Econstudentlog

A recent perspective on invariant theory

Some time ago I covered here on the blog a lecture with a somewhat technical introduction to invariant theory. Even though I didn’t recommend the lecture, I do recommend that you don’t watch the lecture above without first knowing the sort of stuff that might have been covered in that lecture (for all you know, that is), as well as some other lectures on related topics – to be more specific, to get anything out of this lecture you need some linear algebra, you need graph theory, you need some understanding of group theory, you need to know a little about computational complexity, it’ll probably help if you know a bit about invariant theory already, and surely you need some knowledge of a few other topics I forgot to mention. One of the statements I made about the introductory lecture to which I linked above also applies here: “I had to look up a lot of stuff to just sort-of-kind-of muddle along”.

Below some links to stuff I looked up while watching the lecture:

Algebraically closed field.
Reductive group.
Rational representation.
Group homomorphism.
Morphism of algebraic varieties.
Fixed-point subring.
Graph isomorphism.
Adjacency matrix.
Group action (mathematics).
General linear group.
Special linear group.
Alternating minimization, scaling algorithms, and the null-cone problem from invariant theory. (Bürgisser, Garg, Oliveira, Walter, and Wigderson (2017))
Noether normalization lemma.
Succinct data structure. (This link is actually not directly related to the lecture’s coverage; I came across it by accident while looking for topics he did talk about and I found it interesting, so I decided to include the link here anyway)
Characteristic polynomial.
Matrix similarity.
Monomial.
Associative algebra.
Polynomial degree bounds for matrix semi-invariants (Derksen & Makam, 2015).
Semi-invariant of a quiver.

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July 6, 2019 Posted by | Computer science, Lectures, Mathematics | Leave a comment

On the possibility of an instance-based complexity theory

Below some links related to the lecture’s coverage:

Computational complexity theory.
Minimum cut.
2-satisfiability.
3-SAT.
Worst-case complexity.
Average-case complexity.
Max-Cut.
Karp’s 21 NP-complete problems.
Reduction (complexity).
Levin’s Universal search algorithm – Scholarpedia.
Computational indistinguishability.
Circuit complexity.
Adversarial Perturbations of Deep Neural Networks.
Sherrington–Kirkpatrick model.
Equivalence class.
Hopkins (2018).
Planted clique.
SDP (Semidefinite programming).
Jain, Koehler & Risteski (2018): Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective.
Structural operational semantics (SOS).

July 1, 2019 Posted by | Computer science, Lectures | Leave a comment

The pleasure of finding things out (II)

Here’s my first post about the book. In this post I have included a few more quotes from the last half of the book.

“Are physical theories going to keep getting more abstract and mathematical? Could there be today a theorist like Faraday in the early nineteenth century, not mathematically sophisticated but with a very powerful intuition about physics?
Feynman: I’d say the odds are strongly against it. For one thing, you need the math just to understand what’s been done so far. Beyond that, the behavior of subnuclear systems is so strange compared to the ones the brain evolved to deal with that the analysis has to be very abstract: To understand ice, you have to understand things that are themselves very unlike ice. Faraday’s models were mechanical – springs and wires and tense bands in space – and his images were from basic geometry. I think we’ve understood all we can from that point of view; what we’ve found in this century is different enough, obscure enough, that further progress will require a lot of math.”

“There’s a tendency to pomposity in all this, to make it all deep and profound. My son is taking a course in philosophy, and last night we were looking at something by Spinoza – and there was the most childish reasoning! There were all these Attributes, and Substances, all this meaningless chewing around, and we started to laugh. Now, how could we do that? Here’s this great Dutch philosopher, and we’re laughing at him. It’s because there was no excuse for it! In that same period there was Newton, there was Harvey studying the circulation of the blood, there were people with methods of analysis by which progress was being made! You can take every one of Spinoza’s propositions, and take the contrary propositions, and look at the world – and you can’t tell which is right. Sure, people were awed because he had the courage to take on these great questions, but it doesn’t do any good to have the courage if you can’t get anywhere with the question. […] It isn’t the philosophy that gets me, it’s the pomposity. If they’d just laugh at themselves! If they’d just say, “I think it’s like this, but von Leipzig thought it was like that, and he had a good shot at it, too.” If they’d explain that this is their best guess … But so few of them do”.

“The lesson you learn as you grow older in physics is that what we can do is a very small fraction of what there is. Our theories are really very limited.”

“The first principle is that you must not fool yourself – and you are the easiest person to fool. So you have to be very careful about that. After you’ve not fooled yourself, it’s easy not to fool other scientists. You just have to be honest in a conventional way after that.”

“When I was an undergraduate I worked with Professor Wheeler* as a research assistant, and we had worked out together a new theory about how light worked, how the interaction between atoms in different places worked; and it was at that time an apparently interesting theory. So Professor Wigner†, who was in charge of the seminars there [at Princeton], suggested that we give a seminar on it, and Professor Wheeler said that since I was a young man and hadn’t given seminars before, it would be a good opportunity to learn how to do it. So this was the first technical talk that I ever gave. I started to prepare the thing. Then Wigner came to me and said that he thought the work was important enough that he’d made special invitations to the seminar to Professor Pauli, who was a great professor of physics visiting from Zurich; to Professor von Neumann, the world’s greatest mathematician; to Henry Norris Russell, the famous astronomer; and to Albert Einstein, who was living near there. I must have turned absolutely white or something because he said to me, “Now don’t get nervous about it, don’t be worried about it. First of all, if Professor Russell falls asleep, don’t feel bad, because he always falls asleep at lectures. When Professor Pauli nods as you go along, don’t feel good, because he always nods, he has palsy,” and so on. That kind of calmed me down a bit”.

“Well, for the problem of understanding the hadrons and the muons and so on, I can see at the present time no practical applications at all, or virtually none. In the past many people have said that they could see no applications and then later they found applications. Many people would promise under those circumstances that something’s bound to be useful. However, to be honest – I mean he looks foolish; saying there will never be anything useful is obviously a foolish thing to do. So I’m going to be foolish and say these damn things will never have any application, as far as I can tell. I’m too dumb to see it. All right? So why do you do it? Applications aren’t the only thing in the world. It’s interesting in understanding what the world is made of. It’s the same interest, the curiosity of man that makes him build telescopes. What is the use of discovering the age of the universe? Or what are these quasars that are exploding at long distances? I mean what’s the use of all that astronomy? There isn’t any. Nonetheless, it’s interesting. So it’s the same kind of exploration of our world that I’m following and it’s curiosity that I’m satisfying. If human curiosity represents a need, the attempt to satisfy curiosity, then this is practical in the sense that it is that. That’s the way I would look at it at the present time. I would not put out any promise that it would be practical in some economic sense.”

“To science we also bring, besides the experiment, a tremendous amount of human intellectual attempt at generalization. So it’s not merely a collection of all those things which just happen to be true in experiments. It’s not just a collection of facts […] all the principles must be as wide as possible, must be as general as possible, and still be in complete accord with experiment, that’s the challenge. […] Evey one of the concepts of science is on a scale graduated somewhere between, but at neither end of, absolute falsity or absolute truth. It is necessary, I believe, to accept this idea, not only for science, but also for other things; it is of great value to acknowledge ignorance. It is a fact that when we make decisions in our life, we don’t necessarily know that we are making them correctly; we only think that we are doing the best we can – and that is what we should do.”

“In this age of specialization, men who thoroughly know one field are often incompetent to discuss another.”

“I believe that moral questions are outside of the scientific realm. […] The typical human problem, and one whose answer religion aims to supply, is always of the following form: Should I do this? Should we do this? […] To answer this question we can resolve it into two parts: First – If I do this, what will happen? – and second – Do I want that to happen? What would come of it of value – of good? Now a question of the form: If I do this, what will happen? is strictly scientific. […] The technique of it, fundamentally, is: Try it and see. Then you put together a large amount of information from such experiences. All scientists will agree that a question – any question, philosophical or other – which cannot be put into the form that can be tested by experiment (or, in simple terms, that cannot be put into the form: If I do this, what will happen?) is not a scientific question; it is outside the realm of science.”

June 26, 2019 Posted by | Astronomy, Mathematics, Philosophy, Physics, Quotes/aphorisms, Science | Leave a comment

Promoting the unknown…

i.

ii.

iii.

iv.

v.

June 22, 2019 Posted by | Music | Leave a comment

The pleasure of finding things out (I?)

As I put it in my goodreads review of the book, “I felt in good company while reading this book“. Some of the ideas in the book are by now well known, for example some of the interview snippets also included in the book have been added to youtube and have been viewed by hundreds of thousands of people (I added a couple of them to my ‘about’ page some years ago, and they’re still there, these are enjoyable videos to watch and they have aged well!) (the overlap between the book’s text and the sound recordings available is not 100 % for this material, but it’s close enough that I assume these were the same interviews). Others ideas and pieces I would assume to be less well known, for example Feynman’s encounter with Uri Geller in the latter’s hotel room, where he was investigating the latter’s supposed abilities related to mind reading and key bending..

I have added some sample quotes from the book below. It’s a good book, recommended.

“My interest in science is to simply find out about the world, and the more I find out the better it is, like, to find out. […] You see, one thing is, I can live with doubt and uncertainty and not knowing. I think it’s much more interesting to live not knowing than to have answers which might be wrong. I have approximate answers and possible beliefs and different degrees of certainty about different things, but I’m not absolutely sure of anything and there are many things I don’t know anything about […] I don’t have to know an answer, I don’t feel frightened by not knowing things, by being lost in a mysterious universe without having any purpose, which is the way it really is so far as I can tell. It doesn’t frighten me.”

“Some people look at the activity of the brain in action and see that in many respects it surpasses the computer of today, and in many other respects the computer surpasses ourselves. This inspires people to design machines that can do more. What often happens is that an engineer has an idea of how the brain works (in his opinion) and then designs a machine that behaves that way. This new machine may in fact work very well. But, I must warn you that that does not tell us anything about how the brain actually works, nor is it necessary to ever really know that, in order to make a computer very capable. It is not necessary to understand the way birds flap their wings and how the feathers are designed in order to make a flying machine. It is not necessary to understand the lever system in the legs of a cheetah – an animal that runs fast – in order to make an automobile with wheels that goes very fast. It is therefore not necessary to imitate the behavior of Nature in detail in order to engineer a device which can in many respects surpass Nature’s abilities.”

“These ideas and techniques [of scientific investigation] , of course, you all know. I’ll just review them […] The first is the matter of judging evidence – well, the first thing really is, before you begin you must not know the answer. So you begin by being uncertain as to what the answer is. This is very, very important […] The question of doubt and uncertainty is what is necessary to begin; for if you already know the answer there is no need to gather any evidence about it. […] We absolutely must leave room for doubt or there is no progress and there is no learning. There is no learning without having to pose a question. And a question requires doubt. […] Authority may be a hint as to what the truth is, but it is not the source of information. As long as it’s possible, we should disregard authority whenever the observations disagree with it. […] Science is the belief in the ignorance of experts.”

“If we look away from the science and look at the world around us, we find out something rather pitiful: that the environment that we live in is so actively, intensely unscientific. Galileo could say: “I noticed that Jupiter was a ball with moons and not a god in the sky. Tell me, what happened to the astrologers?” Well, they print their results in the newspapers, in the United States at least, in every daily paper every day. Why do we still have astrologers? […] There is always some crazy stuff. There is an infinite amount of crazy stuff, […] the environment is actively, intensely unscientific. There is talk about telepathy still, although it’s dying out. There is faith-healing galore, all over. There is a whole religion of faith-healing. There’s a miracle at Lourdes where healing goes on. Now, it might be true that astrology is right. It might be true that if you go to the dentist on the day that Mars is at right angles to Venus, that it is better than if you go on a different day. It might be true that you can be cured by the miracle of Lourdes. But if it is true it ought to be investigated. Why? To improve it. If it is true then maybe we can find out if the stars do influence life; that we could make the system more powerful by investigating statistically, scientifically judging the evidence objectively, more carefully. If the healing process works at Lourdes, the question is how far from the site of the miracle can the person, who is ill, stand? Have they in fact made a mistake and the back row is really not working? Or is it working so well that there is plenty of room for more people to be arranged near the place of the miracle? Or is it possible, as it is with the saints which have recently been created in the United States–there is a saint who cured leukemia apparently indirectly – that ribbons that are touched to the sheet of the sick person (the ribbon having previously touched some relic of the saint) increase the cure of leukemia–the question is, is it gradually being diluted? You may laugh, but if you believe in the truth of the healing, then you are responsible to investigate it, to improve its efficiency and to make it satisfactory instead of cheating. For example, it may turn out that after a hundred touches it doesn’t work anymore. Now it’s also possible that the results of this investigation have other consequences, namely, that nothing is there.”

“I believe that a scientist looking at nonscientific problems is just as dumb as the next guy – and when he talks about a nonscientific matter, he will sound as naive as anyone untrained in the matter.”

“If we want to solve a problem that we have never solved before, we must leave the door to the unknown ajar.”

“For a successful technology, reality must take precedence over public relations, for nature cannot be fooled.”

“I would like to say a word or two […] about words and definitions, because it is necessary to learn the words. It is not science. That doesn’t mean just because it is not science that we don’t have to teach the words. We are not talking about what to teach; we are talking about what science is. It is not science to know how to change centigrade to Fahrenheit. It’s necessary, but it is not exactly science. […] I finally figured out a way to test whether you have taught an idea or you have only taught a definition. Test it this way: You say, “Without using the new word which you have just learned, try to rephrase what you have just learned in your own language.”

“My father dealt a little bit with energy and used the term after I got a little bit of the idea about it. […] He would say, “It [a toy dog] moves because the sun is shining,” […]. I would say “No. What has that to do with the sun shining? It moved because I wound up the springs.” “And why, my friend, are you able to move to wind up this spring?” “I eat.” “What, my friend, do you eat?” “I eat plants.” “And how do they grow?” “They grow because the sun is shining.” […] The only objection in this particular case was that this was the first lesson. It must certainly come later, telling you what energy is, but not to such a simple question as “What makes a [toy] dog move?” A child should be given a child’s answer. “Open it up; let’s look at it.””

“Now the point of this is that the result of observation, even if I were unable to come to the ultimate conclusion, was a wonderful piece of gold, with a marvelous result. It was something marvelous. Suppose I were told to observe, to make a list, to write down, to do this, to look, and when I wrote my list down, it was filed with 130 other lists in the back of a notebook. I would learn that the result of observation is relatively dull, that nothing much comes of it. I think it is very important – at least it was to me – that if you are going to teach people to make observations, you should show that something wonderful can come from them. […] [During my life] every once in a while there was the gold of a new understanding that I had learned to expect when I was a kid, the result of observation. For I did not learn that observation was not worthwhile. […] The world looks so different after learning science. For example, the trees are made of air, primarily. When they are burned, they go back to air, and in the flaming heat is released the flaming heat of the sun which was bound in to convert the air into trees, and in the ash is the small remnant of the part which did not come from air, that came from the solid earth, instead. These are beautiful things, and the content of science is wonderfully full of them. They are very inspiring, and they can be used to inspire others.”

