Econstudentlog

Radioactivity

A few quotes from the book and some related links below. Here’s my very short goodreads review of the book.

Quotes:

“The main naturally occurring radionuclides of primordial origin are uranium-235, uranium-238, thorium-232, their decay products, and potassium-40. The average abundance of uranium, thorium, and potassium in the terrestrial crust is 2.6 parts per million, 10 parts per million, and 1% respectively. Uranium and thorium produce other radionuclides via neutron- and alpha-induced reactions, particularly deeply underground, where uranium and thorium have a high concentration. […] A weak source of natural radioactivity derives from nuclear reactions of primary and secondary cosmic rays with the atmosphere and the lithosphere, respectively. […] Accretion of extraterrestrial material, intensively exposed to cosmic rays in space, represents a minute contribution to the total inventory of radionuclides in the terrestrial environment. […] Natural radioactivity is [thus] mainly produced by uranium, thorium, and potassium. The total heat content of the Earth, which derives from this radioactivity, is 12.6 × 1024 MJ (one megajoule = 1 million joules), with the crust’s heat content standing at 5.4 × 1021 MJ. For comparison, this is significantly more than the 6.4 × 1013 MJ globally consumed for electricity generation during 2011. This energy is dissipated, either gradually or abruptly, towards the external layers of the planet, but only a small fraction can be utilized. The amount of energy available depends on the Earth’s geological dynamics, which regulates the transfer of heat to the surface of our planet. The total power dissipated by the Earth is 42 TW (one TW = 1 trillion watts): 8 TW from the crust, 32.3 TW from the mantle, 1.7 TW from the core. This amount of power is small compared to the 174,000 TW arriving to the Earth from the Sun.”

“Charged particles such as protons, beta and alpha particles, or heavier ions that bombard human tissue dissipate their energy locally, interacting with the atoms via the electromagnetic force. This interaction ejects electrons from the atoms, creating a track of electron–ion pairs, or ionization track. The energy that ions lose per unit path, as they move through matter, increases with the square of their charge and decreases linearly with their energy […] The energy deposited in the tissues and organs of your body by ionizing radiation is defined absorbed dose and is measured in gray. The dose of one gray corresponds to the energy of one joule deposited in one kilogram of tissue. The biological damage wrought by a given amount of energy deposited depends on the kind of ionizing radiation involved. The equivalent dose, measured in sievert, is the product of the dose and a factor w related to the effective damage induced into the living matter by the deposit of energy by specific rays or particles. For X-rays, gamma rays, and beta particles, a gray corresponds to a sievert; for neutrons, a dose of one gray corresponds to an equivalent dose of 5 to 20 sievert, and the factor w is equal to 5–20 (depending on the neutron energy). For protons and alpha particles, w is equal to 5 and 20, respectively. There is also another weighting factor taking into account the radiosensitivity of different organs and tissues of the body, to evaluate the so-called effective dose. Sometimes the dose is still quoted in rem, the old unit, with 100 rem corresponding to one sievert.”

“Neutrons emitted during fission reactions have a relatively high velocity. When still in Rome, Fermi had discovered that fast neutrons needed to be slowed down to increase the probability of their reaction with uranium. The fission reaction occurs with uranium-235. Uranium-238, the most common isotope of the element, merely absorbs the slow neutrons. Neutrons slow down when they are scattered by nuclei with a similar mass. The process is analogous to the interaction between two billiard balls in a head-on collision, in which the incoming ball stops and transfers all its kinetic energy to the second one. ‘Moderators’, such as graphite and water, can be used to slow neutrons down. […] When Fermi calculated whether a chain reaction could be sustained in a homogeneous mixture of uranium and graphite, he got a negative answer. That was because most neutrons produced by the fission of uranium-235 were absorbed by uranium-238 before inducing further fissions. The right approach, as suggested by Szilárd, was to use separated blocks of uranium and graphite. Fast neutrons produced by the splitting of uranium-235 in the uranium block would slow down, in the graphite block, and then produce fission again in the next uranium block. […] A minimum mass – the critical mass – is required to sustain the chain reaction; furthermore, the material must have a certain geometry. The fissile nuclides, capable of sustaining a chain reaction of nuclear fission with low-energy neutrons, are uranium-235 […], uranium-233, and plutonium-239. The last two don’t occur in nature but can be produced artificially by irradiating with neutrons thorium-232 and uranium-238, respectively – via a reaction called neutron capture. Uranium-238 (99.27%) is fissionable, but not fissile. In a nuclear weapon, the chain reaction occurs very rapidly, releasing the energy in a burst.”

“The basic components of nuclear power reactors, fuel, moderator, and control rods, are the same as in the first system built by Fermi, but the design of today’s reactors includes additional components such as a pressure vessel, containing the reactor core and the moderator, a containment vessel, and redundant and diverse safety systems. Recent technological advances in material developments, electronics, and information technology have further improved their reliability and performance. […] The moderator to slow down fast neutrons is sometimes still the graphite used by Fermi, but water, including ‘heavy water’ – in which the water molecule has a deuterium atom instead of a hydrogen atom – is more widely used. Control rods contain a neutron-absorbing material, such as boron or a combination of indium, silver, and cadmium. To remove the heat generated in the reactor core, a coolant – either a liquid or a gas – is circulating through the reactor core, transferring the heat to a heat exchanger or directly to a turbine. Water can be used as both coolant and moderator. In the case of boiling water reactors (BWRs), the steam is produced in the pressure vessel. In the case of pressurized water reactors (PWRs), the steam generator, which is the secondary side of the heat exchanger, uses the heat produced by the nuclear reactor to make steam for the turbines. The containment vessel is a one-metre-thick concrete and steel structure that shields the reactor.”

“Nuclear energy contributed 2,518 TWh of the world’s electricity in 2011, about 14% of the global supply. As of February 2012, there are 435 nuclear power plants operating in 31 countries worldwide, corresponding to a total installed capacity of 368,267 MW (electrical). There are 63 power plants under construction in 13 countries, with a capacity of 61,032 MW (electrical).”

“Since the first nuclear fusion, more than 60 years ago, many have argued that we need at least 30 years to develop a working fusion reactor, and this figure has stayed the same throughout those years.”

“[I]onizing radiation is […] used to improve many properties of food and other agricultural products. For example, gamma rays and electron beams are used to sterilize seeds, flour, and spices. They can also inhibit sprouting and destroy pathogenic bacteria in meat and fish, increasing the shelf life of food. […] More than 60 countries allow the irradiation of more than 50 kinds of foodstuffs, with 500,000 tons of food irradiated every year. About 200 cobalt-60 sources and more than 10 electron accelerators are dedicated to food irradiation worldwide. […] With the help of radiation, breeders can increase genetic diversity to make the selection process faster. The spontaneous mutation rate (number of mutations per gene, for each generation) is in the range 10-8–10-5. Radiation can increase this mutation rate to 10-5–10-2. […] Long-lived cosmogenic radionuclides provide unique methods to evaluate the ‘age’ of groundwaters, defined as the mean subsurface residence time after the isolation of the water from the atmosphere. […] Scientists can date groundwater more than a million years old, through chlorine-36, produced in the atmosphere by cosmic-ray reactions with argon.”

“Radionuclide imaging was developed in the 1950s using special systems to detect the emitted gamma rays. The gamma-ray detectors, called gamma cameras, use flat crystal planes, coupled to photomultiplier tubes, which send the digitized signals to a computer for image reconstruction. Images show the distribution of the radioactive tracer in the organs and tissues of interest. This method is based on the introduction of low-level radioactive chemicals into the body. […] More than 100 diagnostic tests based on radiopharmaceuticals are used to examine bones and organs such as lungs, intestines, thyroids, kidneys, the liver, and gallbladder. They exploit the fact that our organs preferentially absorb different chemical compounds. […] Many radiopharmaceuticals are based on technetium-99m (an excited state of technetium-99 – the ‘m’ stands for ‘metastable’ […]). This radionuclide is used for the imaging and functional examination of the heart, brain, thyroid, liver, and other organs. Technetium-99m is extracted from molybdenum-99, which has a much longer half-life and is therefore more transportable. It is used in 80% of the procedures, amounting to about 40,000 per day, carried out in nuclear medicine. Other radiopharmaceuticals include short-lived gamma-emitters such as cobalt-57, cobalt-58, gallium-67, indium-111, iodine-123, and thallium-201. […] Methods routinely used in medicine, such as X-ray radiography and CAT, are increasingly used in industrial applications, particularly in non-destructive testing of containers, pipes, and walls, to locate defects in welds and other critical parts of the structure.”

“Today, cancer treatment with radiation is generally based on the use of external radiation beams that can target the tumour in the body. Cancer cells are particularly sensitive to damage by ionizing radiation and their growth can be controlled or, in some cases, stopped. High-energy X-rays produced by a linear accelerator […] are used in most cancer therapy centres, replacing the gamma rays produced from cobalt-60. The LINAC produces photons of variable energy bombarding a target with a beam of electrons accelerated by microwaves. The beam of photons can be modified to conform to the shape of the tumour, which is irradiated from different angles. The main problem with X-rays and gamma rays is that the dose they deposit in the human tissue decreases exponentially with depth. A considerable fraction of the dose is delivered to the surrounding tissues before the radiation hits the tumour, increasing the risk of secondary tumours. Hence, deep-seated tumours must be bombarded from many directions to receive the right dose, while minimizing the unwanted dose to the healthy tissues. […] The problem of delivering the needed dose to a deep tumour with high precision can be solved using collimated beams of high-energy ions, such as protons and carbon. […] Contrary to X-rays and gamma rays, all ions of a given energy have a certain range, delivering most of the dose after they have slowed down, just before stopping. The ion energy can be tuned to deliver most of the dose to the tumour, minimizing the impact on healthy tissues. The ion beam, which does not broaden during the penetration, can follow the shape of the tumour with millimetre precision. Ions with higher atomic number, such as carbon, have a stronger biological effect on the tumour cells, so the dose can be reduced. Ion therapy facilities are [however] still very expensive – in the range of hundreds of millions of pounds – and difficult to operate.”

“About 50 million years ago, a global cooling trend took our planet from the tropical conditions at the beginning of the Tertiary to the ice ages of the Quaternary, when the Arctic ice cap developed. The temperature decrease was accompanied by a decrease in atmospheric CO2 from 2,000 to 300 parts per million. The cooling was probably caused by a reduced greenhouse effect and also by changes in ocean circulation due to plate tectonics. The drop in temperature was not constant as there were some brief periods of sudden warming. Ocean deep-water temperatures dropped from 12°C, 50 million years ago, to 6°C, 30 million years ago, according to archives in deep-sea sediments (today, deep-sea waters are about 2°C). […] During the last 2 million years, the mean duration of the glacial periods was about 26,000 years, while that of the warm periods – interglacials – was about 27,000 years. Between 2.6 and 1.1 million years ago, a full cycle of glacial advance and retreat lasted about 41,000 years. During the past 1.2 million years, this cycle has lasted 100,000 years. Stable and radioactive isotopes play a crucial role in the reconstruction of the climatic history of our planet”.

Links:

CUORE (Cryogenic Underground Observatory for Rare Events).
Borexino.
Lawrence Livermore National Laboratory.
Marie Curie. Pierre Curie. Henri Becquerel. Wilhelm Röntgen. Joseph Thomson. Ernest Rutherford. Hans Geiger. Ernest Marsden. Niels Bohr.
Ruhmkorff coil.
Electroscope.
Pitchblende (uraninite).
Mache.
Polonium. Becquerel.
Radium.
Alpha decay. Beta decay. Gamma radiation.
Plum pudding model.
Spinthariscope.
Robert Boyle. John Dalton. Dmitri Mendeleev. Frederick Soddy. James Chadwick. Enrico Fermi. Lise Meitner. Otto Frisch.
Periodic Table.
Exponential decay. Decay chain.
Positron.
Particle accelerator. Cockcroft-Walton generator. Van de Graaff generator.
Barn (unit).
Nuclear fission.
Manhattan Project.
Chernobyl disaster. Fukushima Daiichi nuclear disaster.
Electron volt.
Thermoluminescent dosimeter.
Silicon diode detector.
Enhanced geothermal system.
Chicago Pile Number 1. Experimental Breeder Reactor 1. Obninsk Nuclear Power Plant.
Natural nuclear fission reactor.
Gas-cooled reactor.
Generation I reactors. Generation II reactor. Generation III reactor. Generation IV reactor.
Nuclear fuel cycle.
Accelerator-driven subcritical reactor.
Thorium-based nuclear power.
Small, sealed, transportable, autonomous reactor.
Fusion power. P-p (proton-proton) chain reaction. CNO cycle. Tokamak. ITER (International Thermonuclear Experimental Reactor).
Sterile insect technique.
Phase-contrast X-ray imaging. Computed tomography (CT). SPECT (Single-photon emission computed tomography). PET (positron emission tomography).
Boron neutron capture therapy.
Radiocarbon dating. Bomb pulse.
Radioactive tracer.
Radithor. The Radiendocrinator.
Radioisotope heater unit. Radioisotope thermoelectric generator. Seebeck effect.
Accelerator mass spectrometry.
Atomic bombings of Hiroshima and Nagasaki. Treaty on the Non-Proliferation of Nuclear Weapons. IAEA.
Nuclear terrorism.
Swiss light source. Synchrotron.
Chronology of the universe. Stellar evolution. S-process. R-process. Red giant. Supernova. White dwarf.
Victor Hess. Domenico Pacini. Cosmic ray.
Allende meteorite.
Age of the Earth. History of Earth. Geomagnetic reversal. Uranium-lead dating. Clair Cameron Patterson.
Glacials and interglacials.
Taung child. Lucy. Ardi. Ardipithecus kadabba. Acheulean tools. Java Man. Ötzi.
Argon-argon dating. Fission track dating.

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November 28, 2017 Posted by | Archaeology, Astronomy, Biology, Books, Cancer/oncology, Chemistry, Engineering, Geology, History, Medicine, Physics | Leave a comment

Isotopes

A decent book. Below some quotes and links.

“[A]ll mass spectrometers have three essential components — an ion source, a mass filter, and some sort of detector […] Mass spectrometers need to achieve high vacuum to allow the uninterrupted transmission of ions through the instrument. However, even high-vacuum systems contain residual gas molecules which can impede the passage of ions. Even at very high vacuum there will still be residual gas molecules in the vacuum system that present potential obstacles to the ion beam. Ions that collide with residual gas molecules lose energy and will appear at the detector at slightly lower mass than expected. This tailing to lower mass is minimized by improving the vacuum as much as possible, but it cannot be avoided entirely. The ability to resolve a small isotope peak adjacent to a large peak is called ‘abundance sensitivity’. A single magnetic sector TIMS has abundance sensitivity of about 1 ppm per mass unit at uranium masses. So, at mass 234, 1 ion in 1,000,000 will actually be 235U not 234U, and this will limit our ability to quantify the rare 234U isotope. […] AMS [accelerator mass spectrometry] instruments use very high voltages to achieve high abundance sensitivity. […] As I write this chapter, the human population of the world has recently exceeded seven billion. […] one carbon atom in 1012 is mass 14. So, detecting 14C is far more difficult than identifying a single person on Earth, and somewhat comparable to identifying an individual leaf in the Amazon rain forest. Such is the power of isotope ratio mass spectrometry.”

14C is produced in the Earth’s atmosphere by the interaction between nitrogen and cosmic ray neutrons that releases a free proton turning 147N into 146C in a process that we call an ‘n-p’ reaction […] Because the process is driven by cosmic ray bombardment, we call 14C a ‘cosmogenic’ isotope. The half-life of 14C is about 5,000 years, so we know that all the 14C on Earth is either cosmogenic or has been created by mankind through nuclear reactors and bombs — no ‘primordial’ 14C remains because any that originally existed has long since decayed. 14C is not the only cosmogenic isotope; 16O in the atmosphere interacts with cosmic radiation to produce the isotope 10Be (beryllium). […] The process by which a high energy cosmic ray particle removes several nucleons is called ‘spallation’. 10Be production from 16O is not restricted to the atmosphere but also occurs when cosmic rays impact rock surfaces. […] when cosmic rays hit a rock surface they don’t bounce off but penetrate the top 2 or 3 metres (m) — the actual ‘attenuation’ depth will vary for particles of different energy. Most of the Earth’s crust is made of silicate minerals based on bonds between oxygen and silicon. So, the same spallation process that produces 10Be in the atmosphere also occurs in rock surfaces. […] If we know the flux of cosmic rays impacting a surface, the rate of production of the cosmogenic isotopes with depth below the rock surface, and the rate of radioactive decay, it should be possible to convert the number of cosmogenic atoms into an exposure age. […] Rocks on Earth which are shielded from much of the cosmic radiation have much lower levels of isotopes like 10Be than have meteorites which, before they arrive on Earth, are exposed to the full force of cosmic radiation. […] polar scientists have used cores drilled through ice sheets in Antarctica and Greenland to compare 10Be at different depths and thereby reconstruct 10Be production through time. The 14C and 10Be records are closely correlated indicating the common response to changes in the cosmic ray flux.”

“[O]nce we have credible cosmogenic isotope production rates, […] there are two classes of applications, which we can call ‘exposure’ and ‘burial’ methodologies. Exposure studies simply measure the accumulation of the cosmogenic nuclide. Such studies are simplest when the cosmogenic nuclide is a stable isotope like 3He and 21Ne. These will just accumulate continuously as the sample is exposed to cosmic radiation. Slightly more complicated are cosmogenic isotopes that are radioactive […]. These isotopes accumulate through exposure but will also be destroyed by radioactive decay. Eventually, the isotopes achieve the condition known as ‘secular equilibrium’ where production and decay are balanced and no chronological information can be extracted. Secular equilibrium is achieved after three to four half-lives […] Imagine a boulder that has been transported from its place of origin to another place within a glacier — what we call a glacial erratic. While the boulder was deeply covered in ice, it would not have been exposed to cosmic radiation. Its cosmogenic isotopes will only have accumulated since the ice melted. So a cosmogenic isotope exposure age tells us the date at which the glacier retreated, and, by examining multiple erratics from different locations along the course of the glacier, allows us to construct a retreat history for the de-glaciation. […] Burial methodologies using cosmogenic isotopes work in situations where a rock was previously exposed to cosmic rays but is now located in a situation where it is shielded.”

“Cosmogenic isotopes are also being used extensively to recreate the seismic histories of tectonically active areas. Earthquakes occur when geological faults give way and rock masses move. A major earthquake is likely to expose new rock to the Earth’s surface. If the field geologist can identify rocks in a fault zone that (s)he is confident were brought to the surface in an earthquake, then a cosmogenic isotope exposure age would date the fault — providing, of course, that subsequent erosion can be ruled out or quantified. Precarious rocks are rock outcrops that could reasonably be expected to topple if subjected to a significant earthquake. Dating the exposed surface of precarious rocks with cosmogenic isotopes can reveal the amount of time that has elapsed since the last earthquake of a magnitude that would have toppled the rock. Constructing records of seismic history is not merely of academic interest; some of the world’s seismically active areas are also highly populated and developed.”

“One aspect of the natural decay series that acts in favour of the preservation of accurate age information is the fact that most of the intermediate isotopes are short-lived. For example, in both the U series the radon (Rn) isotopes, which might be expected to diffuse readily out of a mineral, have half-lives of only seconds or days, too short to allow significant losses. Some decay series isotopes though do have significantly long half-lives which offer the potential to be geochronometers in their own right. […] These techniques depend on the tendency of natural decay series to evolve towards a state of ‘secular equilibrium’ in which the activity of all species in the decay series is equal. […] at secular equilibrium, isotopes with long half-lives (i.e. small decay constants) will have large numbers of atoms whereas short-lived isotopes (high decay constants) will only constitute a relatively small number of atoms. Since decay constants vary by several orders of magnitude, so will the numbers of atoms of each isotope in the equilibrium decay series. […] Geochronological applications of natural decay series depend upon some process disrupting the natural decay series to introduce either a deficiency or an excess of an isotope in the series. The decay series will then gradually return to secular equilibrium and the geochronometer relies on measuring the extent to which equilibrium has been approached.”

“The ‘ring of fire’ volcanoes around the margin of the Pacific Ocean are a manifestation of subduction in which the oldest parts of the Pacific Ocean crust are being returned to the mantle below. The oldest parts of the Pacific Ocean crust are about 150 million years (Ma) old, with anything older having already disappeared into the mantle via subduction zones. The Atlantic Ocean doesn’t have a ring of fire because it is a relatively young ocean which started to form about 60 Ma ago, and its oldest rocks are not yet ready to form subduction zones. Thus, while continental crust persists for billions of years, oceanic crust is a relatively transient (in terms of geological time) phenomenon at the Earth’s surface.”

“Mantle rocks typically contain minerals such as olivine, pyroxene, spinel, and garnet. Unlike say ice, which melts to form water, mixtures of minerals do not melt in the proportions in which they occur in the rock. Rather, they undergo partial melting in which some minerals […] melt preferentially leaving a solid residue enriched in refractory minerals […]. We know this from experimentally melting mantle-like rocks in the laboratory, but also because the basalts produced by melting of the mantle are closer in composition to Ca-rich (clino-) pyroxene than to the olivine-rich rocks that dominate the solid pieces (or xenoliths) of mantle that are sometimes transferred to the surface by certain types of volcanic eruptions. […] Thirty years ago geologists fiercely debated whether the mantle was homogeneous or heterogeneous; mantle isotope geochemistry hasn’t yet elucidated all the details but it has put to rest the initial conundrum; Earth’s mantle is compositionally heterogeneous.”

Links:

Frederick Soddy.
Rutherford–Bohr model.
Isotopes of hydrogen.
Radioactive decay. Types of decay. Alpha decay. Beta decay. Electron capture decay. Branching fraction. Gamma radiation. Spontaneous fission.
Promethium.
Lanthanides.
Radiocarbon dating.
Hessel de Vries.
Dendrochronology.
Suess effect.
Bomb pulse.
Delta notation (non-wiki link).
Isotopic fractionation.
C3 carbon fixation. C4 carbon fixation.
Nitrogen-15 tracing.
Isotopes of strontium. Strontium isotope analysis.
Ötzi.
Mass spectrometry.
Geiger counter.
Townsend avalanche.
Gas proportional counter.
Scintillation detector.
Liquid scintillation spectometry. Photomultiplier tube.
Dynode.
Thallium-doped sodium iodide detectors. Semiconductor-based detectors.
Isotope separation (-enrichment).
Doubly labeled water.
Urea breath test.
Radiation oncology.
Brachytherapy.
Targeted radionuclide therapy.
Iodine-131.
MIBG scan.
Single-photon emission computed tomography.
Positron emission tomography.
Inductively coupled plasma (ICP) mass spectrometry.
Secondary ion mass spectrometry.
Faraday cup (-detector).
δ18O.
Stadials and interstadials. Oxygen isotope ratio cycle.
Insolation.
Gain and phase model.
Milankovitch cycles.
Perihelion and aphelion. Precession.
Equilibrium Clumped-Isotope Effects in Doubly Substituted Isotopologues of Ethane (non-wiki link).
Age of the Earth.
Uranium–lead dating.
Geochronology.
Cretaceous–Paleogene boundary.
Argon-argon dating.
Nuclear chain reaction. Critical mass.
Fukushima Daiichi nuclear disaster.
Natural nuclear fission reactor.
Continental crust. Oceanic crust. Basalt.
Core–mantle boundary.
Chondrite.
Ocean Island Basalt.
Isochron dating.

November 23, 2017 Posted by | Biology, Books, Botany, Chemistry, Geology, Medicine, Physics | Leave a comment

Materials (I)…

Useful matter is a good definition of materials. […] Materials are materials because inventive people find ingenious things to do with them. Or just because people use them. […] Materials science […] explains how materials are made and how they behave as we use them.”

I recently read this book, which I liked. Below I have added some quotes from the first half of the book, with some added hopefully helpful links, as well as a collection of links at the bottom of the post to other topics covered.

