of a faulty gene by introducing a correct version of that gene. […] a cloned gene is transferred into the cells of a patient. Once inside the cell, the protein encoded by the gene is produced and the defect is corrected. […] there are major hurdles to be overcome for gene therapy to be effective. One is the gene construct has to be delivered to the diseased cells or tissues. This can often be difficult […] Mammalian cells […] have complex mechanisms that have evolved to prevent unwanted material such as foreign DNA getting in. Second, introduction of any genetic construct is likely to trigger the patient’s immune response, which can be fatal […] once delivered, expression of the gene product has to be sustained to be effective. One approach to delivering genes to the cells is to use genetically engineered viruses constructed so that most of the viral genome is deleted […] Once inside the cell, some viral vectors such as the retroviruses integrate into the host genome […]. This is an advantage as it provides long-lasting expression of the gene product. However, it also poses a safety risk, as there is little control over where the viral vector will insert into the patient’s genome. If the insertion occurs within a coding gene, this may inactivate gene function. If it integrates close to transcriptional start sites, where promoters and enhancer sequences are located, inappropriate gene expression can occur. This was observed in early gene therapy trials [where some patients who got this type of treatment developed cancer as a result of it. A few more details hereUS] […] Adeno-associated viruses (AAVs) […] are often used in gene therapy applications as they are non-infectious, induce only a minimal immune response, and can be engineered to integrate into the host genome […] However, AAVs can only carry a small gene insert and so are limited to use with genes that are of a small size. […] An alternative delivery system to viruses is to package the DNA into liposomes that are then taken up by the cells. This is safer than using viruses as liposomes do not integrate into the host genome and are not very immunogenic. However, liposome uptake by the cells can be less efficient, resulting in lower expression of the gene.”

Links:

One gene–one enzyme hypothesis.
Molecular chaperone.
Protein turnover.
Isoelectric point.
Gel electrophoresis. Polyacrylamide.
Two-dimensional gel electrophoresis.
Mass spectrometry.
Proteomics.
Peptide mass fingerprinting.
Worldwide Protein Data Bank.
Nuclear magnetic resonance spectroscopy of proteins.
Immunoglobulins. Epitope.
Western blot.
Immunohistochemistry.
Crystallin. β-catenin.
Protein isoform.
Prion.
Gene expression. Transcriptional regulation. Chromatin. Transcription factor. Gene silencing. Histone. NF-κB. Chromatin immunoprecipitation.
The agouti mouse model.
X-inactive specific transcript (Xist).
Cell cycle. Cyclin. Cyclin-dependent kinase.
Retinoblastoma protein pRb.
Cytochrome c. CaspaseBcl-2 family. Bcl-2-associated X protein.
Hybridoma technology. Muromonab-CD3.
Recombinant vaccines and the development of new vaccine strategies.
Knockout mouse.
Adenovirus Vectors for Gene Therapy, Vaccination and Cancer Gene Therapy.
Genetically modified food. Bacillus thuringiensis. Golden rice.

 

May 29, 2018 Posted by | Biology, Books, Chemistry, Diabetes, Engineering, Genetics, Immunology, Medicine, Molecular biology, Pharmacology | Leave a comment

Molecular biology (I?)

“This is a great publication, considering the format. These authors in my opinion managed to get quite close to what I’d consider to be ‘the ideal level of coverage’ for books of this nature.”

The above was what I wrote in my short goodreads review of the book. In this post I’ve added some quotes from the first chapters of the book and some links to topics covered.

Quotes:

“Once the base-pairing double helical structure of DNA was understood it became apparent that by holding and preserving the genetic code DNA is the source of heredity. The heritable material must also be capable of faithful duplication every time a cell divides. The DNA molecule is ideal for this. […] The effort then concentrated on how the instructions held by the DNA were translated into the choice of the twenty different amino acids that make up proteins. […] George Gamov [yes, that George Gamov! – US] made the suggestion that information held in the four bases of DNA (A, T, C, G) must be read as triplets, called codons. Each codon, made up of three nucleotides, codes for one amino acid or a ‘start’ or ‘stop’ signal. This information, which determines an organism’s biochemical makeup, is known as the genetic code. An encryption based on three nucleotides means that there are sixty-four possible three-letter combinations. But there are only twenty amino acids that are universal. […] some amino acids can be coded for by more than one codon.”

“The mechanism of gene expression whereby DNA transfers its information into proteins was determined in the early 1960s by Sydney Brenner, Francois Jacob, and Matthew Meselson. […] Francis Crick proposed in 1958 that information flowed in one direction only: from DNA to RNA to protein. This was called the ‘Central Dogma‘ and describes how DNA is transcribed into RNA, which then acts as a messenger carrying the information to be translated into proteins. Thus the flow of information goes from DNA to RNA to proteins and information can never be transferred back from protein to nucleic acid. DNA can be copied into more DNA (replication) or into RNA (transcription) but only the information in mRNA [messenger RNA] can be translated into protein”.

“The genome is the entire DNA contained within the forty-six chromosomes located in the nucleus of each human somatic (body) cell. […] The complete human genome is composed of over 3 billion bases and contain approximately 20,000 genes that code for proteins. This is much lower than earlier estimates of 80,000 to 140,000 and astonished the scientific community when revealed through human genome sequencing. Equally surprising was the finding that genomes of much simpler organisms sequenced at the same time contained a higher number of protein-coding genes than humans. […] It is now clear that the size of the genome does not correspond with the number of protein-coding genes, and these do not determine the complexity of an organism. Protein-coding genes can be viewed as ‘transcription units’. These are made up of sequences called exons that code for amino acids, and separated by by non-coding sequences called introns. Associated with these are additional sequences termed promoters and enhancers that control the expression of that gene.”

“Some sections of the human genome code for RNA molecules that do not have the capacity to produce proteins. […] it is now becoming apparent that many play a role in controlling gene expression. Despite the importance of proteins, less than 1.5 per cent of the genome is made up of exon sequences. A recent estimate is that about 80 per cent of the genome is transcribed or involved in regulatory functions with the rest mainly composed of repetitive sequences. […] Satellite DNA […] is a short sequence repeated many thousands of times in tandem […] A second type of repetitive DNA is the telomere sequence. […] Their role is to prevent chromosomes from shortening during DNA replication […] Repetitive sequences can also be found distributed or interspersed throughout the genome. These repeats have the ability to move around the genome and are referred to as mobile or transposable DNA. […] Such movements can be harmful sometimes as gene sequences can be disrupted causing disease. […] The vast majority of transposable sequences are no longer able to move around and are considered to be ‘silent’. However, these movements have contributed, over evolutionary time, to the organization and evolution of the genome, by creating new or modified genes leading to the production of proteins with novel functions.”

“A very important property of DNA is that it can make an accurate copy of itself. This is necessary since cells die during the normal wear and tear of tissues and need to be replenished. […] DNA replication is a highly accurate process with an error occurring every 10,000 to 1 million bases in human DNA. This low frequency is because the DNA polymerases carry a proofreading function. If an incorrect nucleotide is incorporated during DNA synthesis, the polymerase detects the error and excises the incorrect base. Following excision, the polymerase reinserts the correct base and replication continues. Any errors that are not corrected through proofreading are repaired by an alternative mismatch repair mechanism. In some instances, proofreading and repair mechanisms fail to correct errors. These become permanent mutations after the next cell division cycle as they are no longer recognized as errors and are therefore propagated each time the DNA replicates.”

DNA sequencing identifies the precise linear order of the nucleotide bases A, C, G, T, in a DNA fragment. It is possible to sequence individual genes, segments of a genome, or whole genomes. Sequencing information is fundamental in helping us understand how our genome is structured and how it functions. […] The Human Genome Project, which used Sanger sequencing, took ten years to sequence and cost 3 billion US dollars. Using high-throughput sequencing, the entire human genome can now be sequenced in a few days at a cost of 3,000 US dollars. These costs are continuing to fall, making it more feasible to sequence whole genomes. The human genome sequence published in 2003 was built from DNA pooled from a number of donors to generate a ‘reference’ or composite genome. However, the genome of each individual is unique and so in 2005 the Personal Genome Project was launched in the USA aiming to sequence and analyse the genomes of 100,000 volunteers across the world. Soon after, similar projects followed in Canada and Korea and, in 2013, in the UK. […] To store and analyze the huge amounts of data, computational systems have developed in parallel. This branch of biology, called bioinformatics, has become an extremely important collaborative research area for molecular biologists drawing on the expertise of computer scientists, mathematicians, and statisticians.”

“[T]he structure of RNA differs from DNA in three fundamental ways. First, the sugar is a ribose, whereas in DNA it is a deoxyribose. Secondly, in RNA the nucleotide bases are A, G, C, and U (uracil) instead of A, G, C, and T. […] Thirdly, RNA is a single-stranded molecule unlike double-stranded DNA. It is not helical in shape but can fold to form a hairpin or stem-loop structure by base-pairing between complementary regions within the same RNA molecule. These two-dimensional secondary structures can further fold to form complex three-dimensional, tertiary structures. An RNA molecule is able to interact not only with itself, but also with other RNAs, with DNA, and with proteins. These interactions, and the variety of conformations that RNAs can adopt, enables them to carry out a wide range of functions. […] RNAs can influence many normal cellular and disease processes by regulating gene expression. RNA interference […] is one of the main ways in which gene expression is regulated.”