“Physicists are trying to find out how nature behaves; they may talk carelessly about some “ultimate particle” because that’s the way nature looks at a given moment, but . . . Suppose people are exploring a new continent, OK? They see water coming along the ground, they’ve seen that before, and they call it “rivers.” So they say they’re exploring to find the headwaters, they go upriver, and sure enough, there they are, it’s all going very well. But lo and behold, when they get up far enough they find the whole system’s different: There’s a great big lake, or springs, or the rivers run in a circle. You might say, “Aha! They’ve failed!” but not at all! The real reason they were doing it was to explore the land. If it turned out not to be headwaters, they might be slightly embarrassed at their carelessness in explaining themselves, but no more than that. As long as it looks like the way things are built is wheels within wheels, then you’re looking for the innermost wheel – but it might not be that way, in which case you’re looking for whatever the hell it is that you find!”

 

June 20, 2019 Posted by | Books, Physics, Science | Leave a comment

Quotes

i. “Roughly, religion is a community’s costly and hard-to-fake commitment to a counterfactual and counterintuitive world of supernatural agents who master people’s existential anxieties, such as death and deception.” (Scott Atran)

ii. “The more one accepts what is materially false to be really true, and the more one spends material resources in displays of such acceptance, the more others consider one’s faith deep and one’s commitment sincere.” (-ll-)

iii. “Cultures and religions do not exist apart from the individual minds that constitute them and the environments that constrain them, any more than biological species and varieties exist independently of the individual organisms that compose them and the environments that conform them. They are not well-bounded systems or definite clusters of beliefs, practices, and artifacts, but more or less regular distributions of causally connected thoughts, behaviors, material products, and environmental objects. To naturalistically understand what “cultures” are is to describe and explain the material causes responsible for reliable differences in these distributions.” (-ll-)

iv. “Religions are not adaptations and they have no evolutionary functions as such.” (-ll-)

v. “Mature cognitions of folkpsychology and agency include metarepresentation. This involves the ability to track and build a notion of self over time, to model other minds and worlds, and to represent beliefs about the actual world as being true or false. It also makes lying and deception possible. This threatens any social order. But this same metarepresentational capacity provides the hope and promise of open-ended solutions to problems of moral relativity. It does so by enabling people to conjure up counterintuitive supernatural worlds that cannot be verified or falsified, either logically or empirically. Religious beliefs minimally violate ordinary intuitions about the world, with its inescapable problems, such as death. This frees people to imagine minimally impossible worlds that seem to solve existential dilemmas, including death and deception. […] Religion survives science and secular ideology not because it is prior to or more primitive than science or secular reasoning, but because of what it affectively and collectively secures for people.” (-ll-)

vi. “Don’t worry about people stealing an idea. If it’s original, you will have to ram it down their throats.” (Howard H. Aiken)

vii. “As long as scientists are free to pursue the truth wherever it may lead, there will be a flow of new scientific knowledge to those who can apply it to practical problems.” (Vannevar Bush)

viii. “Scientific progress on a broad front results from the free play of free intellects, working on subjects of their own choice, in the manner dictated by their curiosity for exploration of the unknown.” (-ll-)

ix. “There are some people who imagine that older adults don’t know how to use the internet. My immediate reaction is, “I’ve got news for you, we invented it.”” (Vinton Cerf)

x. “When we are young we are often puzzled by the fact that each person we admire seems to have a different version of what life ought to be, what a good man is, how to live, and so on. If we are especially sensitive it seems more than puzzling, it is disheartening. What most people usually do is follow one person’s ideas and then another’s depending on who looms largest on one’s horizon at the time. The one with the deepest voice, the strongest appearance, the most authority and success, is usually the one who gets our momentary allegiance; and we try to pattern our ideals after him. […] Each person thinks that he has the formula for triumphing over life’s limitations and knows with authority what it means to be a man, and he usually tries to win a following for his particular patent. Today we know that people try so hard to win converts for their point of view because it is more than merely an outlook on life: it is an immortality formula.” (Ernest Becker)

xi. “A human being cannot survive alone and be entirely human.” (Peter Farb)

xii. “The members of a society do not make conscious choices in arriving at a particular way of life. Rather, they make unconscious adaptations. …they know only that a particular choice works, even though it may appear bizarre to an outsider.” (-ll-)

xiii. “To say that the invention “was in the air” or “the times were ripe for it” are just other ways of stating that the inventors did not do the inventing, but that the cultures did.” (-ll-)

xiv. “Culture is best seen not as complexes of concrete behavior patterns — customs, usages, traditions, habit clusters — as has, by and large, been the case up to now, but as a set of control mechanisms — plans, recipes, rules, instructions (what computer engineers call “programs”) — for the governing of behavior.” (Clifford Geertz)

xv. “In the status game […] the working-class child starts out with a handicap and, to the extent that he cares what the middle-class persons think of him or has internalised the dominant middle-class attitudes toward social class position, he may be expected to feel some ‘shame’.” (Albert Cohen)

xvi. “It is nationalism which engenders nations, and not the other way round.” (Ernest Gellner)

xvii. “Doubt is the offspring of knowledge” (William Winwood Reade)

xviii. “Civilization after civilization, it is the same. The world falls to tyranny with a whisper. The frightened are ever keen to bow to a perceived necessity, in the belief that necessity forces conformity, and conformity a certain stability. In a world shaped into conformity, dissidents stand out, are easily branded and dealt with. There is no multitude of perspectives, no dialogue. The victim assumes the face of the tyrant, self-righteous and intransigent, and wars breed like vermin. And people die.” (Steven Erikson)

xix. “Helping myself is even harder than helping others.” (Gerald Weinberg)

xx. “Science is the study of those things that can be reduced to the study of other things. ” (-ll-)

June 15, 2019 Posted by | Quotes/aphorisms | Leave a comment

A few diabetes papers of interest

i. The dynamic origins of type 1 diabetes.

“Over a century ago, there was diabetes and only diabetes. Subsequently, diabetes came to be much more discretely defined (1) by age at onset (childhood or adult onset), clinical phenotype (lean or obese), treatment (insulin dependent or not insulin dependent), and, more recently, immune genotype (type 1 or type 2 diabetes). Although these categories broadly describe groups, they are often insufficient to categorize specific individuals, such as children having non–insulin-dependent diabetes and adults having type 1 diabetes (T1D) even when not requiring insulin. Indeed, ketoacidosis at presentation can be a feature of either T1D or type 2 diabetes. That heterogeneity extends to the origins and character of both major types of diabetes. In this issue of Diabetes Care, Redondo et al. (2) leverage the TrialNet study of subjects with a single diabetes-associated autoantibody at screening in order to explore factors determining progression to multiple autoantibodies and, subsequently, the pathogenesis of T1D.

T1D is initiated by presumed nongenetic event(s) operating in children with potent genetic susceptibility. But there is substantial heterogeneity even within the origins of this disease. Those nongenetic events evoke different autoantibodies such that T1D patients with insulin autoantibodies (IAA) have different features from those with GAD autoantibodies (GADA) (3,4). The former, in contrast with the latter, are younger both at seroconversion and at development of clinical diabetes, the two groups having different genetic risk and those with IAA having greater insulin secretory loss […]. These observations hint at distinct disease-associated networks leading to T1D, perhaps induced by distinct nongenetic events. Such disease-associated pathways could operate in unison, especially in children with T1D, who often have multiple autoantibodies. […]

Genetic analyses of autoimmune diseases suggest that only a small number of pathways contribute to disease risk. These pathways include NF-κB signaling, T-cell costimulation, interleukin-2, and interleukin-21 pathways and type 1 interferon antiviral responses (5,6). T1D shares most risk loci with celiac disease and rheumatoid arthritis (5), while paradoxically most risk loci shared with inflammatory bowel disease are protective or involve different haplotypes at the same locus. […] Events leading to islet autoimmunity may be encountered very early in life and invoke disease risk or disease protection (4,7) […]. Islet autoantibodies rarely appear before age 6 months, and among children with a family history of T1D there are two peaks for autoantibody seroconversion (3,4), the first for IAA at approximately age 1–2 years, while GADA-restricted autoimmunity develops after age 3 years up to adolescence, with a peak at about age 11 years”

“The precise nature of […] disease-associated nongenetic events remains unclear, but knowledge of the disease heterogeneity (1,9) has cast light on their character. Nongenetic events are implicated in increasing disease incidence, disease discordance even between identical twins, and geographical variation; e.g., Finland has 100-fold greater childhood T1D incidence than China (9,10). That effect likely increases with older age at onset […] disease incidence in Finland is sixfold greater than in an adjacent, relatively impoverished Russian province, despite similar racial origins and frequencies of high-risk HLA DQ genotypes […] Viruses, especially enteroviruses, and dietary factors have been invoked (1215). The former have been implicated because of the genetic association with antiviral interferon networks, seasonal pattern of autoantibody conversion, seroconversion being associated with enterovirus infections, and protection from seroconversion by maternal gestational respiratory infection, while respiratory infections even in the first year of life predispose to seroconversion (14) […]. Dietary factors also predispose to seroconversion and include the time of introduction of solid foods and the use of vitamin C and vitamin D (13,15). The Diabetes Autoimmunity Study in the Young (DAISY) found that early exposure to solid food (1–3 months of age) and vitamin C and late exposure to vitamin D and gluten (after 6 and 9 months of age, respectively) are T1D risk factors, leading the researchers to suggest that genetically at-risk children should have solid foods introduced at about 4 months of age with a diet high in dairy and fruit (13).” [my bold, US]

“This TCF7L2 locus is of particular interest in the context of T1D (9) as it is usually seen as the major type 2 diabetes signal worldwide. The rs7903146 SNP optimally captures that TCF7L2 disease association and is likely the causal variant. Intriguingly, this locus is associated, in some populations, with those adult-onset autoimmune diabetes patients with GADA alone who masquerade as having type 2 diabetes, since they initially do not require insulin therapy, and also markedly increases the diabetes risk in cystic fibrosis patients. One obvious explanation for these associations is that adult-onset autoimmune diabetes is simply a heterogeneous disease, an admixture of both T1D and type 2 diabetes (9), in which shared genes alter the threshold for diabetes. […] A high proportion of T1D cases present in adulthood (17,18), likely more than 50%, and many do not require insulin initially. The natural history, phenotype, and metabolic changes in adult-onset diabetes with GADA resemble a separate cluster of cases with type 2 diabetes but without GADA, which together constitute up to 24% of adult-onset diabetes (19). […] Knowledge of heterogeneity enables understanding of disease processes. In particular, identification of distinct pathways to clinical diabetes offers the possibility of defining distinct nongenetic events leading to T1D and, by implication, modulating those events could limit or eliminate disease progression. There is a growing appreciation that the two major types of diabetes may share common etiopathological factors. Just as there are a limited number of genes and pathways contributing to autoimmunity risk, there may also be a restricted number of pathways contributing to β-cell fragility.”

ii. The Association of Severe Diabetic Retinopathy With Cardiovascular Outcomes in Long-standing Type 1 Diabetes: A Longitudinal Follow-up.

OBJECTIVE It is well established that diabetic nephropathy increases the risk of cardiovascular disease (CVD), but how severe diabetic retinopathy (SDR) impacts this risk has yet to be determined.

RESEARCH DESIGN AND METHODS The cumulative incidence of various CVD events, including coronary heart disease (CHD), peripheral artery disease (PAD), and stroke, retrieved from registries, was evaluated in 1,683 individuals with at least a 30-year duration of type 1 diabetes drawn from the Finnish Diabetic Nephropathy Study (FinnDiane).”

RESULTS During 12,872 person-years of follow-up, 416 incident CVD events occurred. Even in the absence of DKD [Diabetic Kidney Disease], SDR increased the risk of any CVD (hazard ratio 1.46 [95% CI 1.11–1.92]; P < 0.01), after adjustment for diabetes duration, age at diabetes onset, sex, smoking, blood pressure, waist-to-hip ratio, history of hypoglycemia, and serum lipids. In particular, SDR alone was associated with the risk of PAD (1.90 [1.13–3.17]; P < 0.05) and CHD (1.50 [1.09–2.07; P < 0.05) but not with any stroke. Moreover, DKD increased the CVD risk further (2.85 [2.13–3.81]; P < 0.001). […]

CONCLUSIONS SDR alone, even without DKD, increases cardiovascular risk, particularly for PAD, independently of common cardiovascular risk factors in long-standing type 1 diabetes. More remains to be done to fully understand the link between SDR and CVD. This knowledge could help combat the enhanced cardiovascular risk beyond currently available regimens.”

“The 15-year cumulative incidence of any CVD in patients with and without SDR was 36.8% (95% CI 33.4–40.1) and 27.3% (23.3–31.0), respectively (P = 0.0004 for log-rank test) […] Patients without DKD and SDR at baseline had 4.0-fold (95% CI 3.3–4.7) increased risk of CVD compared with control subjects without diabetes up to 70 years of age […]. Intriguingly, after this age, the CVD incidence was similar to that in the matched control subjects (SIR 0.9 [95% CI 0.3–1.9]) in this subgroup of patients with diabetes. However, in patients without DKD but with SDR, the CVD risk was still increased after the patients had reached 70 years of age (SIR 3.4 [95% CI 1.8–6.2]) […]. Of note, in patients with both DKD and SDR, the CVD burden was high already at young ages.”

“This study highlights the role of SDR on a complete range of CVD outcomes in a large sample of patients with long-standing T1D and longitudinal follow-up. We showed that SDR alone, without concomitant DKD, increases the risk of macrovascular disease, independently of the traditional risk factors. The risk is further increased in case of accompanying DKD, especially if SDR is present together with DKD. Findings from this large and well-characterized cohort of patients have a direct impact on clinical practice, emphasizing the importance of regular screening for SDR in individuals with T1D and intensive multifactorial interventions for CVD prevention throughout their life span.

This study also confirms and complements previous data on the continuum of diabetic vascular disease, by which microvascular and macrovascular disease do not seem to be separate diseases, but rather interconnected (10,12,18). The link is most obvious for DKD, which clearly emerges as a major predictor of cardiovascular morbidity and mortality (2,24,25). The association of SDR with CVD is less clear. However, our recent cross-sectional study with the Joslin Medalist Study showed that the CVD risk was in fact increased in patients with SDR on top of DKD compared with DKD alone (19). In the present longitudinal study, we were able to extend those results also to show that SDR alone, without DKD and after the adjustment for other traditional risk factors, increases CVD risk substantially. SDR further increases CVD risk in case DKD is present as well. In addition, the role of SDR as an independent CVD risk predictor is also supported by our data using albuminuria as a marker of DKD. This is important because albuminuria is a known predictor of diabetic retinopathy progression (26) as well as a recognized biomarker for CVD.”

“A novel finding is that, independently of any signs of DKD, the risk of PAD is increased twofold in the presence of SDR. Although this association has recently been highlighted in individuals with type 2 diabetes (10,29), the data in T1D are scarce (16,30). Notably, the previous studies mostly lack adjustments for DKD, the major predictor of mortality in patients with shorter diabetes duration. Both complications, besides sharing some conventional cardiovascular risk factors, may be linked by additional pathological processes involving changes in the microvasculature in both the retina and the vasa vasorum of the conductance vessels (31). […] Patients with T1D duration of >30 years face a continuously increased CVD risk that is further increased by the occurrence of advanced PDR. Therefore, by examining the retina, additional insight into individual CVD risk is gained and can guide the clinician to a more tailored approach to CVD prevention. Moreover, our findings suggest that the link between SDR and CVD is at least partially independent of traditional risk factors, and the mechanism behind the phenomenon warrants further research, aiming to find new therapies to alleviate the CVD burden more efficiently.”