“We understand all materials by knowing about composition and microstructure. Despite their extraordinary minuteness, the atoms are the fundamental units, and they are real, with precise attributes, not least size. Solid materials tend towards crystallinity (for the good thermodynamic reason that it is the arrangement of lowest energy), and they usually achieve it, though often in granular, polycrystalline forms. Processing conditions greatly influence microstructures which may be mobile and dynamic, particularly at high temperatures. […] The idea that we can understand materials by looking at their internal structure in finer and finer detail goes back to the beginnings of microscopy […]. This microstructural view is more than just an important idea, it is the explanatory framework at the core of materials science. Many other concepts and theories exist in materials science, but this is the framework. It says that materials are intricately constructed on many length-scales, and if we don’t understand the internal structure we shall struggle to explain or to predict material behaviour.”

“Oxygen is the most abundant element in the earth’s crust and silicon the second. In nature, silicon occurs always in chemical combination with oxygen, the two forming the strong Si–O chemical bond. The simplest combination, involving no other elements, is silica; and most grains of sand are crystals of silica in the form known as quartz. […] The quartz crystal comes in right- and left-handed forms. Nothing like this happens in metals but arises frequently when materials are built from molecules and chemical bonds. The crystal structure of quartz has to incorporate two different atoms, silicon and oxygen, each in a repeating pattern and in the precise ratio 1:2. There is also the severe constraint imposed by the Si–O chemical bonds which require that each Si atom has four O neighbours arranged around it at the corners of a tetrahedron, every O bonded to two Si atoms. The crystal structure which quartz adopts (which of all possibilities is the one of lowest energy) is made up of triangular and hexagonal units. But within this there are buried helixes of Si and O atoms, and a helix must be either right- or left-handed. Once a quartz crystal starts to grow as right- or left-handed, its structure templates all the other helices with the same handedness. Equal numbers of right- and left-handed crystals occur in nature, but each is unambiguously one or the other.”

“In the living tree, and in the harvested wood that we use as a material, there is a hierarchy of structural levels, climbing all the way from the molecular to the scale of branch and trunk. The stiff cellulose chains are bundled into fibrils, which are themselves bonded by other organic molecules to build the walls of cells; which in turn form channels for the transport of water and nutrients, the whole having the necessary mechanical properties to support its weight and to resist the loads of wind and rain. In the living tree, the structure allows also for growth and repair. There are many things to be learned from biological materials, but the most universal is that biology builds its materials at many structural levels, and rarely makes a distinction between the material and the organism. Being able to build materials with hierarchical architectures is still more or less out of reach in materials engineering. Understanding how materials spontaneously self-assemble is the biggest challenge in contemporary nanotechnology.”

“The example of diamond shows two things about crystalline materials. First, anything we know about an atom and its immediate environment (neighbours, distances, angles) holds for every similar atom throughout a piece of material, however large; and second, everything we know about the unit cell (its size, its shape, and its symmetry) also applies throughout an entire crystal […] and by extension throughout a material made of a myriad of randomly oriented crystallites. These two general propositions provide the basis and justification for lattice theories of material behaviour which were developed from the 1920s onwards. We know that every solid material must be held together by internal cohesive forces. If it were not, it would fly apart and turn into a gas. A simple lattice theory says that if we can work out what forces act on the atoms in one unit cell, then this should be enough to understand the cohesion of the entire crystal. […] In lattice models which describe the cohesion and dynamics of the atoms, the role of the electrons is mainly in determining the interatomic bonding and the stiffness of the bond-spring. But in many materials, and especially in metals and semiconductors, some of the electrons are free to move about within the lattice. A lattice model of electron behaviour combines a geometrical description of the lattice with a more or less mechanical view of the atomic cores, and a fully quantum theoretical description of the electrons themselves. We need only to take account of the outer electrons of the atoms, as the inner electrons are bound tightly into the cores and are not itinerant. The outer electrons are the ones that form chemical bonds, so they are also called the valence electrons.”

“It is harder to push atoms closer together than to pull them further apart. While atoms are soft on the outside, they have harder cores, and pushed together the cores start to collide. […] when we bring a trillion atoms together to form a crystal, it is the valence electrons that are disturbed as the atoms approach each other. As the atomic cores come close to the equilibrium spacing of the crystal, the electron states of the isolated atoms morph into a set of collective states […]. These collective electron states have a continuous distribution of energies up to a top level, and form a ‘band’. But the separation of the valence electrons into distinct electron-pair states is preserved in the band structure, so that we find that the collective states available to the entire population of valence electrons in the entire crystal form a set of bands […]. Thus in silicon, there are two main bands.”

“The perfect crystal has atoms occupying all the positions prescribed by the geometry of its crystal lattice. But real crystalline materials fall short of perfection […] For instance, an individual site may be unoccupied (a vacancy). Or an extra atom may be squeezed into the crystal at a position which is not a lattice position (an interstitial). An atom may fall off its lattice site, creating a vacancy and an interstitial at the same time. Sometimes a site is occupied by the wrong kind of atom. Point defects of this kind distort the crystal in their immediate neighbourhood. Vacancies free up diffusional movement, allowing atoms to hop from site to site. Larger scale defects invariably exist too. A complete layer of atoms or unit cells may terminate abruptly within the crystal to produce a line defect (a dislocation). […] There are materials which try their best to crystallize, but find it hard to do so. Many polymer materials are like this. […] The best they can do is to form small crystalline regions in which the molecules lie side by side over limited distances. […] Often the crystalline domains comprise about half the material: it is a semicrystal. […] Crystals can be formed from the melt, from solution, and from the vapour. All three routes are used in industry and in the laboratory. As a rule, crystals that grow slowly are good crystals. Geological time can give wonderful results. Often, crystals are grown on a seed, a small crystal of the same material deliberately introduced into the crystallization medium. If this is a melt, the seed can gradually be pulled out, drawing behind it a long column of new crystal material. This is the Czochralski process, an important method for making semiconductors. […] However it is done, crystals invariably grow by adding material to the surface of a small particle to make it bigger.”

“As we go down the Periodic Table of elements, the atoms get heavier much more quickly than they get bigger. The mass of a single atom of uranium at the bottom of the Table is about 25 times greater than that of an atom of the lightest engineering metal, beryllium, at the top, but its radius is only 40 per cent greater. […] The density of solid materials of every kind is fixed mainly by where the constituent atoms are in the Periodic Table. The packing arrangement in the solid has only a small influence, although the crystalline form of a substance is usually a little denser than the amorphous form […] The range of solid densities available is therefore quite limited. At the upper end we hit an absolute barrier, with nothing denser than osmium (22,590 kg/m3). At the lower end we have some slack, as we can make lighter materials by the trick of incorporating holes to make foams and sponges and porous materials of all kinds. […] in the entire catalogue of available materials there is a factor of about a thousand for ingenious people to play with, from say 20 to 20,000 kg/m3.”

“The expansion of materials as we increase their temperature is a universal tendency. It occurs because as we raise the temperature the thermal energy of the atoms and molecules increases correspondingly, and this fights against the cohesive forces of attraction. The mean distance of separation between atoms in the solid (or the liquid) becomes larger. […] As a general rule, the materials with small thermal expansivities are metals and ceramics with high melting temperatures. […] Although thermal expansion is a smooth process which continues from the lowest temperatures to the melting point, it is sometimes interrupted by sudden jumps […]. Changes in crystal structure at precise temperatures are commonplace in materials of all kinds. […] There is a cluster of properties which describe the thermal behaviour of materials. Besides the expansivity, there is the specific heat, and also the thermal conductivity. These properties show us, for example, that it takes about four times as much energy to increase the temperature of 1 kilogram of aluminium by 1°C as 1 kilogram of silver; and that good conductors of heat are usually also good conductors of electricity. At everyday temperatures there is not a huge difference in specific heat between materials. […] In all crystalline materials, thermal conduction arises from the diffusion of phonons from hot to cold regions. As they travel, the phonons are subject to scattering both by collisions with other phonons, and with defects in the material. This picture explains why the thermal conductivity falls as temperature rises”.

 

Materials science.
Metals.
Inorganic compound.
Organic compound.
Solid solution.
Copper. Bronze. Brass. Alloy.
Electrical conductivity.
Steel. Bessemer converter. Gamma iron. Alpha iron. Cementite. Martensite.
Phase diagram.
Equation of state.
Calcite. Limestone.
Birefringence.
Portland cement.
Cellulose.
Wood.
Ceramic.
Mineralogy.
Crystallography.
Laue diffraction pattern.
Silver bromide. Latent image. Photographic film. Henry Fox Talbot.
Graphene. Graphite.
Thermal expansion.
Invar.
Dulong–Petit law.
Wiedemann–Franz law.

 

November 14, 2017 Posted by | Biology, Books, Chemistry, Engineering, Physics | Leave a comment

Organic Chemistry (II)

I have included some observations from the second half of the book below, as well as some links to topics covered.

“[E]nzymes are used routinely to catalyse reactions in the research laboratory, and for a variety of industrial processes involving pharmaceuticals, agrochemicals, and biofuels. In the past, enzymes had to be extracted from natural sources — a process that was both expensive and slow. But nowadays, genetic engineering can incorporate the gene for a key enzyme into the DNA of fast growing microbial cells, allowing the enzyme to be obtained more quickly and in far greater yield. Genetic engineering has also made it possible to modify the amino acids making up an enzyme. Such modified enzymes can prove more effective as catalysts, accept a wider range of substrates, and survive harsher reaction conditions. […] New enzymes are constantly being discovered in the natural world as well as in the laboratory. Fungi and bacteria are particularly rich in enzymes that allow them to degrade organic compounds. It is estimated that a typical bacterial cell contains about 3,000 enzymes, whereas a fungal cell contains 6,000. Considering the variety of bacterial and fungal species in existence, this represents a huge reservoir of new enzymes, and it is estimated that only 3 per cent of them have been investigated so far.”

“One of the most important applications of organic chemistry involves the design and synthesis of pharmaceutical agents — a topic that is defined as medicinal chemistry. […] In the 19th century, chemists isolated chemical components from known herbs and extracts. Their aim was to identify a single chemical that was responsible for the extract’s pharmacological effects — the active principle. […] It was not long before chemists synthesized analogues of active principles. Analogues are structures which have been modified slightly from the original active principle. Such modifications can often improve activity or reduce side effects. This led to the concept of the lead compound — a compound with a useful pharmacological activity that could act as the starting point for further research. […] The first half of the 20th century culminated in the discovery of effective antimicrobial agents. […] The 1960s can be viewed as the birth of rational drug design. During that period there were important advances in the design of effective anti-ulcer agents, anti-asthmatics, and beta-blockers for the treatment of high blood pressure. Much of this was based on trying to understand how drugs work at the molecular level and proposing theories about why some compounds were active and some were not.”

“[R]ational drug design was boosted enormously towards the end of the century by advances in both biology and chemistry. The sequencing of the human genome led to the identification of previously unknown proteins that could serve as potential drug targets. […] Advances in automated, small-scale testing procedures (high-throughput screening) also allowed the rapid testing of potential drugs. In chemistry, advances were made in X-ray crystallography and NMR spectroscopy, allowing scientists to study the structure of drugs and their mechanisms of action. Powerful molecular modelling software packages were developed that allowed researchers to study how a drug binds to a protein binding site. […] the development of automated synthetic methods has vastly increased the number of compounds that can be synthesized in a given time period. Companies can now produce thousands of compounds that can be stored and tested for pharmacological activity. Such stores have been called chemical libraries and are routinely tested to identify compounds capable of binding with a specific protein target. These advances have boosted medicinal chemistry research over the last twenty years in virtually every area of medicine.”

“Drugs interact with molecular targets in the body such as proteins and nucleic acids. However, the vast majority of clinically useful drugs interact with proteins, especially receptors, enzymes, and transport proteins […] Enzymes are […] important drug targets. Drugs that bind to the active site and prevent the enzyme acting as a catalyst are known as enzyme inhibitors. […] Enzymes are located inside cells, and so enzyme inhibitors have to cross cell membranes in order to reach them—an important consideration in drug design. […] Transport proteins are targets for a number of therapeutically important drugs. For example, a group of antidepressants known as selective serotonin reuptake inhibitors prevent serotonin being transported into neurons by transport proteins.”

“The main pharmacokinetic factors are absorption, distribution, metabolism, and excretion. Absorption relates to how much of an orally administered drug survives the digestive enzymes and crosses the gut wall to reach the bloodstream. Once there, the drug is carried to the liver where a certain percentage of it is metabolized by metabolic enzymes. This is known as the first-pass effect. The ‘survivors’ are then distributed round the body by the blood supply, but this is an uneven process. The tissues and organs with the richest supply of blood vessels receive the greatest proportion of the drug. Some drugs may get ‘trapped’ or sidetracked. For example fatty drugs tend to get absorbed in fat tissue and fail to reach their target. The kidneys are chiefly responsible for the excretion of drugs and their metabolites.”

“Having identified a lead compound, it is important to establish which features of the compound are important for activity. This, in turn, can give a better understanding of how the compound binds to its molecular target. Most drugs are significantly smaller than molecular targets such as proteins. This means that the drug binds to quite a small region of the protein — a region known as the binding site […]. Within this binding site, there are binding regions that can form different types of intermolecular interactions such as van der Waals interactions, hydrogen bonds, and ionic interactions. If a drug has functional groups and substituents capable of interacting with those binding regions, then binding can take place. A lead compound may have several groups that are capable of forming intermolecular interactions, but not all of them are necessarily needed. One way of identifying the important binding groups is to crystallize the target protein with the drug bound to the binding site. X-ray crystallography then produces a picture of the complex which allows identification of binding interactions. However, it is not always possible to crystallize target proteins and so a different approach is needed. This involves synthesizing analogues of the lead compound where groups are modified or removed. Comparing the activity of each analogue with the lead compound can then determine whether a particular group is important or not. This is known as an SAR study, where SAR stands for structure–activity relationships.” Once the important binding groups have been identified, the pharmacophore for the lead compound can be defined. This specifies the important binding groups and their relative position in the molecule.”

“One way of identifying the active conformation of a flexible lead compound is to synthesize rigid analogues where the binding groups are locked into defined positions. This is known as rigidification or conformational restriction. The pharmacophore will then be represented by the most active analogue. […] A large number of rotatable bonds is likely to have an adverse effect on drug activity. This is because a flexible molecule can adopt a large number of conformations, and only one of these shapes corresponds to the active conformation. […] In contrast, a totally rigid molecule containing the required pharmacophore will bind the first time it enters the binding site, resulting in greater activity. […] It is also important to optimize a drug’s pharmacokinetic properties such that it can reach its target in the body. Strategies include altering the drug’s hydrophilic/hydrophobic properties to improve absorption, and the addition of substituents that block metabolism at specific parts of the molecule. […] The drug candidate must [in general] have useful activity and selectivity, with minimal side effects. It must have good pharmacokinetic properties, lack toxicity, and preferably have no interactions with other drugs that might be taken by a patient. Finally, it is important that it can be synthesized as cheaply as possible”.

“Most drugs that have reached clinical trials for the treatment of Alzheimer’s disease have failed. Between 2002 and 2012, 244 novel compounds were tested in 414 clinical trials, but only one drug gained approval. This represents a failure rate of 99.6 per cent as against a failure rate of 81 per cent for anti-cancer drugs.”

“It takes about ten years and £160 million to develop a new pesticide […] The volume of global sales increased 47 per cent in the ten-year period between 2002 and 2012, while, in 2012, total sales amounted to £31 billion. […] In many respects, agrochemical research is similar to pharmaceutical research. The aim is to find pesticides that are toxic to ‘pests’, but relatively harmless to humans and beneficial life forms. The strategies used to achieve this goal are also similar. Selectivity can be achieved by designing agents that interact with molecular targets that are present in pests, but not other species. Another approach is to take advantage of any metabolic reactions that are unique to pests. An inactive prodrug could then be designed that is metabolized to a toxic compound in the pest, but remains harmless in other species. Finally, it might be possible to take advantage of pharmacokinetic differences between pests and other species, such that a pesticide reaches its target more easily in the pest. […] Insecticides are being developed that act on a range of different targets as a means of tackling resistance. If resistance should arise to an insecticide acting on one particular target, then one can switch to using an insecticide that acts on a different target. […] Several insecticides act as insect growth regulators (IGRs) and target the moulting process rather than the nervous system. In general, IGRs take longer to kill insects but are thought to cause less detrimental effects to beneficial insects. […] Herbicides control weeds that would otherwise compete with crops for water and soil nutrients. More is spent on herbicides than any other class of pesticide […] The synthetic agent 2,4-D […] was synthesized by ICI in 1940 as part of research carried out on biological weapons […] It was first used commercially in 1946 and proved highly successful in eradicating weeds in cereal grass crops such as wheat, maize, and rice. […] The compound […] is still the most widely used herbicide in the world.”

“The type of conjugated system present in a molecule determines the specific wavelength of light absorbed. In general, the more extended the conjugation, the higher the wavelength absorbed. For example, β-carotene […] is the molecule responsible for the orange colour of carrots. It has a conjugated system involving eleven double bonds, and absorbs light in the blue region of the spectrum. It appears red because the reflected light lacks the blue component. Zeaxanthin is very similar in structure to β-carotene, and is responsible for the yellow colour of corn. […] Lycopene absorbs blue-green light and is responsible for the red colour of tomatoes, rose hips, and berries. Chlorophyll absorbs red light and is coloured green. […] Scented molecules interact with olfactory receptors in the nose. […] there are around 400 different olfactory protein receptors in humans […] The natural aroma of a rose is due mainly to 2-phenylethanol, geraniol, and citronellol.”

“Over the last fifty years, synthetic materials have largely replaced natural materials such as wood, leather, wool, and cotton. Plastics and polymers are perhaps the most visible sign of how organic chemistry has changed society. […] It is estimated that production of global plastics was 288 million tons in 2012 […] Polymerization involves linking molecular strands called polymers […]. By varying the nature of the monomer, a huge range of different polymers can be synthesized with widely differing properties. The idea of linking small molecular building blocks into polymers is not a new one. Nature has been at it for millions of years using amino acid building blocks to make proteins, and nucleotide building blocks to make nucleic acids […] The raw materials for plastics come mainly from oil, which is a finite resource. Therefore, it makes sense to recycle or depolymerize plastics to recover that resource. Virtually all plastics can be recycled, but it is not necessarily economically feasible to do so. Traditional recycling of polyesters, polycarbonates, and polystyrene tends to produce inferior plastics that are suitable only for low-quality goods.”

Adipic acid.
Protease. Lipase. Amylase. Cellulase.
Reflectin.
Agonist.
Antagonist.
Prodrug.
Conformational change.
Process chemistry (chemical development).
Clinical trial.
Phenylbutazone.
Pesticide.
Dichlorodiphenyltrichloroethane.
Aldrin.
N-Methyl carbamate.
Organophosphates.
Pyrethrum.
Neonicotinoid.
Colony collapse disorder.
Ecdysone receptor.
Methoprene.
Tebufenozide.
Fungicide.
Quinone outside inhibitors (QoI).
Allelopathy.
Glyphosate.
11-cis retinal.
Chromophore.
Synthetic dyes.
Methylene blue.
Cryptochrome.
Pheromone.
Artificial sweeteners.
Miraculin.
Addition polymer.
Condensation polymer.
Polyethylene.
Polypropylene.
Polyvinyl chloride.
Bisphenol A.
Vulcanization.
Kevlar.
Polycarbonate.
Polyhydroxyalkanoates.
Bioplastic.
Nanochemistry.
Allotropy.
Allotropes of carbon.
Carbon nanotube.
Rotaxane.
π-interactions.
Molecular switch.

November 11, 2017 Posted by | Biology, Books, Botany, Chemistry, Medicine, Pharmacology, Zoology | Leave a comment

Organic Chemistry (I)

This book‘s a bit longer than most ‘A very short introduction to…‘ publications, and it’s quite dense at times and included a lot of interesting stuff. It took me a while to finish it as I put it away a while back when I hit some of the more demanding content, but I did pick it up later and I really enjoyed most of the coverage. In the end I decided that I wouldn’t be doing the book justice if I were to limit my coverage of it to just one post, so this will be only the first of two posts of coverage of this book, covering roughly the first half of it.

As usual I have included in my post both some observations from the book (…and added a few links to these quotes where I figured they might be helpful) as well as some wiki links to topics discussed in the book.

“Organic chemistry is a branch of chemistry that studies carbon-based compounds in terms of their structure, properties, and synthesis. In contrast, inorganic chemistry covers the chemistry of all the other elements in the periodic table […] carbon-based compounds are crucial to the chemistry of life. [However] organic chemistry has come to be defined as the chemistry of carbon-based compounds, whether they originate from a living system or not. […] To date, 16 million compounds have been synthesized in organic chemistry laboratories across the world, with novel compounds being synthesized every day. […] The list of commodities that rely on organic chemistry include plastics, synthetic fabrics, perfumes, colourings, sweeteners, synthetic rubbers, and many other items that we use every day.”

“For a neutral carbon atom, there are six electrons occupying the space around the nucleus […] The electrons in the outer shell are defined as the valence electrons and these determine the chemical properties of the atom. The valence electrons are easily ‘accessible’ compared to the two electrons in the first shell. […] There is great significance in carbon being in the middle of the periodic table. Elements which are close to the left-hand side of the periodic table can lose their valence electrons to form positive ions. […] Elements on the right-hand side of the table can gain electrons to form negatively charged ions. […] The impetus for elements to form ions is the stability that is gained by having a full outer shell of electrons. […] Ion formation is feasible for elements situated to the left or the right of the periodic table, but it is less feasible for elements in the middle of the table. For carbon to gain a full outer shell of electrons, it would have to lose or gain four valence electrons, but this would require far too much energy. Therefore, carbon achieves a stable, full outer shell of electrons by another method. It shares electrons with other elements to form bonds. Carbon excels in this and can be considered chemistry’s ultimate elemental socialite. […] Carbon’s ability to form covalent bonds with other carbon atoms is one of the principle reasons why so many organic molecules are possible. Carbon atoms can be linked together in an almost limitless way to form a mind-blowing variety of carbon skeletons. […] carbon can form a bond to hydrogen, but it can also form bonds to atoms such as nitrogen, phosphorus, oxygen, sulphur, fluorine, chlorine, bromine, and iodine. As a result, organic molecules can contain a variety of different elements. Further variety can arise because it is possible for carbon to form double bonds or triple bonds to a variety of other atoms. The most common double bonds are formed between carbon and oxygen, carbon and nitrogen, or between two carbon atoms. […] The most common triple bonds are found between carbon and nitrogen, or between two carbon atoms.”

[C]hirality has huge importance. The two enantiomers of a chiral molecule behave differently when they interact with other chiral molecules, and this has important consequences in the chemistry of life. As an analogy, consider your left and right hands. These are asymmetric in shape and are non-superimposable mirror images. Similarly, a pair of gloves are non-superimposable mirror images. A left hand will fit snugly into a left-hand glove, but not into a right-hand glove. In the molecular world, a similar thing occurs. The proteins in our bodies are chiral molecules which can distinguish between the enantiomers of other molecules. For example, enzymes can distinguish between the two enantiomers of a chiral compound and catalyse a reaction with one of the enantiomers but not the other.”

“A key concept in organic chemistry is the functional group. A functional group is essentially a distinctive arrangement of atoms and bonds. […] Functional groups react in particular ways, and so it is possible to predict how a molecule might react based on the functional groups that are present. […] it is impossible to build a molecule atom by atom. Instead, target molecules are built by linking up smaller molecules. […] The organic chemist needs to have a good understanding of the reactions that are possible between different functional groups when choosing the molecular building blocks to be used for a synthesis. […] There are many […] reasons for carrying out FGTs [functional group transformations], especially when synthesizing complex molecules. For example, a starting material or a synthetic intermediate may lack a functional group at a key position of the molecular structure. Several reactions may then be required to introduce that functional group. On other occasions, a functional group may be added to a particular position then removed at a later stage. One reason for adding such a functional group would be to block an unwanted reaction at that position of the molecule. Another common situation is where a reactive functional group is converted to a less reactive functional group such that it does not interfere with a subsequent reaction. Later on, the original functional group is restored by another functional group transformation. This is known as a protection/deprotection strategy. The more complex the target molecule, the greater the synthetic challenge. Complexity is related to the number of rings, functional groups, substituents, and chiral centres that are present. […] The more reactions that are involved in a synthetic route, the lower the overall yield. […] retrosynthesis is a strategy by which organic chemists design a synthesis before carrying it out in practice. It is called retrosynthesis because the design process involves studying the target structure and working backwards to identify how that molecule could be synthesized from simpler starting materials. […] a key stage in retrosynthesis is identifying a bond that can be ‘disconnected’ to create those simpler molecules.”