“Translation of the mRNA to a protein takes place in the cell cytoplasm on ribosomes. Ribosomes are cellular structures made up primarily of rRNA and proteins. At the ribosomes, the mRNA is decoded to produce a specific protein according to the rules defined by the genetic code. The correct amino acids are brought to the mRNA at the ribosomes by molecules called transfer RNAs (tRNAs). […] At the start of translation, a tRNA binds to the mRNA at the start codon AUG. This is followed by the binding of a second tRNA matching the adjacent mRNA codon. The two neighbouring amino acids linked to the tRNAs are joined together by a chemical bond called the peptide bond. Once the peptide bond forms, the first tRNA detaches leaving its amino acid behind. The ribosome then moves one codon along the mRNA and a third tRNA binds. In this way, tRNAs sequentially bind to the mRNA as the ribosome moves from codon to codon. Each time a tRNA molecule binds, the linked amino acid is transferred to the growing amino acid chain. Thus the mRNA sequence is translated into a chain of amino acids connected by peptide bonds to produce a polypeptide chain. Translation is terminated when the ribosome encounters a stop codon […]. After translation, the chain is folded and very often modified by the addition of sugar or other molecules to produce fully functional proteins.”

“The naturally occurring RNAi pathway is now extensively exploited in the laboratory to study the function of genes. It is possible to design synthetic siRNA molecules with a sequence complementary to the gene under study. These double-stranded RNA molecules are then introduced into the cell by special techniques to temporarily knock down the expression of that gene. By studying the phenotypic effects of this severe reduction of gene expression, the function of that gene can be identified. Synthetic siRNA molecules also have the potential to be used to treat diseases. If a disease is caused or enhanced by a particular gene product, then siRNAs can be designed against that gene to silence its expression. This prevents the protein which drives the disease from being produced. […] One of the major challenges to the use of RNAi as therapy is directing siRNA to the specific cells in which gene silencing is required. If released directly into the bloodstream, enzymes in the bloodstream degrade siRNAs. […] Other problems are that siRNAs can stimulate the body’s immune response and can produce off-target effects by silencing RNA molecules other than those against which they were specifically designed. […] considerable attention is currently focused on designing carrier molecules that can transport siRNA through the bloodstream to the diseased cell.”

“Both Northern blotting and RT-PCR enable the expression of one or a few genes to be measured simultaneously. In contrast, the technique of microarrays allows gene expression to be measured across the full genome of an organism in a single step. This massive scale genome analysis technique is very useful when comparing gene expression profiles between two samples. […] This can identify gene subsets that are under- or over-expressed in one sample relative to the second sample to which it is compared.”

Links:

Molecular biology.
Charles Darwin. Alfred Wallace. Gregor Mendel. Wilhelm Johannsen. Heinrich Waldeyer. Theodor Boveri. Walter Sutton. Friedrich Miescher. Phoebus Levene. Oswald Avery. Colin MacLeod. Maclyn McCarty. James Watson. Francis Crick. Rosalind Franklin. Andrew Fire. Craig Mello.
Gene. Genotype. Phenotype. Chromosome. Nucleotide. DNA. RNA. Protein.
Chargaff’s rules.
Photo 51.
Human Genome Project.
Long interspersed nuclear elements (LINEs). Short interspersed nuclear elements (SINEs).
Histone. Nucleosome.
Chromatin. Euchromatin. Heterochromatin.
Mitochondrial DNA.
DNA replication. Helicase. Origin of replication. DNA polymeraseOkazaki fragments. Leading strand and lagging strand. DNA ligase. Semiconservative replication.
Mutation. Point mutation. Indel. Frameshift mutation.
Genetic polymorphism. Single-nucleotide polymorphism (SNP).
Genome-wide association study (GWAS).
Molecular cloning. Restriction endonuclease. Multiple cloning site (MCS). Bacterial artificial chromosome.
Gel electrophoresis. Southern blot. Polymerase chain reaction (PCR). Reverse transcriptase PCR (RT-PCR). Quantitative PCR (qPCR).
GenBank. European Molecular Biology Laboratory (EMBL). Encyclopedia of DNA Elements (ENCODE).
RNA polymerase II. TATA box. Transcription factor IID. Stop codon.
Protein biosynthesis.
SmRNA (small nuclear RNA).
Untranslated region (/UTR sequences).
Transfer RNA.
Micro RNA (miRNA).
Dicer (enzyme).
RISC (RNA-induced silencing complex).
Argonaute.
Lipid-Based Nanoparticles for siRNA Delivery in Cancer Therapy.
Long non-coding RNA.
Ribozyme/catalytic RNA.
RNA-sequencing (RNA-seq).

May 5, 2018 Posted by | Biology, Books, Chemistry, Genetics, Medicine, Molecular biology | Leave a comment

A few diabetes papers of interest

i. Economic Costs of Diabetes in the U.S. in 2017.

“This study updates previous estimates of the economic burden of diagnosed diabetes and quantifies the increased health resource use and lost productivity associated with diabetes in 2017. […] The total estimated cost of diagnosed diabetes in 2017 is $327 billion, including $237 billion in direct medical costs and $90 billion in reduced productivity. For the cost categories analyzed, care for people with diagnosed diabetes accounts for 1 in 4 health care dollars in the U.S., and more than half of that expenditure is directly attributable to diabetes. People with diagnosed diabetes incur average medical expenditures of ∼$16,750 per year, of which ∼$9,600 is attributed to diabetes. People with diagnosed diabetes, on average, have medical expenditures ∼2.3 times higher than what expenditures would be in the absence of diabetes. Indirect costs include increased absenteeism ($3.3 billion) and reduced productivity while at work ($26.9 billion) for the employed population, reduced productivity for those not in the labor force ($2.3 billion), inability to work because of disease-related disability ($37.5 billion), and lost productivity due to 277,000 premature deaths attributed to diabetes ($19.9 billion). […] After adjusting for inflation, economic costs of diabetes increased by 26% from 2012 to 2017 due to the increased prevalence of diabetes and the increased cost per person with diabetes. The growth in diabetes prevalence and medical costs is primarily among the population aged 65 years and older, contributing to a growing economic cost to the Medicare program.”

The paper includes a lot of details about how they went about estimating these things, but I decided against including these details here – read the full paper if you’re interested. I did however want to add some additional details, so here goes:

Absenteeism is defined as the number of work days missed due to poor health among employed individuals, and prior research finds that people with diabetes have higher rates of absenteeism than the population without diabetes. Estimates from the literature range from no statistically significant diabetes effect on absenteeism to studies reporting 1–6 extra missed work days (and odds ratios of more absences ranging from 1.5 to 3.3) (1214). Analyzing 2014–2016 NHIS data and using a negative binomial regression to control for overdispersion in self-reported missed work days, we estimate that people with diabetes have statistically higher missed work days—ranging from 1.0 to 4.2 additional days missed per year by demographic group, or 1.7 days on average — after controlling for age-group, sex, race/ethnicity, diagnosed hypertension status (yes/no), and body weight status (normal, overweight, obese, unknown). […] Presenteeism is defined as reduced productivity while at work among employed individuals and is generally measured through worker responses to surveys. Multiple recent studies report that individuals with diabetes display higher rates of presenteeism than their peers without diabetes (12,1517). […] We model productivity loss associated with diabetes-attributed presenteeism using the estimate (6.6%) from the 2012 study—which is toward the lower end of the 1.8–38% range reported in the literature. […] Reduced performance at work […] accounted for 30% of the indirect cost of diabetes.”

It is of note that even with a somewhat conservative estimate of presenteeism, this cost component is an order of magnitude larger than the absenteeism variable. It is worth keeping in mind that this ratio is likely to be different elsewhere; due to the way the American health care system is structured/financed – health insurance is to a significant degree linked to employment – you’d expect the estimated ratio to be different from what you might observe in countries like the UK or Denmark. Some more related numbers from the paper:

Inability to work associated with diabetes is estimated using a conservative approach that focuses on unemployment related to long-term disability. Logistic regression with 2014–2016 NHIS data suggests that people aged 18–65 years with diabetes are significantly less likely to be in the workforce than people without diabetes. […] we use a conservative approach (which likely underestimates the cost associated with inability to work) to estimate the economic burden associated with reduced labor force participation. […] Study results suggest that people with diabetes have a 3.1 percentage point higher rate of being out of the workforce and receiving disability payments compared with their peers without diabetes. The diabetes effect increases with age and varies by demographic — ranging from 2.1 percentage points for non-Hispanic white males aged 60–64 years to 10.6 percentage points for non-Hispanic black females aged 55–59 years.”