The model selection method employed in the paper is far from optimal [“Variables for the model were chosen based on significant univariable associations.” – This is not the way to do things!], but regardless these are interesting results.

iii. Fasting Glucose Variability in Young Adulthood and Cognitive Function in Middle Age: The Coronary Artery Risk Development in Young Adults (CARDIA) Study.

“Individuals with type 2 diabetes (T2D) have 50% greater risk for the development of neurocognitive dysfunction relative to those without T2D (13). The American Diabetes Association recommends screening for the early detection of cognitive impairment for adults ≥65 years of age with diabetes (4). Coupled with the increasing prevalence of prediabetes and diabetes, this calls for better understanding of the impact of diabetes on cerebral structure and function (5,6). Among older individuals with diabetes, higher intraindividual variability in glucose levels around the mean is associated with worse cognition and the development of Alzheimer disease (AD) (7,8). […] Our objectives were to characterize fasting glucose (FG) variability during young adulthood before the onset of diabetes and to assess whether such variability in FG is associated with cognitive function in middle adulthood. We hypothesized that a higher variability of FG during young adulthood would be associated with a lower level of cognitive function in midlife compared with lower FG variability.”

“We studied 3,307 CARDIA (Coronary Artery Risk Development in Young Adults) Study participants (age range 18–30 years and enrolled in 1985–1986) at baseline and calculated two measures of long-term glucose variability: the coefficient of variation about the mean FG (CV-FG) and the absolute difference between successive FG measurements (average real variability [ARV-FG]) before the onset of diabetes over 25 and 30 years of follow-up. Cognitive function was assessed at years 25 (2010–2011) and 30 (2015–2016) with the Digit Symbol Substitution Test (DSST), Rey-Auditory Verbal Learning Test (RAVLT), Stroop Test, Montreal Cognitive Assessment, and category and letter fluency tests. We estimated the association between glucose variability and cognitive function test score with adjustment for clinical and behavioral risk factors, mean FG level, change in FG level, and diabetes development, medication use, and duration.

RESULTS After multivariable adjustment, 1-SD increment of CV-FG was associated with worse cognitive scores at year 25: DSST, standardized regression coefficient −0.95 (95% CI −1.54, −0.36); RAVLT, −0.14 (95% CI −0.27, −0.02); and Stroop Test, 0.49 (95% CI 0.04, 0.94). […] We did not find evidence for effect modification by race or sex for any variability-cognitive function association”

CONCLUSIONS Higher intraindividual FG variability during young adulthood below the threshold of diabetes was associated with worse processing speed, memory, and language fluency in midlife independent of FG levels. […] In this cohort of black and white adults followed from young adulthood into middle age, we observed that greater intraindividual variability in FG below a diabetes threshold was associated with poorer cognitive function independent of behavioral and clinical risk factors. This association was observed above and beyond adjustment for concurrent glucose level; change in FG level during young adulthood; and diabetes status, duration, and medication use. Intraindividual glucose variability as determined by CV was more strongly associated with cognitive function than was absolute average glucose variability.”

iv. Maternal Antibiotic Use During Pregnancy and Type 1 Diabetes in Children — A National Prospective Cohort Study. It is important that papers like these get published and read, even if the results may not sound particularly exciting:

“Prenatal prescription of antibiotics is common but may perturb the composition of the intestinal microbiota in the offspring. In childhood the latter may alter the developing immune system to affect the pathogenesis of type 1 diabetes (1). Previous epidemiological studies reported conflicting results regarding the association between early exposure to antibiotics and childhood type 1 diabetes (2,3). Here we investigated the association in a Danish register setting.

The Danish National Birth Cohort (DNBC) provided data from 100,418 pregnant women recruited between 1996 and 2002 and their children born between 1997 and 2003 (n = 96,840). The women provided information on exposures during and after pregnancy. Antibiotic prescription during pregnancy was obtained from the Danish National Prescription Registry (anatomical therapeutic chemical code J01) [it is important to note that: “In Denmark, purchasing antibiotics requires a prescription, and all purchases are registered at the Danish National Prescription Registry”], and type 1 diabetes diagnoses (diagnostic codes DE10 and DE14) during childhood and adolescence were obtained from the Danish National Patient Register. The children were followed until 2014 (mean follow-up time 14.3 years [range 11.5–18.4 years, SD 1.4]).”

“A total of 336 children developed type 1 diabetes during follow-up. Neither overall exposure (hazard ratio [HR] 0.90; 95% CI 0.68–1.18), number of courses (HR 0.36–0.97[…]), nor trimester-specific exposure (HR 0.81–0.89 […]) of antibiotics in utero was associated with childhood diabetes. Moreover, exposure to specific types of antibiotics in utero did not change the risk of childhood type 1 diabetes […] This large prospective Danish cohort study demonstrated that maternal use of antibiotics during pregnancy was not associated with childhood type 1 diabetes. Thus, the results from this study do not support a revision of the clinical recommendations on treatment with antibiotics during pregnancy.”

v. Decreasing Cumulative Incidence of End-Stage Renal Disease in Young Patients With Type 1 Diabetes in Sweden: A 38-Year Prospective Nationwide Study.

“Diabetic nephropathy is a devastating complication to diabetes. It can lead to end-stage renal disease (ESRD), which demands renal replacement therapy (RRT) with dialysis or kidney transplantation. In addition, diabetic nephropathy is associated with increased risk of cardiovascular morbidity and mortality (1,2). As a nation, Sweden, next to Finland, has the highest incidence of type 1 diabetes in the world (3), and the incidence of childhood-onset diabetes is increasing globally (4,5). The incidence of ESRD caused by diabetic nephropathy in these Nordic countries is fairly low as shown in recent studies, 3–8% at maximum 30 years’ of diabetes duration (6,7). This is to be compared with studies from Denmark in the 1980s that showed a cumulative incidence of diabetic nephropathy of 41% at 40 years of diabetes duration. Older, hospital-based cohort studies found that the incidence of persistent proteinuria seemed to peak at 25 years of diabetes duration; after that, the incidence levels off (8,9). This implies the importance of genetic susceptibility as a risk factor for diabetic nephropathy, which has also been indicated in recent genome-wide scan studies (10,11). Still, modifiable factors such as metabolic control are clearly of major importance in the development of diabetic nephropathy (1215). Already in 1994, a decreasing incidence of diabetic nephropathy was seen in a hospital-based study in Sweden, and the authors concluded that this was mainly driven by better metabolic control (16). Young age at onset of diabetes has previously been found to protect, or postpone, the development of ESRD caused by diabetic nephropathy, while diabetes onset at older ages is associated with increased risk (7,9,17). In a previous study, we found that age at onset of diabetes affects men and women differently (7). Earlier studies have indicated a male predominance (8,18), while our previous study showed that the incidence of ESRD was similar in men and women with diabetes onset before 20 years of age, but with diabetes onset after 20 years of age, men had increased risk of developing ESRD compared with women. The current study analyzes the incidence of ESRD due to type 1 diabetes, and changes over time, in a large Swedish population-based cohort with a maximum follow-up of 38 years.”

“Earlier studies have shown that it takes ∼15 years to develop persistent proteinuria and another 10 to proceed to ESRD (9,25). In the current study population, no patients developed ESRD because of type 1 diabetes at a duration <14 years; thus only patients with diabetes duration of ≥14 years were included in the study. […] A total of 18,760 unique patients were included in the study: 10,560 (56%) men and 8,200 (44%) women. The mean age at the end of the study was somewhat lower for women, 38.9 years, compared with 40.2 years for men. Women tend to develop type 1 diabetes about a year earlier than men: mean age 15.0 years for women compared with 16.5 years for men. There was no difference regarding mean diabetes duration between men and women in the study (23.8 years for women and 23.7 years for men). A total of 317 patients had developed ESRD due to diabetes. The maximum diabetes duration was 38.1 years for patients in the SCDR and 32.6 years for the NDR and the DISS. The median time from onset of diabetes to ESRD was 22.9 years (minimum 14.1 and maximum 36.6). […] At follow-up, 77 patients with ESRD and 379 without ESRD had died […]. The risk of dying during the course of the study was almost 12 times higher among the ESRD patients (HR 11.9 [95% CI 9.3–15.2]) when adjusted for sex and age. Males had almost twice as high a risk of dying as female patients (HR 1.7 [95% CI 1.4–2.1]), adjusted for ESRD and age.”

“The overall incidence rate of ESRD during 445,483 person-years of follow-up was 0.71 per 1,000 person-years. […] The incidence rate increases with diabetes duration. For patients with diabetes onset at 0–9 and 10–19 years of age, there was an increase in incidence up to 36 years of duration; at longer durations, the number of cases is too small and results must be interpreted with caution. With diabetes onset at 20–34 years of age the incidence rate increases until 25 years of diabetes duration, and then a decrease can be observed […] In comparison of different time periods, the risk of developing ESRD was lower in patients with diabetes onset in 1991–2001 compared with onset in 1977–1984 (HR 3.5 [95% CI 2.3–5.3]) and 1985–1990 (HR 2.6 [95% CI 1.7–3.8]), adjusted for age at follow-up and sex. […] The lowest risk of developing ESRD was found in the group with onset of diabetes before the age of 10 years — both for males and females […]. With this group as reference, males diagnosed with diabetes at 10–19 or 20–34 years of age had increased risk of ESRD (HR 2.4 [95% CI 1.6–3.5] and HR 2.2 [95% CI 1.4–3.3]), respectively. For females, the risk of developing ESRD was also increased with diabetes onset at 10–19 years of age (HR 2.4 [95% CI 1.5–3.6]); however, when diabetes was diagnosed after the age of 20 years, the risk of developing ESRD was not increased compared with an early onset of diabetes (HR 1.4 [95% CI 0.8–3.4]).”

“By combining data from the SCDR, DISS, and NDR registers and identifying ESRD cases via the SRR, we have included close to all patients with type 1 diabetes in Sweden with diabetes duration >14 years who developed ESRD since 1991. The cumulative incidence of ESRD in this study is low: 5.6% (5.9% and 5.3% for males and females, respectively) at maximum 38 years of diabetes duration. For the first time, we could see a clear decrease in ESRD incidence in Sweden by calendar year of diabetes onset. The results are in line with a recent study from Norway that reported a modest incidence of 5.3% after 40 years of diabetes duration (27). In the current study, we found a decrease in the incidence rate after 25 years of diabetes duration in the group with diabetes onset at 20–34 years. With age at onset of diabetes 0–9 or 10–19 years, the ESRD incidence rate increases until 35 years of diabetes duration, but owing to the limited number of patients with longer duration we cannot determine whether the peak incidence has been reached or not. We can, however, conclude that the onset of ESRD has been postponed at least 10 years compared with that in older prospective cohort studies (8,9). […] In conclusion, this large population-based study shows a low incidence of ESRD in Swedish patients with onset of type 1 diabetes after 1977 and an encouraging decrease in risk of ESRD, which is probably an effect of improved diabetes care. We confirm that young age at onset of diabetes protects against, or prolongs, the time until development of severe complications.”

vi. Hypoglycemia and Incident Cognitive Dysfunction: A Post Hoc Analysis From the ORIGIN Trial. Another potentially important negative result, this one related to the link between hypoglycemia and cognitive impairment:

“Epidemiological studies have reported a relationship between severe hypoglycemia, cognitive dysfunction, and dementia in middle-aged and older people with type 2 diabetes. However, whether severe or nonsevere hypoglycemia precedes cognitive dysfunction is unclear. Thus, the aim of this study was to analyze the relationship between hypoglycemia and incident cognitive dysfunction in a group of carefully followed patients using prospectively collected data in the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial.”

“This prospective cohort analysis of data from a randomized controlled trial included individuals with dysglycemia who had additional cardiovascular risk factors and a Mini-Mental State Examination (MMSE) score ≥24 (N = 11,495). Severe and nonsevere hypoglycemic events were collected prospectively during a median follow-up time of 6.2 years. Incident cognitive dysfunction was defined as either reported dementia or an MMSE score of <24. The hazard of at least one episode of severe or nonsevere hypoglycemia for incident cognitive dysfunction (i.e., the dependent variable) from the time of randomization was estimated using a Cox proportional hazards model after adjusting for baseline cardiovascular disease, diabetes status, treatment allocation, and a propensity score for either form of hypoglycemia.

RESULTS This analysis did not demonstrate an association between severe hypoglycemia and incident cognitive impairment either before (hazard ratio [HR] 1.16; 95% CI 0.89, 1.52) or after (HR 1.00; 95% CI 0.76, 1.31) adjusting for the severe hypoglycemia propensities. Conversely, nonsevere hypoglycemia was inversely related to incident cognitive impairment both before (HR 0.59; 95% CI 0.52, 0.68) and after (HR 0.58; 95% CI 0.51, 0.67) adjustment.

CONCLUSIONS Hypoglycemia did not increase the risk of incident cognitive dysfunction in 11,495 middle-aged individuals with dysglycemia. […] These findings provide no support for the hypothesis that hypoglycemia causes long-term cognitive decline and are therefore reassuring for patients and their health care providers.”

vii. Effects of Severe Hypoglycemia on Cardiovascular Outcomes and Death in the Veterans Affairs Diabetes Trial.

“The VADT was a large randomized controlled trial aimed at determining the effects of intensive treatment of T2DM in U.S. veterans (9). In the current study, we examine predictors and consequences of severe hypoglycemia within the VADT and report several key findings. First, we identified risk factors for severe hypoglycemia that included intensive therapy, insulin use, proteinuria, and autonomic neuropathy. Consistent with prior reports in glucose-lowering studies, severe hypoglycemia occurred at a threefold significantly greater rate in those assigned to intensive glucose lowering. Second, severe hypoglycemia was associated with an increased risk of cardiovascular events, cardiovascular mortality, and all-cause mortality in both the standard and the intensive treatment groups. Of importance, however, severe hypoglycemia was associated with an even greater risk of all-cause mortality in the standard compared with the intensive treatment group. Third, the association between severe hypoglycemia and serious cardiovascular events was greater in individuals with an elevated risk for CVD at baseline.”

“Mean participant characteristics were as follows: age, 60.4 years; duration of diabetes, 11.5 years; BMI, 31.3 kg/m2; and HbA1c, 9.4%. Seventy-two percent had hypertension, 40% had a previous cardiovascular event, 62% had a microvascular complication, and 52% had baseline insulin use. The standard and intensive treatment groups included 899 and 892 participants, respectively. […] During the study, the standard treatment group averaged 3.7 severe hypoglycemic events per 100 patient-years versus 10.3 events per 100 patient-years in the intensive treatment group (P < 0.001). Overall, the combined rate of severe hypoglycemia during follow-up in the VADT from both study arms was 7.0 per 100 patient-years. […] Severe hypoglycemia within the prior 3 months was associated with an increased risk for composite cardiovascular outcome (HR 1.9 [95% CI 1.1, 3.5]; P = 0.03), cardiovascular mortality (3.7 [1.3, 10.4]; P = 0.01), and all-cause mortality (2.4 [1.1, 5.1]; P = 0.02) […]. More distant hypoglycemia (4–6 months prior) had no independently associated increased risk with adverse events or death. The association of severe hypoglycemia with cardiovascular events or cardiovascular mortality were not significantly different between the intensive and standard treatment groups […]. In contrast, the association of severe hypoglycemia with all-cause mortality was significantly greater in the standard versus the intensive treatment group (6.7 [2.7, 16.6] vs. 0.92 [0.2, 3.8], respectively; P = 0.019 for interaction). Because of the relative paucity of repeated severe hypoglycemic events in either study group, there was insufficient power to determine whether more than one episode of severe hypoglycemia increased the risk of subsequent outcomes.”