“[V]ery few reactions produce the spectacular visual and audible effects observed in chemistry demonstrations. More typically, reactions involve mixing together two colourless solutions to produce another colourless solution. Temperature changes are a bit more informative. […] However, not all reactions generate heat, and monitoring the temperature is not a reliable way of telling whether the reaction has gone to completion or not. A better approach is to take small samples of the reaction solution at various times and to test these by chromatography or spectroscopy. […] If a reaction is taking place very slowly, different reaction conditions could be tried to speed it up. This could involve heating the reaction, carrying out the reaction under pressure, stirring the contents vigorously, ensuring that the reaction is carried out in a dry atmosphere, using a different solvent, using a catalyst, or using one of the reagents in excess. […] There are a large number of variables that can affect how efficiently reactions occur, and organic chemists in industry are often employed to develop the ideal conditions for a specific reaction. This is an area of organic chemistry known as chemical development. […] Once a reaction has been carried out, it is necessary to isolate and purify the reaction product. This often proves more time-consuming than carrying out the reaction itself. Ideally, one would remove the solvent used in the reaction and be left with the product. However, in most reactions this is not possible as other compounds are likely to be present in the reaction mixture. […] it is usually necessary to carry out procedures that will separate and isolate the desired product from these other compounds. This is known as ‘working up’ the reaction.”

“Proteins are large molecules (macromolecules) which serve a myriad of purposes, and are essentially polymers constructed from molecular building blocks called amino acids […]. In humans, there are twenty different amino acids having the same ‘head group’, consisting of a carboxylic acid and an amine attached to the same carbon atom […] The amino acids are linked up by the carboxylic acid of one amino acid reacting with the amine group of another to form an amide link. Since a protein is being produced, the amide bond is called a peptide bond, and the final protein consists of a polypeptide chain (or backbone) with different side chains ‘hanging off’ the chain […]. The sequence of amino acids present in the polypeptide sequence is known as the primary structure. Once formed, a protein folds into a specific 3D shape […] Nucleic acids […] are another form of biopolymer, and are formed from molecular building blocks called nucleotides. These link up to form a polymer chain where the backbone consists of alternating sugar and phosphate groups. There are two forms of nucleic acid — deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). In DNA, the sugar is deoxyribose , whereas the sugar in RNA is ribose. Each sugar ring has a nucleic acid base attached to it. For DNA, there are four different nucleic acid bases called adenine (A), thymine (T), cytosine (C), and guanine (G) […]. These bases play a crucial role in the overall structure and function of nucleic acids. […] DNA is actually made up of two DNA strands […] where the sugar-phosphate backbones are intertwined to form a double helix. The nucleic acid bases point into the centre of the helix, and each nucleic acid base ‘pairs up’ with a nucleic acid base on the opposite strand through hydrogen bonding. The base pairing is specifically between adenine and thymine, or between cytosine and guanine. This means that one polymer strand is complementary to the other, a feature that is crucial to DNA’s function as the storage molecule for genetic information. […]  [E]ach strand […] act as the template for the creation of a new strand to produce two identical ‘daughter’ DNA double helices […] [A] genetic alphabet of four letters (A, T, G, C) […] code for twenty amino acids. […] [A]n amino acid is coded, not by one nucleotide, but by a set of three. The number of possible triplet combinations using four ‘letters’ is more than enough to encode all the amino acids.”

“Proteins have a variety of functions. Some proteins, such as collagen, keratin, and elastin, have a structural role. Others catalyse life’s chemical reactions and are called enzymes. They have a complex 3D shape, which includes a cavity called the active site […]. This is where the enzyme binds the molecules (substrates) that undergo the enzyme-catalysed reaction. […] A substrate has to have the correct shape to fit an enzyme’s active site, but it also needs binding groups to interact with that site […]. These interactions hold the substrate in the active site long enough for a reaction to occur, and typically involve hydrogen bonds, as well as van der Waals and ionic interactions. When a substrate binds, the enzyme normally undergoes an induced fit. In other words, the shape of the active site changes slightly to accommodate the substrate, and to hold it as tightly as possible. […] Once a substrate is bound to the active site, amino acids in the active site catalyse the subsequent reaction.”

“Proteins called receptors are involved in chemical communication between cells and respond to chemical messengers called neurotransmitters if they are released from nerves, or hormones if they are released by glands. Most receptors are embedded in the cell membrane, with part of their structure exposed on the outer surface of the cell membrane, and another part exposed on the inner surface. On the outer surface they contain a binding site that binds the molecular messenger. An induced fit then takes place that activates the receptor. This is very similar to what happens when a substrate binds to an enzyme […] The induced fit is crucial to the mechanism by which a receptor conveys a message into the cell — a process known as signal transduction. By changing shape, the protein initiates a series of molecular events that influences the internal chemistry within the cell. For example, some receptors are part of multiprotein complexes called ion channels. When the receptor changes shape, it causes the overall ion channel to change shape. This opens up a central pore allowing ions to flow across the cell membrane. The ion concentration within the cell is altered, and that affects chemical reactions within the cell, which ultimately lead to observable results such as muscle contraction. Not all receptors are membrane-bound. For example, steroid receptors are located within the cell. This means that steroid hormones need to cross the cell membrane in order to reach their target receptors. Transport proteins are also embedded in cell membranes and are responsible for transporting polar molecules such as amino acids into the cell. They are also important in controlling nerve action since they allow nerves to capture released neurotransmitters, such that they have a limited period of action.”

“RNA […] is crucial to protein synthesis (translation). There are three forms of RNA — messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). mRNA carries the genetic code for a particular protein from DNA to the site of protein production. Essentially, mRNA is a single-strand copy of a specific section of DNA. The process of copying that information is known as transcription. tRNA decodes the triplet code on mRNA by acting as a molecular adaptor. At one end of tRNA, there is a set of three bases (the anticodon) that can base pair to a set of three bases on mRNA (the codon). An amino acid is linked to the other end of the tRNA and the type of amino acid present is related to the anticodon that is present. When tRNA with the correct anticodon base pairs to the codon on mRNA, it brings the amino acid encoded by that codon. rRNA is a major constituent of a structure called a ribosome, which acts as the factory for protein production. The ribosome binds mRNA then coordinates and catalyses the translation process.”

Organic chemistry.
Carbon.
Stereochemistry.
Delocalization.
Hydrogen bond.
Van der Waals forces.
Ionic bonding.
Chemoselectivity.
Coupling reaction.
Chemical polarity.
Crystallization.
Elemental analysis.
NMR spectroscopy.
Polymerization.
Miller–Urey experiment.
Vester-Ulbricht hypothesis.
Oligonucleotide.
RNA world.
Ribozyme.

November 9, 2017 Posted by | Biology, Books, Chemistry, Genetics | Leave a comment

Molecules

This book is almost exclusively devoted to covering biochemistry topics. When the coverage is decent I find biochemistry reasonably interesting – for example I really liked Beer, Björk & Beardall’s photosynthesis book – and the coverage here was okay, but not more than that. I think that Ball was trying to cover a bit too much ground, or perhaps that there was really too much ground to cover for it to even make sense to try to write a book on this particular topic in a series like this. I learned a lot though.

As usual I’ve added some quotes from the coverage below, as well as some additional links to topics/concepts/people/etc. covered in the book.

“Most atoms on their own are highly reactive – they have a predisposition to join up with other atoms. Molecules are collectives of atoms, firmly welded together into assemblies that may contain anything up to many millions of them. […] By molecules, we generally mean assemblies of a discrete, countable number of atoms. […] Some pure elements adopt molecular forms; others do not. As a rough rule of thumb, metals are non-molecular […] whereas non-metals are molecular. […] molecules are the smallest units of meaning in chemistry. It is through molecules, not atoms, that one can tell stories in the sub-microscopic world. They are the words; atoms are just the letters. […] most words are distinct aggregates of several letters arranged in a particular order. We often find that longer words convey subtler and more finely nuanced meanings. And in molecules, as in words, the order in which the component parts are put together matters: ‘save’ and ‘vase’ do not mean the same thing.”

“There are something like 60,000 different varieties of protein molecule in human cells, each conducting a highly specialized task. It would generally be impossible to guess what this task is merely by looking at a protein. They are undistinguished in appearance, mostly globular in shape […] and composed primarily of carbon, hydrogen, nitrogen, oxygen, and a little sulphur. […] There are twenty varieties of amino acids in natural proteins. In the chain, one amino acid is linked to the next via a covalent bond called a peptide bond. Both molecules shed a few extraneous atoms to make this linkage, and the remainder – another link in the chain – is called a residue. The chain itself is termed a polypeptide. Any string of amino acid residues is a polypeptide. […] In a protein the order of amino acids along the chain – the sequence – is not arbitrary. It is selected […] to ensure that the chain will collapse and curl up in water into the precisely determined globular form of the protein, with all parts of the chain in the right place. This shape can be destroyed by warming the protein, a process called denaturation. But many proteins will fold up again spontaneously into the same globular structure when cooled. In other words, the chain has a kind of memory of its folded shape. The details of this folding process are still not fully understood – it is, in fact, one of the central unsolved puzzles of molecular biology. […] proteins are made not in the [cell] nucleus but in a different compartment called the endoplasmic reticulum […]. The gene is transcribed first into a molecule related to DNA, called RNA (ribonucleic acid). The RNA molecules travel from the nucleus to the endoplasmic reticulum, where they are translated to proteins. The proteins are then shipped off to where they are needed.”

[M]icrofibrils aggregate together in various ways. For example, they can gather in a staggered arrangement to form thick strands called banded fibrils. […] Banded fibrils constitute the connective tissues between cells – they are the cables that hold our flesh together. Bone consists of collagen banded fibrils sprinkled with tiny crystals of the mineral hydroxyapatite, which is basically calcium phosphate. Because of the high protein content of bone, it is flexible and resilient as well as hard. […] In contrast to the disorderly tangle of connective tissue, the eye’s cornea contains collagen fibrils packed side by side in an orderly manner. These fibrils are too small to scatter light, and so the material is virtually transparent. The basic design principle – one that recurs often in nature – is that, by tinkering with the chemical composition and, most importantly, the hierarchical arrangement of the same basic molecules, it is possible to extract several different kinds of material properties. […] cross-links determine the strength of the material: hair and fingernail are more highly cross-linked than skin. Curly or frizzy hair can be straightened by breaking some of [the] sulphur cross-links to make the hairs more pliable. […] Many of the body’s structural fabrics are proteins. Unlike enzymes, structural proteins do not have to conduct any delicate chemistry, but must simply be (for instance) tough, or flexible, or waterproof. In principle many other materials besides proteins would suffice; and indeed, plants use cellulose (a sugar-based polymer) to make their tissues.”

“In many ways, it is metabolism and not replication that provides the best working definition of life. Evolutionary biologists would say that we exist in order to reproduce – but we are not, even the most amorous of us, trying to reproduce all the time. Yet, if we stop metabolizing, even for a minute or two, we are done for. […] Whether waking or asleep, our bodies stay close to a healthy temperature of 37 °C. There is only one way of doing this: our cells are constantly pumping out heat, a by-product of metabolism. Heat is not really the point here – it is simply unavoidable, because all conversion of energy from one form to another squanders some of it this way. Our metabolic processes are primarily about making molecules. Cells cannot survive without constantly reinventing themselves: making new amino acids for proteins, new lipids for membranes, new nucleic acids so that they can divide.”

“In the body, combustion takes place in a tightly controlled, graded sequence of steps, and some chemical energy is drawn off and stored at each stage. […] A power station burns coal, oil, or gas […]. Burning is just a means to an end. The heat is used to turn water into steam; the pressure of the steam drives turbines; the turbines spin and send wire coils whirling in the arms of great magnets, which induces an electrical current in the wire. Energy is passed on, from chemical to heat to mechanical to electrical. And every plant has a barrage of regulatory and safety mechanisms. There are manual checks on pressure gauges and on the structural integrity of moving parts. Automatic sensors make the measurements. Failsafe devices avert catastrophic failure. Energy generation in the cell is every bit as complicated. […] The cell seems to have thought of everything, and has protein devices for fine-tuning it all.”

ATP is the key to the maintenance of cellular integrity and organization, and so the cell puts a great deal of effort into making as much of it as possible from each molecule of glucose that it burns. About 40 per cent of the energy released by the combustion of food is conserved in ATP molecules. ATP is rich in energy because it is like a coiled spring. It contains three phosphate groups, linked like so many train carriages. Each of these phosphate groups has a negative charge; this means that they repel one another. But because they are joined by chemical bonds, they cannot escape one another […]. Straining to get away, the phosphates pull an energetically powerful punch. […] The links between phosphates can be snipped in a reaction that involves water […] called hydrolysis (‘splitting with water’). Each time a bond is hydrolysed, energy is released. Setting free the outermost phosphate converts ATP to adenosine diphosphate (ADP); cleave the second phosphate and it becomes adenosine monophosphate (AMP). Both severances release comparable amounts of energy.”

“Burning sugar is a two-stage process, beginning with its transformation to a molecule called pyruvate in a process known as glycolysis […]. This involves a sequence of ten enzyme-catalysed steps. The first five of these split glucose in half […], powered by the consumption of ATP molecules: two of them are ‘decharged’ to ADP for every glucose molecule split. But the conversion of the fragments to pyruvate […] permits ATP to be recouped from ADP. Four ATP molecules are made this way, so that there is an overall gain of two ATP molecules per glucose molecule consumed. Thus glycolysis charges the cell’s batteries. Pyruvate then normally enters the second stage of the combustion process: the citric acid cycle, which requires oxygen. But if oxygen is scarce – that is, under anaerobic conditions – a contingency plan is enacted whereby pyruvate is instead converted to the molecule lactate. […] The first thing a mitochondrion does is convert pyruvate enzymatically to a molecule called acetyl coenzyme A (CoA). The breakdown of fatty acids and glycerides from fats also eventually generates acetyl CoA. The [citric acid] cycle is a sequence of eight enzyme-catalysed reactions that transform acetyl CoA first to citric acid and then to various other molecules, ending with […] oxaloacetate. This end is a new beginning, for oxaloacetate reacts with acetyl CoA to make citric acid. In some of the steps of the cycle, carbon dioxide is generated as a by-product. It dissolves in the bloodstream and is carried off to the lungs to be exhaled. Thus in effect the carbon in the original glucose molecules is syphoned off into the end product carbon dioxide, completing the combustion process. […] Also syphoned off from the cycle are electrons – crudely speaking, the citric acid cycle sends an electrical current to a different part of the mitochondrion. These electrons are used to convert oxygen molecules and positively charged hydrogen ions to water – an energy-releasing process. The energy is captured and used to make ATP in abundance.”

“While mammalian cells have fuel-burning factories in the form of mitochondria, the solar-power centres in the cells of plant leaves are compartments called chloroplasts […] chloroplast takes carbon dioxide and water, and from them constructs […] sugar. […] In the first part of photosynthesis, light is used to convert NADP to an electron carrier (NADPH) and to transform ADP to ATP. This is effectively a charging-up process that primes the chloroplast for glucose synthesis. In the second part, ATP and NADPH are used to turn carbon dioxide into sugar, in a cyclic sequence of steps called the Calvin–Benson cycle […] There are several similarities between the processes of aerobic metabolism and photosynthesis. Both consist of two distinct sub-processes with separate evolutionary origins: a linear sequence of reactions coupled to a cyclic sequence that regenerates the molecules they both need. The bridge between glycolysis and the citric acid cycle is the electron-ferrying NAD molecule; the two sub-processes of photosynthesis are bridged by the cycling of an almost identical molecule, NAD phosphate (NADP).”

“Despite the variety of messages that hormones convey, the mechanism by which the signal is passed from a receptor protein at the cell surface to the cell’s interior is the same in almost all cases. It involves a sequence of molecular interactions in which molecules transform one another down a relay chain. In cell biology this is called signal transduction. At the same time as relaying the message, these interactions amplify the signal so that the docking of a single hormone molecule to a receptor creates a big response inside the cell. […] The receptor proteins span the entire width of the membrane; the hormone-binding site protrudes on the outer surface, while the base of the receptor emerges from the inner surface […]. When the receptor binds its target hormone, a shape change is transmitted to the lower face of the protein, which enables it to act as an enzyme. […] The participants of all these processes [G protein, guanosine diphosphate and -triphosphate, adenylate cyclase… – figured it didn’t matter if I left out a few details – US…] are stuck to the cell wall. But cAMP floats freely in the cell’s cytoplasm, and is able to carry the signal into the cell interior. It is called a ‘second messenger’, since it is the agent that relays the signal of the ‘first messenger’ (the hormone) into the community of the cell. Cyclic AMP becomes attached to protein molecules called protein kinases, whereupon they in turn become activated as enzymes. Most protein kinases switch other enzymes on and off by attaching phosphate groups to them – a reaction called phosphorylation. […] The process might sound rather complicated, but it is really nothing more than a molecular relay. The signal is passed from the hormone to its receptor, then to the G protein, on to an enzyme and thence to the second messenger, and further on to a protein kinase, and so forth. The G-protein mechanism of signal transduction was discovered in the 1970s by Alfred Gilman and Martin Rodbell, for which they received the 1994 Nobel Prize for medicine. It represents one of the most widespread means of getting a message across a cell membrane. […] it is not just hormonal signalling that makes use of the G-protein mechanism. Our senses of vision and smell, which also involve the transmission of signals, employ the same switching process.”

“Although axon signals are electrical, they differ from those in the metal wires of electronic circuitry. The axon is basically a tubular cell membrane decorated along its length with channels that let sodium and potassium ions in and out. Some of these ion channels are permanently open; others are ‘gated’, opening or closing in response to electrical signals. And some are not really channels at all but pumps, which actively transport sodium ions out of the cell and potassium ions in. These sodium-potassium pumps can move ions […] powered by ATP. […] Drugs that relieve pain typically engage with inhibitory receptors. Morphine, the main active ingredient of opium, binds to so-called opioid receptors in the spinal cord, which inhibit the transmission of pain signals to the brain. There are also opioid receptors in the brain itself, which is why morphine and related opiate drugs have a mental as well as a somatic effect. These receptors in the brain are the binding sites of peptide molecules called endorphins, which the brain produces in response to pain. Some of these are themselves extremely powerful painkillers. […] Not all pain-relieving drugs (analgesics) work by blocking the pain signal. Some prevent the signal from ever being sent. Pain signals are initiated by peptides called prostaglandins, which are manufactured and released by distressed cells. Aspirin (acetylsalicylic acid) latches onto and inhibits one of the enzymes responsible for prostaglandin synthesis, cutting off the cry of pain at its source. Unfortunately, prostaglandins are also responsible for making the mucus that protects the stomach lining […], so one of the side effects of aspirin is the risk of ulcer formation.”

“Shape changes […] are common when a receptor binds its target. If binding alone is the objective, a big shape change is not terribly desirable, since the internal rearrangements of the receptor make heavy weather of the binding event and may make it harder to achieve. This is why many supramolecular hosts are designed so that they are ‘pre-organized’ to receive their guests, minimizing the shape change caused by binding.”

“The way that a protein chain folds up is determined by its amino-acid sequence […] so the ‘information’ for making a protein is uniquely specified by this sequence. DNA encodes this information using […] groups of three bases [to] represent each amino acid. This is the genetic code.* How a particular protein sequence determines the way its chain folds is not yet fully understood. […] Nevertheless, the principle of information flow in the cell is clear. DNA is a manual of information about proteins. We can think of each chromosome as a separate chapter, each gene as a word in that chapter (they are very long words!), and each sequential group of three bases in the gene as a character in the word. Proteins are translations of the words into another language, whose characters are amino acids. In general, only when the genetic language is translated can we understand what it means.”

“It is thought that only about 2–3 per cent of the entire human genome codes for proteins. […] Some people object to genetic engineering on the grounds that it is ethically wrong to tamper with the fundamental material of life – DNA – whether it is in bacteria, humans, tomatoes, or sheep. One can understand such objections, and it would be arrogant to dismiss them as unscientific. Nevertheless, they do sit uneasily with what we now know about the molecular basis of life. The idea that our genetic make-up is sacrosanct looks hard to sustain once we appreciate how contingent, not to say arbitrary, that make-up is. Our genomes are mostly parasite-riddled junk, full of the detritus of over three billion years of evolution.”

Links:

Roald Hoffmann.
Molecular solid.
Covalent bond.
Visible spectrum.
X-ray crystallography.
Electron microscope.
Valence (chemistry).
John Dalton.
Isomer.
Lysozyme.
Organic chemistry.
Synthetic dye industry/Alizarin.
Paul Ehrlich (staining).
Retrosynthetic analysis. [I would have added a link to ‘rational synthesis as well here if there’d been a good article on that topic, but I wasn’t able to find one. Anyway: “Organic chemists call [the] kind of procedure […] in which a starting molecule is converted systematically, bit by bit, to the desired product […] a rational synthesis.”]
Paclitaxel synthesis.
Protein.
Enzyme.
Tryptophan synthase.
Ubiquitin.
Amino acid.
Protein folding.
Peptide bond.
Hydrogen bond.
Nucleotide.
Chromosome.
Structural gene. Regulatory gene.
Operon.
Gregor Mendel.
Mitochondrial DNA.
RNA world.
Ribozyme.
Artificial gene synthesis.
Keratin.
Silk.
Vulcanization.
Aramid.
Microtubule.
Tubulin.
Carbon nanotube.
Amylase/pepsin/glycogen/insulin.
Cytochrome c oxidase.
ATP synthase.
Haemoglobin.
Thylakoid membrane.
Chlorophyll.
Liposome.
TNT.
Motor protein. Dynein. Kinesin.
Sarcomere.
Sliding filament theory of muscle action.
Photoisomerization.
Supramolecular chemistry.
Hormone. Endocrine system.
Neurotransmitter.
Ionophore.
DNA.
Mutation.
Intron. Exon.
Transposon.
Molecular electronics.

October 30, 2017 Posted by | Biology, Books, Botany, Chemistry, Genetics, Neurology, Pharmacology | Leave a comment

Physical chemistry

This is a good book, I really liked it, just as I really liked the other book in the series which I read by the same author, the one about the laws of thermodynamics (blog coverage here). I know much, much more about physics than I do about chemistry and even though some of it was review I learned a lot from this one. Recommended, certainly if you find the quotes below interesting. As usual, I’ve added some observations from the book and some links to topics/people/etc. covered/mentioned in the book below.

Some quotes:

“Physical chemists pay a great deal of attention to the electrons that surround the nucleus of an atom: it is here that the chemical action takes place and the element expresses its chemical personality. […] Quantum mechanics plays a central role in accounting for the arrangement of electrons around the nucleus. The early ‘Bohr model’ of the atom, […] with electrons in orbits encircling the nucleus like miniature planets and widely used in popular depictions of atoms, is wrong in just about every respect—but it is hard to dislodge from the popular imagination. The quantum mechanical description of atoms acknowledges that an electron cannot be ascribed to a particular path around the nucleus, that the planetary ‘orbits’ of Bohr’s theory simply don’t exist, and that some electrons do not circulate around the nucleus at all. […] Physical chemists base their understanding of the electronic structures of atoms on Schrödinger’s model of the hydrogen atom, which was formulated in 1926. […] An atom is often said to be mostly empty space. That is a remnant of Bohr’s model in which a point-like electron circulates around the nucleus; in the Schrödinger model, there is no empty space, just a varying probability of finding the electron at a particular location.”

“No more than two electrons may occupy any one orbital, and if two do occupy that orbital, they must spin in opposite directions. […] this form of the principle [the Pauli exclusion principleUS] […] is adequate for many applications in physical chemistry. At its very simplest, the principle rules out all the electrons of an atom (other than atoms of one-electron hydrogen and two-electron helium) having all their electrons in the 1s-orbital. Lithium, for instance, has three electrons: two occupy the 1s orbital, but the third cannot join them, and must occupy the next higher-energy orbital, the 2s-orbital. With that point in mind, something rather wonderful becomes apparent: the structure of the Periodic Table of the elements unfolds, the principal icon of chemistry. […] The first electron can enter the 1s-orbital, and helium’s (He) second electron can join it. At that point, the orbital is full, and lithium’s (Li) third electron must enter the next higher orbital, the 2s-orbital. The next electron, for beryllium (Be), can join it, but then it too is full. From that point on the next six electrons can enter in succession the three 2p-orbitals. After those six are present (at neon, Ne), all the 2p-orbitals are full and the eleventh electron, for sodium (Na), has to enter the 3s-orbital. […] Similar reasoning accounts for the entire structure of the Table, with elements in the same group all having analogous electron arrangements and each successive row (‘period’) corresponding to the next outermost shell of orbitals.”