“In 2017, an estimated 24.7 million people in the U.S. are diagnosed with diabetes, representing ∼7.6% of the total population (and 9.7% of the adult population). The estimated national cost of diabetes in 2017 is $327 billion, of which $237 billion (73%) represents direct health care expenditures attributed to diabetes and $90 billion (27%) represents lost productivity from work-related absenteeism, reduced productivity at work and at home, unemployment from chronic disability, and premature mortality. Particularly noteworthy is that excess costs associated with medications constitute 43% of the total direct medical burden. This includes nearly $15 billion for insulin, $15.9 billion for other antidiabetes agents, and $71.2 billion in excess use of other prescription medications attributed to higher disease prevalence associated with diabetes. […] A large portion of medical costs associated with diabetes costs is for comorbidities.”

Insulin is ~$15 billion/year, out of a total estimated cost of $327 billion. This is less than 5% of the total cost. Take note of the 70 billion. I know I’ve said this before, but it bears repeating: Most of diabetes-related costs are not related to insulin.

“…of the projected 162 million hospital inpatient days in the U.S. in 2017, an estimated 40.3 million days (24.8%) are incurred by people with diabetes [who make up ~7.6% of the population – see above], of which 22.6 million days are attributed to diabetes. About one-fourth of all nursing/residential facility days are incurred by people with diabetes. About half of all physician office visits, emergency department visits, hospital outpatient visits, and medication prescriptions (excluding insulin and other antidiabetes agents) incurred by people with diabetes are attributed to their diabetes. […] The largest contributors to the cost of diabetes are higher use of prescription medications beyond antihyperglycemic medications ($71.2 billion), higher use of hospital inpatient services ($69.7 billion), medications and supplies to directly treat diabetes ($34.6 billion), and more office visits to physicians and other health providers ($30.0 billion). Approximately 61% of all health care expenditures attributed to diabetes are for health resources used by the population aged ≥65 years […] we estimate the average annual excess expenditures for the population aged <65 years and ≥65 years, respectively, at $6,675 and $13,239. Health care expenditures attributed to diabetes generally increase with age […] The population with diabetes is older and sicker than the population without diabetes, and consequently annual medical expenditures are much higher (on average) than for people without diabetes“.

“Of the estimated 24.7 million people with diagnosed diabetes, analysis of NHIS data suggests that ∼8.1 million are in the workforce. If people with diabetes participated in the labor force at rates similar to their peers without diabetes, there would be ∼2 million additional people aged 18–64 years in the workforce.”

Comparing the 2017 estimates with those produced for 2012, the overall cost of diabetes appears to have increased by ∼25% after adjusting for inflation, reflecting an 11% increase in national prevalence of diagnosed diabetes and a 13% increase in the average annual diabetes-attributed cost per person with diabetes.”

ii. Current Challenges and Opportunities in the Prevention and Management of Diabetic Foot Ulcers.

“Diabetic foot ulcers remain a major health care problem. They are common, result in considerable suffering, frequently recur, and are associated with high mortality, as well as considerable health care costs. While national and international guidance exists, the evidence base for much of routine clinical care is thin. It follows that many aspects of the structure and delivery of care are susceptible to the beliefs and opinion of individuals. It is probable that this contributes to the geographic variation in outcome that has been documented in a number of countries. This article considers these issues in depth and emphasizes the urgent need to improve the design and conduct of clinical trials in this field, as well as to undertake systematic comparison of the results of routine care in different health economies. There is strong suggestive evidence to indicate that appropriate changes in the relevant care pathways can result in a prompt improvement in clinical outcomes.”

“Despite considerable advances made over the last 25 years, diabetic foot ulcers (DFUs) continue to present a very considerable health care burden — one that is widely unappreciated. DFUs are common, the median time to healing without surgery is of the order of 12 weeks, and they are associated with a high risk of limb loss through amputation (14). The 5-year survival following presentation with a new DFU is of the order of only 50–60% and hence worse than that of many common cancers (4,5). While there is evidence that mortality is improving with more widespread use of cardiovascular risk reduction (6), the most recent data — derived from a Veterans Health Adminstration population—reported that 1-, 2-, and 5-year survival was only 81, 69, and 29%, respectively, and the association between mortality and DFU was stronger than that of any macrovascular disease (7). […] There is […] wide variation in clinical outcome within the same country (1315), suggesting that some people are being managed considerably less well than others.”

“Data on community-wide ulcer incidence are very limited. Overall incidences of 5.8 and 6.0% have been reported in selected populations of people with diabetes in the U.S. (2,12,20) while incidences of 2.1 and 2.2% have been reported from less selected populations in Europe—either in all people with diabetes (21) or in those with type 2 disease alone (22). It is not known whether the incidence is changing […] Although a number of risk factors associated with the development of ulceration are well recognized (23), there is no consensus on which dominate, and there are currently no reports of any studies that might justify the adoption of any specific strategy for population selection in primary prevention.”

“The incidence of major amputation is used as a surrogate measure of the failure of DFUs to heal. Its main value lies in the relative ease of data capture, but its value is limited because it is essentially a treatment and not a true measure of disease outcome. In no other major disease (including malignancies, cardiovascular disease, or cerebrovascular disease) is the number of treatments used as a measure of outcome. But despite this and other limitations of major amputation as an outcome measure (36), there is evidence that the overall incidence of major amputation is falling in some countries with nationwide databases (37,38). Perhaps the most convincing data come from the U.K., where the unadjusted incidence has fallen dramatically from about 3.0–3.5 per 1,000 people with diabetes per year in the mid-1990s to 1.0 or less per 1,000 per year in both England and Scotland (14,39).”

New ulceration after healing is high, with ∼40% of people having a new ulcer (whether at the same site or another) within 12 months (10). This is a critical aspect of diabetic foot disease—emphasizing that when an ulcer heals, foot disease must be regarded not as cured, but in remission (10). In this respect, diabetic foot disease is directly analogous to malignancy. It follows that the person whose foot disease is in remission should receive the same structured follow-up as a person who is in remission following treatment for cancer. Of all areas concerned with the management of DFUs, this long-term need for specialist surveillance is arguably the one that should command the greatest attention.

“There is currently little evidence to justify the adoption of very many of the products and procedures currently promoted for use in clinical practice. Guidelines are required to encourage clinicians to adopt only those treatments that have been shown to be effective in robust studies and principally in RCTs. The design and conduct of such RCTs needs improved governance because many are of low standard and do not always provide the evidence that is claimed.”

Incidence numbers like the ones included above will not always give you the full picture when there are a lot of overlapping data points in the sample (due to recurrence), but sometimes that’s all you have. However in the type 1 context we also do have some additional numbers that make it easier to appreciate the scale of the problem in that context. Here are a few additional data from a related publication I blogged some time ago (do keep in mind that estimates are likely to be lower in community samples of type 2 diabetics, even if perhaps nobody actually know precisely how much lower):

“The rate of nontraumatic amputation in T1DM is high, occurring at 0.4–7.2% per year (28). By 65 years of age, the cumulative probability of lower-extremity amputation in a Swedish administrative database was 11% for women with T1DM and 20.7% for men (10). In this Swedish population, the rate of lower-extremity amputation among those with T1DM was nearly 86-fold that of the general population.” (link)

Do keep in mind that people don’t stop getting ulcers once they reach retirement age (the 11%/20.7% is not lifetime risk, it’s a biased lower bound).

iii. Excess Mortality in Patients With Type 1 Diabetes Without Albuminuria — Separating the Contribution of Early and Late Risks.

“The current study investigated whether the risk of mortality in patients with type 1 diabetes without any signs of albuminuria is different than in the general population and matched control subjects without diabetes.”

“Despite significant improvements in management, type 1 diabetes remains associated with an increase in mortality relative to the age- and sex-matched general population (1,2). Acute complications of diabetes may initially account for this increased risk (3,4). However, with increasing duration of disease, the leading contributor to excess mortality is its vascular complications including diabetic kidney disease (DKD) and cardiovascular disease (CVD). Consequently, patients who subsequently remain free of complications may have little or no increased risk of mortality (1,2,5).”

“Mortality was evaluated in a population-based cohort of 10,737 children (aged 0–14 years) with newly diagnosed type 1 diabetes in Finland who were listed on the National Public Health Institute diabetes register, Central Drug Register, and Hospital Discharge Register in 1980–2005 […] We excluded patients with type 2 diabetes and diabetes occurring secondary to other conditions, such as steroid use, Down syndrome, and congenital malformations of the pancreas. […] FinnDiane participants who died were more likely to be male, older, have a longer duration of diabetes, and later age of diabetes onset […]. Notably, none of the conventional variables associated with complications (e.g., HbA1c, hypertension, smoking, lipid levels, or AER) were associated with all-cause mortality in this cohort of patients without albuminuria. […] The most frequent cause of death in the FinnDiane cohort was IHD [ischaemic heart disease, US] […], largely driven by events in patients with long-standing diabetes and/or previously established CVD […]. The mortality rate ratio for IHD was 4.34 (95% CI 2.49–7.57, P < 0.0001). There remained a number of deaths due to acute complications of diabetes, including ketoacidosis and hypoglycemia. This was most significant in patients with a shorter duration of diabetes but still apparent in those with long-standing diabetes[…]. Notably, deaths due to “risk-taking behavior” were lower in adults with type 1 diabetes compared with matched individuals without diabetes: mortality rate ratio was 0.42 (95% CI 0.22–0.79, P = 0.006) […] This was largely driven by the 80% reduction (95% CI 0.06–0.66) in deaths due to alcohol and drugs in males with type 1 diabetes (Table 3). No reduction was observed in female patients (rate ratio 0.90 [95% CI 0.18–4.44]), although the absolute event rate was already more than seven times lower in Finnish women than in men.”