“Although recent severe hypoglycemia increased the risk of major cardiovascular events for those with a 10-year cardiovascular risk score of 35% (HR 2.88 [95% CI 1.57, 5.29]; absolute risk increase per 10 episodes = 0.252; number needed to harm = 4), hypoglycemia was not significantly associated with increased major cardiovascular events for those with a risk score of ≤7.5%. The absolute associated risk of major adverse cardiovascular events, cardiovascular mortality, and all-cause mortality increased with higher CVD risk for all three outcomes […]. We were not able to identify, however, any group of patients in either treatment arm in which severe hypoglycemia did not increase the risk of CVD events and mortality at least to some degree.”

“Although the explanation for the relatively greater risk of serious adverse events after severe hypoglycemia in the standard treatment group is unknown, we agree with previous reports that milder episodes of hypoglycemia, which are more frequent in the intensive treatment group, may quantitatively blunt the release of neuroendocrine and autonomic nervous system responses and their resultant metabolic and cardiovascular responses to hypoglycemia, thereby lessening the impact of subsequent severe hypoglycemic episodes (18,19). Episodes of prior hypoglycemia have rapid and significant effects on reducing (i.e., blunting) subsequent counterregulatory responses to a falling plasma glucose level (20,21). Thus, if one of the homeostatic counterregulatory responses (e.g., epinephrine) also can initiate unwanted intravascular atherothrombotic consequences, it may follow that severe hypoglycemia in a more intensively treated and metabolically well-controlled individual would provoke a reduced counterregulatory response. Although hypoglycemia frequency may be increased in these individuals, this may also lower unwanted and deleterious effects on the vasculature from counterregulatory responses. On the other hand, an isolated severe hypoglycemic event in a less well-controlled individual could provoke a relatively greater counterregulatory response with a proportionally attendant elevated risk for adverse vascular effects (22). In support of this, we previously reported in a subset of VADT participants that despite more frequent serious hypoglycemia in the intensive therapy group, progression of coronary artery calcium scores after severe hypoglycemia only occurred in the standard treatment group (23).”

“In the current study, we demonstrate that the association of severe hypoglycemia with subsequent serious adverse cardiovascular events and death occurred within the preceding 3 months but not beyond. The temporal relationship and proximity of severe hypoglycemia to a subsequent serious cardiovascular event and/or death has been investigated in a number of recent clinical trials in T2DM (25,13,14). All these trials consistently reported an association between severe hypoglycemic and subsequent serious adverse events. However, the proximity of severe hypoglycemic events to subsequent adverse events and death varies. In ADVANCE, a severe hypoglycemic episode increased the risk of major cardiovascular events for both the next 3 months and the following 6 months. In A Trial Comparing Cardiovascular Safety of Insulin Degludec Versus Insulin Glargine in Subjects With Type 2 Diabetes at High Risk of Cardiovascular Events (DEVOTE) and the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results (LEADER) trial, there was an increased risk of either serious cardiovascular events or all-cause mortality starting 15 days and extending (albeit with decreasing risk) up to 1 year after severe hypoglycemia (13,14).”

June 15, 2019 Posted by | Cardiology, Diabetes, Epidemiology, Genetics, Nephrology, Neurology, Ophthalmology, Studies | 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

Quotes

i. “The surest way to be deceived is to think oneself more clever than others.” (Rochefoucauld)

ii. “It is more trouble to make a maxim than it is to do right.” (Mark Twain)

iii. “Between us, we cover all knowledge; he knows all that can be known, and I know the rest.” (-ll-)

iv. “Customs do not concern themselves with right or wrong or reason. But they have to be obeyed; one reasons all around them until he is tired, but he must not transgress them, it is sternly forbidden.” (-ll-)

v. “Every one is a moon, and has a dark side which he never shows to anybody.” (-ll-)

vi. “Often, the surest way to convey misinformation is to tell the strict truth.” (-ll-)

vii. “A man is never more truthful than when he acknowledges himself a liar.” (-ll-)

viii. “It is not worth while to try to keep history from repeating itself, for man’s character will always make the preventing of the repetitions impossible.” (-ll-)

ix. “Man will do many things to get himself loved; he will do all things to get himself envied.” (-ll-)

x. “Grief can take care of itself, but to get the full value of a joy you must have somebody to divide it with.” (-ll-)

xi. “Science, at bottom, is really anti-intellectual. It always distrusts pure reason, and demands the production of objective fact.” (H. L. Mencken)

xii. “It is the natural tendency of the ignorant to believe what is not true. In order to overcome that tendency it is not sufficient to exhibit the true; it is also necessary to expose and denounce the false.” (-ll-)

xiii. “It is the dull man who is always sure, and the sure man who is always dull.” (-ll-)

xiv. “…enlightenment, among mankind, is very narrowly dispersed. It is common to assume that human progress affects everyone-that even the dullest man, in these bright days, knows more than any man of, say, the Eighteenth Century, and is, far more civilized. This assumption is quite erroneous.” (-ll-)

xv. “…there are more viruses in the world than all other forms of life added together.” (Dorothy H. Crawford, Viruses: A Very Short Introduction).

xvi. “People don’t think about you nearly as much as you think about people thinking about you.” (‘Abstrusegoose‘)

xvii. “Most people are not intellectuals — a fact that intellectuals have terrible trouble coming to terms with.” (John Derbyshire)

xviii. “Few of the great tragedies of history were created by the village idiot, and many by the village genius.” (Thomas Sowell)

xix. “If I have not seen as far as others, it is because giants were standing on my shoulders.” (Hal Abelson)

xx. “If I have seen further than others, it is because I am surrounded by dwarfs.” (Murray Gell-Mann. RIP.)

 

May 25, 2019 Posted by | Quotes/aphorisms | Leave a comment

Cardiology: Diabetes Mellitus

Despite the title this is mainly a pharmacology lecture. It’s a bit dated, but on the other hand the action mechanism of a major drug class usually doesn’t change dramatically in a semi-decade, so the fact that the lecture is a few years old I don’t think is that much of a problem. This is not in my opinion a great lecture, but it was worth watching.

A few random links related to topics covered in the talk:

Thiazolidinedione.
PPAR agonist.
Pioglitazone.
Dipeptidyl peptidase-4 inhibitor.
Glucagon-like peptide-1 receptor agonist.
Pregnancy categories.
Alpha-glucosidase inhibitor.
Sulfonylurea.
SGLT2 inhibitor.
Pramlintide.

May 25, 2019 Posted by | Cardiology, Diabetes, Lectures, Pharmacology | Leave a comment

Quotes

i. “Everyone knows that he and his friends and the people he knows will die sooner or later, and after a while the thought recedes to the back of your mind, being a problem that must be faced when the time comes. But you feel that you at least have a right to some notice of such an event, to give you time to prepare your mind for it.” (Tom Holt, The Walled Orchard)

ii. “He was speaking tremendously well, even I could tell that; but he didn’t actually seem to be saying anything.” (-ll-)

iii. “As we walked we saw another column of smoke coming up from a sheltered little combe below us, but this time we didn’t try and interfere. ‘Callicrates,’ I said as we hurried along. ‘Do the Spartans always do things like that? I haven’t heard any stories about it.’ ‘Only the last year or so,’ Callicrates said, ‘ever since we started doing that sort of thing in Messenia when we go raiding there.’ I was horrified. ‘You mean we started it,’ I said. ‘We’re in the wrong.’ ‘What do you mean, in the wrong?’ Callicrates replied. ‘It’s a war, things like that happen. And they only happen when people are stupid enough to hang around when the enemy are approaching.’ I couldn’t believe what I was hearing. ‘Are you trying to say it was their fault they got killed?’ I asked. Callicrates stopped walking and looked at me. ‘Don’t you understand anything?’ he said. ‘It’s nobody’s fault. It’s just the way things are. Why does everything have to be somebody’s fault all the time?’” (-ll-)

iv. “I hate posterity – it’s so fond of having the last word” (Saki, The Complete Saki: 144 Collected Novels and Short Stories)

v. “The young have aspirations that never come to pass, the old have reminiscences of what never happened.” (-ll-)

vi. “In the minds of those who come after us we may be remembered for qualities and successes which we quite left out of the reckoning.” (-ll-)

vii. “Lost dignity is not a possession which can be restored at a moment’s notice” (-ll-)

viii. “My mother is thinking of getting married.” “Again!” “It’s the first time.” “Of course, you ought to know. I was under the impression that she’d been married once or twice at least.” “Three times, to be mathematically exact. I meant that it was the first time she’d thought about getting married; the other times she did it without thinking.” (-ll-)

ix. “It is the tragedy of human endeavour that it works so often unseen and unguessed.” (-ll-)

x. ““Tell me a story,” said the Baroness […] “What sort of story?” […] “One just true enough to be interesting and not true enough to be tiresome[”]”. (-ll-)

xi. “It is the golden rule of all religions that no one should really live up to their precepts; when a man observes the principles of his religion too exactly he is in immediate danger of founding a new sect.” (-ll-)

xii. “Given how evolution operates on populations subjected to different selective pressures it seems one would have to invoke divine intervention for human intelligence to be unvaried completely across the board. Everything else varies; variation in intelligence would be expected.” (‘Young’, from this westhunt comment thread)

xiii. “As long as there have been cells on Earth there have been viruses infecting them.” (John M. Archibald, Genomics, A Very Short Introduction)

xiv. “Giraffes are so tall because their ancestors ate the top branches of trees. The shorter Giraffes could not reach the top branches and died off. Why the shorter Giraffes did not eat the tops of shorter trees seems very strange. Perhaps it never occurred to them.1 When standing beside a mimosa the Giraffe is indistinguishable from the tree except that he has four legs and a head and a tail. Some hunters will stalk a mimosa tree for days without getting results. Others take to stalking apple trees. […] The herd is governed by an experienced male who is governed by several experienced females.” (Will Cuppy, How to Tell Your Friends From the Apes)

xv. “The Screech Owl makes a most amusing pet. The bird flies at visitors and buries its talons in their scalps, sometimes causing them to break a leg in their headlong flight, to the accompaniment of gales of laughter from the owners. After a mass meeting of neighbors, the bird sometimes disappears as suddenly as it came. The owners often disappear, too.” (-ll-)

xvi. “The Man-eating Tiger is old and decrepit. He has lost his strength and vigor and we should feel sorry for him. Young normal Tigers do not eat people. If eaten by a Tiger you may rest assured that he was abnormal. Once in a while a normal Tiger will eat somebody but he doesn’t mean anything by it.” (-ll-)

xvii. “During my labors I found time for my first intensive study of Aristotle, whose “History of Animals” provided me with a footnote or two. The more one peruses this author, and ponders upon him, the more one realizes the wide range, the almost universal scope of his misinformation.” (-ll-)

xviii. “Sometimes you have to give weight to a principle to keep it from being taken away in a storm.” (Jim Butcher, Brief Cases)

xix. “Harry told me once that you can always tell when you’re about to rationalize your way to a bad decision. It’s when you start using phrases such as It would be wrong, but … His advice was to leave the conjunction out of the sentence: It would be wrong. Period.” (-ll-)

xx. “He met his day in the shower, washing his hair with shampoo that was guaranteed to have never been put in a bunny’s eyes and from which ten percent of the profits went to save the whales. He lathered his face with shaving cream free of chlorofluorocarbons, thereby saving the ozone layer. He breakfasted on fertile eggs laid by sexually satisfied chickens that were allowed to range while listening to Brahms, and muffins made with pesticide-free grain, so no eagle-egg shells were weakened by his thoughtless consumption. He scrambled the eggs in margarine free of tropical oils, thus preserving the rain forest, and he added milk from a carton made of recycled paper and shipped from a small family farm. By the time he finished his second cup of coffee, which would presumably help to educate the children of a poor peasant farmer named Juan Valdez, Sam was on the verge of congratulating himself for single-handedly saving the planet just by getting up in the morning.” (Christopher Moore, Coyote Blue)

May 6, 2019 Posted by | Books, Quotes/aphorisms | Leave a comment

Successes and Challenges in Neural Models for Speech and Language

Some links related to the coverage:
Speech recognition.
Machine translation.
Supervised learning.
Parsing.
Context-free grammar.
Kernel Approximation Methods for Speech Recognition.
Convolutional neural network.
Dependency parsing | NLP-progress.
Natural Language Processing (Almost) from Scratch (Collobert et al.).
A Fast and Accurate Dependency Parser using Neural Networks (Chen and Manning, 2014).
Question answering.
Natural Questions: a Benchmark for Question Answering Research (Kwiatkowski et al.)
Attention Is All You Need (Vaswani et al. 2017).
Softmax function.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al.).

May 5, 2019 Posted by | Computer science, Language, Lectures | Leave a comment

Reproducible, Reusable, and Robust Reinforcement Learning

This pdf was created some time before the lecture took place, but it seems to contains all the slides included in the lecture – so if you want a short version of the talk I guess you can read that. I’ve added a few other lecture-relevant links below.

REPRODUCIBILITY, REPLICABILITY, AND GENERALIZATION IN THE SOCIAL, BEHAVIORAL, AND ECONOMIC SCIENCES (Bollen et al. 2015).
1,500 scientists lift the lid on reproducibility (Nature).
Reinforcement learning.
AlphaGo. Libratus.
Adaptive control of epileptiform excitability in an in vitro model of limbic seizures (Panuccio, Guez, Vincent, Avoli and Pineau, 2013)
Deep Reinforcement Learning that Matters (Henderson et al, 2019).
Policy gradient methods.
Hyperparameter (machine learning).
Transfer learning.

May 1, 2019 Posted by | Computer science, Data, Lectures, Statistics | Leave a comment

A few diabetes papers of interest

i. Glycemic Control and Risk of Infections Among People With Type 1 or Type 2 Diabetes in a Large Primary Care Cohort Study. From the paper:

“Infections are widely considered to be a source of significant health care costs and to reduce quality of life among people with diabetes mellitus (DM) (1). Nevertheless, relatively few, large, well-designed, epidemiological studies have explored relationships between poorer control of DM and infections; previous studies have important limitations (1). Most randomized controlled trials (RCTs) of DM control have not investigated the effect of improved glycemic control on infections and are unlikely to do so at present because of the high cost and lack of good-quality supporting observational evidence. […] A recent review of higher-quality population-based epidemiological studies found clinically important (∼1.5–3.5 times higher) infection risks associated with poorer DM control in some studies (usually defined as a glycated hemoglobin [HbA1c] level >7–8% [53–64 mmol/mol]) (1). However, the studies were inconsistent, generating uncertainty about the evidence.