“[O]n crossing the [Periodic] Table from left to right, atoms become smaller: even though they have progressively more electrons, the nuclear charge increases too, and draws the clouds in to itself. On descending a group, atoms become larger because in successive periods new outermost shells are started (as in going from lithium to sodium) and each new coating of cloud makes the atom bigger […] the ionization energy [is] the energy needed to remove one or more electrons from the atom. […] The ionization energy more or less follows the trend in atomic radii but in an opposite sense because the closer an electron lies to the positively charged nucleus, the harder it is to remove. Thus, ionization energy increases from left to right across the Table as the atoms become smaller. It decreases down a group because the outermost electron (the one that is most easily removed) is progressively further from the nucleus. […] the electron affinity [is] the energy released when an electron attaches to an atom. […] Electron affinities are highest on the right of the Table […] An ion is an electrically charged atom. That charge comes about either because the neutral atom has lost one or more of its electrons, in which case it is a positively charged cation […] or because it has captured one or more electrons and has become a negatively charged anion. […] Elements on the left of the Periodic Table, with their low ionization energies, are likely to lose electrons and form cations; those on the right, with their high electron affinities, are likely to acquire electrons and form anions. […] ionic bonds […] form primarily between atoms on the left and right of the Periodic Table.”

“Although the Schrödinger equation is too difficult to solve for molecules, powerful computational procedures have been developed by theoretical chemists to arrive at numerical solutions of great accuracy. All the procedures start out by building molecular orbitals from the available atomic orbitals and then setting about finding the best formulations. […] Depictions of electron distributions in molecules are now commonplace and very helpful for understanding the properties of molecules. It is particularly relevant to the development of new pharmacologically active drugs, where electron distributions play a central role […] Drug discovery, the identification of pharmacologically active species by computation rather than in vivo experiment, is an important target of modern computational chemistry.”

Work […] involves moving against an opposing force; heat […] is the transfer of energy that makes use of a temperature difference. […] the internal energy of a system that is isolated from external influences does not change. That is the First Law of thermodynamics. […] A system possesses energy, it does not possess work or heat (even if it is hot). Work and heat are two different modes for the transfer of energy into or out of a system. […] if you know the internal energy of a system, then you can calculate its enthalpy simply by adding to U the product of pressure and volume of the system (H = U + pV). The significance of the enthalpy […] is that a change in its value is equal to the output of energy as heat that can be obtained from the system provided it is kept at constant pressure. For instance, if the enthalpy of a system falls by 100 joules when it undergoes a certain change (such as a chemical reaction), then we know that 100 joules of energy can be extracted as heat from the system, provided the pressure is constant.”

“In the old days of physical chemistry (well into the 20th century), the enthalpy changes were commonly estimated by noting which bonds are broken in the reactants and which are formed to make the products, so A → B might be the bond-breaking step and B → C the new bond-formation step, each with enthalpy changes calculated from knowledge of the strengths of the old and new bonds. That procedure, while often a useful rule of thumb, often gave wildly inaccurate results because bonds are sensitive entities with strengths that depend on the identities and locations of the other atoms present in molecules. Computation now plays a central role: it is now routine to be able to calculate the difference in energy between the products and reactants, especially if the molecules are isolated as a gas, and that difference easily converted to a change of enthalpy. […] Enthalpy changes are very important for a rational discussion of changes in physical state (vaporization and freezing, for instance) […] If we know the enthalpy change taking place during a reaction, then provided the process takes place at constant pressure we know how much energy is released as heat into the surroundings. If we divide that heat transfer by the temperature, then we get the associated entropy change in the surroundings. […] provided the pressure and temperature are constant, a spontaneous change corresponds to a decrease in Gibbs energy. […] the chemical potential can be thought of as the Gibbs energy possessed by a standard-size block of sample. (More precisely, for a pure substance the chemical potential is the molar Gibbs energy, the Gibbs energy per mole of atoms or molecules.)”

“There are two kinds of work. One kind is the work of expansion that occurs when a reaction generates a gas and pushes back the atmosphere (perhaps by pressing out a piston). That type of work is called ‘expansion work’. However, a chemical reaction might do work other than by pushing out a piston or pushing back the atmosphere. For instance, it might do work by driving electrons through an electric circuit connected to a motor. This type of work is called ‘non-expansion work’. […] a change in the Gibbs energy of a system at constant temperature and pressure is equal to the maximum non-expansion work that can be done by the reaction. […] the link of thermodynamics with biology is that one chemical reaction might do the non-expansion work of building a protein from amino acids. Thus, a knowledge of the Gibbs energies changes accompanying metabolic processes is very important in bioenergetics, and much more important than knowing the enthalpy changes alone (which merely indicate a reaction’s ability to keep us warm).”

“[T]he probability that a molecule will be found in a state of particular energy falls off rapidly with increasing energy, so most molecules will be found in states of low energy and very few will be found in states of high energy. […] If the temperature is low, then the distribution declines so rapidly that only the very lowest levels are significantly populated. If the temperature is high, then the distribution falls off very slowly with increasing energy, and many high-energy states are populated. If the temperature is zero, the distribution has all the molecules in the ground state. If the temperature is infinite, all available states are equally populated. […] temperature […] is the single, universal parameter that determines the most probable distribution of molecules over the available states.”

“Mixing adds disorder and increases the entropy of the system and therefore lowers the Gibbs energy […] In the absence of mixing, a reaction goes to completion; when mixing of reactants and products is taken into account, equilibrium is reached when both are present […] Statistical thermodynamics, through the Boltzmann distribution and its dependence on temperature, allows physical chemists to understand why in some cases the equilibrium shifts towards reactants (which is usually unwanted) or towards products (which is normally wanted) as the temperature is raised. A rule of thumb […] is provided by a principle formulated by Henri Le Chatelier […] that a system at equilibrium responds to a disturbance by tending to oppose its effect. Thus, if a reaction releases energy as heat (is ‘exothermic’), then raising the temperature will oppose the formation of more products; if the reaction absorbs energy as heat (is ‘endothermic’), then raising the temperature will encourage the formation of more product.”

“Model building pervades physical chemistry […] some hold that the whole of science is based on building models of physical reality; much of physical chemistry certainly is.”

“For reasonably light molecules (such as the major constituents of air, N2 and O2) at room temperature, the molecules are whizzing around at an average speed of about 500 m/s (about 1000 mph). That speed is consistent with what we know about the propagation of sound, the speed of which is about 340 m/s through air: for sound to propagate, molecules must adjust their position to give a wave of undulating pressure, so the rate at which they do so must be comparable to their average speeds. […] a typical N2 or O2 molecule in air makes a collision every nanosecond and travels about 1000 molecular diameters between collisions. To put this scale into perspective: if a molecule is thought of as being the size of a tennis ball, then it travels about the length of a tennis court between collisions. Each molecule makes about a billion collisions a second.”

“X-ray diffraction makes use of the fact that electromagnetic radiation (which includes X-rays) consists of waves that can interfere with one another and give rise to regions of enhanced and diminished intensity. This so-called ‘diffraction pattern’ is characteristic of the object in the path of the rays, and mathematical procedures can be used to interpret the pattern in terms of the object’s structure. Diffraction occurs when the wavelength of the radiation is comparable to the dimensions of the object. X-rays have wavelengths comparable to the separation of atoms in solids, so are ideal for investigating their arrangement.”

“For most liquids the sample contracts when it freezes, so […] the temperature does not need to be lowered so much for freezing to occur. That is, the application of pressure raises the freezing point. Water, as in most things, is anomalous, and ice is less dense than liquid water, so water expands when it freezes […] when two gases are allowed to occupy the same container they invariably mix and each spreads uniformly through it. […] the quantity of gas that dissolves in any liquid is proportional to the pressure of the gas. […] When the temperature of [a] liquid is raised, it is easier for a dissolved molecule to gather sufficient energy to escape back up into the gas; the rate of impacts from the gas is largely unchanged. The outcome is a lowering of the concentration of dissolved gas at equilibrium. Thus, gases appear to be less soluble in hot water than in cold. […] the presence of dissolved substances affects the properties of solutions. For instance, the everyday experience of spreading salt on roads to hinder the formation of ice makes use of the lowering of freezing point of water when a salt is present. […] the boiling point is raised by the presence of a dissolved substance [whereas] the freezing point […] is lowered by the presence of a solute.”

“When a liquid and its vapour are present in a closed container the vapour exerts a characteristic pressure (when the escape of molecules from the liquid matches the rate at which they splash back down into it […][)] This characteristic pressure depends on the temperature and is called the ‘vapour pressure’ of the liquid. When a solute is present, the vapour pressure at a given temperature is lower than that of the pure liquid […] The extent of lowering is summarized by yet another limiting law of physical chemistry, ‘Raoult’s law’ [which] states that the vapour pressure of a solvent or of a component of a liquid mixture is proportional to the proportion of solvent or liquid molecules present. […] Osmosis [is] the tendency of solvent molecules to flow from the pure solvent to a solution separated from it by a [semi-]permeable membrane […] The entropy when a solute is present in a solvent is higher than when the solute is absent, so an increase in entropy, and therefore a spontaneous process, is achieved when solvent flows through the membrane from the pure liquid into the solution. The tendency for this flow to occur can be overcome by applying pressure to the solution, and the minimum pressure needed to overcome the tendency to flow is called the ‘osmotic pressure’. If one solution is put into contact with another through a semipermeable membrane, then there will be no net flow if they exert the same osmotic pressures and are ‘isotonic’.”

“Broadly speaking, the reaction quotient [‘Q’] is the ratio of concentrations, with product concentrations divided by reactant concentrations. It takes into account how the mingling of the reactants and products affects the total Gibbs energy of the mixture. The value of Q that corresponds to the minimum in the Gibbs energy […] is called the equilibrium constant and denoted K. The equilibrium constant, which is characteristic of a given reaction and depends on the temperature, is central to many discussions in chemistry. When K is large (1000, say), we can be reasonably confident that the equilibrium mixture will be rich in products; if K is small (0.001, say), then there will be hardly any products present at equilibrium and we should perhaps look for another way of making them. If K is close to 1, then both reactants and products will be abundant at equilibrium and will need to be separated. […] Equilibrium constants vary with temperature but not […] with pressure. […] van’t Hoff’s equation implies that if the reaction is strongly exothermic (releases a lot of energy as heat when it takes place), then the equilibrium constant decreases sharply as the temperature is raised. The opposite is true if the reaction is strongly endothermic (absorbs a lot of energy as heat). […] Typically it is found that the rate of a reaction [how fast it progresses] decreases as it approaches equilibrium. […] Most reactions go faster when the temperature is raised. […] reactions with high activation energies proceed slowly at low temperatures but respond sharply to changes of temperature. […] The surface area exposed by a catalyst is important for its function, for it is normally the case that the greater that area, the more effective is the catalyst.”

Links:

John Dalton.
Atomic orbital.
Electron configuration.
S,p,d,f orbitals.
Computational chemistry.
Atomic radius.
Covalent bond.
Gilbert Lewis.
Valence bond theory.
Molecular orbital theory.
Orbital hybridisation.
Bonding and antibonding orbitals.
Schrödinger equation.
Density functional theory.
Chemical thermodynamics.
Laws of thermodynamics/Zeroth law/First law/Second law/Third Law.
Conservation of energy.
Thermochemistry.
Bioenergetics.
Spontaneous processes.
Entropy.
Rudolf Clausius.
Chemical equilibrium.
Heat capacity.
Compressibility.
Statistical thermodynamics/statistical mechanics.
Boltzmann distribution.
State of matter/gas/liquid/solid.
Perfect gas/Ideal gas law.
Robert Boyle/Joseph Louis Gay-Lussac/Jacques Charles/Amedeo Avogadro.
Equation of state.
Kinetic theory of gases.
Van der Waals equation of state.
Maxwell–Boltzmann distribution.
Thermal conductivity.
Viscosity.
Nuclear magnetic resonance.
Debye–Hückel equation.
Ionic solids.
Catalysis.
Supercritical fluid.
Liquid crystal.
Graphene.
Benoît Paul Émile Clapeyron.
Phase (matter)/phase diagram/Gibbs’ phase rule.
Ideal solution/regular solution.
Henry’s law.
Chemical kinetics.
Electrochemistry.
Rate equation/First order reactions/Second order reactions.
Rate-determining step.
Arrhenius equation.
Collision theory.
Diffusion-controlled and activation-controlled reactions.
Transition state theory.
Photochemistry/fluorescence/phosphorescence/photoexcitation.
Photosynthesis.
Redox reactions.
Electrochemical cell.
Fuel cell.
Reaction dynamics.
Spectroscopy/emission spectroscopy/absorption spectroscopy/Raman spectroscopy.
Raman effect.
Magnetic resonance imaging.
Fourier-transform spectroscopy.
Electron paramagnetic resonance.
Mass spectrum.
Electron spectroscopy for chemical analysis.
Scanning tunneling microscope.
Chemisorption/physisorption.

October 5, 2017 Posted by | Biology, Books, Chemistry, Pharmacology, Physics | Leave a comment

Earth System Science

I decided not to rate this book. Some parts are great, some parts I didn’t think were very good.

I’ve added some quotes and links below. First a few links (I’ve tried not to add links here which I’ve also included in the quotes below):

Carbon cycle.
Origin of water on Earth.
Gaia hypothesis.
Albedo (climate and weather).
Snowball Earth.
Carbonate–silicate cycle.
Carbonate compensation depth.
Isotope fractionation.
CLAW hypothesis.
Mass-independent fractionation.
δ13C.
Great Oxygenation Event.
Acritarch.
Grypania.
Neoproterozoic.
Rodinia.
Sturtian glaciation.
Marinoan glaciation.
Ediacaran biota.
Cambrian explosion.
Quarternary.
Medieval Warm Period.
Little Ice Age.
Eutrophication.
Methane emissions.
Keeling curve.
CO2 fertilization effect.
Acid rain.
Ocean acidification.
Earth systems models.
Clausius–Clapeyron relation.
Thermohaline circulation.
Cryosphere.
The limits to growth.
Exoplanet Biosignature Gases.
Transiting Exoplanet Survey Satellite (TESS).
James Webb Space Telescope.
Habitable zone.
Kepler-186f.

A few quotes from the book:

“The scope of Earth system science is broad. It spans 4.5 billion years of Earth history, how the system functions now, projections of its future state, and ultimate fate. […] Earth system science is […] a deeply interdisciplinary field, which synthesizes elements of geology, biology, chemistry, physics, and mathematics. It is a young, integrative science that is part of a wider 21st-century intellectual trend towards trying to understand complex systems, and predict their behaviour. […] A key part of Earth system science is identifying the feedback loops in the Earth system and understanding the behaviour they can create. […] In systems thinking, the first step is usually to identify your system and its boundaries. […] what is part of the Earth system depends on the timescale being considered. […] The longer the timescale we look over, the more we need to include in the Earth system. […] for many Earth system scientists, the planet Earth is really comprised of two systems — the surface Earth system that supports life, and the great bulk of the inner Earth underneath. It is the thin layer of a system at the surface of the Earth […] that is the subject of this book.”

“Energy is in plentiful supply from the Sun, which drives the water cycle and also fuels the biosphere, via photosynthesis. However, the surface Earth system is nearly closed to materials, with only small inputs to the surface from the inner Earth. Thus, to support a flourishing biosphere, all the elements needed by life must be efficiently recycled within the Earth system. This in turn requires energy, to transform materials chemically and to move them physically around the planet. The resulting cycles of matter between the biosphere, atmosphere, ocean, land, and crust are called global biogeochemical cycles — because they involve biological, geological, and chemical processes. […] The global biogeochemical cycling of materials, fuelled by solar energy, has transformed the Earth system. […] It has made the Earth fundamentally different from its state before life and from its planetary neighbours, Mars and Venus. Through cycling the materials it needs, the Earth’s biosphere has bootstrapped itself into a much more productive state.”

“Each major element important for life has its own global biogeochemical cycle. However, every biogeochemical cycle can be conceptualized as a series of reservoirs (or ‘boxes’) of material connected by fluxes (or flows) of material between them. […] When a biogeochemical cycle is in steady state, the fluxes in and out of each reservoir must be in balance. This allows us to define additional useful quantities. Notably, the amount of material in a reservoir divided by the exchange flux with another reservoir gives the average ‘residence time’ of material in that reservoir with respect to the chosen process of exchange. For example, there are around 7 × 1016 moles of carbon dioxide (CO2) in today’s atmosphere, and photosynthesis removes around 9 × 1015 moles of CO2 per year, giving each molecule of CO2 a residence time of roughly eight years in the atmosphere before it is taken up, somewhere in the world, by photosynthesis. […] There are 3.8 × 1019 moles of molecular oxygen (O2) in today’s atmosphere, and oxidative weathering removes around 1 × 1013 moles of O2 per year, giving oxygen a residence time of around four million years with respect to removal by oxidative weathering. This makes the oxygen cycle […] a geological timescale cycle.”

“The water cycle is the physical circulation of water around the planet, between the ocean (where 97 per cent is stored), atmosphere, ice sheets, glaciers, sea-ice, freshwaters, and groundwater. […] To change the phase of water from solid to liquid or liquid to gas requires energy, which in the climate system comes from the Sun. Equally, when water condenses from gas to liquid or freezes from liquid to solid, energy is released. Solar heating drives evaporation from the ocean. This is responsible for supplying about 90 per cent of the water vapour to the atmosphere, with the other 10 per cent coming from evaporation on the land and freshwater surfaces (and sublimation of ice and snow directly to vapour). […] The water cycle is intimately connected to other biogeochemical cycles […]. Many compounds are soluble in water, and some react with water. This makes the ocean a key reservoir for several essential elements. It also means that rainwater can scavenge soluble gases and aerosols out of the atmosphere. When rainwater hits the land, the resulting solution can chemically weather rocks. Silicate weathering in turn helps keep the climate in a state where water is liquid.”

“In modern terms, plants acquire their carbon from carbon dioxide in the atmosphere, add electrons derived from water molecules to the carbon, and emit oxygen to the atmosphere as a waste product. […] In energy terms, global photosynthesis today captures about 130 terrawatts (1 TW = 1012 W) of solar energy in chemical form — about half of it in the ocean and about half on land. […] All the breakdown pathways for organic carbon together produce a flux of carbon dioxide back to the atmosphere that nearly balances photosynthetic uptake […] The surface recycling system is almost perfect, but a tiny fraction (about 0.1 per cent) of the organic carbon manufactured in photosynthesis escapes recycling and is buried in new sedimentary rocks. This organic carbon burial flux leaves an equivalent amount of oxygen gas behind in the atmosphere. Hence the burial of organic carbon represents the long-term source of oxygen to the atmosphere. […] the Earth’s crust has much more oxygen trapped in rocks in the form of oxidized iron and sulphur, than it has organic carbon. This tells us that there has been a net source of oxygen to the crust over Earth history, which must have come from the loss of hydrogen to space.”

“The oxygen cycle is relatively simple, because the reservoir of oxygen in the atmosphere is so massive that it dwarfs the reservoirs of organic carbon in vegetation, soils, and the ocean. Hence oxygen cannot get used up by the respiration or combustion of organic matter. Even the combustion of all known fossil fuel reserves can only put a small dent in the much larger reservoir of atmospheric oxygen (there are roughly 4 × 1017 moles of fossil fuel carbon, which is only about 1 per cent of the O2 reservoir). […] Unlike oxygen, the atmosphere is not the major surface reservoir of carbon. The amount of carbon in global vegetation is comparable to that in the atmosphere and the amount of carbon in soils (including permafrost) is roughly four times that in the atmosphere. Even these reservoirs are dwarfed by the ocean, which stores forty-five times as much carbon as the atmosphere, thanks to the fact that CO2 reacts with seawater. […] The exchange of carbon between the atmosphere and the land is largely biological, involving photosynthetic uptake and release by aerobic respiration (and, to a lesser extent, fires). […] Remarkably, when we look over Earth history there are fluctuations in the isotopic composition of carbonates, but no net drift up or down. This suggests that there has always been roughly one-fifth of carbon being buried in organic form and the other four-fifths as carbonate rocks. Thus, even on the early Earth, the biosphere was productive enough to support a healthy organic carbon burial flux.”

“The two most important nutrients for life are phosphorus and nitrogen, and they have very different biogeochemical cycles […] The largest reservoir of nitrogen is in the atmosphere, whereas the heavier phosphorus has no significant gaseous form. Phosphorus thus presents a greater recycling challenge for the biosphere. All phosphorus enters the surface Earth system from the chemical weathering of rocks on land […]. Phosphorus is concentrated in rocks in grains or veins of the mineral apatite. Natural selection has made plants on land and their fungal partners […] very effective at acquiring phosphorus from rocks, by manufacturing and secreting a range of organic acids that dissolve apatite. […] The average terrestrial ecosystem recycles phosphorus roughly fifty times before it is lost into freshwaters. […] The loss of phosphorus from the land is the ocean’s gain, providing the key input of this essential nutrient. Phosphorus is stored in the ocean as phosphate dissolved in the water. […] removal of phosphorus into the rock cycle balances the weathering of phosphorus from rocks on land. […] Although there is a large reservoir of nitrogen in the atmosphere, the molecules of nitrogen gas (N2) are extremely strongly bonded together, making nitrogen unavailable to most organisms. To split N2 and make nitrogen biologically available requires a remarkable biochemical feat — nitrogen fixation — which uses a lot of energy. In the ocean the dominant nitrogen fixers are cyanobacteria with a direct source of energy from sunlight. On land, various plants form a symbiotic partnership with nitrogen fixing bacteria, making a home for them in root nodules and supplying them with food in return for nitrogen. […] Nitrogen fixation and denitrification form the major input and output fluxes of nitrogen to both the land and the ocean, but there is also recycling of nitrogen within ecosystems. […] There is an intimate link between nutrient regulation and atmospheric oxygen regulation, because nutrient levels and marine productivity determine the source of oxygen via organic carbon burial. However, ocean nutrients are regulated on a much shorter timescale than atmospheric oxygen because their residence times are much shorter—about 2,000 years for nitrogen and 20,000 years for phosphorus.”

“[F]orests […] are vulnerable to increases in oxygen that increase the frequency and ferocity of fires. […] Combustion experiments show that fires only become self-sustaining in natural fuels when oxygen reaches around 17 per cent of the atmosphere. Yet for the last 370 million years there is a nearly continuous record of fossil charcoal, indicating that oxygen has never dropped below this level. At the same time, oxygen has never risen too high for fires to have prevented the slow regeneration of forests. The ease of combustion increases non-linearly with oxygen concentration, such that above 25–30 per cent oxygen (depending on the wetness of fuel) it is hard to see how forests could have survived. Thus oxygen has remained within 17–30 per cent of the atmosphere for at least the last 370 million years.”

“[T]he rate of silicate weathering increases with increasing CO2 and temperature. Thus, if something tends to increase CO2 or temperature it is counteracted by increased CO2 removal by silicate weathering. […] Plants are sensitive to variations in CO2 and temperature, and together with their fungal partners they greatly amplify weathering rates […] the most pronounced change in atmospheric CO2 over Phanerozoic time was due to plants colonizing the land. This started around 470 million years ago and escalated with the first forests 370 million years ago. The resulting acceleration of silicate weathering is estimated to have lowered the concentration of atmospheric CO2 by an order of magnitude […], and cooled the planet into a series of ice ages in the Carboniferous and Permian Periods.”