The chief determinant of excess mortality in patients with type 1 diabetes is its complications. In the first 10 years of type 1 diabetes, the acute complications of diabetes dominate and result in excess mortality — more than twice that observed in the age- and sex-matched general population. This early excess explains why registry studies following patients with type 1 diabetes from diagnosis have consistently reported reduced life expectancy, even in patients free of chronic complications of diabetes (68). By contrast, studies of chronic complications, like FinnDiane and the Pittsburgh Epidemiology of Diabetes Complications Study (1,2), have followed participants with, usually, >10 years of type 1 diabetes at baseline. In these patients, the presence or absence of chronic complications of diabetes is critical for survival. In particular, the presence and severity of albuminuria (as a marker of vascular burden) is strongly associated with mortality outcomes in type 1 diabetes (1). […] the FinnDiane normoalbuminuric patients showed increased all-cause mortality compared with the control subjects without diabetes in contrast to when the comparison was made with the Finnish general population, as in our previous publication (1). Two crucial causes behind the excess mortality were acute diabetes complications and IHD. […] Comparisons with the general population, rather than matched control subjects, may overestimate expected mortality, diluting the SMR estimate”.

Despite major improvements in the delivery of diabetes care and other technological advances, acute complications remain a major cause of death both in children and in adults with type 1 diabetes. Indeed, the proportion of deaths due to acute events has not changed significantly over the last 30 years. […] Even in patients with long-standing diabetes (>20 years), the risk of death due to hypoglycemia or ketoacidosis remains a constant companion. […] If it were possible to eliminate all deaths from acute events, the observed mortality rate would have been no different from the general population in the early cohort. […] In long-term diabetes, avoiding chronic complications may be associated with mortality rates comparable with those of the general population; although death from IHD remains increased, this is offset by reduced risk-taking behavior, especially in men.”

“It is well-known that CVD is strongly associated with DKD (15). However, in the current study, mortality from IHD remained higher in adults with type 1 diabetes without albuminuria compared with matched control subjects in both men and women. This is concordant with other recent studies also reporting increased mortality from CVD in patients with type 1 diabetes in the absence of DKD (7,8) and reinforces the need for aggressive cardiovascular risk reduction even in patients without signs of microvascular disease. However, it is important to note that the risk of death from CVD, though significant, is still at least 10-fold lower than observed in patients with albuminuria (1). Alcohol- and drug-related deaths were substantially lower in patients with type 1 diabetes compared with the age-, sex-, and region-matched control subjects. […] This may reflect a selection bias […] Nonparticipation in health studies is associated with poorer health, stress, and lower socioeconomic status (17,18), which are in turn associated with increased risk of premature mortality. It can be speculated that with inclusion of patients with risk-taking behavior, the mortality rate in patients with diabetes would be even higher and, consequently, the SMR would also be significantly higher compared with the general population. Selection of patients who despite long-standing diabetes remained free of albuminuria may also have included individuals more accepting of general health messages and less prone to depression and nihilism arising from treatment failure.”

I think the selection bias problem is likely to be quite significant, as these results don’t really match what I’ve seen in the past. For example a recent Norwegian study on young type 1 diabetics found high mortality in their sample in significant degree due to alcohol-related causes and suicide: “A relatively high proportion of deaths were related to alcohol. […] Death was related to alcohol in 15% of cases. SMR for alcohol-related death was 6.8 (95% CI 4.5–10.3), for cardiovascular death was 7.3 (5.4–10.0), and for violent death was 3.6 (2.3–5.3).” That doesn’t sound very similar to the study above, and that study’s also from Scandinavia. In this study, in which they used data from diabetic organ donors, they found that a large proportion of the diabetics included in the study used illegal drugs: “we observed a high rate of illicit substance abuse: 32% of donors reported or tested positive for illegal substances (excluding marijuana), and multidrug use was common.”

Do keep in mind that one of the main reasons why ‘alcohol-related’ deaths are higher in diabetes is likely to be that ‘drinking while diabetic’ is a lot more risky than is ‘drinking while not diabetic’. On a related note, diabetics may not appreciate the level of risk they’re actually exposed to while drinking, due to community norms etc., so there might be a disconnect between risk preferences and observed behaviour (i.e., a diabetic might be risk averse but still engage in risky behaviours because he doesn’t know how risky those behaviours in which he’s engaging actually are).

Although the illicit drugs study indicates that diabetics at least in some samples are not averse to engaging in risky behaviours, a note of caution is probably warranted in the alcohol context: High mortality from alcohol-mediated acute complications needn’t be an indication that diabetics drink more than non-diabetics; that’s a separate question, you might see numbers like these even if they in general drink less. And a young type 1 diabetic who suffers a cardiac arrhythmia secondary to long-standing nocturnal hypoglycemia and subsequently is found ‘dead in bed’ after a bout of drinking is conceptually very different from a 50-year old alcoholic dying from a variceal bleed or acute pancreatitis. Parenthetically, if it is true that illicit drugs use is common in type 1 diabetics one reason might be that they are aware of the risks associated with alcohol (which is particularly nasty in terms of the metabolic/glycemic consequences in diabetes, compared to some other drugs) and thus they deliberately make a decision to substitute this drug with other drugs less likely to cause acute complications like severe hypoglycemic episodes or DKA (depending on the setting and the specifics, alcohol might be a contributor to both of these complications). If so, classical ‘risk behaviours’ may not always be ‘risk behaviours’ in diabetes. You need to be careful, this stuff’s complicated.

iv. Are All Patients With Type 1 Diabetes Destined for Dialysis if They Live Long Enough? Probably Not.

“Over the past three decades there have been numerous innovations, supported by large outcome trials that have resulted in improved blood glucose and blood pressure control, ultimately reducing cardiovascular (CV) risk and progression to nephropathy in type 1 diabetes (T1D) (1,2). The epidemiological data also support the concept that 25–30% of people with T1D will progress to end-stage renal disease (ESRD). Thus, not everyone develops progressive nephropathy that ultimately requires dialysis or transplantation. This is a result of numerous factors […] Data from two recent studies reported in this issue of Diabetes Care examine the long-term incidence of chronic kidney disease (CKD) in T1D. Costacou and Orchard (7) examined a cohort of 932 people evaluated for 50-year cumulative kidney complication risk in the Pittsburgh Epidemiology of Diabetes Complications study. They used both albuminuria levels and ESRD/transplant data for assessment. By 30 years’ duration of diabetes, ESRD affected 14.5% and by 40 years it affected 26.5% of the group with onset of T1D between 1965 and 1980. For those who developed diabetes between 1950 and 1964, the proportions developing ESRD were substantially higher at 34.6% at 30 years, 48.5% at 40 years, and 61.3% at 50 years. The authors called attention to the fact that ESRD decreased by 45% after 40 years’ duration between these two cohorts, emphasizing the beneficial roles of improved glycemic control and blood pressure control. It should also be noted that at 40 years even in the later cohort (those diagnosed between 1965 and 1980), 57.3% developed >300 mg/day albuminuria (7).”

Numbers like these may seem like ancient history (data from the 60s and 70s), but it’s important to keep in mind that many type 1 diabetics are diagnosed in early childhood, and that they don’t ‘get better’ later on – if they’re still alive, they’re still diabetic. …And very likely macroalbuminuric, at least if they’re from Pittsburgh. I was diagnosed in ’87.

“Gagnum et al. (8), using data from a Norwegian registry, also examined the incidence of CKD development over a 42-year follow-up period in people with childhood-onset (<15 years of age) T1D (8). The data from the Norwegian registry noted that the cumulative incidence of ESRD was 0.7% after 20 years and 5.3% after 40 years of T1D. Moreover, the authors noted the risk of developing ESRD was lower in women than in men and did not identify any difference in risk of ESRD between those diagnosed with diabetes in 1973–1982 and those diagnosed in 1989–2012. They concluded that there is a very low incidence of ESRD among patients with childhood-onset T1D diabetes in Norway, with a lower risk in women than men and among those diagnosed at a younger age. […] Analyses of population-based studies, similar to the Pittsburgh and Norway studies, showed that after 30 years of T1D the cumulative incidences of ESRD were only 10% for those diagnosed with T1D in 1961–1984 and 3% for those diagnosed in 1985–1999 in Japan (11), 3.3% for those diagnosed with T1D in 1977–2007 in Sweden (12), and 7.8% for those diagnosed with T1D in 1965–1999 in Finland (13) (Table 1).”