A key concern with previous work is that the measurement of HbA1c usually was made at or near to the time of the infection, so any association could be explained by reverse causality. Any infectious disease episode can itself have an adverse effect on glycemic control, a process known as stress hyperglycemia (4); hence, blood glucose or HbA1c measurements near the time of an infection may be elevated, rendering determination of the chronology and relationship between the two difficult. Several studies with serial HbA1c measurements have shown that the stress hyperglycemia response can be substantial (46). Another important issue is that studies of incident DM often use measurements of HbA1c obtained during initial presentation, and these typically do not represent subsequent levels after initiation of treatment; use of such measurements may obscure associations between usual HbA1c level and infection risk. Other limitations of previous work include a lack of consideration of type of DM (especially T1DM) and fewer older people with DM. The current study uses a large English primary care database with repeated HbA1c measurements wherein we can classify individuals more precisely in terms of their baseline glycemic control as well as ensure that these HbA1c measurements were made before the infection episode.”

“With the use of English primary care data, average glycated hemoglobin (HbA1c) during 2008–2009 was estimated for 85,312 patients with DM ages 40–89 years. Infection rates during 2010–2015 compiled from primary care, linked hospital, and mortality records were estimated across 18 infection categories and further summarized as any requiring a prescription or hospitalization or as cause of death. Poisson regression was used to estimate adjusted incidence rate ratios (IRRs) by HbA1c categories across all DM, and type 1 and type 2 DM separately. IRRs also were compared with 153,341 age-sex-practice–matched controls without DM. Attributable fractions (AF%) among patients with DM were estimated for an optimal control scenario (HbA1c 6–7% [42–53 mmol/mol]).”

“Crude infection rates during 2010–2015 estimated across 18 different categories confirmed consistently higher rates among patients with DM […]. Long-term infection risk rose with increasing HbA1c for most outcomes. Compared with patients without DM, those with DM and optimal control (HbA1c 6–7% [42–53 mmol/mol], IRR 1.41 [95% CI 1.36–1.47]) and poor control (≥11% [97 mmol/mol], 4.70 [4.24–5.21]) had elevated hospitalization risks for infection. In patients with type 1 DM and poor control, this risk was even greater (IRR 8.47 [5.86–12.24]). Comparisons within patients with DM confirmed the risk of hospitalization with poor control (2.70 [2.43–3.00]) after adjustment for duration and other confounders. AF% of poor control were high for serious infections, particularly bone and joint (46%), endocarditis (26%), tuberculosis (24%), sepsis (21%), infection-related hospitalization (17%), and mortality (16%). […] even patients with DM with good control were at an increased risk compared with matched controls without DM. Thus, compared with patients without DM, patients with DM and good control (mean HbA1c 6–7%, IRR 1.41 [95% CI 1.36–1.47]) and those with poor control (≥11%, 4.70 [4.24–5.21]) had elevated hospitalization risks for infection. These risks were higher among patients with T1DM. For example, patients with T1DM with a mean HbA1c ≥11%, had more than eight times the risk of hospitalization than their matched controls without DM (IRR 8.47 [5.86–12.24]), whereas for T2DM, this was four times higher (4.31 [3.88–4.80]). […] Patients with T1DM […] had higher rates of hospitalization (1.12 [1.01–1.24]) and death as a result of infection (1.42 [1.03–1.96]) than patients with T2DM, even after accounting for duration of DM.”

“In terms of the overall population effect, almost one-half of bone and joint infections among patients with DM were attributed to poor control. […] The most novel and concerning finding is the substantial proportion of other serious infections statistically attributable to poor glycemic control, particularly endocarditis, tuberculosis, and sepsis. Between 20 and 30% of these infections in the English DM population could be attributed to poor control. […] [W]e estimated AF% for the three summary groupings […] plus individual infection types […] across HbA1c categories for patients with DM compared with the optimal control scenario of 6–7%. The largest AF% estimate was for bone and joint infections, with 46.0% of hospitalizations being attributed to HbA1c values outside of the range 6–7%. Other large AF% estimates were observed for endocarditis (26.2%) and tuberculosis (23.7%), but CIs were wide. Sepsis (20.8%), pneumonia (15.3%), skin infections (cellulitis 14.0%, other 12.1%), and candidiasis (16.5%) all produced AF% estimates of ≥10%. Overall, 15.7% of infection-related deaths, 16.5% of infection-related hospitalizations, and 6.8% of infections requiring a prescription were attributed to values of HbA1c outside the 6–7% range.”

“Prevalence of diagnosed T2DM has tripled in the U.K. over the past 20 years (17). Although some improvements in glycemic control also have been observed over this period, our analyses show that substantial numbers of patients still have very poor glycemic control (e.g., 16% of patients with T2DM and 41% of patients with T1DM had a mean HbA1c >9%). […] 14% of patients with DM in the current study were hospitalized for infection during follow-up […] The U.K. has a relatively low prevalence of DM and good control on the basis of international comparisons (18); therefore, in many low- and middle-income countries, the burden of infections attributable to poor glycemic control could be substantially higher (19).”

“A variety of mechanisms may link DM and hyperglycemia with infection response (1,2022). Diabetes progression itself is associated with immune dysfunction; autoimmunity in T1DM and low-grade chronic inflammation in T2DM (1). Hyperglycemia may also have adverse effects on several types of immune cells (19,23); alter cytokine and chemokine gene expression (24), and inhibit effects of complement (25). Other important mechanisms may include peripheral diabetic neuropathy because this results in a loss of sensation and reduced awareness of minor injuries (13). Alongside ischemia, often as a result of related peripheral arterial disease, neuropathy can result in impaired barrier defenses, skin ulcers, and lesions with poor wound healing and an increased risk of secondary infections (19). Although numerous mechanisms exist, nearly all involve poor glycemic control. Thus, that improved control would reduce infections seems likely […] Overall, the current analyses demonstrate a strong and likely causal association between hyperglycemia and infection risk for both T1DM and T2DM. DM duration and other markers of severity cannot explain the increased risk, nor can longer duration explain the increased risk for T1DM compared with T2DM. This remains the case in older people in whom infections are common and often severe and more uncertainty exists about the vascular benefits of improving DM control. Substantial proportions of serious infections can be attributed to poor control, even though DM is managed well in the U.K. by international standards. Interventions to reduce infection risk largely have been ignored by the DM community and should be a high priority for future research.”

ii. Poor Metabolic Control in Children and Adolescents With Type 1 Diabetes and Psychiatric Comorbidity. Some observations from the paper:

“Type 1 diabetes in childhood has been found to be associated with an increased risk of psychiatric comorbidities (13), which might intensify the burden of disease and accelerate metabolic deterioration (46), subsequently increasing the risk of mortality and long-term complications such as retinopathy, nephropathy, and neuropathy (79).

Metabolic dysregulation is closely linked to age and diabetes duration, showing a peak in adolescence and early adulthood (10,11). Early adolescence is also characterized as a time of psychological vulnerability (12), in which the incidence of major psychiatric disorders increases (13). A diagnosis of type 1 diabetes in early adolescence seems to increase psychological distress (1,2), and three large population-based studies have shown higher rates of psychiatric disorders in children and adolescents with type 1 diabetes compared with the general population (13). In particular, increased risk was seen for depression, anxiety, and eating disorders, where the pathogenesis is considered to involve reactive mechanisms and imbalances in the diathesis-stress system (13,14).”

“Despite clinical and research evidence that a child with type 1 diabetes often receives more than one psychiatric diagnosis (1,3), most studies evaluate one disorder at a time (46,1620). Motivated by findings that Danish children and adolescents with type 1 diabetes have a higher risk of developing a psychiatric disorder compared with the background population (2), we performed two studies based on the NPR and the Danish Registry of Childhood and Adolescent Diabetes (DanDiabKids). […] The NPR contains psychiatric and somatic diagnoses from all inpatient admissions to Danish public hospitals since 1977. […] The register has used the ICD-10 since 1994 (22,23). Data on registration of psychiatric and type 1 diabetes diagnoses were collected from the NPR, covering 1996 to April 2015. DanDiabKids collects information on all children and adolescents diagnosed with type 1 diabetes before the age of 15 years and monitors them until they are transferred to adult clinics at ∼18 years of age. All public hospital pediatric units must supply annual data on all patients with diabetes to DanDiabKids. […] DanDiabKids contains annual data on all registered patients since 1996, including information on quality indicators, demographic variables, associated conditions, diabetes classification, diabetes family history, growth, self-management, and treatment variables. DanDiabKids now covers 99% of all Danish children and adolescents diagnosed with type 1 diabetes before the age of 15 years. […] Our study population was generated by merging data from DanDiabKids and the NPR. The inclusion criteria were registration with type 1 diabetes in DanDiabKids, age at onset <15 years, year of onset 1995–2014, and year of birth after 1980.”

“After merging DanDiabKids with the NPR, 4,725 children and adolescents with type 1 diabetes were identified […]. Characteristics for the included subjects were as follows: mean age at onset of diabetes was 8.98 years (SD 3.81), birth year ranged from 1980 to 2013, mean age at last visit was 14.6 years (3.7), 2,462 (52.1%) were boys, mean duration of diabetes at last visit was 5.65 years (3.7), 4,434 (93.8%) were of Danish origin, 254 (5.4%) were immigrants or offspring of immigrants, and 36 (0.8%) had unknown ethnicity. […] The observed number of SH [severe hypoglycemia, US] and DKA events per 100 person-years was respectively 10.7 (SH) and 3.2 (DKA) in patients with neurodevelopmental/constitutional psychiatric disorder, 12.1 (SH) and 3.7 (DKA) in patients with potentially reactive psychiatric disorder, 12.3 (SH) and 6.4 (DKA) in patients with both types of psychiatric disorders, and 8.1 (SH) and 1.8 (DKA) in patients without psychiatric disorder. […] Among the 4,725 children and adolescents included in the study, 1,035 were diagnosed with at least one psychiatric disorder at some point. Of these, a total of 175 received their first psychiatric diagnosis before the onset of type 1 diabetes, 575 during pediatric care, and 285 were diagnosed after referral to adult care. […] Anxiety disorders were the most common (n = 492), followed by “behavioral and emotional disorders” (n = 310), mood disorders (n = 205), psychoactive substance misuse disorders (n = 190), and disorders of inattention and hyperactivity (ADHD/attention-deficit disorder [ADD]) (n = 172). Of the 1,035 patients, 46% were diagnosed with two or more psychiatric disorders and 22.8% were diagnosed with three or more psychiatric disorders.”

“Shortly after type 1 diabetes diagnosis, a higher estimated risk of psychiatric disorders was evident among patients who were 10–15 years old at onset of type 1 diabetes. However, after 15–20 years with diabetes, the differences among the groups leveled out at a risk of ∼30% […] Children with high mean HbA1c levels (>8.5% [>70 mmol/mol]) during the first 2 years showed the highest estimated risk of developing a psychiatric disorder, although these differences also appear to level out after 15–20 years with type 1 diabetes. […] The mean HbA1c level was higher in children with a psychiatric disorder (0.22% [95% CI 0.15; 0.29]; 2.45 mmol/mol [1.67; 3.22]) compared with children with no psychiatric disorder (P < 0.001) […] High HbA1c levels in the early period after type 1 diabetes onset seem to be a possible indicator for subsequent psychiatric disorders, and having a psychiatric disorder was associated with higher HbA1c levels, especially in patients with disorders of putative reactive pathogenesis. Given that the Kaplan-Meier plots showed that the estimated risk of being diagnosed with a psychiatric disorder within a period of 15–20 years of type 1 diabetes onset was close to 30% in most groups, our finding highlights an important clinical problem.”

“The estimated risk of developing a psychiatric disorder during the 15–20 years after type 1 diabetes diagnosis is high. The most vulnerable period appeared to be adolescence. Patients with poorly regulated diabetes shortly after onset had a higher estimated risk of developing psychiatric comorbidities. Young patients diagnosed with a psychiatric disorder had more episodes of DKA, and those diagnosed within the reactive spectrum had higher HbA1c levels. Children and adolescents with type 1 diabetes, and in particular those who fail to reach treatment goals, should be systematically evaluated regarding psychological vulnerabilities.”

iii. Development of Microvascular Complications and Effect of Concurrent Risk Factors in Type 1 Diabetes: A Multistate Model From an Observational Clinical Cohort Study.

“The prevalence of type 1 diabetes has increased over the past decades (1,2). Increased life expectancy means that people live longer with diabetes (35); thus, potentially more years are lived with both macrovascular and microvascular complications (6,7). Type 1 diabetes is a complex disease, which develops in various complication states, and co-occurrence of multiple microvascular complications frequently is seen (8). So far, most studies are of a single complication, and the association between the worsening of one complication and the incidence of another is well described, although independently of other complications (9,10). At the same time, a sizeable group of individuals seems to be protected from microvascular complications (1114), and some live several decades with type 1 diabetes without developing complications. Advanced statistical models, such as multistate models, offer an opportunity to explore the transition through various disease states and to quantify progression rates while considering the concurrent complication burden (15,16), that is, the complication burden at a given time point in the observation window.

Strong evidence indicates that some risk factors play a role in all types of microvascular complications. For example, the effects of the duration of diabetes and poor glycemic control are well documented (1720). For other risk factors, such as hypertension, an association has been established mainly for retinopathy and diabetic kidney disease (21,22). Adverse cholesterol levels and previous cardiovascular disease (CVD) are indisputably associated with a higher risk of macrovascular complications (23) and may play a role in the development of microvascular complications (24). […] The complex interplay between microvascular complications and risk factors has been explored only to a limited extent. In this study, we developed a multistate model of microvascular complications to describe in detail complication development in type 1 diabetes. We describe the development of sequences of diabetes-related microvascular complications at various states and examine the associations between selected risk factors, both alone and combined with existing complication burden, and incidence of (further) microvascular complications.”

“In total, 5,031 individuals with type 1 diabetes were registered at the SDCC during the study period. We excluded 1,203 because of missing data for diabetic kidney disease, retinopathy, and/or neuropathy, which left 3,828 eligible individuals to be included in the study. Of these, 242 were first seen in the final state with three complications, which left 3,586 available for analysis, corresponding to 22,946 person-years (PY) […] The median follow-up time was 7.8 years (25th–75th percentile 3.3–10.7 years). HbA1c level at the end of follow-up was lower than at entry, whereas the levels of blood pressure, lipids, and BMI were unchanged. An increase in the use of all cardioprotective medications was observed.”

“We identified 523 individuals who developed diabetic kidney disease during the study. Of these, 84 events occurred in individuals with no complications (IR 12.9 per 1,000 PY), 221 in individuals with retinopathy (25.7 per 1,000 PY), 27 in individuals with neuropathy (36.6 per 1,000 PY), and 191 in individuals with both neuropathy and retinopathy (61.8 per 1,000 PY). […] In the adjusted model, individuals with both retinopathy and neuropathy had a threefold higher risk of diabetic kidney disease than individuals without complications. […] A total of 482 individuals developed neuropathy during follow-up. Of these, 75 incidents occurred in individuals with no complications (IR 11.5 per 1,000 PY), 14 in individuals with diabetic kidney disease (20.6 per 1,000 PY), 234 in individuals with retinopathy (27.2 per 1,000 PY), and 159 in individuals with both retinopathy and diabetic kidney disease (50.2 per 1,000 PY). Individuals with both retinopathy and diabetic kidney disease had a 70% higher risk of developing neuropathy than individuals without complications […] In total, we recorded 649 individuals with incident retinopathy from any previous complication state. Of these, 459 incidents occurred in individuals with no complications (IR 70.7 per 1,000 PY), 74 in individuals with diabetic kidney disease (109.1 per 1,000 PY), 71 in individuals with neuropathy (96.6 per 1,000 PY), and 45 in individuals with both neuropathy and diabetic kidney disease (224.7 per 1,000 PY). Individuals with both diabetic kidney disease and neuropathy had a twofold higher IRR of developing retinopathy than individuals without complications”.