“The first photosynthesis was not the kind we are familiar with, which splits water and spits out oxygen as a waste product. Instead, early photosynthesis was ‘anoxygenic’ — meaning it didn’t produce oxygen. […] It could have used a range of compounds, in place of water, as a source of electrons with which to fix carbon from carbon dioxide and reduce it to sugars. Potential electron donors include hydrogen (H2) and hydrogen sulphide (H2S) in the atmosphere, or ferrous iron (Fe2+) dissolved in the ancient oceans. All of these are easier to extract electrons from than water. Hence they require fewer photons of sunlight and simpler photosynthetic machinery. The phylogenetic tree of life confirms that several forms of anoxygenic photosynthesis evolved very early on, long before oxygenic photosynthesis. […] If the early biosphere was fuelled by anoxygenic photosynthesis, plausibly based on hydrogen gas, then a key recycling process would have been the biological regeneration of this gas. Calculations suggest that once such recycling had evolved, the early biosphere might have achieved a global productivity up to 1 per cent of the modern marine biosphere. If early anoxygenic photosynthesis used the supply of reduced iron upwelling in the ocean, then its productivity would have been controlled by ocean circulation and might have reached 10 per cent of the modern marine biosphere. […] The innovation that supercharged the early biosphere was the origin of oxygenic photosynthesis using abundant water as an electron donor. This was not an easy process to evolve. To split water requires more energy — i.e. more high-energy photons of sunlight — than any of the earlier anoxygenic forms of photosynthesis. Evolution’s solution was to wire together two existing ‘photosystems’ in one cell and bolt on the front of them a remarkable piece of biochemical machinery that can rip apart water molecules. The result was the first cyanobacterial cell — the ancestor of all organisms performing oxygenic photosynthesis on the planet today. […] Once oxygenic photosynthesis had evolved, the productivity of the biosphere would no longer have been restricted by the supply of substrates for photosynthesis, as water and carbon dioxide were abundant. Instead, the availability of nutrients, notably nitrogen and phosphorus, would have become the major limiting factors on the productivity of the biosphere — as they still are today.” [If you’re curious to know more about how that fascinating ‘biochemical machinery’ works, this is a great book on these and related topics – US].

“On Earth, anoxygenic photosynthesis requires one photon per electron, whereas oxygenic photosynthesis requires two photons per electron. On Earth it took up to a billion years to evolve oxygenic photosynthesis, based on two photosystems that had already evolved independently in different types of anoxygenic photosynthesis. Around a fainter K- or M-type star […] oxygenic photosynthesis is estimated to require three or more photons per electron — and a corresponding number of photosystems — making it harder to evolve. […] However, fainter stars spend longer on the main sequence, giving more time for evolution to occur.”

“There was a lot more energy to go around in the post-oxidation world, because respiration of organic matter with oxygen yields an order of magnitude more energy than breaking food down anaerobically. […] The revolution in biological complexity culminated in the ‘Cambrian Explosion’ of animal diversity 540 to 515 million years ago, in which modern food webs were established in the ocean. […] Since then the most fundamental change in the Earth system has been the rise of plants on land […], beginning around 470 million years ago and culminating in the first global forests by 370 million years ago. This doubled global photosynthesis, increasing flows of materials. Accelerated chemical weathering of the land surface lowered atmospheric carbon dioxide levels and increased atmospheric oxygen levels, fully oxygenating the deep ocean. […] Although grasslands now cover about a third of the Earth’s productive land surface they are a geologically recent arrival. Grasses evolved amidst a trend of declining atmospheric carbon dioxide, and climate cooling and drying, over the past forty million years, and they only became widespread in two phases during the Miocene Epoch around seventeen and six million years ago. […] Since the rise of complex life, there have been several mass extinction events. […] whilst these rolls of the extinction dice marked profound changes in evolutionary winners and losers, they did not fundamentally alter the operation of the Earth system.” [If you’re interested in this kind of stuff, the evolution of food webs and so on, Herrera et al.’s wonderful book is a great place to start – US]

“The Industrial Revolution marks the transition from societies fuelled largely by recent solar energy (via biomass, water, and wind) to ones fuelled by concentrated ‘ancient sunlight’. Although coal had been used in small amounts for millennia, for example for iron making in ancient China, fossil fuel use only took off with the invention and refinement of the steam engine. […] With the Industrial Revolution, food and biomass have ceased to be the main source of energy for human societies. Instead the energy contained in annual food production, which supports today’s population, is at fifty exajoules (1 EJ = 1018 joules), only about a tenth of the total energy input to human societies of 500 EJ/yr. This in turn is equivalent to about a tenth of the energy captured globally by photosynthesis. […] solar energy is not very efficiently converted by photosynthesis, which is 1–2 per cent efficient at best. […] The amount of sunlight reaching the Earth’s land surface (2.5 × 1016 W) dwarfs current total human power consumption (1.5 × 1013 W) by more than a factor of a thousand.”

“The Earth system’s primary energy source is sunlight, which the biosphere converts and stores as chemical energy. The energy-capture devices — photosynthesizing organisms — construct themselves out of carbon dioxide, nutrients, and a host of trace elements taken up from their surroundings. Inputs of these elements and compounds from the solid Earth system to the surface Earth system are modest. Some photosynthesizers have evolved to increase the inputs of the materials they need — for example, by fixing nitrogen from the atmosphere and selectively weathering phosphorus out of rocks. Even more importantly, other heterotrophic organisms have evolved that recycle the materials that the photosynthesizers need (often as a by-product of consuming some of the chemical energy originally captured in photosynthesis). This extraordinary recycling system is the primary mechanism by which the biosphere maintains a high level of energy capture (productivity).”

“[L]ike all stars on the ‘main sequence’ (which generate energy through the nuclear fusion of hydrogen into helium), the Sun is burning inexorably brighter with time — roughly 1 per cent brighter every 100 million years — and eventually this will overheat the planet. […] Over Earth history, the silicate weathering negative feedback mechanism has counteracted the steady brightening of the Sun by removing carbon dioxide from the atmosphere. However, this cooling mechanism is near the limits of its operation, because CO2 has fallen to limiting levels for the majority of plants, which are key amplifiers of silicate weathering. Although a subset of plants have evolved which can photosynthesize down to lower CO2 levels [the author does not go further into this topic, but here’s a relevant link – US], they cannot draw CO2 down lower than about 10 ppm. This means there is a second possible fate for life — running out of CO2. Early models projected either CO2 starvation or overheating […] occurring about a billion years in the future. […] Whilst this sounds comfortingly distant, it represents a much shorter future lifespan for the Earth’s biosphere than its past history. Earth’s biosphere is entering its old age.”

September 28, 2017 Posted by | Astronomy, Biology, Books, Botany, Chemistry, Geology, Paleontology, Physics | Leave a comment

The Biology of Moral Systems (III)

This will be my last post about the book. It’s an important work which deserves to be read by far more people than have already read it. I have added some quotes and observations from the last chapters of the book below.

“If egoism, as self-interest in the biologists’ sense, is the reason for the promotion of ethical behavior, then, paradoxically, it is expected that everyone will constantly promote the notion that egoism is not a suitable theory of action, and, a fortiori, that he himself is not an egoist. Most of all he must present this appearance to his closest associates because it is in his best interests to do so – except, perhaps, to his closest relatives, to whom his egoism may often be displayed in cooperative ventures from which some distant- or non-relative suffers. Indeed, it may be arguable that it will be in the egoist’s best interest not to know (consciously) or to admit to himself that he is an egoist because of the value to himself of being able to convince others he is not.”

“The function of [societal] punishments and rewards, I have suggested, is to manipulate the behavior of participating individuals, restricting individual efforts to serve their own interests at others’ expense so as to promote harmony and unity within the group. The function of harmony and unity […] is to allow the group to compete against hostile forces, especially other human groups. It is apparent that success of the group may serve the interests of all individuals in the group; but it is also apparent that group success can be achieved with different patterns of individual success differentials within the group. So […] it is in the interests of those who are differentially successful to promote both unity and the rules so that group success will occur without necessitating changes deleterious to them. Similarly, it may be in the interests of those individuals who are relatively unsuccessful to promote dissatisfaction with existing rules and the notion that group success would be more likely if the rules were altered to favor them. […] the rules of morality and law alike seem not to be designed explicitly to allow people to live in harmony within societies but to enable societies to be sufficiently united to deter their enemies. Within-society harmony is the means not the end. […] extreme within-group altruism seems to correlate with and be historically related to between-group strife.”

“There are often few or no legitimate or rational expectations of reciprocity or “fairness” between social groups (especially warring or competing groups such as tribes or nations). Perhaps partly as a consequence, lying, deceit, or otherwise nasty or even heinous acts committed against enemies may sometimes not be regarded as immoral by others withing the group of those who commit them. They may even be regarded as highly moral if they seem dramatically to serve the interests of the group whose members commit them.”

“Two major assumptions, made universally or most of the time by philosophers, […] are responsible for the confusion that prevents philosophers from making sense out of morality […]. These assumptions are the following: 1. That proximate and ultimate mechanisms or causes have the same kind of significance and can be considered together as if they were members of the same class of causes; this is a failure to understand that proximate causes are evolved because of ultimate causes, and therefore may be expected to serve them, while the reverse is not true. Thus, pleasure is a proximate mechanism that in the usual environments of history is expected to impel us toward behavior that will contribute to our reproductive success. Contrarily, acts leading to reproductive success are not proximate mechanisms that evolved because they served the ultimate function of bringing us pleasure. 2. That morality inevitably involves some self-sacrifice. This assumption involves at least three elements: a. Failure to consider altruism as benefits to the actor. […] b. Failure to comprehend all avenues of indirect reciprocity within groups. c. Failure to take into account both within-group and between-group benefits.”

“If morality means true sacrifice of one’s own interests, and those of his family, then it seems to me that we could not have evolved to be moral. If morality requires ethical consistency, whereby one does not do socially what he would not advocate and assist all others also to do, then, again, it seems to me that we could not have evolved to be moral. […] humans are not really moral at all, in the sense of “true sacrifice” given above, but […] the concept of morality is useful to them. […] If it is so, then we might imagine that, in the sense and to the extent that they are anthropomorphized, the concepts of saints and angels, as well as that of God, were also created because of their usefulness to us. […] I think there have been far fewer […] truly self-sacrificing individuals than might be supposed, and most cases that might be brought forward are likely instead to be illustrations of the complexity and indirectness of reciprocity, especially the social value of appearing more altruistic than one is. […] I think that […] the concept of God must be viewed as originally generated and maintained for the purpose – now seen by many as immoral – of furthering the interests of one group of humans at the expense of one or more other groups. […] Gods are inventions originally developed to extend the notion that some have greater rights than others to design and enforce rules, and that some are more destined to be leaders, others to be followers. This notion, in turn, arose out of prior asymmetries in both power and judgment […] It works when (because) leaders are (have been) valuable, especially in the context of intergroup competition.”

“We try to move moral issues in the direction of involving no conflict of interest, always, I suggest, by seeking universal agreement with our own point of view.”

“Moral and legal systems are commonly distinguished by those, like moral philosophers, who study them formally. I believe, however, that the distinction between them is usually poorly drawn, and based on a failure to realize that moral as well as legal behavior occurs as a result of probably and possible punishments and reward. […] we often internalize the rules of law as well as the rules of morality – and perhaps by the same process […] It would seem that the rules of law are simply a specialized, derived aspect of what in earlier societies would have been a part of moral rules. On the other hand, law covers only a fraction of the situations in which morality is involved […] Law […] seems to be little more than ethics written down.”

“Anyone who reads the literature on dispute settlement within different societies […] will quickly understand that genetic relatedness counts: it allows for one-way flows of benefits and alliances. Long-term association also counts; it allows for reliability and also correlates with genetic relatedness. […] The larger the social group, the more fluid its membership; and the more attenuated the social interactions of its membership, the more they are forced to rely on formal law”.

“[I]ndividuals have separate interests. They join forces (live in groups; become social) when they share certain interests that can be better realized for all by close proximity or some forms of cooperation. Typically, however, the overlaps of interests rarely are completely congruent with those of either other individuals or the rest of the group. This means that, even during those times when individual interests within a group are most broadly overlapping, we may expect individuals to temper their cooperation with efforts to realize their own interests, and we may also expect them to have evolved to be adept at using others, or at thwarting the interests of others, to serve themselves (and their relatives). […] When the interests of all are most nearly congruent, it is essentially always due to a threat shared equally. Such threats almost always have to be external (or else they are less likely to affect everyone equally […] External threats to societies are typically other societies. Maintenance of such threats can yield situations in which everyone benefits from rigid, hierarchical, quasi-military, despotic government. Liberties afforded leaders – even elaborate perquisites of dictators – may be tolerated because such threats are ever-present […] Extrinsic threats, and the governments they produce, can yield inflexibilities of political structures that can persist across even lengthy intervals during which the threats are absent. Some societies have been able to structure their defenses against external threats as separate units (armies) within society, and to keep them separate. These rigidly hierarchical, totalitarian, and dictatorial subunits rise and fall in size and influence according to the importance of the external threat. […] Discussion of liberty and equality in democracies closely parallels discussions of morality and moral systems. In either case, adding a perspective from evolutionary biology seems to me to have potential for clarification.”

“It is indeed common, if not universal, to regard moral behavior as a kind of altruism that necessarily yields the altruist less than he gives, and to see egoism as either the opposite of morality or the source of immorality; but […] this view is usually based on an incomplete understanding of nepotism, reciprocity, and the significance of within-group unity for between-group competition. […] My view of moral systems in the real world, however, is that they are systems in which costs and benefits of specific actions are manipulated so as to produce reasonably harmonious associations in which everyone nevertheless pursues his own (in evolutionary terms) self-interest. I do not expect that moral and ethical arguments can ever be finally resolved. Compromises and contracts, then, are (at least currently) the only real solutions to actual conflicts of interest. This is why moral and ethical decisions must arise out of decisions of the collective of affected individuals; there is no single source of right and wrong.

I would also argue against the notion that rationality can be easily employed to produce a world of humans that self-sacrifice in favor of other humans, not to say nonhuman animals, plants, and inanimate objects. Declarations of such intentions may themselves often be the acts of self-interested persons developing, consciously or not, a socially self-benefiting view of themselves as extreme altruists. In this connection it is not irrelevant that the more dissimilar a species or object is to one’s self the less likely it is to provide a competitive threat by seeking the same resources. Accordingly, we should not be surprised to find humans who are highly benevolent toward other species or inanimate objects (some of which may serve them uncomplainingly), yet relatively hostile and noncooperative with fellow humans. As Darwin (1871) noted with respect to dogs, we have selected our domestic animals to return our altruism with interest.”

“It is not easy to discover precisely what historical differences have shaped current male-female differences. If, however, humans are in a general way similar to other highly parental organisms that live in social groups […] then we can hypothesize as follows: for men much of sexual activity has had as a main (ultimate) significance the initiating of pregnancies. It would follow that when a man avoids copulation it is likely to be because (1) there is no likelihood of pregnancy or (2) the costs entailed (venereal disease, danger from competition with other males, lowered status if the event becomes public, or an undesirable commitment) are too great in comparison with the probability that pregnancy will be induced. The man himself may be judging costs against the benefits of immediate sensory pleasures, such as orgasms (i.e., rather than thinking about pregnancy he may say that he was simply uninterested), but I am assuming that selection has tuned such expectations in terms of their probability of leading to actual reproduction […]. For women, I hypothesize, sexual activity per se has been more concerned with the securing of resources (again, I am speaking of ultimate and not necessarily conscious concerns) […]. Ordinarily, when women avoid or resist copulation, I speculate further, the disinterest, aversion, or inhibition may be traceable eventually to one (or more) of three causes: (1) there is no promise of commitment (of resources), (2) there is a likelihood of undesirable commitment (e.g., to a man with inadequate resources), or (3) there is a risk of loss of interest by a man with greater resources, than the one involved […] A man behaving so as to avoid pregnancies, and who derives from an evolutionary background of avoiding pregnancies, should be expected to favor copulation with women who are for age or other reasons incapable of pregnancy. A man derived from an evolutionary process in which securing of pregnancies typically was favored, may be expected to be most interested sexually in women most likely to become pregnant and near the height of the reproductive probability curve […] This means that men should usually be expected to anticipate the greatest sexual pleasure with young, healthy, intelligent women who show promise of providing superior parental care. […] In sexual competition, the alternatives of a man without resources are to present himself as a resource (i.e., as a mimic of one with resources or as one able and likely to secure resources because of his personal attributes […]), to obtain sex by force (rape), or to secure resources through a woman (e.g., allow himself to be kept by a relatively undesired woman, perhaps as a vehicle to secure liaisons with other women). […] in nonhuman species of higher animals, control of the essential resources of parenthood by females correlates with lack of parental behavior by males, promiscuous polygyny, and absence of long-term pair bonds. There is some evidence of parallel trends within human societies (cf. Flinn, 1981).” [It’s of some note that quite a few good books have been written on these topics since Alexander first published his book, so there are many places to look for detailed coverage of topics like these if you’re curious to know more – I can recommend both Kappeler & van Schaik (a must-read book on sexual selection, in my opinion) & Bobby Low. I didn’t think too highly of Miller or Meston & Buss, but those are a few other books on these topics which I’ve read – US].

“The reason that evolutionary knowledge has no moral content is [that] morality is a matter of whose interests one should, by conscious and willful behavior, serve, and how much; evolutionary knowledge contains no messages on this issue. The most it can do is provide information about the reasons for current conditions and predict some consequences of alternative courses of action. […] If some biologists and nonbiologists make unfounded assertions into conclusions, or develop pernicious and fallible arguments, then those assertions and arguments should be exposed for what they are. The reason for doing this, however, is not […should not be..? – US] to prevent or discourage any and all analyses of human activities, but to enable us to get on with a proper sort of analysis. Those who malign without being specific; who attack people rather than ideas; who gratuitously translate hypotheses into conclusions and then refer to them as “explanations,” “stories,” or “just-so-stories”; who parade the worst examples of argument and investigation with the apparent purpose of making all efforts at human self-analysis seem silly and trivial, I see as dangerously close to being ideologues at least as worrisome as those they malign. I cannot avoid the impression that their purpose is not to enlighten, but to play upon the uneasiness of those for whom the approach of evolutionary biology is alien and disquieting, perhaps for political rather than scientific purposes. It is more than a little ironic that the argument of politics rather than science is their own chief accusation with respect to scientists seeking to analyze human behavior in evolutionary terms (e.g. Gould and Levontin, 1979 […]).”

“[C]urrent selective theory indicates that natural selection has never operated to prevent species extinction. Instead it operates by saving the genetic materials of those individuals or families that outreproduce others. Whether species become extinct or not (and most have) is an incidental or accidental effect of natural selection. An inference from this is that the members of no species are equipped, as a direct result of their evolutionary history, with traits designed explicitly to prevent extinction when that possibility looms. […] Humans are no exception: unless their comprehension of the likelihood of extinction is so clear and real that they perceive the threat to themselves as individuals, and to their loved ones, they cannot be expected to take the collective action that will be necessary to reduce the risk of extinction.”

“In examining ourselves […] we are forced to use the attributes we wish to analyze to carry out the analysis, while resisting certain aspects of the analysis. At the very same time, we pretend that we are not resisting at all but are instead giving perfectly legitimate objections; and we use our realization that others will resist the analysis, for reasons as arcane as our own, to enlist their support in our resistance. And they very likely will give it. […] If arguments such as those made here have any validity it follows that a problem faced by everyone, in respect to morality, is that of discovering how to subvert or reduce some aspects of individual selfishness that evidently derive from our history of genetic individuality.”

“Essentially everyone thinks of himself as well-meaning, but from my viewpoint a society of well-meaning people who understand themselves and their history very well is a better milieu than a society of well-meaning people who do not.”

September 22, 2017 Posted by | Anthropology, Biology, Books, Evolutionary biology, Genetics, Philosophy, Psychology, Religion | Leave a comment

How Species Interact

There are multiple reasons why I have not covered Arditi and Ginzburg’s book before, but none of them are related to the quality of the book’s coverage. It’s a really nice book. However the coverage is somewhat technical and model-focused, which makes it harder to blog than other kinds of books. Also, the version of the book I read was a hardcover ‘paper book’ version, and ‘paper books’ take a lot more work for me to cover than do e-books.

I should probably get it out of the way here at the start of the post that if you’re interested in ecology, predator-prey dynamics, etc., this book is a book you would be well advised to read; or, if you don’t read the book, you should at least familiarize yourself with the ideas therein e.g. through having a look at some of Arditi & Ginzburg’s articles on these topics. I should however note that I don’t actually think skipping the book and having a look at some articles instead will necessarily be a labour-saving strategy; the book is not particularly long and it’s to the point, so although it’s not a particularly easy read their case for ratio dependence is actually somewhat easy to follow – if you take the effort – in the sense that I believe how different related ideas and observations are linked is quite likely better expounded upon in the book than they might have been in their articles. The presumably wrote the book precisely in order to provide a concise yet coherent overview.

I have had some trouble figuring out how to cover this book, and I’m still not quite sure what might be/have been the best approach; when covering technical books I’ll often skip a lot of detail and math and try to stick to what might be termed ‘the main ideas’ when quoting from such books, but there’s a clear limit as to how many of the technical details included in a book like this it is possible to skip if you still want to actually talk about the stuff covered in the work, and this sometimes make blogging such books awkward. These authors spend a lot of effort talking about how different ecological models work and which sort of conclusions these different models may lead to in different contexts, and this kind of stuff is a very big part of the book. I’m not sure if you strictly need to have read an ecology textbook or two before you read this one in order to be able to follow the coverage, but I know that I personally derived some benefit from having read Gurney & Nisbet’s ecology text in the past and I did look up stuff in that book a few times along the way, e.g. when reminding myself what a Holling type 2 functional response is and how models with such a functional response pattern behave. ‘In theory’ I assume one might argue that you could theoretically look up all the relevant concepts along the way without any background knowledge of ecology – assuming you have a decent understanding of basic calculus/differential equations, linear algebra, equilibrium dynamics, etc. (…systems analysis? It’s hard for me to know and outline exactly which sources I’ve read in the past which helped make this book easier to read than it otherwise would have been, but suffice it to say that if you look at the page count and think that this will be an quick/easy read, it will be that only if you’ve read more than a few books on ‘related topics’, broadly defined, in the past), but I wouldn’t advise reading the book if all you know is high school math – the book will be incomprehensible to you, and you won’t make it. I ended up concluding that it would simply be too much work to try to make this post ‘easy’ to read for people who are unfamiliar with these topics and have not read the book, so although I’ve hardly gone out of my way to make the coverage hard to follow, the blog coverage that is to follow is mainly for my own benefit.

First a few relevant links, then some quotes and comments.

Lotka–Volterra equations.
Ecosystem model.
Arditi–Ginzburg equations. (Yep, these equations are named after the authors of this book).
Nicholson–Bailey model.
Functional response.
Monod equation.
Rosenzweig-MacArthur predator-prey model.
Trophic cascade.
Underestimation of mutual interference of predators.
Coupling in predator-prey dynamics: Ratio Dependence.
Michaelis–Menten kinetics.
Trophic level.
Advection–diffusion equation.
Paradox of enrichment. [Two quotes from the book: “actual systems do not behave as Rosensweig’s model predict” + “When ecologists have looked for evidence of the paradox of enrichment in natural and laboratory systems, they often find none and typically present arguments about why it was not observed”]
Predator interference emerging from trophotaxis in predator–prey systems: An individual-based approach.
Directed movement of predators and the emergence of density dependence in predator-prey models.

“Ratio-dependent predation is now covered in major textbooks as an alternative to the standard prey-dependent view […]. One of this book’s messages is that the two simple extreme theories, prey dependence and ratio dependence, are not the only alternatives: they are the ends of a spectrum. There are ecological domains in which one view works better than the other, with an intermediate view also being a possible case. […] Our years of work spent on the subject have led us to the conclusion that, although prey dependence might conceivably be obtained in laboratory settings, the common case occurring in nature lies close to the ratio-dependent end. We believe that the latter, instead of the prey-dependent end, can be viewed as the “null model of predation.” […] we propose the gradual interference model, a specific form of predator-dependent functional response that is approximately prey dependent (as in the standard theory) at low consumer abundances and approximately ratio dependent at high abundances. […] When density is low, consumers do not interfere and prey dependence works (as in the standard theory). When consumers density is sufficiently high, interference causes ratio dependence to emerge. In the intermediate densities, predator-dependent models describe partial interference.”

“Studies of food chains are on the edge of two domains of ecology: population and community ecology. The properties of food chains are determined by the nature of their basic link, the interaction of two species, a consumer and its resource, a predator and its prey.1 The study of this basic link of the chain is part of population ecology while the more complex food webs belong to community ecology. This is one of the main reasons why understanding the dynamics of predation is important for many ecologists working at different scales.”