Do note that ESRD (end stage renal disease) is not the same thing as DKD (diabetic kidney disease), and that e.g. many of the Norwegians who did not develop ESRD nevertheless likely have kidney complications from their diabetes. That 5.3% is not the number of diabetics in that cohort who developed diabetes-related kidney complications, it’s the proportion of them who did and as a result of this needed a new kidney or dialysis in order not to die very soon. Do also keep in mind that both microalbuminuria and macroalbuminuria will substantially increase the risk of cardiovascular disease and -cardiac death. I recall a study where they looked at the various endpoints and found that more diabetics with microalbuminuria eventually died of cardiovascular disease than did ever develop kidney failure – cardiac risk goes up a lot long before end-stage renal disease. ESRD estimates don’t account for the full risk profile, and even if you look at mortality risk the number accounts for perhaps less than half of the total risk attributable to DKD. One thing the ESRD diagnosis does have going for it is that it’s a much more reliable variable indicative of significant pathology than is e.g. microalbuminuria (see e.g. this paper). The paper is short and not at all detailed, but they do briefly discuss/mention these issues:

“…there is a substantive difference between the numbers of people with stage 3 CKD (estimated glomerular filtration rate [eGFR] 30–59 mL/min/1.73 m2) versus those with stages 4 and 5 CKD (eGFR <30 mL/min/1.73 m2): 6.7% of the National Health and Nutrition Examination Survey (NHANES) population compared with 0.1–0.3%, respectively (14). This is primarily because of competing risks, such as death from CV disease that occurs in stage 3 CKD; hence, only the survivors are progressing into stages 4 and 5 CKD. Overall, these studies are very encouraging. Since the 1980s, risk of ESRD has been greatly reduced, while risk of CKD progression persists but at a slower rate. This reduced ESRD rate and slowed CKD progression is largely due to improvements in glycemic and blood pressure control and probably also to the institution of RAAS blockers in more advanced CKD. These data portend even better future outcomes if treatment guidance is followed. […] many medications are effective in blood pressure control, but RAAS blockade should always be a part of any regimen when very high albuminuria is present.”

v. New Understanding of β-Cell Heterogeneity and In Situ Islet Function.

“Insulin-secreting β-cells are heterogeneous in their regulation of hormone release. While long known, recent technological advances and new markers have allowed the identification of novel subpopulations, improving our understanding of the molecular basis for heterogeneity. This includes specific subpopulations with distinct functional characteristics, developmental programs, abilities to proliferate in response to metabolic or developmental cues, and resistance to immune-mediated damage. Importantly, these subpopulations change in disease or aging, including in human disease. […] We will discuss recent findings revealing functional β-cell subpopulations in the intact islet, the underlying basis for these identified subpopulations, and how these subpopulations may influence in situ islet function.”

I won’t cover this one in much detail, but this part was interesting:

“Gap junction (GJ) channels electrically couple β-cells within mouse and human islets (25), serving two main functions. First, GJ channels coordinate oscillatory dynamics in electrical activity and Ca2+ under elevated glucose or GLP-1, allowing pulsatile insulin secretion (26,27). Second, GJ channels lower spontaneous elevations in Ca2+ under low glucose levels (28). GJ coupling is also heterogeneous within the islet (29), leading to some β-cells being highly coupled and others showing negligible coupling. Several studies have examined how electrically heterogeneous cells interact via GJ channels […] This series of experiments indicate a “bistability” in islet function, where a threshold number of poorly responsive β-cells is sufficient to totally suppress islet function. Notably, when islets lacking GJ channels are treated with low levels of the KATP activator diazoxide or the GCK inhibitor mannoheptulose, a subpopulation of cells are silenced, presumably corresponding to the less functional population (30). Only diazoxide/mannoheptulose concentrations capable of silencing >40% of these cells will fully suppress Ca2+ elevations in normal islets. […] this indicates that a threshold number of poorly responsive cells can inhibit the whole islet. Thus, if there exists a threshold number of functionally competent β-cells (∼60–85%), then the islet will show coordinated elevations in Ca2+ and insulin secretion.

Below this threshold number, the islet will lack Ca2+ elevation and insulin secretion (Fig. 2). The precise threshold depends on the characteristics of the excitable and inexcitable populations: small numbers of inexcitable cells will increase the number of functionally competent cells required for islet activity, whereas small numbers of highly excitable cells will do the opposite. However, if GJ coupling is lowered, then inexcitable cells will exert a reduced suppression, also decreasing the threshold required. […] Paracrine communication between β-cells and other endocrine cells is also important for regulating insulin secretion. […] Little is known how these paracrine and juxtacrine mechanisms impact heterogeneous cells.”

vi. Closing in on the Mechanisms of Pulsatile Insulin Secretion.

“Insulin secretion from pancreatic islet β-cells occurs in a pulsatile fashion, with a typical period of ∼5 min. The basis of this pulsatility in mouse islets has been investigated for more than four decades, and the various theories have been described as either qualitative or mathematical models. In many cases the models differ in their mechanisms for rhythmogenesis, as well as other less important details. In this Perspective, we describe two main classes of models: those in which oscillations in the intracellular Ca2+ concentration drive oscillations in metabolism, and those in which intrinsic metabolic oscillations drive oscillations in Ca2+ concentration and electrical activity. We then discuss nine canonical experimental findings that provide key insights into the mechanism of islet oscillations and list the models that can account for each finding. Finally, we describe a new model that integrates features from multiple earlier models and is thus called the Integrated Oscillator Model. In this model, intracellular Ca2+ acts on the glycolytic pathway in the generation of oscillations, and it is thus a hybrid of the two main classes of models. It alone among models proposed to date can explain all nine key experimental findings, and it serves as a good starting point for future studies of pulsatile insulin secretion from human islets.”

This one covers material closely related to the study above, so if you find one of these papers interesting you might want to check out the other one as well. The paper is quite technical but if you were wondering why people are interested in this kind of stuff, one reason is that there’s good evidence at this point that insulin pulsativity is disturbed in type 2 diabetics and so it’d be nice to know why that is so that new drugs can be developed to correct this.

April 25, 2018 Posted by | Biology, Cardiology, Diabetes, Epidemiology, Health Economics, Medicine, Nephrology, Pharmacology, Studies | Leave a comment

Networks

I actually think this was a really nice book, considering the format – I gave it four stars on goodreads. One of the things I noticed people didn’t like about it in the reviews is that it ‘jumps’ a bit in terms of topic coverage; it covers a wide variety of applications and analytical settings. I mostly don’t consider this a weakness of the book – even if occasionally it does get a bit excessive – and I can definitely understand the authors’ choice of approach; it’s sort of hard to illustrate the potential the analytical techniques described within this book have if you’re not allowed to talk about all the areas in which they have been – or could be gainfully – applied. A related point is that many people who read the book might be familiar with the application of these tools in specific contexts but have perhaps not thought about the fact that similar methods are applied in many other areas (and they might all of them be a bit annoyed the authors don’t talk more about computer science applications, or foodweb analyses, or infectious disease applications, or perhaps sociometry…). Most of the book is about graph-theory-related stuff, but a very decent amount of the coverage deals with applications, in a broad sense of the word at least, not theory. The discussion of theoretical constructs in the book always felt to me driven to a large degree by their usefulness in specific contexts.

I have covered related topics before here on the blog, also quite recently – e.g. there’s at least some overlap between this book and Holland’s book about complexity theory in the same series (I incidentally think these books probably go well together) – and as I found the book slightly difficult to blog as it was I decided against covering it in as much detail as I sometimes do when covering these texts – this means that I decided to leave out the links I usually include in posts like these.

Below some quotes from the book.

“The network approach focuses all the attention on the global structure of the interactions within a system. The detailed properties of each element on its own are simply ignored. Consequently, systems as different as a computer network, an ecosystem, or a social group are all described by the same tool: a graph, that is, a bare architecture of nodes bounded by connections. […] Representing widely different systems with the same tool can only be done by a high level of abstraction. What is lost in the specific description of the details is gained in the form of universality – that is, thinking about very different systems as if they were different realizations of the same theoretical structure. […] This line of reasoning provides many insights. […] The network approach also sheds light on another important feature: the fact that certain systems that grow without external control are still capable of spontaneously developing an internal order. […] Network models are able to describe in a clear and natural way how self-organization arises in many systems. […] In the study of complex, emergent, and self-organized systems (the modern science of complexity), networks are becoming increasingly important as a universal mathematical framework, especially when massive amounts of data are involved. […] networks are crucial instruments to sort out and organize these data, connecting individuals, products, news, etc. to each other. […] While the network approach eliminates many of the individual features of the phenomenon considered, it still maintains some of its specific features. Namely, it does not alter the size of the system — i.e. the number of its elements — or the pattern of interaction — i.e. the specific set of connections between elements. Such a simplified model is nevertheless enough to capture the properties of the system. […] The network approach [lies] somewhere between the description by individual elements and the description by big groups, bridging the two of them. In a certain sense, networks try to explain how a set of isolated elements are transformed, through a pattern of interactions, into groups and communities.”