“Baseline and concurrent values of HbA1c, systolic blood pressure, eGFR, and baseline CVD status were all strongly associated with a higher risk of developing diabetic kidney disease. […] The analysis that included complication state revealed that individuals without any other complications than CVD had an almost three times higher risk of diabetic kidney disease than individuals without either CVD or microvascular complications. […] Duration of diabetes, baseline and concurrent value of HbA1c, systolic blood pressure, and baseline LDL cholesterol values were all factors associated with a higher risk of developing retinopathy. None of the effects of the modifiable risk factors on retinopathy were modified by complication burden. […] men with diabetic kidney disease had a higher risk of developing retinopathy than women with diabetic kidney disease. […] All investigated risk factors, except LDL cholesterol, were associated with incidence of neuropathy at both baseline and concurrent levels.”

“[W]e conducted a sensitivity analysis with retinopathy defined as severe nonproliferative or proliferative retinopathy. The prevalence and incidence of retinopathy were much lower, but all associations were similar to the main analysis […]. We found no effect modification by lipid-lowering or antihypertensive treatment. […] We found a stepwise higher risk of any microvascular complication in individuals with higher concurrent complication burden. Baseline and concurrent HbA1c levels, systolic blood pressure, and duration of diabetes were associated with the development of all three microvascular complications. For most risk factors, we did not find evidence that concurrent complication burden modified the association with complication development. […] Concurrent HbA1c level was a strong risk factor for all microvascular complications, even when we adjusted for age, duration, and other traditional risk factors. The overall effects were of similar magnitude to the effect of baseline levels of HbA1c and to other reports (11,29).”

“The presented results are interpreted in the frame of a multistate model design, and the use of clinical data makes the results highly relevant in similar health care settings. However, because of the observational study design, we cannot draw conclusions about causality. The positive associations among complications might reflect that diabetic kidney disease takes the longest time to develop, whereas retinopathy and neuropathy develop faster. Associations of two disease complications to a third might not be causal. However, that the risk of a third complication, even after adjustment for multiple confounders, is higher regardless of the previous combination of complications indicates that an association cannot be explained by these risk factors alone. In addition, concurrent risk factor levels may be subject to reverse causality. The current results should be seen as a benchmark for others who aim to explore the occurrence of microvascular complications as a function of the concurrent total complication burden in individuals with type 1 diabetes. […] The findings demonstrate that high concurrent complication burden elevates the risk of all three investigated microvascular complications: diabetic kidney disease, retinopathy, and neuropathy. This means that if an individual develops a complication, the clinician should be aware of the increased risk of developing more complications. […] For most risk factors, including HbA1c, we found no evidence that the effect on the development of microvascular complications was modified by the burden of concurrent complications.”

iv. Long-term Glycemic Control and Dementia Risk in Type 1 Diabetes.

“[P]rior work has established type 1 diabetes as a risk factor for dementia (15). However, the relationship between glycemic control and subsequent risk of dementia in those with type 1 diabetes remains unclear. Hemoglobin A1c (HbA1c) is an established measure that integrates glucose control over the prior 2–3 months and is widely used to guide clinical management of type 1 diabetes (16,17). Cumulative glycemic exposure, as measured by multiple HbA1c measures over time, has previously been used to evaluate glycemic trajectories and their association with a number of diabetes complications (18,19). Electronic health records capture HbA1c values collected over time allowing for a more thorough long-term characterization of glycemic exposure than is reflected by a single HbA1c measure. In this study, we leverage data [from northern California, US] collected over a span of 19 years to examine the association of cumulative glycemic exposure, as measured by repeated HbA1c values, with incident dementia among older adults with type 1 diabetes. We also examine the potential for a threshold of glycemic exposure above or below which risk of dementia increases.”

“The final analytic cohort consisted of 3,433 individuals (mean age at cohort entry = 56.1 years old; 47.1% female) […]. On average, individuals who developed dementia during follow-up were older at cohort entry (64.4 vs. 55.7 years) and were more likely to have a history of stroke (7.7% compared with 3.5%) at baseline. The mean follow-up time was 6.3 years (median 4.8 years [interquartile range (IQR) 1.7, 9.9]), and the mean number of HbA1c measurements was 13.5 (median 9.0 [IQR 3.0, 20.00]). By the end of follow-up on 30 September 2015, 155 members (4.5%) were diagnosed with dementia, 860 (25.1%) had a lapse of at least 90 days in membership coverage, 519 (15.1%) died without a dementia diagnosis, and 1,899 (55.3%) were still alive without dementia diagnosis. Among the 155 members who developed dementia over follow-up, the mean age at dementia diagnosis was 64.6 years (median 63.6 years [IQR 56.1, 72.3]).”

“In Cox proportional hazards models, dementia risk was higher in those with increased exposure to HbA1c 8–8.9% (64–74 mmol/mol) and ≥9% (≥75 mmol/mol) and lower in those with HbA1c 6–6.9% (42–52 mmol/mol) and 7–7.9% (53–63 mmol/mol). In fully adjusted models, compared with those with minimal exposure (<10% of HbA1c measurements) to HbA1c 8–8.9% and ≥9%, those with prolonged exposure (≥75% of measurements) were 2.51 and 2.13 times more likely to develop dementia, respectively (HbA1c 8–8.9% fully adjusted hazard ratio [aHR] 2.51 [95% CI 1.23, 5.11] and HbA1c ≥9% aHR 2.13 [95% CI 1.13, 4.01]) […]. In contrast, prolonged exposure to HbA1c 6–6.9 and 7–7.9% was associated with a 58% lower and 61% lower risk of dementia, respectively (HbA1c 6–6.9% aHR 0.42 [95% CI 0.21, 0.83] and HbA1c 7–7.9% aHR 0.39 [95% CI 0.18, 0.83]). […] Results were similar in Cox models examining cumulative glycemic exposure based on whether a majority (>50%) of an individual’s available HbA1c measurements fell into the following categories of HbA1c: <6, 6–6.9, 7–7.9, 8–8.9, and ≥9% […]. Majority exposure to HbA1c 8–8.9 and ≥9% was associated with an increased risk of dementia (HbA1c 8–8.9% aHR 1.65 [95% CI 1.06, 2.57] and HbA1c ≥9% aHR 1.79 [95% CI 1.11, 2.90]), while majority exposure to HbA1c 6–6.9 and 7–7.9% was associated with a reduced risk of dementia (HbA1c 6–6.9% aHR 0.55 [95% CI 0.34, 0.88] and HbA1c 7–7.9% aHR 0.55 [95% CI 0.37, 0.82]). Majority exposure to HbA1c <6% (<42 mmol/mol) was associated with increased dementia risk in age-adjusted models (HR 2.06 [95% CI 1.11, 3.82]), though findings did not remain significant in fully adjusted models (aHR 1.45 [95% CI 0.71, 2.92]). Findings were similar in sensitivity analyses among the subset of members who were ≥65 years of age at baseline (n = 1,082 [32% of the sample]), though the increased risk associated with majority time at HbA1c ≥9% was no longer statistically significant”.

“In this large sample of older adults with type 1 diabetes, we found that cumulative exposure to higher levels of HbA1c (8–8.9 and ≥9%) was associated with an increased risk of dementia, while cumulative exposure to well-controlled HbA1c (6–6.9 and 7–7.9%) was associated with a decreased risk of dementia. In fully adjusted models, compared with those with minimal exposure to HbA1c 8–8.9% and HbA1c ≥9%, those with prolonged exposure were more than twice as likely to develop dementia over the course of follow-up […]. By contrast, dementia risk was ∼60% lower among those with prolonged exposure to well-controlled HbA1c (6–6.9 and 7–7.9%) compared with those with minimal time at well-controlled levels of HbA1c.”

“Our results complement and extend previous studies that have reported an association between chronic hyperglycemia and decreased cognitive function in children and adolescents with type 1 diabetes (25,26), as well as studies reporting an association between poor glycemic control and decreased cognitive functioning in middle-aged adults with type 1 diabetes and older adults with type 2 diabetes (711). Our findings are also consistent with previous studies that found an increased dementia risk associated with poorer glycemic control among adults with type 2 diabetes and adults without diabetes (1113). Whether these findings applied to dementia risk among older adults with type 1 diabetes was previously unknown.”

“In our study of 3,433 older adults with type 1 diabetes, 155 (4.5%) individuals developed dementia over an average of 6.3 years of follow-up. Among those who developed dementia, the average age at dementia diagnosis was 64.6 years. A large-scale study using administrative health data from 1998 to 2011 in England reported a similar incidence of dementia among a subset of adults aged ≥50 years with type 1 diabetes (3.99% developed dementia), though the average length of follow-up was not reported for this specific age-group (15). Prior studies have also found type 1 diabetes to be a risk factor for dementia (15) and have reported the average age at onset of dementia to be 2–5 years earlier in those with diabetes compared with those without diabetes (27,28). Taken together, these results provide further evidence that older adults with type 1 diabetes are at increased risk of developing dementia and may have increased risk at younger ages than the general population. Our results, however, suggest that effective glycemic control could be an important tool for reducing risk of dementia among older adults with type 1 diabetes.”

“Pathophysiological mechanisms by which glycemic control may affect dementia risk are still poorly understood but are hypothesized to result from structural brain abnormalities stemming from chronic exposure to hyperglycemia and/or recurrent severe hypoglycemia. Studies in adults and youth with type 1 diabetes have reported an association between chronic hyperglycemia (defined using lifetime HbA1c history and using retinopathy as an indicator of chronic exposure) and gray matter density loss (3537). Studies examining the association between severe hypoglycemic events and changes in brain structure have been less consistent, with some reporting increased gray matter density loss and a higher prevalence of cortical atrophy in those with a history of frequent exposure to severe hypoglycemia (36,38), while another study reported no association (37). In the ACCORD MIND (Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes) trial, compared with standard glycemic control, intensive glycemic control was associated with greater total brain volume, suggesting that intensive glycemic control may reduce brain atrophy related to diabetes (39). […] Understanding why glycemic patterns are associated with dementia is a much-needed area for future study, particularly with regard of the potential role of intercurrent micro- and macrovascular complications.”

v. A Comparison of the 2017 American College of Cardiology/American Heart Association Blood Pressure Guideline and the 2017 American Diabetes Association Diabetes and Hypertension Position Statement for U.S. Adults With Diabetes.

“Hypertension is one of the most common comorbidities among adults with diabetes. Prior studies have estimated the prevalence of hypertension to be twice as high among adults with diabetes compared with age-matched control subjects without diabetes (1,2). Among adults with diabetes, the presence of hypertension has been associated with a two times higher risk for cardiovascular disease (CVD) events and mortality (3,4).

The 2017 American College of Cardiology (ACC)/American Heart Association (AHA) Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults provides a comprehensive set of recommendations for the diagnosis and treatment of hypertension among adults, including those with diabetes (5). This guideline defines hypertension in adults, including those with diabetes, as an average systolic blood pressure (SBP) ≥130 mmHg or diastolic blood pressure (DBP) ≥80 mmHg […]. According to this guideline, pharmacological antihypertensive treatment should be initiated in adults with diabetes if they have an average SBP ≥130 mmHg or DBP ≥80 mmHg, and the treatment goal is SBP <130 mmHg and DBP <80 mmHg (5).

The American Diabetes Association (ADA) published a position statement on diabetes and hypertension in 2017 that recommends blood pressure (BP) levels different from the ACC/AHA guideline for defining hypertension and for initiating pharmacological antihypertensive treatment (for both, SBP ≥140 mmHg or DBP ≥90 mmHg) (6). The ADA position statement recommends that BP goals should be individualized based on patient priorities and clinician judgment. Treatment goals for those taking antihypertensive medication are SBP <140 mmHg and DBP <90 mmHg, with SBP <130 mmHg and DBP <80 mmHg to be considered for those with high CVD risk as long as these levels can be achieved without undo treatment burden.

The purpose of the current study was to estimate the impact of differences in the definition of hypertension and recommendations for pharmacological antihypertensive treatment initiation and intensification of therapy in U.S. adults with diabetes according to the ACC/AHA guideline and the ADA diabetes and hypertension position statement (5,6). To accomplish these goals, we analyzed data from the U.S. National Health and Nutrition Examination Survey (NHANES).”

“According to data from NHANES 2011–2016, 56.6% (95% CI 53.3, 59.9) of U.S. adults with diabetes were taking antihypertensive medication. Of U.S. adults with diabetes, 57.4% (53.1, 61.6) of those not taking and 80.2% (76.6, 83.4) of those taking antihypertensive medication had high CVD risk. Among U.S. adults with diabetes, those with high CVD risk (history of CVD or 10-year ASCVD risk ≥10%) were on average 15–20 years older and the prevalence of smoking and chronic kidney disease was 10–20% higher when compared with their counterparts without high CVD risk […]. Among U.S. adults with diabetes without high CVD risk, the mean 10-year and 30-year predicted CVD risks were 3.8% (3.5, 4.2) and 25.0% (23.4, 26.6), respectively, for those not taking antihypertensive medication and 5.8% (5.3, 6.4) and 37.4% (34.5, 40.3), respectively, for those taking antihypertensive medication.

The prevalence of hypertension was 77.1% (95% CI 73.9, 80.0) according to the ACC/AHA guideline and 66.3% (63.4, 69.1) according to the ADA position statement […]. Overall, 10.8% (9.0, 12.8) of U.S. adults with diabetes had hypertension according to the ACC/AHA guideline but not the ADA position statement. Among U.S. adults with diabetes not taking antihypertensive medication, 52.8% (47.7, 57.8), 24.8% (20.6, 29.6) and 22.4% (19.2, 25.9) were recommended antihypertensive medication initiation by neither document, by the 2017 ACC/AHA guideline only, and by both documents, respectively […]. Among U.S. adults with diabetes taking antihypertensive medication, 45.3% (41.3, 49.4), 4.3% (2.8, 6.6), and 50.4% (46.5, 54.2) had an average BP that met the goal in both documents, was above the ACC/AHA goal but not the ADA goal, and was above the goals in both documents, respectively […] The overall agreement between the ACC/AHA guideline and the ADA position statement was 89.2% (87.2, 91.0) for the presence of hypertension, 75.2% (70.4, 79.4) for the recommendation to initiate antihypertensive medication, and 95.7% (93.4, 97.2) for having a BP above the recommended treatment goal. “

“Based on both the ACC/AHA guideline and ADA position statement, 17.8 (95% CI 16.2, 19.3) million U.S. adults with diabetes had hypertension […]. An additional 2.9 (2.3, 3.5) million U.S. adults had hypertension based on the ACC/AHA guideline only. Among U.S. adults with diabetes not taking antihypertensive medication, 2.6 (2.1, 3.1) million were recommended to initiate antihypertensive medication by both the ACC/AHA guideline and the ADA position statement with an additional 2.9 (2.3, 3.5) million recommended to initiate antihypertensive medication by the ACC/AHA guideline only […]. Among U.S. adults with diabetes taking antihypertensive medication, 7.6 (6.8, 8.5) million had a BP above the goal in both documents, with an additional 700,000 (400,000, 900,000) having a BP above the goal recommended in the ACC/AHA guideline only […]. Among U.S. adults with diabetes not taking antihypertensive medication, the mean 10-year CVD risk was 10.7% (95% CI 9.4, 12.0) for those not recommended treatment initiation by either the ACC/AHA guideline or the ADA position statement, 14.6% (11.5, 17.6) for those recommended treatment initiation by the ACC/AHA guideline but not the ADA position statement, and 23.2% (19.5, 27.0) among those recommended treatment initiation by the ACC/AHA guideline and the ADA position statement […]. The mean 30-year CVD risk exceeded 25% in each of these groups. Among U.S. adults with diabetes taking antihypertensive medication, the mean 10-year CVD risk was 10.6% (9.4, 12.0), 6.5% (CI 5.6, 7.3), and 33.8% (32.1, 35.5) among those with above-goal BP according to neither document, the ACC/AHA guideline only, and both documents, respectively […]. The 30-year CVD risk exceeded 40% in each group.”