“We have named predator-dependent the functional responses of the form g = g(N,P), where the predator density P acts (in addition to N [prey abundance, US]) as an independent variable to determine the per capita kill rate […] predator-dependent functional response models have one more parameter than the prey-dependent or the ratio-dependent models. […] The main interest that we see in these intermediate models is that the additional parameter can provide a way to quantify the position of a specific predator-prey pair of species along a spectrum with prey dependence at one end and ratio dependence at the other end:

g(N) <- g(N,P) -> g(N/P) (1.21)

In the Hassell-Varley and Arditi-Akçakaya models […] the mutual interference parameter m plays the role of a cursor along this spectrum, from m = 0 for prey dependence to m = 1 for ratio dependence. Note that this theory does not exclude that strong interference goes “beyond ratio dependence,” with m > 1.2 This is also called overcompensation. […] In this book, rather than being interested in the interference parameters per se, we use predator-dependent models to determine, either parametrically or nonparametrically, which of the ends of the spectrum (1.21) better describes predator-prey systems in general.”

“[T]he fundamental problem of the Lotka-Volterra and the Rosensweig-MacArthur dynamic models lies in the functional response and in the fact that this mathematical function is assumed not to depend on consumer density. Since this function measures the number of prey captured per consumer per unit time, it is a quantity that should be accessible to observation. This variable could be apprehended either on the fast behavioral time scale or on the slow demographic time scale. These two approaches need not necessarily reveal the same properties: […] a given species could display a prey-dependent response on the fast scale and a predator-dependent response on the slow scale. The reason is that, on a very short scale, each predator individually may “feel” virtually alone in the environment and react only to the prey that it encounters. On the long scale, the predators are more likely to be affected by the presence of conspecifics, even without direct encounters. In the demographic context of this book, it is the long time scale that is relevant. […] if predator dependence is detected on the fast scale, then it can be inferred that it must be present on the slow scale; if predator dependence is not detected on the fast scale, it cannot be inferred that it is absent on the slow scale.”

Some related thoughts. A different way to think about this – which they don’t mention in the book, but which sprang to mind to me as I was reading it – is to think about this stuff in terms of a formal predator territorial overlap model and then asking yourself this question: Assume there’s zero territorial overlap – does this fact mean that the existence of conspecifics does not matter? The answer is of course no. The sizes of the individual patches/territories may be greatly influenced by the predator density even in such a context. Also, the territorial area available to potential offspring (certainly a fitness-relevant parameter) may be greatly influenced by the number of competitors inhabiting the surrounding territories. In relation to the last part of the quote it’s easy to see that in a model with significant territorial overlap you don’t need direct behavioural interaction among predators for the overlap to be relevant; even if two bears never meet, if one of them eats a fawn the other one would have come across two days later, well, such indirect influences may be important for prey availability. Of course as prey tend to be mobile, even if predator territories are static and non-overlapping in a geographic sense, they might not be in a functional sense. Moving on…

“In [chapter 2 we] attempted to assess the presence and the intensity of interference in all functional response data sets that we could gather in the literature. Each set must be trivariate, with estimates of the prey consumed at different values of prey density and different values of predator densities. Such data sets are not very abundant because most functional response experiments present in the literature are simply bivariate, with variations of the prey density only, often with a single predator individual, ignoring the fact that predator density can have an influence. This results from the usual presentation of functional responses in textbooks, which […] focus only on the influence of prey density.
Among the data sets that we analyzed, we did not find a single one in which the predator density did not have a significant effect. This is a powerful empirical argument against prey dependence. Most systems lie somewhere on the continuum between prey dependence (m=0) and ratio dependence (m=1). However, they do not appear to be equally distributed. The empirical evidence provided in this chapter suggests that they tend to accumulate closer to the ratio-dependent end than to the prey-dependent end.”

“Equilibrium properties result from the balanced predator-prey equations and contain elements of the underlying dynamic model. For this reason, the response of equilibria to a change in model parameters can inform us about the structure of the underlying equations. To check the appropriateness of the ratio-dependent versus prey-dependent views, we consider the theoretical equilibrium consequences of the two contrasting assumptions and compare them with the evidence from nature. […] According to the standard prey-dependent theory, in reference to [an] increase in primary production, the responses of the populations strongly depend on their level and on the total number of trophic levels. The last, top level always responds proportionally to F [primary input]. The next to the last level always remains constant: it is insensitive to enrichment at the bottom because it is perfectly controled [sic] by the last level. The first, primary producer level increases if the chain length has an odd number of levels, but declines (or stays constant with a Lotka-Volterra model) in the case of an even number of levels. According to the ratio-dependent theory, all levels increase proportionally, independently of how many levels are present. The present purpose of this chapter is to show that the second alternative is confirmed by natural data and that the strange predictions of the prey-dependent theory are unsupported.”

“If top predators are eliminated or reduced in abundance, models predict that the sequential lower trophic levels must respond by changes of alternating signs. For example, in a three-level system of plants-herbivores-predators, the reduction of predators leads to the increase of herbivores and the consequential reduction in plant abundance. This response is commonly called the trophic cascade. In a four-level system, the bottom level will increase in response to harvesting at the top. These predicted responses are quite intuitive and are, in fact, true for both short-term and long-term responses, irrespective of the theory one employs. […] A number of excellent reviews have summarized and meta-analyzed large amounts of data on trophic cascades in food chains […] In general, the cascading reaction is strongest in lakes, followed by marine systems, and weakest in terrestrial systems. […] Any theory that claims to describe the trophic chain equilibria has to produce such cascading when top predators are reduced or eliminated. It is well known that the standard prey-dependent theory supports this view of top-down cascading. It is not widely appreciated that top-down cascading is likewise a property of ratio-dependent trophic chains. […] It is [only] for equilibrial responses to enrichment at the bottom that predictions are strikingly different according to the two theories”.

As the book does spend a little time on this I should perhaps briefly interject here that the above paragraph should not be taken to indicate that the two types of models provide identical predictions in the top-down cascading context in all cases; both predict cascading, but there are even so some subtle differences between the models here as well. Some of these differences are however quite hard to test.

“[T]he traditional Lotka-Volterra interaction term […] is nothing other than the law of mass action of chemistry. It assumes that predator and prey individuals encounter each other randomly in the same way that molecules interact in a chemical solution. Other prey-dependent models, like Holling’s, derive from the same idea. […] an ecological system can only be described by such a model if conspecifics do not interfere with each other and if the system is sufficiently homogeneous […] we will demonstrate that spatial heterogeneity, be it in the form of a prey refuge or in the form of predator clusters, leads to emergence of gradual interference or of ratio dependence when the functional response is observed at the population level. […] We present two mechanistic individual-based models that illustrate how, with gradually increasing predator density and gradually increasing predator clustering, interference can become gradually stronger. Thus, a given biological system, prey dependent at low predator density, can gradually become ratio dependent at high predator density. […] ratio dependence is a simple way of summarizing the effects induced by spatial heterogeneity, while the prey dependent [models] (e.g., Lotka-Volterra) is more appropriate in homogeneous environments.”

“[W]e consider that a good model of interacting species must be fundamentally invariant to a proportional change of all abundances in the system. […] Allowing interacting populations to expand in balanced exponential growth makes the laws of ecology invariant with respect to multiplying interacting abundances by the same constant, so that only ratios matter. […] scaling invariance is required if we wish to preserve the possibility of joint exponential growth of an interacting pair. […] a ratio-dependent model allows for joint exponential growth. […] Neither the standard prey-dependent models nor the more general predator-dependent models allow for balanced growth. […] In our view, communities must be expected to expand exponentially in the presence of unlimited resources. Of course, limiting factors ultimately stop this expansion just as they do for a single species. With our view, it is the limiting resources that stop the joint expansion of the interacting populations; it is not directly due to the interactions themselves. This partitioning of the causes is a major simplification that traditional theory implies only in the case of a single species.”

August 1, 2017 Posted by | Biology, Books, Chemistry, Ecology, Mathematics, Studies | Leave a comment

Melanoma therapeutic strategies that select against resistance

A short lecture, but interesting:

If you’re not an oncologist, these two links in particular might be helpful to have a look at before you start out: BRAF (gene) & Myc. A very substantial proportion of the talk is devoted to math and stats methodology (which some people will find interesting and others …will not).

July 3, 2017 Posted by | Biology, Cancer/oncology, Genetics, Lectures, Mathematics, Medicine, Statistics | Leave a comment

The Antarctic

“A very poor book with poor coverage, mostly about politics and history (and a long collection of names of treaties and organizations). I would definitely not have finished it if it were much longer than it is.”

That was what I wrote about the book in my goodreads review. I was strongly debating whether or not to blog it at all, but I decided in the end to just settle for some very lazy coverage of the book, only consisting of links to content covered in the book. I only cover the book here to at least have some chance of remembering which kinds of things were covered in the book later on.

If you’re interested enough in the Antarctic to read a book about it, read Scott’s Last Expedition instead of this one (here’s my goodreads review of Scott).

Links:

Antarctica (featured).
Antarctic Convergence.
Antarctic Circle.
Southern Ocean.
Antarctic Circumpolar Current.
West Antarctic Ice Sheet.
East Antarctic Ice Sheet.
McMurdo Dry Valleys.
Notothenioidei.
Patagonian toothfish.
Antarctic krill.
Fabian Gottlieb von Bellingshausen.
Edward Bransfield.
James Clark Ross.
United States Exploring Expedition.
Heroic Age of Antarctic Exploration (featured).
Nimrod Expedition (featured).
Roald Amundsen.
Wilhelm Filchner.
Japanese Antarctic Expedition.
Terra Nova Expedition (featured).
Lincoln Ellsworth.
British Graham Land expedition.
German Antarctic Expedition (1938–1939).
Operation Highjump.
Operation Windmill.
Operation Deep Freeze.
Commonwealth Trans-Antarctic Expedition.
Caroline Mikkelsen.
International Association of Antarctica Tour Operators.
Territorial claims in Antarctica.
International Geophysical Year.
Antarctic Treaty System.
Operation Tabarin.
Scientific Committee on Antarctic Research.
United Nations Convention on the Law of the Sea.
Convention on the Continental Shelf.
Council of Managers of National Antarctic Programs.
British Antarctic Survey.
International Polar Year.
Antarctic ozone hole.
Gamburtsev Mountain Range.
Pine Island Glacier (‘good article’).
Census of Antarctic Marine Life.
Lake Ellsworth Consortium.
Antarctic fur seal.
Southern elephant seal.
Grytviken (whaling-related).
International Convention for the Regulation of Whaling.
International Whaling Commission.
Ocean Drilling Program.
Convention on the Regulation of Antarctic Mineral Resource Activities.
Agreement on the Conservation of Albatrosses and Petrels.

July 3, 2017 Posted by | Biology, Books, Geography, Geology, History, Wikipedia | Leave a comment

The Biology of Moral Systems (II)

There are multiple really great books I have read ‘recently’ and which I have either not blogged at all, or not blogged in anywhere near the amount of detail they deserve; Alexander’s book is one of those books. I hope to get rid of some of the backlog soon. You can read my first post about the book here, and it might be a good idea to do so as I won’t allude to material covered in the first post here. In this post I have added some quotes from and comments related to the book’s second chapter, ‘A Biological View of Morality’.

“Moral systems are systems of indirect reciprocity. They exist because confluences of interest within groups are used to deal with conflicts of interest between groups. Indirect reciprocity develops because interactions are repeated, or flow among a society’s members, and because information about subsequent interactions can be gleaned from observing the reciprocal interactions of others.
To establish moral rules is to impose rewards and punishments (typically assistance and ostracism, respectively) to control social acts that, respectively, help or hurt others. To be regarded as moral, a rule typically must represent widespread opinion, reflecting the fact that it must apply with a certain degree of indiscrimininateness.”

“Moral philosophers have not treated the beneficence of humans as a part, somehow, of their selfishness; yet, as Trivers (1971) suggested, the biologist’s view of lifetimes leads directly to this argument. In other words, the normally expressed beneficence, or altruism, of parenthood and nepotism and the temporary altruism (or social investment) of reciprocity are expected to result in greater returns than their alternatives.
If biologists are correct, all that philosophers refer to as altruistic or utilitarian behavior by individuals will actually represent either the temporary altruism (phenotypic beneficence or social investment) of indirect somatic effort [‘Direct somatic effort refers to self-help that involves no other persons. Indirect somatic effort involves reciprocity, which may be direct or indirect. Returns from direct and indirect reciprocity may be immediate or delayed’ – Alexander spends some pages classifying human effort in terms of such ‘atoms of sociality’, which are useful devices for analytical purposes, but I decided not to cover that stuff in detail here – US] or direct and indirect nepotism. The exceptions are what might be called evolutionary mistakes or accidents that result in unreciprocated or “genetic” altruism, deleterious to both the phenotype and genotype of the altruist; such mistakes can occur in all of the above categories” [I should point out that Boyd and Richerson’s book Not by Genes Alone – another great book which I hope to blog soon – is worth having a look at if after reading Alexander’s book you think that he does not cover the topic of how and why such mistakes might happen in the amount of detail it deserves; they also cover related topics in some detail, from a different angle – US]

“It is my impression that many moral philosophers do not approach the problem of morality and ethics as if it arose as an effort to resolve conflicts of interests. Their involvement in conflicts of interest seems to come about obliquely through discussions of individuals’ views with respect to moral behavior, or their proximate feelings about morality – almost as if questions about conflicts of interest arise only because we operate under moral systems, rather than vice versa.”

“The problem, in developing a theory of moral systems that is consistent with evolutionary theory from biology, is in accounting for the altruism of moral behavior in genetically selfish terms. I believe this can be done by interpreting moral systems as systems of indirect reciprocity.
I regard indirect reciprocity as a consequence of direct reciprocity occurring in the presence of interested audiences – groups of individuals who continually evaluate the members of their society as possible future interactants from whom they would like to gain more than they lose […] Even in directly reciprocal interactions […] net losses to self […] may be the actual aim of one or even both individuals, if they are being scrutinized by others who are likely to engage either individual subsequently in reciprocity of greater significance than that occurring in the scrutinized acts. […] Systems of indirect reciprocity, and therefore moral systems, are social systems structured around the importance of status. The concept of status implies that an individual’s privileges, or its access to resources, are controlled in part by how others collectively think of him (hence, treat him) as a result of past interactions (including observations of interactions with others). […] The consequences of indirect reciprocity […] include the concomitant spread of altruism (as social investment genetically valuable to the altruist), rules, and efforts to cheat […]. I would not contend that we always carry out cost-benefit analyses on these issues deliberately or consciously. I do, however, contend that such analyses occur, sometimes consciously, sometimes not, and that we are evolved to be exceedingly accurate and quick at making them […] [A] conscience [is what] I have interpreted (Alexander, 1979a) as the “still small voice that tells us how far we can go in serving our own interests without incurring intolerable risks.””

“The long-term existence of complex patterns of indirect reciprocity […] seems to favor the evolution of keen abilities to (1) make one’s self seem more beneficent than is the case; and (2) influence others to be beneficent in such fashions as to be deleterious to themselves and beneficial to the moralizer, e.g. to lead others to (a) invest too much, (b) invest wrongly in the moralizer or his relatives and friends, or (c) invest indiscriminately on a larger scale than would otherwise be the case. According to this view, individuals are expected to parade the idea of much beneficence, and even of indiscriminate altruism as beneficial, so as to encourage people in general to engage in increasing amounts of social investment whether or not it is beneficial to their interests. […] They may also be expected to depress the fitness of competitors by identifying them, deceptively or not, as reciprocity cheaters (in other words, to moralize and gossip); to internalize rules or evolve the ability to acquire a conscience, interpreted […] as the ability to use or own judgment to serve our own interests; and to self-deceive and display false sincerity as defenses against detection of cheating and attributions of deliberateness in cheating […] Everyone will with to appear more beneficent than he is. There are two reasons: (1) this appearance, if credible, is more likely to lead to direct social rewards than its alternatives; (2) it is also more likely to encourage others to be more beneficent.”

“Consciousness and related aspects of the human psyche (self-awareness, self-reflection, foresight, planning, purpose, conscience, free will, etc.) are here hypothesized to represent a system for competing with other humans for status, resources, and eventually reproductive success. More specifically, the collection of these attributes is viewed as a means of seeing ourselves and our life situations as others see us and our life situations – most particularly in ways that will cause (the most and the most important of) them to continue to interact with us in fashions that will benefit us and seem to benefit them.
Consciousness, then, is a game of life in which the participants are trying to comprehend what is in one another’s minds before, and more effectively than, it can be done in reverse.”

“Provided with a means of relegating our deceptions to the subconsciousness […] false sincerity becomes easier and detection more difficult. There are reasons for believing that one does not need to know his own personal interests consciously in order to serve them as much as he needs to know the interests of others to thwart them. […] I have suggested that consciousness is a way of making our social behavior so unpredictable as to allow us to outmaneuver others; and that we press into subconsciousness (as opposed to forgetting) those things that remain useful to us but would be detrimental to us if others knew about them, and on which we are continually tested and would have to lie deliberately if they remained in our conscious mind […] Conscious concealment of interests, or disavowal, is deliberate deception, considered more reprehensible than anything not conscious. Indeed, if one does not know consciously what his interests are, he cannot, in some sense, be accused of deception even though he may be using an evolved ability of self-deception to deceive others. So it is not always – maybe not usually – in our evolutionary or surrogate-evolutionary interests to make them conscious […] If people can be fooled […] then there will be continual selection for becoming better at fooling others […]. This may include causing them to think that it will be best for them to help you when it is not. This ploy works because of the thin line everybody must continually tread with respect to not showing selfishness. If some people are self-destructively beneficent (i.e., make altruistic mistakes), and if people often cannot tell if one is such a mistake-maker, it might be profitable even to try to convince others that one is such a mistake-maker so as to be accepted as a cooperator or so that the other will be beneficent in expectation of large returns (through “mistakes”) later. […] Reciprocity may work this way because it is grounded evolutionarily in nepotism, appropriate dispensing of nepotism (as well as reciprocity) depends upon learning, and the wrong things can be learned. [Boyd and Richerson talk about this particular aspect, the learning part, in much more detail in their books – US] Self-deception, then may not be a pathological or detrimental trait, at least in most people most of the time. Rather, it may have evolved as a way to deceive others.”

“The only time that utilitarianism (promoting the greatest good to the greatest number) is predicted by evolutionary theory is when the interests of the group (the “greatest number”) and the individual coincide, and in such cases utilitarianism is not really altruistic in either the biologists’ or the philosophers’ sense of the term. […] If Kohlberg means to imply that a significant proportion of the populace of the world either implicitly or explicitly favors a system in which everyone (including himself) behaves so as to bring the greatest good to the greatest number, then I simply believe that he is wrong. If he supposes that only a relatively few – particularly moral philosophers and some others like them – have achieved this “stage,” then I also doubt the hypothesis. I accept that many people are aware of this concept of utility, that a small minority may advocate it, and that an even smaller minority may actually believe that they behave according to it. I speculate, however, that with a few inadvertent or accidental exceptions, no one actually follows this precept. I see the concept as having its main utility as a goal towards which one may exhort others to aspire, and towards which one may behave as if (or talk as if) aspiring, which actually practicing complex forms of self-interest.”

“Generally speaking, the bigger the group, the more complex the social organization, and the greater the group’s unity of purpose the more limited is individual entrepreneurship.”

“The function or raison d’etre [sic] of moral systems is evidently to provide the unity required to enable the group to compete successfully with other human groups. […] the argument that human evolution has been guided to some large extent by intergroup competition and aggression […] is central to the theory of morality presented here”.

June 29, 2017 Posted by | Anthropology, Biology, Books, Evolutionary biology, Genetics, Philosophy | Leave a comment

Quotes

(The Pestallozzi quotes below are from The Education of Man, a short and poor aphorism collection I can not possibly recommend despite the inclusion of quotes from it in this post.)

i. “Only a good conscience always gives man the courage to handle his affairs straightforwardly, openly and without evasion.” (Johann Heinrich Pestalozzi)

ii. “An intimate relationship in its full power is always a source of human wisdom and strength in relationships less intimate.” (-ll-)

iii. “Whoever is unwilling to help himself can be helped by no one.” (-ll-)

iv. “He who has filled his pockets in the service of injustice will have little good to say on behalf of justice.” (-ll-)

v. “It is Man’s fate that no one knows the truth alone; we all possess it, but it is divided up among us. He who learns from one man only, will never learn what the others know.” (-ll-)

vi. “No scoundrel is so wicked that he cannot at some point truthfully reprove some honest man” (-ll-)

vii. “The man too keenly aware of his good reputation is likely to have a bad one.” (-ll-)

viii. “Many words make an excuse anything but convincing.” (-ll-)

ix. “Fashions are usually seen in their true perspective only when they have gone out of fashion.” (-ll-)

x. “A thing that nobody looks for is seldom found.” (-ll-)

xi. “Many discoveries must have been stillborn or smothered at birth. We know only those which survived.” (William Ian Beardmore Beveridge)

xii. “Time is the most valuable thing a man can spend.” (Theophrastus)

xiii. “The only man who makes no mistakes is the man who never does anything.” (Theodore Roosevelt)

xiv. “It is hard to fail, but it is worse never to have tried to succeed.” (-ll-)

xv. “From their appearance in the Triassic until the end of the Creta­ceous, a span of 140 million years, mam­mals remained small and inconspicuous while all the ecological roles of large ter­restrial herbivores and carnivores were monopolized by dinosaurs; mammals did not begin to radiate and produce large species until after the dinosaurs had al­ready become extinct at the end of the Cretaceous. One is forced to conclude that dinosaurs were competitively su­perior to mammals as large land vertebrates.” (Robert T. Bakker)

xvi. “Plants and plant-eaters co-evolved. And plants aren’t the passive partners in the chain of terrestrial life. […] A birch tree doesn’t feel cosmic fulfillment when a moose munches its leaves; the tree species, in fact, evolves to fight the moose, to keep the animal’s munching lips away from vulnerable young leaves and twigs. In the final analysis, the merciless hand of natural selection will favor the birch genes that make the tree less and less palatable to the moose in generation after generation. No plant species could survive for long by offering itself as unprotected fodder.” (-ll-)

xvii. “… if you look at crocodiles today, they aren’t really representative of what the lineage of crocodiles look like. Crocodiles are represented by about 23 species, plus or minus a couple. Along that lineage the more primitive members weren’t aquatic. A lot of them were bipedal, a lot of them looked like little dinosaurs. Some were armored, others had no teeth. They were all fully terrestrial. So this is just the last vestige of that radiation that we’re seeing. And the ancestor of both dinosaurs and crocodiles would have, to the untrained eye, looked much more like a dinosaur.” (Mark Norell)

xviii. “If we are to understand the interactions of a large number of agents, we must first be able to describe the capabilities of individual agents.” (John Henry Holland)

xix. “Evolution continually innovates, but at each level it conserves the elements that are recombined to yield the innovations.” (-ll-)

xx. “Model building is the art of selecting those aspects of a process that are relevant to the question being asked. […] High science depends on this art.” (-ll-)

June 19, 2017 Posted by | Biology, Books, Botany, Evolutionary biology, Paleontology, Quotes/aphorisms | Leave a comment

Imported Plant Diseases

I found myself debating whether or not I should read Lewis, Petrovskii, and Potts’ text The Mathematics Behind Biological Invasions a while back, but at the time I in the end decided that it would simply be too much work to justify the potential payoff – so instead of reading the book, I decided to just watch the above lecture and leave it at that. This lecture is definitely a very poor textbook substitute, and I was strongly debating whether or not to blog it because it just isn’t very good; the level of coverage is very low. Which is sad, because some of the diseases discussed in the lecture – like e.g. wheat leaf rust – are really important and worth knowing about. One of the important points made in the lecture is that in the context of potential epidemics, it can be difficult to know when and how to intervene because of the uncertainty involved; early action may be the more efficient choice in terms of resource use, but the earlier you intervene, the less certain will be the intervention payoff and the less you’ll know about stuff like transmission patterns (…would outbreak X ever really have spread very wide if we had not intervened? We don’t observe the counterfactual…). Such aspects of course are not only relevant to plant-diseases, and the lecture also contains other basic insights from epidemiology which apply to other types of disease – but if you’ve ever opened a basic epidemiology text you’ll know all these things already.