“[T]he random graph model is very important because it quantifies the properties of a totally random network. Random graphs can be used as a benchmark, or null case, for any real network. This means that a random graph can be used in comparison to a real-world network, to understand how much chance has shaped the latter, and to what extent other criteria have played a role. The simplest recipe for building a random graph is the following. We take all the possible pair of vertices. For each pair, we toss a coin: if the result is heads, we draw a link; otherwise we pass to the next pair, until all the pairs are finished (this means drawing the link with a probability p = ½, but we may use whatever value of p). […] Nowadays [the random graph model] is a benchmark of comparison for all networks, since any deviations from this model suggests the presence of some kind of structure, order, regularity, and non-randomness in many real-world networks.”

“…in networks, topology is more important than metrics. […] In the network representation, the connections between the elements of a system are much more important than their specific positions in space and their relative distances. The focus on topology is one of its biggest strengths of the network approach, useful whenever topology is more relevant than metrics. […] In social networks, the relevance of topology means that social structure matters. […] Sociology has classified a broad range of possible links between individuals […]. The tendency to have several kinds of relationships in social networks is called multiplexity. But this phenomenon appears in many other networks: for example, two species can be connected by different strategies of predation, two computers by different cables or wireless connections, etc. We can modify a basic graph to take into account this multiplexity, e.g. by attaching specific tags to edges. […] Graph theory [also] allows us to encode in edges more complicated relationships, as when connections are not reciprocal. […] If a direction is attached to the edges, the resulting structure is a directed graph […] In these networks we have both in-degree and out-degree, measuring the number of inbound and outbound links of a node, respectively. […] in most cases, relations display a broad variation or intensity [i.e. they are not binary/dichotomous]. […] Weighted networks may arise, for example, as a result of different frequencies of interactions between individuals or entities.”

“An organism is […] the outcome of several layered networks and not only the deterministic result of the simple sequence of genes. Genomics has been joined by epigenomics, transcriptomics, proteomics, metabolomics, etc., the disciplines that study these layers, in what is commonly called the omics revolution. Networks are at the heart of this revolution. […] The brain is full of networks where various web-like structures provide the integration between specialized areas. In the cerebellum, neurons form modules that are repeated again and again: the interaction between modules is restricted to neighbours, similarly to what happens in a lattice. In other areas of the brain, we find random connections, with a more or less equal probability of connecting local, intermediate, or distant neurons. Finally, the neocortex — the region involved in many of the higher functions of mammals — combines local structures with more random, long-range connections. […] typically, food chains are not isolated, but interwoven in intricate patterns, where a species belongs to several chains at the same time. For example, a specialized species may predate on only one prey […]. If the prey becomes extinct, the population of the specialized species collapses, giving rise to a set of co-extinctions. An even more complicated case is where an omnivore species predates a certain herbivore, and both eat a certain plant. A decrease in the omnivore’s population does not imply that the plant thrives, because the herbivore would benefit from the decrease and consume even more plants. As more species are taken into account, the population dynamics can become more and more complicated. This is why a more appropriate description than ‘foodchains’ for ecosystems is the term foodwebs […]. These are networks in which nodes are species and links represent relations of predation. Links are usually directed (big fishes eat smaller ones, not the other way round). These networks provide the interchange of food, energy, and matter between species, and thus constitute the circulatory system of the biosphere.”

“In the cell, some groups of chemicals interact only with each other and with nothing else. In ecosystems, certain groups of species establish small foodwebs, without any connection to external species. In social systems, certain human groups may be totally separated from others. However, such disconnected groups, or components, are a strikingly small minority. In all networks, almost all the elements of the systems take part in one large connected structure, called a giant connected component. […] In general, the giant connected component includes not less than 90 to 95 per cent of the system in almost all networks. […] In a directed network, the existence of a path from one node to another does not guarantee that the journey can be made in the opposite direction. Wolves eat sheep, and sheep eat grass, but grass does not eat sheep, nor do sheep eat wolves. This restriction creates a complicated architecture within the giant connected component […] according to an estimate made in 1999, more than 90 per cent of the WWW is composed of pages connected to each other, if the direction of edges is ignored. However, if we take direction into account, the proportion of nodes mutually reachable is only 24 per cent, the giant strongly connected component. […] most networks are sparse, i.e. they tend to be quite frugal in connections. Take, for example, the airport network: the personal experience of every frequent traveller shows that direct flights are not that common, and intermediate stops are necessary to reach several destinations; thousands of airports are active, but each city is connected to less than 20 other cities, on average. The same happens in most networks. A measure of this is given by the mean number of connection of their nodes, that is, their average degree.”

“[A] puzzling contradiction — a sparse network can still be very well connected — […] attracted the attention of the Hungarian mathematicians […] Paul Erdős and Alfréd Rényi. They tackled it by producing different realizations of their random graph. In each of them, they changed the density of edges. They started with a very low density: less than one edge per node. It is natural to expect that, as the density increases, more and more nodes will be connected to each other. But what Erdős and Rényi found instead was a quite abrupt transition: several disconnected components coalesced suddenly into a large one, encompassing almost all the nodes. The sudden change happened at one specific critical density: when the average number of links per node (i.e. the average degree) was greater than one, then the giant connected component suddenly appeared. This result implies that networks display a very special kind of economy, intrinsic to their disordered structure: a small number of edges, even randomly distributed between nodes, is enough to generate a large structure that absorbs almost all the elements. […] Social systems seem to be very tightly connected: in a large enough group of strangers, it is not unlikely to find pairs of people with quite short chains of relations connecting them. […] The small-world property consists of the fact that the average distance between any two nodes (measured as the shortest path that connects them) is very small. Given a node in a network […], few nodes are very close to it […] and few are far from it […]: the majority are at the average — and very short — distance. This holds for all networks: starting from one specific node, almost all the nodes are at very few steps from it; the number of nodes within a certain distance increases exponentially fast with the distance. Another way of explaining the same phenomenon […] is the following: even if we add many nodes to a network, the average distance will not increase much; one has to increase the size of a network by several orders of magnitude to notice that the paths to new nodes are (just a little) longer. The small-world property is crucial to many network phenomena. […] The small-world property is something intrinsic to networks. Even the completely random Erdős-Renyi graphs show this feature. By contrast, regular grids do not display it. If the Internet was a chessboard-like lattice, the average distance between two routers would be of the order of 1,000 jumps, and the Net would be much slower [the authors note elsewhere that “The Internet is composed of hundreds of thousands of routers, but just about ten ‘jumps’ are enough to bring an information packet from one of them to any other.”] […] The key ingredient that transforms a structure of connections into a small world is the presence of a little disorder. No real network is an ordered array of elements. On the contrary, there are always connections ‘out of place’. It is precisely thanks to these connections that networks are small worlds. […] Shortcuts are responsible for the small-world property in many […] situations.”

“Body size, IQ, road speed, and other magnitudes have a characteristic scale: that is, an average value that in the large majority of cases is a rough predictor of the actual value that one will find. […] While height is a homogeneous magnitude, the number of social connection[s] is a heterogeneous one. […] A system with this feature is said to be scale-free or scale-invariant, in the sense that it does not have a characteristic scale. This can be rephrased by saying that the individual fluctuations with respect to the average are too large for us to make a correct prediction. […] In general, a network with heterogeneous connectivity has a set of clear hubs. When a graph is small, it is easy to find whether its connectivity is homogeneous or heterogeneous […]. In the first case, all the nodes have more or less the same connectivity, while in the latter it is easy to spot a few hubs. But when the network to be studied is very big […] things are not so easy. […] the distribution of the connectivity of the nodes of the […] network […] is the degree distribution of the graph. […] In homogeneous networks, the degree distribution is a bell curve […] while in heterogeneous networks, it is a power law […]. The power law implies that there are many more hubs (and much more connected) in heterogeneous networks than in homogeneous ones. Moreover, hubs are not isolated exceptions: there is a full hierarchy of nodes, each of them being a hub compared with the less connected ones.”

“Looking at the degree distribution is the best way to check if a network is heterogeneous or not: if the distribution is fat tailed, then the network will have hubs and heterogeneity. A mathematically perfect power law is never found, because this would imply the existence of hubs with an infinite number of connections. […] Nonetheless, a strongly skewed, fat-tailed distribution is a clear signal of heterogeneity, even if it is never a perfect power law. […] While the small-world property is something intrinsic to networked structures, hubs are not present in all kind of networks. For example, power grids usually have very few of them. […] hubs are not present in random networks. A consequence of this is that, while random networks are small worlds, heterogeneous ones are ultra-small worlds. That is, the distance between their vertices is relatively smaller than in their random counterparts. […] Heterogeneity is not equivalent to randomness. On the contrary, it can be the signature of a hidden order, not imposed by a top-down project, but generated by the elements of the system. The presence of this feature in widely different networks suggests that some common underlying mechanism may be at work in many of them. […] the Barabási–Albert model gives an important take-home message. A simple, local behaviour, iterated through many interactions, can give rise to complex structures. This arises without any overall blueprint”.