“In conclusion, the current study demonstrates a high degree of concordance between the 2017 ACC/AHA BP guideline and the 2017 ADA position statement on diabetes and hypertension. Using either document, the majority of U.S. adults with diabetes have hypertension. A substantial proportion of U.S. adults with diabetes not taking antihypertensive medication are recommended to initiate treatment by both documents […] Among U.S. adults with diabetes not taking antihypertensive medication, 75.2% had an identical recommendation for initiation of antihypertensive drug therapy according to the ACC/AHA guideline and the ADA position statement. The majority of those who were recommended to initiate pharmacological antihypertensive therapy according to the ACC/AHA guideline but not the ADA position statement had high CVD risk. […] At the population level, the ACC/AHA guideline and ADA position statement have more similarities than differences. However, at the individual level, some patients with diabetes will have fundamental changes in their care depending on which advice is followed. The decision to initiate and intensify antihypertensive medication should always be individualized, based on discussions between patients and their clinicians. Both the ACC/AHA BP guideline and ADA position statement acknowledge the need to individualize treatment decisions to align with patients’ interests.”

vi. Treatment-induced neuropathy of diabetes: an acute, iatrogenic complication of diabetes.

“Treatment-induced neuropathy in diabetes (also referred to as insulin neuritis) is considered a rare iatrogenic small fibre neuropathy caused by an abrupt improvement in glycaemic control in the setting of chronic hyperglycaemia. The prevalence and risk factors of this disorder are not known. In a retrospective review of all individuals referred to a tertiary care diabetic neuropathy clinic over 5 years, we define the proportion of individuals that present with and the risk factors for development of treatment-induced neuropathy in diabetes. Nine hundred and fifty-four individuals were evaluated for a possible diabetic neuropathy. Treatment-induced neuropathy in diabetes was defined as the acute onset of neuropathic pain and/or autonomic dysfunction within 8 weeks of a large improvement in glycaemic control—specified as a decrease in glycosylated haemoglobin A1C (HbA1c) of ≥2% points over 3 months. Detailed structured neurologic examinations, glucose control logs, pain scores, autonomic symptoms and other microvascular complications were measured every 3–6 months for the duration of follow-up. Of 954 patients evaluated for diabetic neuropathy, 104/954 subjects (10.9%) met criteria for treatment-induced neuropathy in diabetes with an acute increase in neuropathic or autonomic symptoms or signs coinciding with a substantial decrease in HbA1c. Individuals with a decrease in HbA1c had a much greater risk of developing a painful or autonomic neuropathy than those individuals with no change in HbA1c (P < 0.001), but also had a higher risk of developing retinopathy (P < 0.001) and microalbuminuria (P < 0.001). There was a strong correlation between the magnitude of decrease in HbA1c, the severity of neuropathic pain (R = 0.84, P < 0.001), the degree of parasympathetic dysfunction (R = −0.52, P < 0.01) and impairment of sympathetic adrenergic function as measured by fall in blood pressure on tilt-table testing (R = −0.63, P < 0.001). With a decrease in HbA1c of 2–3% points over 3 months there was a 20% absolute risk of developing treatment-induced neuropathy in diabetes, with a decrease in HbA1c of >4% points over 3 months the absolute risk of developing treatment-induced neuropathy in diabetes exceeded 80%. Treatment-induced neuropathy of diabetes is an underestimated iatrogenic disorder associated with diffuse microvascular complications. Rapid glycaemic change in patients with uncontrolled diabetes increases the risk of this complication.”

“Typically, individuals with TIND reported the onset of severe burning pain (pain scores 4–10/10) within 2–6 weeks of the improvement in glucose control. Burning pain was present in all subjects with TIND. Paraesthesias were present in 93/104 subjects and shooting pain in 88/104 subjects. Hyperalgesia and allodynia were common in the distribution of the pain. […] Individuals with TIND all reported ongoing sleep disturbances typically described as difficulty with sleep initiation and sleep duration secondary to neuropathic pain. These individuals reported no record of sleep problems prior to the development of TIND. […] Erectile dysfunction was noted in 28/31 males with TIND, compared to 135/417 males without TIND (P < 0.001, X2). […] Seventy-three individuals completed autonomic testing within 2–5 months of the onset of neuropathic pain. […] The results for both groups, in all tests, were abnormal compared to age-related normative values. There were strong correlations between the magnitude of decrease in HbA1c over 3 months and worsening autonomic function. A greater change in HbA1c resulted in worsening parasympathetic function as determined by the expiratory to inspiratory ratio (R = −0.52, P < 0.01) and the Valsalva ratio (R = −0.55, P < 0.01). Greater sympathetic adrenergic dysfunction also correlated with a greater change in HbA1c over 3 months as determined by the fall in systolic blood pressure during tilt-table test (R = −0.63, P < 0.001), the fall in blood pressure during phase 2 of the Valsalva manoeuvre (R = 0.49, P < 0.001), and the diminished phase 4 blood pressure overshoot during the Valsalva manoeuvre (R = −0.59, P < 0.001). […] individuals with type 1 diabetes had greater autonomic dysfunction than those with type 2 diabetes across all tests. The slopes of the regression lines describing the correlation between the change in HbA1c and a particular autonomic test did not differ by the type of diabetes, or by the type of treatment used to control glucose.”

“Most patients with TIND had rapid progression of retinopathy that developed in conjunction with the onset of neuropathic pain […] Prior to development of TIND, 65/104 individuals had no retinopathy, 35/104 had non-proliferative retinopathy, whereas 4/104 had proliferative retinopathy. Twelve months after the development of TIND, 10/104 individuals had no retinopathy, 54/104 had non-proliferative retinopathy and 40/104 had proliferative retinopathy (P < 0.001, Fisher’s exact test). Prior to development of TIND, 18/104 had evidence of microalbuminuria, while 12 months after the development of TIND, 87/104 had evidence of microalbuminuria (P < 0.001, X2).”

“TIND is a small fibre and autonomic neuropathy that appears after rapid improvements in glucose control. In this manuscript, we demonstrate that: (i) there is an unexpectedly high proportion of individuals with TIND in a tertiary referral diabetic clinic; (ii) the risk of developing TIND is associated with the magnitude and rate of change in HbA1c; (iii) neuropathic pain and autonomic dysfunction severity correlate with the magnitude of change in HbA1c; (iv) patients with Type 1 diabetes and a history of eating disorders are at high risk for developing TIND; and (v) TIND can occur with use of insulin or oral hypoglycaemic agents. […] TIND differs from the most prevalent generalized neuropathy of diabetes, the distal sensory-motor polyneuropathy, in several respects. The neuropathic pain has an acute onset, appearing within 8 weeks of glycaemic change, in contrast with the more insidious onset in the distal sensory-motor polyneuropathy […]. The pain in TIND is more severe, and poorly responsive to interventions including opioids, whereas most patients with distal sensory-motor polyneuropathy respond to non-opioid interventions […]. Although the distribution of the pain is length-dependent in individuals with TIND, it is frequently far more extensive than in distal sensory-motor polyneuropathy and the associated allodynia and hyperalgesia are much more prevalent […]. Autonomic symptoms and signs are common, prominent and appear acutely, in contrast to the relatively lower prevalence, gradual onset and slow progression in distal sensory-motor polyneuropathy […]. Finally, both the pain and autonomic features may be reversible in some patients […].

Our data indicate that the severity of TIND is associated with the magnitude of the change of HbA1c, however, it is also clear that the rate of change is important (e.g. a 4% point fall in the HbA1c will have a greater impact if occurring over 3 months than over 6 months). The pathogenic mechanisms whereby this change in glucose results in nerve damage and/or dysfunction are not known. Proposed mechanisms include endoneurial ischaemia due to epineurial arterio-venous shunts […], apoptosis due to glucose deprivation […], microvascular neuronal damage due to recurrent hypoglycaemia […], and ectopic firing of regenerating axon sprouts, but these possibilities are unproven. […] Additional mechanistic studies are necessary to determine the underlying pathophysiology.”

April 28, 2019 Posted by | Cardiology, Diabetes, Epidemiology, Immunology, Medicine, Nephrology, Neurology, Ophthalmology, Psychiatry, Studies | Leave a comment

Words

The words below were mainly words I encountered while reading the books Artificial intelligence, a very short introduction,
Cognitive Neuroscience, -ll-, and The Complete Saki: 144 Collected Novels and Short Stories (…the post only contains words from the first half – this book is very long (…and highly recommended)).

Clapotis. Aedile. Proventriculus. Sortition. Fug. Ecumenical. Credal. Obstreperous. Officiant. Oneirology. Unadulterated. Risible. Onomasti komodein. Recusancy. Saltire. Anent. Propaedeutic. Patristic. Plectrum. Voxel.

Cark. Deimatic. Phasmid. Peptonize. Tomtit. Maffick. Hartebeest. Preceptress. Pavonicide. Halma. Quatrain. Epigrammatic. Missal. Chaffinch. Psalmody. Bittern. Vergeress. Snaffle. Quagga. Heretofore.

Jacquerie. Plaguy. Cajolery. Madder. Picquet. Potage. Votive. Dissention. Begird. Medlar. Whirligig. Recessional. Lory. Ditty. Alarum. Skewbald. Burg. Convolvulus. Stotting. Entr’acte.

Counterfoil. Bandicoot. Tercentenary. Schipperke. Jangle. Serry. Snuggery. Benignant. Jonquil. Wyandot. Francolin. Lanner. Aspic. Paddock. Sloe. Malmaison. Umber. Drake. Pullet. Borzoi.

March 23, 2019 Posted by | Books, Language | Leave a comment

Promoting the unknown

 

February 23, 2019 Posted by | Music | Leave a comment

Quotes

Many of the quotes below are from Stanislaw Jerzy Lec’s book Unkempt Thoughts.

i. “Aristotle’s remark about wit in general, that it is “educated insolence,” frequently applies to the aphorism in particular. […] Dozens of the greater ones dispense life’s bitter rather than its sweet. They are moralists whose barbs do not spur us on to the higher morality. We read them not to improve ourselves but to feel the pleasure that comes of recognizing how unimproved (or, still more gratifying, how unimprovable) are all the other fellows.” (Clifton Fadiman)

ii. “If a nation values anything more than freedom, it will lose its freedom; and the irony of it is that if it is comfort or money that it values more, it will lose that too.” (W. Somerset Maugham)

iii. “The solar system has no anxiety about its reputation.” (Ralph Waldo Emerson)

iv. “In love, as in war, a fortress that parleys is half taken.” (Marguerite de Valois)

v. “He that leaveth nothing to chance will do few things ill, but he will do very few things.” (George Savile).

vi. “You will always find some Eskimos ready to instruct the Congolese on how to cope with heat waves.” (Stanislaw Jerzy Lec)

vii. “Some hide truth because they fear it, others hide truth because they want to save it for the right occasion. Both truths are exactly the same.” (-ll-)

viii. “You have to climb to reach a deep thought.” (-ll-)

ix. “Never open the door to those who open them even without your permission.” (-ll-)

x. “In some lands exile is the greatest punishment; in others, the greatest humanitarians should fight for it.” (-ll-)

xi. “He who has a good memory can forget more, more easily.” (-ll-)

xii. “Some people’s thoughts are so shallow they don’t even reach their heads.” (-ll-)

xiii. “When gossip grows old it becomes myth.” (-ll-)

xiv. “It is unhealthy to live. He who lives, dies.” (-ll-)

xv. “If Newton’s Principia were published today, it would have 4 stars on Amazon. There would be one cluster of 5 star reviews by people saying it had revolutionized their thinking, and another cluster of 1 star reviews by people complaining it was pointless and hard to read.” (Paul Graham)

xvi. “I find I have a splendid appetite for the kindness of those I respect.” (Patrick O’Brian. The Thirteen Gun Salute)

xvii. “Man may sympathize with the underdog, but he wants to side with the winner.” (Erle Stanley Gardner. The Case of the Sulky Girl)

xviii. “Everyone is so proud of their own insignificant little boundaries. Scrupulously they vow, I would never do that! And perhaps they wouldn’t. More likely, they’ll never have to.” (John O’Brien, Leaving Las Vegas)

xix. “In this bundle, he said, I have a very powerful magical object. […] if you know how to use it, it will take you anywhere you wish to go in the twinkling of an eye; it will carry you from Spain to India in a heartbeat, or show you the heart of Asia or the deserts of Africa. It can even take you to where you can see the dead and listen to them talking to you.’ […] As it happens,’ he went on, ‘I have with me just such an object. Look.’ And he dived about inside the sleeve of his robe and pulled out a bronze tube about the size of a cucumber. ‘That’s remarkable,’ I said, wondering what he was looking to achieve. Obviously whatever the thing in the tube was, it couldn’t do all those things he’d said; he’d offer to give me a demonstration, it wouldn’t work, and we’d have a major loss of face and a serious diplomatic incident on our hands. Why me, I thought? But the ambassador just smiled and said, ‘Would you like to see it?’ Well, I couldn’t say no; so he pulled off the lid and started fishing about inside the tube with his fingers. […] ‘Here we are,’ the ambassador said, and he pulled out the contents of the tube; and of course you’re way ahead of me, and you guessed quite some time ago that what he’d got in there wasn’t some scrap of magical cloth but a plain, ordinary book.” (Tom Holt, Meadowland)

xx. “You will meet someone that will make you laugh, will put your stomach in knots every time they talk to you, will make you smile every time their name pops up on your phone, and whose smile you’ll see every night as you fall asleep and every morning as you wake up because it has such a calming and comforting effect on you.

All they’ll have met is someone they kinda like to talk to and see every now and again.” (‘Closer67’, here)

February 3, 2019 Posted by | Books, Quotes/aphorisms | 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

Books 2018

Below I have added a list of books I read in 2018, as well as some comments and observations. As usual ‘f’ = fiction, ‘m’ = miscellaneous, ‘nf’ = non-fiction; the numbers in parentheses indicate my goodreads ratings of the books (from 1-5). The post contains links to the books’ goodreads profiles as well as links to reviews and blog posts I’ve written about them.