May 22, 2017 Posted by | Biology, Botany, Ecology, Epidemiology, Lectures | Leave a comment

Biodemography of aging (IV)

My working assumption as I was reading part two of the book was that I would not be covering that part of the book in much detail here because it would simply be too much work to make such posts legible to the readership of this blog. However I then later, while writing this post, had the thought that given that almost nobody reads along here anyway (I’m not complaining, mind you – this is how I like it these days), the main beneficiary of my blog posts will always be myself, which lead to the related observation/notion that I should not be limiting my coverage of interesting stuff here simply because some hypothetical and probably nonexistent readership out there might not be able to follow the coverage. So when I started out writing this post I was working under the assumption that it would be my last post about the book, but I now feel sure that if I find the time I’ll add at least one more post about the book’s statistics coverage. On a related note I am explicitly making the observation here that this post was written for my benefit, not yours. You can read it if you like, or not, but it was not really written for you.

I have added bold a few places to emphasize key concepts and observations from the quoted paragraphs and in order to make the post easier for me to navigate later (all the italics below are on the other hand those of the authors of the book).

Biodemography is a multidisciplinary branch of science that unites under its umbrella various analytic approaches aimed at integrating biological knowledge and methods and traditional demographic analyses to shed more light on variability in mortality and health across populations and between individuals. Biodemography of aging is a special subfield of biodemography that focuses on understanding the impact of processes related to aging on health and longevity.”

“Mortality rates as a function of age are a cornerstone of many demographic analyses. The longitudinal age trajectories of biomarkers add a new dimension to the traditional demographic analyses: the mortality rate becomes a function of not only age but also of these biomarkers (with additional dependence on a set of sociodemographic variables). Such analyses should incorporate dynamic characteristics of trajectories of biomarkers to evaluate their impact on mortality or other outcomes of interest. Traditional analyses using baseline values of biomarkers (e.g., Cox proportional hazards or logistic regression models) do not take into account these dynamics. One approach to the evaluation of the impact of biomarkers on mortality rates is to use the Cox proportional hazards model with time-dependent covariates; this approach is used extensively in various applications and is available in all popular statistical packages. In such a model, the biomarker is considered a time-dependent covariate of the hazard rate and the corresponding regression parameter is estimated along with standard errors to make statistical inference on the direction and the significance of the effect of the biomarker on the outcome of interest (e.g., mortality). However, the choice of the analytic approach should not be governed exclusively by its simplicity or convenience of application. It is essential to consider whether the method gives meaningful and interpretable results relevant to the research agenda. In the particular case of biodemographic analyses, the Cox proportional hazards model with time-dependent covariates is not the best choice.

“Longitudinal studies of aging present special methodological challenges due to inherent characteristics of the data that need to be addressed in order to avoid biased inference. The challenges are related to the fact that the populations under study (aging individuals) experience substantial dropout rates related to death or poor health and often have co-morbid conditions related to the disease of interest. The standard assumption made in longitudinal analyses (although usually not explicitly mentioned in publications) is that dropout (e.g., death) is not associated with the outcome of interest. While this can be safely assumed in many general longitudinal studies (where, e.g., the main causes of dropout might be the administrative end of the study or moving out of the study area, which are presumably not related to the studied outcomes), the very nature of the longitudinal outcomes (e.g., measurements of some physiological biomarkers) analyzed in a longitudinal study of aging assumes that they are (at least hypothetically) related to the process of aging. Because the process of aging leads to the development of diseases and, eventually, death, in longitudinal studies of aging an assumption of non-association of the reason for dropout and the outcome of interest is, at best, risky, and usually is wrong. As an illustration, we found that the average trajectories of different physiological indices of individuals dying at earlier ages markedly deviate from those of long-lived individuals, both in the entire Framingham original cohort […] and also among carriers of specific alleles […] In such a situation, panel compositional changes due to attrition affect the averaging procedure and modify the averages in the total sample. Furthermore, biomarkers are subject to measurement error and random biological variability. They are usually collected intermittently at examination times which may be sparse and typically biomarkers are not observed at event times. It is well known in the statistical literature that ignoring measurement errors and biological variation in such variables and using their observed “raw” values as time-dependent covariates in a Cox regression model may lead to biased estimates and incorrect inferences […] Standard methods of survival analysis such as the Cox proportional hazards model (Cox 1972) with time-dependent covariates should be avoided in analyses of biomarkers measured with errors because they can lead to biased estimates.

“Statistical methods aimed at analyses of time-to-event data jointly with longitudinal measurements have become known in the mainstream biostatistical literature as “joint models for longitudinal and time-to-event data” (“survival” or “failure time” are often used interchangeably with “time-to-event”) or simply “joint models.” This is an active and fruitful area of biostatistics with an explosive growth in recent years. […] The standard joint model consists of two parts, the first representing the dynamics of longitudinal data (which is referred to as the “longitudinal sub-model”) and the second one modeling survival or, generally, time-to-event data (which is referred to as the “survival sub-model”). […] Numerous extensions of this basic model have appeared in the joint modeling literature in recent decades, providing great flexibility in applications to a wide range of practical problems. […] The standard parameterization of the joint model (11.2) assumes that the risk of the event at age t depends on the current “true” value of the longitudinal biomarker at this age. While this is a reasonable assumption in general, it may be argued that additional dynamic characteristics of the longitudinal trajectory can also play a role in the risk of death or onset of a disease. For example, if two individuals at the same age have exactly the same level of some biomarker at this age, but the trajectory for the first individual increases faster with age than that of the second one, then the first individual can have worse survival chances for subsequent years. […] Therefore, extensions of the basic parameterization of joint models allowing for dependence of the risk of an event on such dynamic characteristics of the longitudinal trajectory can provide additional opportunities for comprehensive analyses of relationships between the risks and longitudinal trajectories. Several authors have considered such extended models. […] joint models are computationally intensive and are sometimes prone to convergence problems [however such] models provide more efficient estimates of the effect of a covariate […] on the time-to-event outcome in the case in which there is […] an effect of the covariate on the longitudinal trajectory of a biomarker. This means that analyses of longitudinal and time-to-event data in joint models may require smaller sample sizes to achieve comparable statistical power with analyses based on time-to-event data alone (Chen et al. 2011).”

“To be useful as a tool for biodemographers and gerontologists who seek biological explanations for observed processes, models of longitudinal data should be based on realistic assumptions and reflect relevant knowledge accumulated in the field. An example is the shape of the risk functions. Epidemiological studies show that the conditional hazards of health and survival events considered as functions of risk factors often have U- or J-shapes […], so a model of aging-related changes should incorporate this information. In addition, risk variables, and, what is very important, their effects on the risks of corresponding health and survival events, experience aging-related changes and these can differ among individuals. […] An important class of models for joint analyses of longitudinal and time-to-event data incorporating a stochastic process for description of longitudinal measurements uses an epidemiologically-justified assumption of a quadratic hazard (i.e., U-shaped in general and J-shaped for variables that can take values only on one side of the U-curve) considered as a function of physiological variables. Quadratic hazard models have been developed and intensively applied in studies of human longitudinal data”.

“Various approaches to statistical model building and data analysis that incorporate unobserved heterogeneity are ubiquitous in different scientific disciplines. Unobserved heterogeneity in models of health and survival outcomes can arise because there may be relevant risk factors affecting an outcome of interest that are either unknown or not measured in the data. Frailty models introduce the concept of unobserved heterogeneity in survival analysis for time-to-event data. […] Individual age trajectories of biomarkers can differ due to various observed as well as unobserved (and unknown) factors and such individual differences propagate to differences in risks of related time-to-event outcomes such as the onset of a disease or death. […] The joint analysis of longitudinal and time-to-event data is the realm of a special area of biostatistics named “joint models for longitudinal and time-to-event data” or simply “joint models” […] Approaches that incorporate heterogeneity in populations through random variables with continuous distributions (as in the standard joint models and their extensions […]) assume that the risks of events and longitudinal trajectories follow similar patterns for all individuals in a population (e.g., that biomarkers change linearly with age for all individuals). Although such homogeneity in patterns can be justifiable for some applications, generally this is a rather strict assumption […] A population under study may consist of subpopulations with distinct patterns of longitudinal trajectories of biomarkers that can also have different effects on the time-to-event outcome in each subpopulation. When such subpopulations can be defined on the base of observed covariate(s), one can perform stratified analyses applying different models for each subpopulation. However, observed covariates may not capture the entire heterogeneity in the population in which case it may be useful to conceive of the population as consisting of latent subpopulations defined by unobserved characteristics. Special methodological approaches are necessary to accommodate such hidden heterogeneity. Within the joint modeling framework, a special class of models, joint latent class models, was developed to account for such heterogeneity […] The joint latent class model has three components. First, it is assumed that a population consists of a fixed number of (latent) subpopulations. The latent class indicator represents the latent class membership and the probability of belonging to the latent class is specified by a multinomial logistic regression function of observed covariates. It is assumed that individuals from different latent classes have different patterns of longitudinal trajectories of biomarkers and different risks of event. The key assumption of the model is conditional independence of the biomarker and the time-to-events given the latent classes. Then the class-specific models for the longitudinal and time-to-event outcomes constitute the second and third component of the model thus completing its specification. […] the latent class stochastic process model […] provides a useful tool for dealing with unobserved heterogeneity in joint analyses of longitudinal and time-to-event outcomes and taking into account hidden components of aging in their joint influence on health and longevity. This approach is also helpful for sensitivity analyses in applications of the original stochastic process model. We recommend starting the analyses with the original stochastic process model and estimating the model ignoring possible hidden heterogeneity in the population. Then the latent class stochastic process model can be applied to test hypotheses about the presence of hidden heterogeneity in the data in order to appropriately adjust the conclusions if a latent structure is revealed.”

The longitudinal genetic-demographic model (or the genetic-demographic model for longitudinal data) […] combines three sources of information in the likelihood function: (1) follow-up data on survival (or, generally, on some time-to-event) for genotyped individuals; (2) (cross-sectional) information on ages at biospecimen collection for genotyped individuals; and (3) follow-up data on survival for non-genotyped individuals. […] Such joint analyses of genotyped and non-genotyped individuals can result in substantial improvements in statistical power and accuracy of estimates compared to analyses of the genotyped subsample alone if the proportion of non-genotyped participants is large. Situations in which genetic information cannot be collected for all participants of longitudinal studies are not uncommon. They can arise for several reasons: (1) the longitudinal study may have started some time before genotyping was added to the study design so that some initially participating individuals dropped out of the study (i.e., died or were lost to follow-up) by the time of genetic data collection; (2) budget constraints prohibit obtaining genetic information for the entire sample; (3) some participants refuse to provide samples for genetic analyses. Nevertheless, even when genotyped individuals constitute a majority of the sample or the entire sample, application of such an approach is still beneficial […] The genetic stochastic process model […] adds a new dimension to genetic biodemographic analyses, combining information on longitudinal measurements of biomarkers available for participants of a longitudinal study with follow-up data and genetic information. Such joint analyses of different sources of information collected in both genotyped and non-genotyped individuals allow for more efficient use of the research potential of longitudinal data which otherwise remains underused when only genotyped individuals or only subsets of available information (e.g., only follow-up data on genotyped individuals) are involved in analyses. Similar to the longitudinal genetic-demographic model […], the benefits of combining data on genotyped and non-genotyped individuals in the genetic SPM come from the presence of common parameters describing characteristics of the model for genotyped and non-genotyped subsamples of the data. This takes into account the knowledge that the non-genotyped subsample is a mixture of carriers and non-carriers of the same alleles or genotypes represented in the genotyped subsample and applies the ideas of heterogeneity analyses […] When the non-genotyped subsample is substantially larger than the genotyped subsample, these joint analyses can lead to a noticeable increase in the power of statistical estimates of genetic parameters compared to estimates based only on information from the genotyped subsample. This approach is applicable not only to genetic data but to any discrete time-independent variable that is observed only for a subsample of individuals in a longitudinal study.

“Despite an existing tradition of interpreting differences in the shapes or parameters of the mortality rates (survival functions) resulting from the effects of exposure to different conditions or other interventions in terms of characteristics of individual aging, this practice has to be used with care. This is because such characteristics are difficult to interpret in terms of properties of external and internal processes affecting the chances of death. An important question then is: What kind of mortality model has to be developed to obtain parameters that are biologically interpretable? The purpose of this chapter is to describe an approach to mortality modeling that represents mortality rates in terms of parameters of physiological changes and declining health status accompanying the process of aging in humans. […] A traditional (demographic) description of changes in individual health/survival status is performed using a continuous-time random Markov process with a finite number of states, and age-dependent transition intensity functions (transitions rates). Transitions to the absorbing state are associated with death, and the corresponding transition intensity is a mortality rate. Although such a description characterizes connections between health and mortality, it does not allow for studying factors and mechanisms involved in the aging-related health decline. Numerous epidemiological studies provide compelling evidence that health transition rates are influenced by a number of factors. Some of them are fixed at the time of birth […]. Others experience stochastic changes over the life course […] The presence of such randomly changing influential factors violates the Markov assumption, and makes the description of aging-related changes in health status more complicated. […] The age dynamics of influential factors (e.g., physiological variables) in connection with mortality risks has been described using a stochastic process model of human mortality and aging […]. Recent extensions of this model have been used in analyses of longitudinal data on aging, health, and longevity, collected in the Framingham Heart Study […] This model and its extensions are described in terms of a Markov stochastic process satisfying a diffusion-type stochastic differential equation. The stochastic process is stopped at random times associated with individuals’ deaths. […] When an individual’s health status is taken into account, the coefficients of the stochastic differential equations become dependent on values of the jumping process. This dependence violates the Markov assumption and renders the conditional Gaussian property invalid. So the description of this (continuously changing) component of aging-related changes in the body also becomes more complicated. Since studying age trajectories of physiological states in connection with changes in health status and mortality would provide more realistic scenarios for analyses of available longitudinal data, it would be a good idea to find an appropriate mathematical description of the joint evolution of these interdependent processes in aging organisms. For this purpose, we propose a comprehensive model of human aging, health, and mortality in which the Markov assumption is fulfilled by a two-component stochastic process consisting of jumping and continuously changing processes. The jumping component is used to describe relatively fast changes in health status occurring at random times, and the continuous component describes relatively slow stochastic age-related changes of individual physiological states. […] The use of stochastic differential equations for random continuously changing covariates has been studied intensively in the analysis of longitudinal data […] Such a description is convenient since it captures the feedback mechanism typical of biological systems reflecting regular aging-related changes and takes into account the presence of random noise affecting individual trajectories. It also captures the dynamic connections between aging-related changes in health and physiological states, which are important in many applications.”

April 23, 2017 Posted by | Biology, Books, Demographics, Genetics, Mathematics, Statistics | Leave a comment

Biodemography of aging (III)

Latent class representation of the Grade of Membership model.
Singular value decomposition.
Affine space.
Lebesgue measure.
General linear position.

The links above are links to topics I looked up while reading the second half of the book. The first link is quite relevant to the book’s coverage as a comprehensive longitudinal Grade of Membership (-GoM) model is covered in chapter 17. Relatedly, chapter 18 covers linear latent structure (-LLS) models, and as observed in the book LLS is a generalization of GoM. As should be obvious from the nature of the links some of the stuff included in the second half of the text is highly technical, and I’ll readily admit I was not fully able to understand all the details included in the coverage of chapters 17 and 18 in particular. On account of the technical nature of the coverage in Part 2 I’m not sure I’ll cover the second half of the book in much detail, though I probably shall devote at least one more post to some of those topics, as they were quite interesting even if some of the details were difficult to follow.

I have almost finished the book at this point, and I have already decided to both give the book five stars and include it on my list of favorite books on goodreads; it’s really well written, and it provides consistently highly detailed coverage of very high quality. As I also noted in the first post about the book the authors have given readability aspects some thought, and I am sure most readers would learn quite a bit from this text even if they were to skip some of the more technical chapters. The main body of Part 2 of the book, the subtitle of which is ‘Statistical Modeling of Aging, Health, and Longevity’, is however probably in general not worth the effort of reading unless you have a solid background in statistics.

This post includes some observations and quotes from the last chapters of the book’s Part 1.

“The proportion of older adults in the U.S. population is growing. This raises important questions about the increasing prevalence of aging-related diseases, multimorbidity issues, and disability among the elderly population. […] In 2009, 46.3 million people were covered by Medicare: 38.7 million of them were aged 65 years and older, and 7.6 million were disabled […]. By 2031, when the baby-boomer generation will be completely enrolled, Medicare is expected to reach 77 million individuals […]. Because the Medicare program covers 95 % of the nation’s aged population […], the prediction of future Medicare costs based on these data can be an important source of health care planning.”

“Three essential components (which could be also referred as sub-models) need to be developed to construct a modern model of forecasting of population health and associated medical costs: (i) a model of medical cost projections conditional on each health state in the model, (ii) health state projections, and (iii) a description of the distribution of initial health states of a cohort to be projected […] In making medical cost projections, two major effects should be taken into account: the dynamics of the medical costs during the time periods comprising the date of onset of chronic diseases and the increase of medical costs during the last years of life. In this chapter, we investigate and model the first of these two effects. […] the approach developed in this chapter generalizes the approach known as “life tables with covariates” […], resulting in a new family of forecasting models with covariates such as comorbidity indexes or medical costs. In sum, this chapter develops a model of the relationships between individual cost trajectories following the onset of aging-related chronic diseases. […] The underlying methodological idea is to aggregate the health state information into a single (or several) covariate(s) that can be determinative in predicting the risk of a health event (e.g., disease incidence) and whose dynamics could be represented by the model assumptions. An advantage of such an approach is its substantial reduction of the degrees of freedom compared with existing forecasting models  (e.g., the FEM model, Goldman and RAND Corporation 2004). […] We found that the time patterns of medical cost trajectories were similar for all diseases considered and can be described in terms of four components having the meanings of (i) the pre-diagnosis cost associated with initial comorbidity represented by medical expenditures, (ii) the cost peak associated with the onset of each disease, (iii) the decline/reduction in medical expenditures after the disease onset, and (iv) the difference between post- and pre-diagnosis cost levels associated with an acquired comorbidity. The description of the trajectories was formalized by a model which explicitly involves four parameters reflecting these four components.”

As I noted earlier in my coverage of the book, I don’t think the model above fully captures all relevant cost contributions of the diseases included, as the follow-up period was too short to capture all relevant costs to be included in the part iv model component. This is definitely a problem in the context of diabetes. But then again nothing in theory stops people from combining the model above with other models which are better at dealing with the excess costs associated with long-term complications of chronic diseases, and the model results were intriguing even if the model likely underperforms in a few specific disease contexts.

Moving on…

“Models of medical cost projections usually are based on regression models estimated with the majority of independent predictors describing demographic status of the individual, patient’s health state, and level of functional limitations, as well as their interactions […]. If the health states needs to be described by a number of simultaneously manifested diseases, then detailed stratification over the categorized variables or use of multivariate regression models allows for a better description of the health states. However, it can result in an abundance of model parameters to be estimated. One way to overcome these difficulties is to use an approach in which the model components are demographically-based aggregated characteristics that mimic the effects of specific states. The model developed in this chapter is an example of such an approach: the use of a comorbidity index rather than of a set of correlated categorical regressor variables to represent the health state allows for an essential reduction in the degrees of freedom of the problem.”

“Unlike mortality, the onset time of chronic disease is difficult to define with high precision due to the large variety of disease-specific criteria for onset/incident case identification […] there is always some arbitrariness in defining the date of chronic disease onset, and a unified definition of date of onset is necessary for population studies with a long-term follow-up.”

“Individual age trajectories of physiological indices are the product of a complicated interplay among genetic and non-genetic (environmental, behavioral, stochastic) factors that influence the human body during the course of aging. Accordingly, they may differ substantially among individuals in a cohort. Despite this fact, the average age trajectories for the same index follow remarkable regularities. […] some indices tend to change monotonically with age: the level of blood glucose (BG) increases almost monotonically; pulse pressure (PP) increases from age 40 until age 85, then levels off and shows a tendency to decline only at later ages. The age trajectories of other indices are non-monotonic: they tend to increase first and then decline. Body mass index (BMI) increases up to about age 70 and then declines, diastolic blood pressure (DBP) increases until age 55–60 and then declines, systolic blood pressure (SBP) increases until age 75 and then declines, serum cholesterol (SCH) increases until age 50 in males and age 70 in females and then declines, ventricular rate (VR) increases until age 55 in males and age 45 in females and then declines. With small variations, these general patterns are similar in males and females. The shapes of the age-trajectories of the physiological variables also appear to be similar for different genotypes. […] The effects of these physiological indices on mortality risk were studied in Yashin et al. (2006), who found that the effects are gender and age specific. They also found that the dynamic properties of the individual age trajectories of physiological indices may differ dramatically from one individual to the next.”

“An increase in the mortality rate with age is traditionally associated with the process of aging. This influence is mediated by aging-associated changes in thousands of biological and physiological variables, some of which have been measured in aging studies. The fact that the age trajectories of some of these variables differ among individuals with short and long life spans and healthy life spans indicates that dynamic properties of the indices affect life history traits. Our analyses of the FHS data clearly demonstrate that the values of physiological indices at age 40 are significant contributors both to life span and healthy life span […] suggesting that normalizing these variables around age 40 is important for preventing age-associated morbidity and mortality later in life. […] results [also] suggest that keeping physiological indices stable over the years of life could be as important as their normalizing around age 40.”

“The results […] indicate that, in the quest of identifying longevity genes, it may be important to look for candidate genes with pleiotropic effects on more than one dynamic characteristic of the age-trajectory of a physiological variable, such as genes that may influence both the initial value of a trait (intercept) and the rates of its changes over age (slopes). […] Our results indicate that the dynamic characteristics of age-related changes in physiological variables are important predictors of morbidity and mortality risks in aging individuals. […] We showed that the initial value (intercept), the rate of changes (slope), and the variability of a physiological index, in the age interval 40–60 years, significantly influenced both mortality risk and onset of unhealthy life at ages 60+ in our analyses of the Framingham Heart Study data. That is, these dynamic characteristics may serve as good predictors of late life morbidity and mortality risks. The results also suggest that physiological changes taking place in the organism in middle life may affect longevity through promoting or preventing diseases of old age. For non-monotonically changing indices, we found that having a later age at the peak value of the index […], a lower peak value […], a slower rate of decline in the index at older ages […], and less variability in the index over time, can be beneficial for longevity. Also, the dynamic characteristics of the physiological indices were, overall, associated with mortality risk more significantly than with onset of unhealthy life.”

“Decades of studies of candidate genes show that they are not linked to aging-related traits in a straightforward manner […]. Recent genome-wide association studies (GWAS) have reached fundamentally the same conclusion by showing that the traits in late life likely are controlled by a relatively large number of common genetic variants […]. Further, GWAS often show that the detected associations are of tiny effect […] the weak effect of genes on traits in late life can be not only because they confer small risks having small penetrance but because they confer large risks but in a complex fashion […] In this chapter, we consider several examples of complex modes of gene actions, including genetic tradeoffs, antagonistic genetic effects on the same traits at different ages, and variable genetic effects on lifespan. The analyses focus on the APOE common polymorphism. […] The analyses reported in this chapter suggest that the e4 allele can be protective against cancer with a more pronounced role in men. This protective effect is more characteristic of cancers at older ages and it holds in both the parental and offspring generations of the FHS participants. Unlike cancer, the effect of the e4 allele on risks of CVD is more pronounced in women. […] [The] results […] explicitly show that the same allele can change its role on risks of CVD in an antagonistic fashion from detrimental in women with onsets at younger ages to protective in women with onsets at older ages. […] e4 allele carriers have worse survival compared to non-e4 carriers in each cohort. […] Sex stratification shows sexual dimorphism in the effect of the e4 allele on survival […] with the e4 female carriers, particularly, being more exposed to worse survival. […] The results of these analyses provide two important insights into the role of genes in lifespan. First, they provide evidence on the key role of aging-related processes in genetic susceptibility to lifespan. For example, taking into account the specifics of aging-related processes gains 18 % in estimates of the RRs and five orders of magnitude in significance in the same sample of women […] without additional investments in increasing sample sizes and new genotyping. The second is that a detailed study of the role of aging-related processes in estimates of the effects of genes on lifespan (and healthspan) helps in detecting more homogeneous [high risk] sub-samples”.