Homogamy, the tendency of like to marry like, is very strong […] Homogamy is a specific instance of homophily: this consists of a general trend of like to link to like, and is a powerful force in shaping social networks […] assortative mixing [is] a special form of homophily, in which nodes tend to connect with others that are similar to them in the number of connections. By contrast [when] high- and low-degree nodes are more connected to each other [it] is called disassortative mixing. Both cases display a form of correlation in the degrees of neighbouring nodes. When the degrees of neighbours are positively correlated, then the mixing is assortative; when negatively, it is disassortative. […] In random graphs, the neighbours of a given node are chosen completely at random: as a result, there is no clear correlation between the degrees of neighbouring nodes […]. On the contrary, correlations are present in most real-world networks. Although there is no general rule, most natural and technological networks tend to be disassortative, while social networks tend to be assortative. […] Degree assortativity and disassortativity are just an example of the broad range of possible correlations that bias how nodes tie to each other.”

“[N]etworks (neither ordered lattices nor random graphs), can have both large clustering and small average distance at the same time. […] in almost all networks, the clustering of a node depends on the degree of that node. Often, the larger the degree, the smaller the clustering coefficient. Small-degree nodes tend to belong to well-interconnected local communities. Similarly, hubs connect with many nodes that are not directly interconnected. […] Central nodes usually act as bridges or bottlenecks […]. For this reason, centrality is an estimate of the load handled by a node of a network, assuming that most of the traffic passes through the shortest paths (this is not always the case, but it is a good approximation). For the same reason, damaging central nodes […] can impair radically the flow of a network. Depending on the process one wants to study, other definitions of centrality can be introduced. For example, closeness centrality computes the distance of a node to all others, and reach centrality factors in the portion of all nodes that can be reached in one step, two steps, three steps, and so on.”

“Domino effects are not uncommon in foodwebs. Networks in general provide the backdrop for large-scale, sudden, and surprising dynamics. […] most of the real-world networks show a doubled-edged kind of robustness. They are able to function normally even when a large fraction of the network is damaged, but suddenly certain small failures, or targeted attacks, bring them down completely. […] networks are very different from engineered systems. In an airplane, damaging one element is enough to stop the whole machine. In order to make it more resilient, we have to use strategies such as duplicating certain pieces of the plane: this makes it almost 100 per cent safe. In contrast, networks, which are mostly not blueprinted, display a natural resilience to a broad range of errors, but when certain elements fail, they collapse. […] A random graph of the size of most real-world networks is destroyed after the removal of half of the nodes. On the other hand, when the same procedure is performed on a heterogeneous network (either a map of a real network or a scale-free model of a similar size), the giant connected component resists even after removing more than 80 per cent of the nodes, and the distance within it is practically the same as at the beginning. The scene is different when researchers simulate a targeted attack […] In this situation the collapse happens much faster […]. However, now the most vulnerable is the second: while in the homogeneous network it is necessary to remove about one-fifth of its more connected nodes to destroy it, in the heterogeneous one this happens after removing the first few hubs. Highly connected nodes seem to play a crucial role, in both errors and attacks. […] hubs are mainly responsible for the overall cohesion of the graph, and removing a few of them is enough to destroy it.”

“Studies of errors and attacks have shown that hubs keep different parts of a network connected. This implies that they also act as bridges for spreading diseases. Their numerous ties put them in contact with both infected and healthy individuals: so hubs become easily infected, and they infect other nodes easily. […] The vulnerability of heterogeneous networks to epidemics is bad news, but understanding it can provide good ideas for containing diseases. […] if we can immunize just a fraction, it is not a good idea to choose people at random. Most of the times, choosing at random implies selecting individuals with a relatively low number of connections. Even if they block the disease from spreading in their surroundings, hubs will always be there to put it back into circulation. A much better strategy would be to target hubs. Immunizing hubs is like deleting them from the network, and the studies on targeted attacks show that eliminating a small fraction of hubs fragments the network: thus, the disease will be confined to a few isolated components. […] in the epidemic spread of sexually transmitted diseases the timing of the links is crucial. Establishing an unprotected link with a person before they establish an unprotected link with another person who is infected is not the same as doing so afterwards.”

April 3, 2018 Posted by | Biology, Books, Ecology, Engineering, Epidemiology, Genetics, Mathematics, Statistics | Leave a comment

Marine Biology (II)

Below some observations and links related to the second half of the book’s coverage:

[C]oral reefs occupy a very small proportion of the planet’s surface – about 284,000 square kilometres – roughly equivalent to the size of Italy [yet they] are home to an incredibly diversity of marine organisms – about a quarter of all marine species […]. Coral reef systems provide food for hundreds of millions of people, with about 10 per cent of all fish consumed globally caught on coral reefs. […] Reef-building corals thrive best at sea temperatures above about 23°C and few exist where sea temperatures fall below 18°C for significant periods of time. Thus coral reefs are absent at tropical latitudes where upwelling of cold seawater occurs, such as the west coasts of South America and Africa. […] they are generally restricted to areas of clear water less than about 50 metres deep. Reef-building corals are very intolerant of any freshening of seawater […] and so do not occur in areas exposed to intermittent influxes of freshwater, such as near the mouths of rivers, or in areas where there are high amounts of rainfall run-off. This is why coral reefs are absent along much of the tropical Atlantic coast of South America, which is exposed to freshwater discharge from the Amazon and Orinoco Rivers. Finally, reef-building corals flourish best in areas with moderate to high wave action, which keeps the seawater well aerated […]. Spectacular and productive coral reef systems have developed in those parts of the Global Ocean where this special combination of physical conditions converges […] Each colony consists of thousands of individual animals called polyps […] all reef-building corals have entered into an intimate relationship with plant cells. The tissues lining the inside of the tentacles and stomach cavity of the polyps are packed with photosynthetic cells called zooxanthellae, which are photosynthetic dinoflagellates […] Depending on the species, corals receive anything from about 50 per cent to 95 per cent of their food from their zooxanthellae. […] Healthy coral reefs are very productive marine systems. This is in stark contrast to the nutrient-poor and unproductive tropical waters adjacent to reefs. Coral reefs are, in general, roughly one hundred times more productive than the surrounding environment”.

“Overfishing constitutes a significant threat to coral reefs at this time. About an eighth of the world’s population – roughly 875 million people – live within 100 kilometres of a coral reef. Most of the people live in developing countries and island nations and depend greatly on fish obtained from coral reefs as a food source. […] Some of the fishing practices are very harmful. Once the large fish are removed from a coral reef, it becomes increasingly more difficult to make a living harvesting the more elusive and lower-value smaller fish that remain. Fishers thus resort to more destructive techniques such as dynamiting parts of the reef and scooping up the dead and stunned fish that float to the surface. People capturing fish for the tropical aquarium trade will often poison parts of the reef with sodium cyanide which paralyses the fish, making them easier to catch. An unfortunate side effect of this practice is that the poison kills corals. […] Coral reefs have only been seriously studied since the 1970s, which in most cases was well after human impacts had commenced. This makes it difficult to define what might actually constitute a ‘natural’ and healthy coral reef system, as would have existed prior to extensive human impacts.”

“Mangrove is a collective term applied to a diverse group of trees and scrubs that colonize protected muddy intertidal areas in tropical and subtropical regions, creating mangrove forests […] Mangroves are of great importance from a human perspective. The sheltered waters of a mangrove forest provide important nursery areas for juvenile fish, crabs, and shrimp. Many commercial fisheries depend on the existence of healthy mangrove forests, including blue crab, shrimp, spiny lobster, and mullet fisheries. Mangrove forests also stabilize the foreshore and protect the adjacent land from erosion, particularly from the effects of large storms and tsunamis. They also act as biological filters by removing excess nutrients and trapping sediment from land run-off before it enters the coastal environment, thereby protecting other habitats such as seagrass meadows and coral reefs. […] [However] mangrove forests are disappearing rapidly. In a twenty-year period between 1980 and 2000 the area of mangrove forest globally declined from around 20 million hectares to below 15 million hectares. In some specific regions the rate of mangrove loss is truly alarming. For example, Puerto Rico lost about 89 per cent of its mangrove forests between 1930 and 1985, while the southern part of India lost about 96 per cent of its mangroves between 1911 and 1989.”