Of the 150 books I read, 40 were non-fiction, 108 were fiction, and 2 I categorized as miscellaneous. You can see an overview of the books on goodreads here. According to the goodreads count, I read 42,069 pages during the year, or slightly more than 115 pages per day. This is significantly less than the -count for 2017, where I read ~125 pages/day. The average page count of the books I read was 280 pages, with a minimum page count of 120 pages and a maximum page count of 934.

2018 was the year where I finished Patrick O’Brian’s Aubrey & Maturin series, as I read the last 17 of the novels in that series. Other fiction authors I’ve read include Tom Holt (33 books), Erle Stanley Gardner (19), P. G. Wodehouse (18) (I’d read all of the Wodehouse books before, but I see no reason not to include them in this list/count despite this fact), and Ngaio Marsh (14). I don’t actually think either Marsh or Gardner’s books are all that good, a fact the ratings of the books below should also indicate, but I don’t like spending a lot of time looking for new books to read and new authors to try out and given that, they were sort of ‘good enough at the time’; however I can’t really recommend these authors. O’Brian’s a different matter, as is Wodehouse – and the good Tom Holt books are actually very decent, even if he’s also written a few which were certainly nothing special.

I have been less active this year than I have been in recent years on the blog, but I did post a total of 83 book-related posts on the blog during 2018 (one every 4,4 days on average). As usual not all book-related posts published on the blog this year are included in/-linked from this post; the posts in which I have included lists of new/interesting words I’ve encountered while reading mainly fiction books are not included, and neither are the posts containing quotes and aphorisms from books I read – I decided some time ago that it’s too much work trying to link that kind of stuff up here as well; people who are interested can check out the relevant categories themselves if they feel like it.

I may update this post later on with more detailed information about my non-fiction reading during the year and/or perhaps some information about the books I did not manage to finish during the year.

1. Complexity: A Very Short Introduction (nf. Oxford University Press). Blog coverage here.

2. Rivers: A Very Short Introduction (1, nf. Oxford University Press). Short goodreads review here. Blog coverage here and here.

3. Something for the Pain: Compassion and Burnout in the ER (2, m. W. W. Norton & Company/Paul Austin).

4. Mountains: A Very Short Introduction (1, nf. Oxford University Press). Short goodreads review here.

5. Water: A Very Short Introduction (4, nf. Oxford University Press). Goodreads review here.

6. Assassin’s Quest (3, f). Robin Hobb. Goodreads review here.

7. Oxford Handbook of Endocrinology and Diabetes (3rd edition) (5, nf. Oxford University Press). Goodreads review here. Blog coverage here, here, here, here, here, and here. I added this book to my list of favourite books on goodreads. Some of the specific chapters included are ‘book-equivalents’; this book is very long and takes a lot of work.

8. Desolation Island (Aubrey & Maturin #5) (3, f). Patrick O’Brian.

9. The Fortune of War (Aubrey & Maturin #6) (4, f). Patrick O’Brian.

10. Lakes: A Very Short Introduction (4, nf. Oxford University Press). Blog coverage here and here.

11. The Surgeon’s Mate (Aubrey & Maturin #7) (4, f). Patrick O’Brian. Short goodreads review here.

12. Domestication of Plants in the Old World: The Origin and Spread of Domesticated Plants in South-West Asia, Europe, and the Mediterranean Basin (5, nf. Oxford University Press). Goodreads review here. I added this book to my list of favourite books on goodreads.

13. The Ionian Mission (Aubrey & Maturin #8) (4, f). Patrick O’Brian.

14. Systems Biology: Functional Strategies of Living Organisms (4, nf. Springer). Blog coverage here, here, and here.

15. Treason’s Harbour (Aubrey & Maturin #9) (4, f). Patrick O’Brian.

16. Peripheral Neuropathy – A New Insight into the Mechanism, Evaluation and Management of a Complex Disorder (3, nf. InTech). Blog coverage here and here.

17. The portable door (5, f). Tom Holt. Goodreads review here.

18. Prevention of Late-Life Depression: Current Clinical Challenges and Priorities (2, nf. Humana Press). Blog coverage here and here.

19. In your dreams (4, f). Tom Holt.

20. Earth, Air, Fire and Custard (3, f). Tom Holt. Short goodreads review here.

21. You Don’t Have to Be Evil to Work Here, But it Helps (3, f). Tom Holt.

22. The Ice Age: A Very Short Introduction (4, nf. Oxford University Press). Blog coverage here and here.

23. The Better Mousetrap (4, f). Tom Holt.

24. May Contain Traces of Magic (2, f). Tom Holt.

25. Expecting Someone Taller (4, f). Tom Holt.

26. The Computer: A Very Short Introduction (2, nf. Oxford University Press). Short goodreads review here. Blog coverage here.

27. Who’s Afraid of Beowulf? (5, f). Tom Holt.

28. Flying Dutch (4, f). Tom Holt.

29. Ye Gods! (2, f). Tom Holt.

30. Marine Biology: A Very Short Introduction (2, nf. Oxford University Press). Blog coverage here and here.

31. Here Comes The Sun (2, f). Tom Holt.

32. Grailblazers (4, f). Tom Holt.

33. Oceans: A Very Short Introduction (2, nf. Oxford University Press). Very short goodreads review here. Blog coverage and here.

34. Oxford Handbook of Medical Statistics (2, nf. Oxford University Press). Long, takes some work. Goodreads review here. Blog coverage here, here, and here.

35. Faust Among Equals (3, f). Tom Holt.

36. My Hero (3, f). Tom Holt. Short goodreads review here.

37. Odds and Gods (3, f). Tom Holt.

38. Networks: A Very Short Introduction (4, nf. Oxford University Press). Blog coverage here.

39. Paint Your Dragon (2, f). Tom Holt. Very short goodreads review here.

40. Wish You Were Here (2, f). Tom Holt.

41. Djinn Rummy (2, f). Tom Holt.

42. Structural Engineering: A Very Short Introduction (2, nf. Oxford University Press). Blog coverage here.

43. Open Sesame (3, f). Tom Holt.

44. The Far Side of the World (Aubrey & Maturin #10) (4, f). Patrick O’Brian.

45. 100 Cases in Surgery (3, nf. CRC Press). Blog coverage here and here.

46. The Reverse of the Medal (Aubrey & Maturin #11) (3, f). Patrick O’Brian.

47. 100 Cases in Emergency Medicine and Critical Care (4, nf. CRC Press). Blog coverage here and here.

48. The Letter of Marque (Aubrey & Maturin #12) (4, f). Patrick O’Brian.

49. Molecular Biology: A Very Short Introduction (5, nf. Oxford University Press). Short goodreads review here. Blog coverage here, here, and here.

50. Frozen Assets (4, f). P. G. Wodehouse.

51. Galahad at Blandings (5, f). P. G. Wodehouse.

52. Spring Fever (4, f). P. G. Wodehouse.

53. Intermediate Excel (Excel Essentials, #2) (nf., M.L. Humphrey). Goodreads review here.

54. A Gentleman of Leisure (3, f). P. G. Wodehouse.

55. Bachelors Anonymous (5, f). P. G. Wodehouse.

56. Money in the Bank (5, f). P. G. Wodehouse.

57. Company for Henry (4, f). P. G. Wodehouse.

58. The Old Reliable (4, f). P. G. Wodehouse.

59. The Thirteen-Gun Salute (Aubrey & Maturin #13) (4, f). Patrick O’Brian.

60. Alcohol and Aging: Clinical and Public Health Perspectives (3, nf. Springer). Blog coverage here and here.

61. Ice in the Bedroom (5, f). P. G. Wodehouse.

62. Enter a Murderer (3, f). Ngaio Marsh.

63. The Nursing Home Murder (2, f). Ngaio Marsh. Very short goodreads review here.

64. Death in Ecstasy (2, f). Ngaio Marsh.

65. Vintage Murder (3, f). Ngaio Marsh.

66. Blood: A Very Short Introduction (3, nf. Oxford University Press). Short goodreads review here. Blog coverage here and here.

67. Artists in Crime (3, f). Ngaio Marsh.

68. Developmental Biology: A Very Short Introduction (5, nf. Oxford University Press). Very short goodreads review here. Blog coverage here and here.

69. Death in a White Tie (2, f). Ngaio Marsh.

70. Robotics: A Very Short Introduction (4, nf. Oxford University Press). Blog coverage here.

71. Overture to death (3, f). Ngaio Marsh. Very short goodreads review here.

72. Death at the bar (3, f). Ngaio Marsh.

73. 100 Cases in Orthopaedics and Rheumatology (2, nf. CRC Press). Blog coverage here and here.

74. A Surfeit of Lampreys (4, f). Ngaio Marsh.

75. Managing Gastrointestinal Complications of Diabetes (4, nf. Adis (/Springer)). Blog coverage here and here.

76. Colour Scheme (2, f). Ngaio Marsh.

77. American Naval History: A Very Short Introduction (2, nf. Oxford University Press). Blog coverage here and here.

78. The Nutmeg of Consolation (Aubrey & Maturin #14) (4, f). Patrick O’Brian.

79. Blonde Bombshell (4, f). Tom Holt.

80. Big Data: A Very Short Introduction (2, nf. Oxford University Press). Blog coverage here.

81. Little People (2, f). Tom Holt.

82. Life, Liberty, and the Pursuit of Sausages (4, f). Tom Holt.

83. Pocket Oncology (2, nf. Wolters Kluwer Health). Goodreads review here. Dense, very informative, takes a lot of work to read from cover to cover. There were very specific reasons why I did not give this book a much higher rating, see the goodreads review for details. Blog coverage here and here.

84. The Girl in Blue (4, f). P. G. Wodehouse.

85. Service With a Smile (5, f). P. G. Wodehouse.

86. Military Strategy: A Very Short Introduction (3, nf. Oxford University Press).

87. Died in the Wool (2, f). Ngaio Marsh.

88. When It’s A Jar (3, f). Tom Holt.

89. The Truelove (Aubrey & Maturin #15) (3, f). Patrick O’Brian.

90. Combinatorics: A Very short Introduction (4, nf. Oxford University Press). Blog coverage here and here.

91. The Wine-Dark Sea (Aubrey & Maturin #16) (3, f). Patrick O’Brian.

92. Jeeves in the Offing (4, f). P. G. Wodehouse.

93. Blandings Castle … and Elsewhere (Blandings Castle #3) (4, f). P. G. Wodehouse.

94. Something Fresh (5, f). P. G. Wodehouse.

95. Psmith, Journalist (4, f). P. G. Wodehouse.

96. Personal Relationships: The Effect on Employee Attitudes, Behavior, and Well-being (SIOP Organizational Frontiers Series) (2, nf. Routledge). Long. Blog coverage here, here, and here.

97. Final Curtain (2, f). Ngaio Marsh.

98. Circadian Rhythms: A Very Short Introduction (4, nf. Oxford University Press). Short goodreads review here. Blog coverage here and here.

99. A Wreath for Rivera (2, f). Ngaio Marsh. Very short goodreads review here.

100. The Commodore (Aubrey & Maturin #17) (3, f). Patrick O’Brian.

101. Quick Service (4, f). P. G. Wodehouse.

102. The Yellow Admiral (Aubrey & Maturin #18) (3, f). Patrick O’Brian.

103. French Leave (4, f). P. G. Wodehouse.

104. Jingo (5, f). Terry Pratchett. I added this book to my list of favourite books on goodreads.

105. Principles of Memory (2, nf. Psychology Press). Blog coverage here, here, and here.

106. Spring Fever (4, f). P. G. Wodehouse.

107. The Hundred Days (Aubrey & Maturin #19) (2, f). Patrick O’Brian.

108. Night at the Vulcan (2, f). Ngaio Marsh.

109. The Overcoat and Other Short Stories (3, f). Nikolai Gogol.

110. Feet of Clay (4, f). Terry Pratchett.

111. Blue at the Mizzen (Aubrey & Maturin #20) (4, f). Patrick O’Brian. Very short goodreads review here.

112. The Case of the Velvet Claws (Perry Mason #1) (3, f). Erle Stanley Gardner.

113. Perception: A Very Short Introduction (3, nf. Oxford University Press). Short goodreads review here. Blog coverage here.

114. The Case of the Sulky Girl (Perry Mason #2) (3, f). Erle Stanley Gardner.

115. The Case of the Howling Dog (2, f). Erle Stanley Gardner.

116. 21: The Final Unfinished Voyage of Jack Aubrey (Aubrey & Maturin #21) (2, f). Patrick O’Brian. Short goodreads review here.

117. The Case of the Curious Bride (2, f). Erle Stanley Gardner.

118. Sleep: A Very Short Introduction (2, nf. Oxford University Press).

119. The Case of the Counterfeit Eye (2, f). Erle Stanley Gardner.

120. The Case of the Caretaker’s Cat (3, f). Erle Stanley Gardner.

121. The Case of the Sleepwalker’s Niece (2, f). Erle Stanley Gardner.

122. The Case of the Dangerous Dowager (3, f). Erle Stanley Gardner.

123. The Case of the Substitute Face (2, f). Erle Stanley Gardner.

124. Geophysics: A Very Short Introduction (5, nf. Oxford University Press). Very short goodreads review here. Blog coverage here and here.

125. The Case of the Lame Canary (2, f). Erle Stanley Gardner.

126. The Case of the Rolling Bones (3, f). Erle Stanley Gardner.

127. The Case of the Silent Partner (2, f). Erle Stanley Gardner. Goodreads review here.

128. The Case of the Haunted Husband (2, f). Erle Stanley Gardner.

129. Reaper Man (5, f). Terry Pratchett.

130. The Case of the Empty Tin (2, f). Erle Stanley Gardner.

131. The Case of the Drowning Duck (2, f). Erle Stanley Gardner.

132. Early Riser (5, f). Jasper Fforde.

133. Bacteria: A Very Short Introduction (3, nf. Oxford University Press).

134. The Outsorcerer’s Apprentice (5, f). Tom Holt. Goodreads review here.

135. Doughnut (3, f). Tom Holt.

136. The Case of the Buried Clock (1, f). Erle Stanley Gardner.

137. The Good, the Bad and the Smug (4, f). Tom Holt.

138. The Management Style of the Supreme Beings (3, f). Tom Holt.

139. Nothing But Blue Skies (2, f). Tom Holt.

140. Leaving Las Vegas (5, f). John O’Brien. Short goodreads review here.

141. Overtime (2, f). Tom Holt.

142. Barking (2, f). Tom Holt.

143. Lucia in Wartime (1, f). Tom Holt. Short goodreads review here.

144. Falling Sideways (2, f). Tom Holt. Very short goodreads review here.

145. Snow White and the Seven Samurai (4, f). Tom Holt.

146. The Case of the Lonely Heiress (3, f). Erle Stanley Gardner.

147. The Case of the Crooked Candle (2, f). Erle Stanley Gardner.

148. The Case of the Gilded Lily (2, f). Erle Stanley Gardner.

149. The Science of Discworld (2, m.). Terry Pratchett, Ian Stewart & Jack Cohen. Goodreads review here.

150. Artificial Intelligence: A Very Short Introduction (2, nf. Oxford University Press).

January 3, 2019 Posted by | Books, Personal | Leave a comment