“[A]bout 80 per cent of the entire volume of the Global Ocean, or roughly one billion cubic kilometres, consists of seawater with depths greater than 1,000 metres […] The deep ocean is a permanently dark environment devoid of sunlight, the last remnants of which cannot penetrate much beyond 200 metres in most parts of the Global Ocean, and no further than 800 metres or so in even the clearest oceanic waters. The only light present in the deep ocean is of biological origin […] Except in a few very isolated places, the deep ocean is a permanently cold environment, with sea temperatures ranging from about 2° to 4°C. […] Since there is no sunlight, there is no plant life, and thus no primary production of organic matter by photosynthesis. The base of the food chain in the deep ocean consists mostly of a ‘rain’ of small particles of organic material sinking down through the water column from the sunlit surface waters of the ocean. This reasonably constant rain of organic material is supplemented by the bodies of large fish and marine mammals that sink more rapidly to the bottom following death, and which provide sporadic feasts for deep-ocean bottom dwellers. […] Since food is a scarce commodity for deep-ocean fish, full advantage must be taken of every meal encountered. This has resulted in a number of interesting adaptations. Compared to fish in the shallow ocean, many deep-ocean fish have very large mouths capable of opening very wide, and often equipped with numerous long, sharp, inward-pointing teeth. […] These fish can capture and swallow whole prey larger than themselves so as not to pass up a rare meal simply because of its size. These fish also have greatly extensible stomachs to accommodate such meals.”

“In the pelagic environment of the deep ocean, animals must be able to keep themselves within an appropriate depth range without using up energy in their food-poor habitat. This is often achieved by reducing the overall density of the animal to that of seawater so that it is neutrally buoyant. Thus the tissues and bones of deep-sea fish are often rather soft and watery. […] There is evidence that deep-ocean organisms have developed biochemical adaptations to maintain the functionality of their cell membranes under pressure, including adjusting the kinds of lipid molecules present in membranes to retain membrane fluidity under high pressure. High pressures also affect protein molecules, often preventing them from folding up into the correct shapes for them to function as efficient metabolic enzymes. There is evidence that deep-ocean animals have evolved pressure-resistant variants of common enzymes that mitigate this problem. […] The pattern of species diversity of the deep-ocean benthos appears to differ from that of other marine communities, which are typically dominated by a small number of abundant and highly visible species which overshadow the presence of a large number of rarer and less obvious species which are also present. In the deep-ocean benthic community, in contrast, no one group of species tends to dominate, and the community consists of a high number of different species all occurring in low abundance. […] In general, species diversity increases with the size of a habitat – the larger the area of a habitat, the more species that have developed ways to successfully live in that habitat. Since the deep-ocean bottom is the largest single habitat on the planet, it follows that species diversity would be expected to be high.”

Seamounts represent a special kind of biological hotspot in the deep ocean. […] In contrast to the surrounding flat, soft-bottomed abyssal plains, seamounts provide a complex rocky platform that supports an abundance of organisms that are distinct from the surrounding deep-ocean benthos. […] Seamounts support a great diversity of fish species […] This [has] triggered the creation of new deep-ocean fisheries focused on seamounts. […] [However these species are generally] very slow-growing and long-lived and mature at a late age, and thus have a low reproductive potential. […] Seamount fisheries have often been described as mining operations rather than sustainable fisheries. They typically collapse within a few years of the start of fishing and the trawlers then move on to other unexplored seamounts to maintain the fishery. The recovery of localized fisheries will inevitably be very slow, if achievable at all, because of the low reproductive potential of these deep-ocean fish species. […] Comparisons of ‘fished’ and ‘unfished’ seamounts have clearly shown the extent of habitat damage and loss of species diversity brought about by trawl fishing, with the dense coral habitats reduced to rubble over much of the area investigated. […] Unfortunately, most seamounts exist in areas beyond national jurisdiction, which makes it very difficult to regulate fishing activities on them, although some efforts are underway to establish international treaties to better manage and protect seamount ecosystems.”

“Hydrothermal vents are unstable and ephemeral features of the deep ocean. […] The lifespan of a typical vent is likely in the order of tens of years. Thus the rich communities surrounding vents have a very limited lifespan. Since many vent animals can live only near vents, and the distance between vent systems can be hundreds to thousands of kilometres, it is a puzzle as to how vent animals escape a dying vent and colonize other distant vents or newly created vents. […] Hydrothermal vents are [however] not the only source of chemical-laden fluids supporting unique chemosynthetic-based communities in the deep ocean. Hydrogen sulphide and methane also ooze from the ocean buttom at some locations at temperatures similar to the surrounding seawater. These so-called ‘cold seeps‘ are often found along continental margins […] The communities associated with cold seeps are similar to hydrothermal vent communities […] Cold seeps appear to be more permanent sources of fluid compared to the ephemeral nature of hot water vents.”

“Seepage of crude oil into the marine environment occurs naturally from oil-containing geological formations below the seabed. It is estimated that around 600,000 tonnes of crude oil seeps into the marine environment each year, which represents almost half of all the crude oil entering the oceans. […] The human activities associated with exploring for and producing oil result in the release on average of an estimated 38,000 tonnes of crude oil into the oceans each year, which is about 6 per cent of the total anthropogenic input of oil into the oceans worldwide. Although small in comparison to natural seepage, crude oil pollution from this source can cause serious damage to coastal ecosystems because it is released near the coast and sometimes in very large, concentrated amounts. […] The transport of oil and oil products around the globe in tankers results in the release of about 150,000 tonnes of oil worldwide each year on average, or about 22 per cent of the total anthropogenic input. […] About 480,000 tonnes of oil make their way into the marine environment each year worldwide from leakage associated with the consumption of oil-derived products in cars and trucks, and to a lesser extent in boats. Oil lost from the operation of cars and trucks collects on paved urban areas from where it is washed off into streams and rivers, and from there into the oceans. Surprisingly, this represents the most significant source of human-derived oil pollution into the marine environment – about 72 per cent of the total. Because it is a very diffuse source of pollution, it is the most difficult to control.”

“Today it has been estimated that virtually all of the marine food resources in the Mediterranean sea have been reduced to less than 50 per cent of their original abundance […] The greatest impact has been on the larger predatory fish, which were the first to be targeted by fishers. […] It is estimated that, collectively, the European fish stocks of today are just one-tenth of their size in 1900. […] In 1950 the total global catch of marine seafood was just less than twenty million tonnes fresh weight. This increased steadily and rapidly until by the late 1980s more than eighty million tonnes were being taken each year […] Starting in the early 1990s, however, yields began to show signs of levelling off. […] By far the most heavily exploited marine fishery in the world is the Peruvian anchoveta (Engraulis ringens) fishery, which can account for 10 per cent or more of the global marine catch of seafood in any particular year. […] The anchoveta is a very oily fish, which makes it less desirable for direct consumption by humans. However, the high oil content makes it ideal for the production of fish meal and fish oil […] the demand for fish meal and fish oil is huge and about a third of the entire global catch of fish is converted into these products rather than consumed directly by humans. Feeding so much fish protein to livestock comes with a considerable loss of potential food energy (around 25 per cent) compared to if it was eaten directly by humans. This could be viewed as a potential waste of available energy for a rapidly growing human population […] around 90 per cent of the fish used to produce fish meal and oil is presently unpalatable to most people and thus unmarketable in large quantities as a human food”.

“On heavily fished areas of the continental shelves, the same parts of the sea floor can be repeatedly trawled many times per year. Such intensive bottom trawling causes great cumulative damage to seabed habitats. The trawls scrape and pulverize rich and complex bottom habitats built up over centuries by living organisms such as tube worms, cold-water corals, and oysters. These habitats are eventually reduced to uniform stretches of rubble and sand. For all intents and purposes these areas are permanently altered and become occupied by a much changed and much less rich community adapted to frequent disturbance.”

“The eighty million tonnes or so of marine seafood caught each year globally equates to about eleven kilograms of wild-caught marine seafood per person on the planet. […] What is perfectly clear […] on the basis of theory backed up by real data on marine fish catches, is that marine fisheries are now fully exploited and that there is little if any headroom for increasing the amount of wild-caught fish humans can extract from the oceans to feed a burgeoning human population. […] This conclusion is solidly supported by the increasingly precarious state of global marine fishery resources. The most recent information from the Food and Agriculture Organization of the United Nations (The State of World Fisheries and Aquaculture 2010) shows that over half (53 per cent of all fish stocks are fully exploited – their current catches are at or close to their maximum sustainable levels of production and there is no scope for further expansion. Another 32 per cent are overexploited and in decline. Of the remaining 15 per cent of stocks, 12 per cent are considered moderately exploited and only 3 per cent underexploited. […] in the mid 1970s 40 per cent of all fish stocks were in [the moderately exploited or unexploited] category as opposed to around 15 per cent now. […] the real question is not so much whether we can get more fish from the sea but whether we can sustain the amount of fish we are harvesting at present”.

Links:

Scleractinia.
Atoll. Fringing reef. Barrier reef.
Corallivore.
Broadcast spawning.
Acanthaster planci.
Coral bleaching. Ocean acidification.
Avicennia germinans. Pneumatophores. Lenticel.
Photophore. Lanternfish. Anglerfish. Black swallower.
Deep scattering layer. Taylor column.
Hydrothermal vent. Black smokers and white smokers. Chemosynthesis. Siboglinidae.
Intertidal zone. Tides. Tidal range.
Barnacle. Mussel.
Clupeidae. Gadidae. Scombridae.

March 16, 2018 Posted by | Biology, Books, Chemistry, Ecology, Evolutionary biology, Geology | Leave a comment