“The aging of populations in developed countries requires effective strategies to extend healthspan. A promising solution could be to yield insights into the genetic predispositions for endophenotypes, diseases, well-being, and survival. It was thought that genome-wide association studies (GWAS) would be a major breakthrough in this endeavor. Various genetic association studies including GWAS assume that there should be a deterministic (unconditional) genetic component in such complex phenotypes. However, the idea of unconditional contributions of genes to these phenotypes faces serious difficulties which stem from the lack of direct evolutionary selection against or in favor of such phenotypes. In fact, evolutionary constraints imply that genes should be linked to age-related phenotypes in a complex manner through different mechanisms specific for given periods of life. Accordingly, the linkage between genes and these traits should be strongly modulated by age-related processes in a changing environment, i.e., by the individuals’ life course. The inherent sensitivity of genetic mechanisms of complex health traits to the life course will be a key concern as long as genetic discoveries continue to be aimed at improving human health.”

“Despite the common understanding that age is a risk factor of not just one but a large portion of human diseases in late life, each specific disease is typically considered as a stand-alone trait. Independence of diseases was a plausible hypothesis in the era of infectious diseases caused by different strains of microbes. Unlike those diseases, the exact etiology and precursors of diseases in late life are still elusive. It is clear, however, that the origin of these diseases differs from that of infectious diseases and that age-related diseases reflect a complicated interplay among ontogenetic changes, senescence processes, and damages from exposures to environmental hazards. Studies of the determinants of diseases in late life provide insights into a number of risk factors, apart from age, that are common for the development of many health pathologies. The presence of such common risk factors makes chronic diseases and hence risks of their occurrence interdependent. This means that the results of many calculations using the assumption of disease independence should be used with care. Chapter 4 argued that disregarding potential dependence among diseases may seriously bias estimates of potential gains in life expectancy attributable to the control or elimination of a specific disease and that the results of the process of coping with a specific disease will depend on the disease elimination strategy, which may affect mortality risks from other diseases.”

April 17, 2017 Posted by | Biology, Books, Cancer/oncology, Demographics, Economics, Epidemiology, Genetics, Health Economics, Medicine, Statistics | Leave a comment

Biodemography of aging (I)

“The goal of this monograph is to show how questions about the connections between and among aging, health, and longevity can be addressed using the wealth of available accumulated knowledge in the field, the large volumes of genetic and non-genetic data collected in longitudinal studies, and advanced biodemographic models and analytic methods. […] This monograph visualizes aging-related changes in physiological variables and survival probabilities, describes methods, and summarizes the results of analyses of longitudinal data on aging, health, and longevity in humans performed by the group of researchers in the Biodemography of Aging Research Unit (BARU) at Duke University during the past decade. […] the focus of this monograph is studying dynamic relationships between aging, health, and longevity characteristics […] our focus on biodemography/biomedical demography meant that we needed to have an interdisciplinary and multidisciplinary biodemographic perspective spanning the fields of actuarial science, biology, economics, epidemiology, genetics, health services research, mathematics, probability, and statistics, among others.”

The quotes above are from the book‘s preface. In case this aspect was not clear from the comments above, this is the kind of book where you’ll randomly encounter sentences like these:

The simplest model describing negative correlations between competing risks is the multivariate lognormal frailty model. We illustrate the properties of such model for the bivariate case.

“The time-to-event sub-model specifies the latent class-specific expressions for the hazard rates conditional on the vector of biomarkers Yt and the vector of observed covariates X …”

…which means that some parts of the book are really hard to blog; it simply takes more effort to deal with this stuff here than it’s worth. As a result of this my coverage of the book will not provide a remotely ‘balanced view’ of the topics covered in it; I’ll skip a lot of the technical stuff because I don’t think it makes much sense to cover specific models and algorithms included in the book in detail here. However I should probably also emphasize while on this topic that although the book is in general not an easy read, it’s hard to read because ‘this stuff is complicated’, not because the authors are not trying. The authors in fact make it clear already in the preface that some chapters are more easy to read than are others and that some chapters are actually deliberately written as ‘guideposts and way-stations‘, as they put it, in order to make it easier for the reader to find the stuff in which he or she is most interested (“the interested reader can focus directly on the chapters/sections of greatest interest without having to read the entire volume“) – they have definitely given readability aspects some thought, and I very much like the book so far; it’s full of great stuff and it’s very well written.

I have had occasion to question a few of the observations they’ve made, for example I was a bit skeptical about a few of the conclusions they drew in chapter 6 (‘Medical Cost Trajectories and Onset of Age-Associated Diseases’), but this was related to what some would certainly consider to be minor details. In the chapter they describe a model of medical cost trajectories where the post-diagnosis follow-up period is 20 months; this is in my view much too short a follow-up period to draw conclusions about medical cost trajectories in the context of type 2 diabetes, one of the diseases included in the model, which I know because I’m intimately familiar with the literature on that topic; you need to look 7-10 years ahead to get a proper sense of how this variable develops over time – and it really is highly relevant to include those later years, because if you do not you may miss out on a large proportion of the total cost given that a substantial proportion of the total cost of diabetes relate to complications which tend to take some years to develop. If your cost analysis is based on a follow-up period as short as that of that model you may also on a related note draw faulty conclusions about which medical procedures and -subsidies are sensible/cost effective in the setting of these patients, because highly adherent patients may be significantly more expensive in a short run analysis like this one (they show up to their medical appointments and take their medications…) but much cheaper in the long run (…because they take their medications they don’t go blind or develop kidney failure). But as I say, it’s a minor point – this was one condition out of 20 included in the analysis they present, and if they’d addressed all the things that pedants like me might take issue with, the book would be twice as long and it would likely no longer be readable. Relatedly, the model they discuss in that chapter is far from unsalvageable; it’s just that one of the components of interest –  ‘the difference between post- and pre-diagnosis cost levels associated with an acquired comorbidity’ – in the case of at least one disease is highly unlikely to be correct (given the authors’ interpretation of the variable), because there’s some stuff of relevance which the model does not include. I found the model quite interesting, despite the shortcomings, and the results were definitely surprising. (No, the above does not in my opinion count as an example of coverage of a ‘specific model […] in detail’. Or maybe it does, but I included no equations. On reflection I probably can’t promise much more than that, sometimes the details are interesting…)

Anyway, below I’ve added some quotes from the first few chapters of the book and a few remarks along the way.

“The genetics of aging, longevity, and mortality has become the subject of intensive analyses […]. However, most estimates of genetic effects on longevity in GWAS have not reached genome-wide statistical significance (after applying the Bonferroni correction for multiple testing) and many findings remain non-replicated. Possible reasons for slow progress in this field include the lack of a biologically-based conceptual framework that would drive development of statistical models and methods for genetic analyses of data [here I was reminded of Burnham & Anderson’s coverage, in particular their criticism of mindless ‘Let the computer find out’-strategies – the authors of that chapter seem to share their skepticism…], the presence of hidden genetic heterogeneity, the collective influence of many genetic factors (each with small effects), the effects of rare alleles, and epigenetic effects, as well as molecular biological mechanisms regulating cellular functions. […] Decades of studies of candidate genes show that they are not linked to aging-related traits in a straightforward fashion (Finch and Tanzi 1997; Martin 2007). Recent genome-wide association studies (GWAS) have supported this finding by showing that the traits in late life are likely controlled by a relatively large number of common genetic variants […]. Further, GWAS often show that the detected associations are of tiny size (Stranger et al. 2011).”

I think this ties in well with what I’ve previously read on these and related topics – see e.g. the second-last paragraph quoted in my coverage of Richard Alexander’s book, or some of the remarks included in Roberts et al. Anyway, moving on:

“It is well known from epidemiology that values of variables describing physiological states at a given age are associated with human morbidity and mortality risks. Much less well known are the facts that not only the values of these variables at a given age, but also characteristics of their dynamic behavior during the life course are also associated with health and survival outcomes. This chapter [chapter 8 in the book, US] shows that, for monotonically changing variables, the value at age 40 (intercept), the rate of change (slope), and the variability of a physiological variable, at ages 40–60, significantly influence both health-span and longevity after age 60. For non-monotonically changing variables, the age at maximum, the maximum value, the rate of decline after reaching the maximum (right slope), and the variability in the variable over the life course may influence health-span and longevity. This indicates that such characteristics can be important targets for preventive measures aiming to postpone onsets of complex diseases and increase longevity.”

The chapter from which the quotes in the next two paragraphs are taken was completely filled with data from the Framingham Heart Study, and it was hard for me to know what to include here and what to leave out – so you should probably just consider the stuff I’ve included below as samples of the sort of observations included in that part of the coverage.

“To mediate the influence of internal or external factors on lifespan, physiological variables have to show associations with risks of disease and death at different age intervals, or directly with lifespan. For many physiological variables, such associations have been established in epidemiological studies. These include body mass index (BMI), diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), blood glucose (BG), serum cholesterol (SCH), hematocrit (H), and ventricular rate (VR). […] the connection between BMI and mortality risk is generally J-shaped […] Although all age patterns of physiological indices are non-monotonic functions of age, blood glucose (BG) and pulse pressure (PP) can be well approximated by monotonically increasing functions for both genders. […] the average values of body mass index (BMI) increase with age (up to age 55 for males and 65 for females), and then decline for both sexes. These values do not change much between ages 50 and 70 for males and between ages 60 and 70 for females. […] Except for blood glucose, all average age trajectories of physiological indices differ between males and females. Statistical analysis confirms the significance of these differences. In particular, after age 35 the female BMI increases faster than that of males. […] [When comparing women with less than or equal to 11 years of education [‘LE’] to women with 12 or more years of education [HE]:] The average values of BG for both groups are about the same until age 45. Then the BG curve for the LE females becomes higher than that of the HE females until age 85 where the curves intersect. […] The average values of BMI in the LE group are substantially higher than those among the HE group over the entire age interval. […] The average values of BG for the HE and LE males are very similar […] However, the differences between groups are much smaller than for females.”

They also in the chapter compared individuals with short life-spans [‘SL’, died before the age of 75] and those with long life-spans [‘LL’, 100 longest-living individuals in the relevant sample] to see if the variables/trajectories looked different. They did, for example: “trajectories for the LL females are substantially different from those for the SL females in all eight indices. Specifically, the average values of BG are higher and increase faster in the SL females. The entire age trajectory of BMI for the LL females is shifted to the right […] The average values of DBP [diastolic blood pressure, US] among the SL females are higher […] A particularly notable observation is the shift of the entire age trajectory of BMI for the LL males and females to the right (towards an older age), as compared with the SL group, and achieving its maximum at a later age. Such a pattern is markedly different from that for healthy and unhealthy individuals. The latter is mostly characterized by the higher values of BMI for the unhealthy people, while it has similar ages at maximum for both the healthy and unhealthy groups. […] Physiological aging changes usually develop in the presence of other factors affecting physiological dynamics and morbidity/mortality risks. Among these other factors are year of birth, gender, education, income, occupation, smoking, and alcohol use. An important limitation of most longitudinal studies is the lack of information regarding external disturbances affecting individuals in their day-today life.”

I incidentally noted while I was reading that chapter that a relevant variable ‘lurking in the shadows’ in the context of the male and female BMI trajectories might be changing smoking habits over time; I have not looked at US data on this topic, but I do know that the smoking patterns of Danish males and females during the latter half of the last century were markedly different and changed really quite dramatically in just a few decades; a lot more males than females smoked in the 60es, whereas the proportions of male- and female smokers today are much more similar, because a lot of males have given up smoking (I refer Danish readers to this blog post which I wrote some years ago on these topics). The authors of the chapter incidentally do look a little at data on smokers and they observe that smokers’ BMI are lower than non-smokers (not surprising), and that the smokers’ BMI curve (displaying the relationship between BMI and age) grows at a slower rate than the BMI curve of non-smokers (that this was to be expected is perhaps less clear, at least to me – the authors don’t interpret these specific numbers, they just report them).

The next chapter is one of the chapters in the book dealing with the SEER data I also mentioned not long ago in the context of my coverage of Bueno et al. Some sample quotes from that chapter below:

“To better address the challenge of “healthy aging” and to reduce economic burdens of aging-related diseases, key factors driving the onset and progression of diseases in older adults must be identified and evaluated. An identification of disease-specific age patterns with sufficient precision requires large databases that include various age-specific population groups. Collections of such datasets are costly and require long periods of time. That is why few studies have investigated disease-specific age patterns among older U.S. adults and there is limited knowledge of factors impacting these patterns. […] Information collected in U.S. Medicare Files of Service Use (MFSU) for the entire Medicare-eligible population of older U.S. adults can serve as an example of observational administrative data that can be used for analysis of disease-specific age patterns. […] In this chapter, we focus on a series of epidemiologic and biodemographic characteristics that can be studied using MFSU.”

“Two datasets capable of generating national level estimates for older U.S. adults are the Surveillance, Epidemiology, and End Results (SEER) Registry data linked to MFSU (SEER-M) and the National Long Term Care Survey (NLTCS), also linked to MFSU (NLTCS-M). […] The SEER-M data are the primary dataset analyzed in this chapter. The expanded SEER registry covers approximately 26 % of the U.S. population. In total, the Medicare records for 2,154,598 individuals are available in SEER-M […] For the majority of persons, we have continuous records of Medicare services use from 1991 (or from the time the person reached age 65 after 1990) to his/her death. […] The NLTCS-M data contain two of the six waves of the NLTCS: namely, the cohorts of years 1994 and 1999. […] In total, 34,077 individuals were followed-up between 1994 and 1999. These individuals were given the detailed NLTCS interview […] which has information on risk factors. More than 200 variables were selected”

In short, these data sets are very large, and contain a lot of information. Here are some results/data:

“Among studied diseases, incidence rates of Alzheimer’s disease, stroke, and heart failure increased with age, while the rates of lung and breast cancers, angina pectoris, diabetes, asthma, emphysema, arthritis, and goiter became lower at advanced ages. [..] Several types of age-patterns of disease incidence could be described. The first was a monotonic increase until age 85–95, with a subsequent slowing down, leveling off, and decline at age 100. This pattern was observed for myocardial infarction, stroke, heart failure, ulcer, and Alzheimer’s disease. The second type had an earlier-age maximum and a more symmetric shape (i.e., an inverted U-shape) which was observed for lung and colon cancers, Parkinson’s disease, and renal failure. The majority of diseases (e.g., prostate cancer, asthma, and diabetes mellitus among them) demonstrated a third shape: a monotonic decline with age or a decline after a short period of increased rates. […] The occurrence of age-patterns with a maximum and, especially, with a monotonic decline contradicts the hypothesis that the risk of geriatric diseases correlates with an accumulation of adverse health events […]. Two processes could be operative in the generation of such shapes. First, they could be attributed to the effect of selection […] when frail individuals do not survive to advanced ages. This approach is popular in cancer modeling […] The second explanation could be related to the possibility of under-diagnosis of certain chronic diseases at advanced ages (due to both less pronounced disease symptoms and infrequent doctor’s office visits); however, that possibility cannot be assessed with the available data […this is because the data sets are based on Medicare claims – US]”

“The most detailed U.S. data on cancer incidence come from the SEER Registry […] about 60 % of malignancies are diagnosed in persons aged 65+ years old […] In the U.S., the estimated percent of cancer patients alive after being diagnosed with cancer (in 2008, by current age) was 13 % for those aged 65–69, 25 % for ages 70–79, and 22 % for ages 80+ years old (compared with 40 % of those aged younger than 65 years old) […] Diabetes affects about 21 % of the U.S. population aged 65+ years old (McDonald et al. 2009). However, while more is known about the prevalence of diabetes, the incidence of this disease among older adults is less studied. […] [In multiple previous studies] the incidence rates of diabetes decreased with age for both males and females. In the present study, we find similar patterns […] The prevalence of asthma among the U.S. population aged 65+ years old in the mid-2000s was as high as 7 % […] older patients are more likely to be underdiagnosed, untreated, and hospitalized due to asthma than individuals younger than age 65 […] asthma incidence rates have been shown to decrease with age […] This trend of declining asthma incidence with age is in agreement with our results.”

“The prevalence and incidence of Alzheimer’s disease increase exponentially with age, with the most notable rise occurring through the seventh and eight decades of life (Reitz et al. 2011). […] whereas dementia incidence continues to increase beyond age 85, the rate of increase slows down [which] suggests that dementia diagnosed at advanced ages might be related not to the aging process per se, but associated with age-related risk factors […] Approximately 1–2 % of the population aged 65+ and up to 3–5 % aged 85+ years old suffer from Parkinson’s disease […] There are few studies of Parkinsons disease incidence, especially in the oldest old, and its age patterns at advanced ages remain controversial”.

“One disadvantage of large administrative databases is that certain factors can produce systematic over/underestimation of the number of diagnosed diseases or of identification of the age at disease onset. One reason for such uncertainties is an incorrect date of disease onset. Other sources are latent disenrollment and the effects of study design. […] the date of onset of a certain chronic disease is a quantity which is not defined as precisely as mortality. This uncertainty makes difficult the construction of a unified definition of the date of onset appropriate for population studies.”

“[W]e investigated the phenomenon of multimorbidity in the U.S. elderly population by analyzing mutual dependence in disease risks, i.e., we calculated disease risks for individuals with specific pre-existing conditions […]. In total, 420 pairs of diseases were analyzed. […] For each pair, we calculated age patterns of unconditional incidence rates of the diseases, conditional rates of the second (later manifested) disease for individuals after onset of the first (earlier manifested) disease, and the hazard ratio of development of the subsequent disease in the presence (or not) of the first disease. […] three groups of interrelations were identified: (i) diseases whose risk became much higher when patients had a certain pre-existing (earlier diagnosed) disease; (ii) diseases whose risk became lower than in the general population when patients had certain pre-existing conditions […] and (iii) diseases for which “two-tail” effects were observed: i.e., when the effects are significant for both orders of disease precedence; both effects can be direct (either one of the diseases from a disease pair increases the risk of the other disease), inverse (either one of the diseases from a disease pair decreases the risk of the other disease), or controversial (one disease increases the risk of the other, but the other disease decreases the risk of the first disease from the disease pair). In general, the majority of disease pairs with increased risk of the later diagnosed disease in both orders of precedence were those in which both the pre-existing and later occurring diseases were cancers, and also when both diseases were of the same organ. […] Generally, the effect of dependence between risks of two diseases diminishes with advancing age. […] Identifying mutual relationships in age-associated disease risks is extremely important since they indicate that development of […] diseases may involve common biological mechanisms.”

“in population cohorts, trends in prevalence result from combinations of trends in incidence, population at risk, recovery, and patients’ survival rates. Trends in the rates for one disease also may depend on trends in concurrent diseases, e.g., increasing survival from CHD contributes to an increase in the cancer incidence rate if the individuals who survived were initially susceptible to both diseases.”

March 1, 2017 Posted by | Biology, Books, Cancer/oncology, Cardiology, Demographics, Diabetes, Epidemiology, Genetics, Health Economics, Medicine, Nephrology, Neurology | Leave a comment

The Ageing Immune System and Health (II)

Here’s the first post about the book. I finished it a while ago but I recently realized I had not completed my intended coverage of the book here on the blog back then, and as some of the book’s material sort-of-kind-of relates to material encountered in a book I’m currently reading (Biodemography of Aging) I decided I might as well finish my coverage of the book now in order to review some things I might have forgot in the meantime, by providing coverage here of some of the material covered in the second half of the book. It’s a nice book with some interesting observations, but as I also pointed out in my first post it is definitely not an easy read. Below I have included some observations from the book’s second half.

Lungs:

“The aged lung is characterised by airspace enlargement similar to, but not identical with acquired emphysema [4]. Such tissue damage is detected even in non-smokers above 50 years of age as the septa of the lung alveoli are destroyed and the enlarged alveolar structures result in a decreased surface for gas exchange […] Additional problems are that surfactant production decreases with age [6] increasing the effort needed to expand the lungs during inhalation in the already reduced thoracic cavity volume where the weakened muscles are unable to thoroughly ventilate. […] As ageing is associated with respiratory muscle strength reduction, coughing becomes difficult making it progressively challenging to eliminate inhaled particles, pollens, microbes, etc. Additionally, ciliary beat frequency (CBF) slows down with age impairing the lungs’ first line of defence: mucociliary clearance [9] as the cilia can no longer repel invading microorganisms and particles. Consequently e.g. bacteria can more easily colonise the airways leading to infections that are frequent in the pulmonary tract of the older adult.”

“With age there are dramatic changes in neutrophil function, including reduced chemotaxis, phagocytosis and bactericidal mechanisms […] reduced bactericidal function will predispose to infection but the reduced chemotaxis also has consequences for lung tissue as this results in increased tissue bystander damage from neutrophil elastases released during migration […] It is currently accepted that alterations in pulmonary PPAR profile, more precisely loss of PPARγ activity, can lead to inflammation, allergy, asthma, COPD, emphysema, fibrosis, and cancer […]. Since it has been reported that PPARγ activity decreases with age, this provides a possible explanation for the increasing incidence of these lung diseases and conditions in older individuals [6].”

Cancer:

“Age is an important risk factor for cancer and subjects aged over 60 also have a higher risk of comorbidities. Approximately 50 % of neoplasms occur in patients older than 70 years […] a major concern for poor prognosis is with cancer patients over 70–75 years. These patients have a lower functional reserve, a higher risk of toxicity after chemotherapy, and an increased risk of infection and renal complications that lead to a poor quality of life. […] [Whereas] there is a difference in organs with higher cancer incidence in developed versus developing countries [,] incidence increases with ageing almost irrespective of country […] The findings from Surveillance, Epidemiology and End Results Program [SEERincidentally I likely shall at some point discuss this one in much more detail, as the aforementioned biodemography textbook covers this data in a lot of detail.. – US] [6] show that almost a third of all cancer are diagnosed after the age of 75 years and 70 % of cancer-related deaths occur after the age of 65 years. […] The traditional clinical trial focus is on younger and healthier patient, i.e. with few or no co-morbidities. These restrictions have resulted in a lack of data about the optimal treatment for older patients [7] and a poor evidence base for therapeutic decisions. […] In the older patient, neutropenia, anemia, mucositis, cardiomyopathy and neuropathy — the toxic effects of chemotherapy — are more pronounced […] The correction of comorbidities and malnutrition can lead to greater safety in the prescription of chemotherapy […] Immunosenescence is a general classification for changes occurring in the immune system during the ageing process, as the distribution and function of cells involved in innate and adaptive immunity are impaired or remodelled […] Immunosenescence is considered a major contributor to cancer development in aged individuals“.

Neurodegenerative diseases:

“Dementia and age-related vision loss are major causes of disability in our ageing population and it is estimated that a third of people aged over 75 are affected. […] age is the largest risk factor for the development of neurodegenerative diseases […] older patients with comorbidities such as atherosclerosis, type II diabetes or those suffering from repeated or chronic systemic bacterial and viral infections show earlier onset and progression of clinical symptoms […] analysis of post-mortem brain tissue from healthy older individuals has provided evidence that the presence of misfolded proteins alone does not correlate with cognitive decline and dementia, implying that additional factors are critical for neural dysfunction. We now know that innate immune genes and life-style contribute to the onset and progression of age-related neuronal dysfunction, suggesting that chronic activation of the immune system plays a key role in the underlying mechanisms that lead to irreversible tissue damage in the CNS. […] Collectively these studies provide evidence for a critical role of inflammation in the pathogenesis of a range of neurodegenerative diseases, but the factors that drive or initiate inflammation remain largely elusive.”

“The effect of infection, mimicked experimentally by administration of bacterial lipopolysaccharide (LPS) has revealed that immune to brain communication is a critical component of a host organism’s response to infection and a collection of behavioural and metabolic adaptations are initiated over the course of the infection with the purpose of restricting the spread of a pathogen, optimising conditions for a successful immune response and preventing the spread of infection to other organisms [10]. These behaviours are mediated by an innate immune response and have been termed ‘sickness behaviours’ and include depression, reduced appetite, anhedonia, social withdrawal, reduced locomotor activity, hyperalgesia, reduced motivation, cognitive impairment and reduced memory encoding and recall […]. Metabolic adaptation to infection include fever, altered dietary intake and reduction in the bioavailability of nutrients that may facilitate the growth of a pathogen such as iron and zinc [10]. These behavioural and metabolic adaptions are evolutionary highly conserved and also occur in humans”.

“Sickness behaviour and transient microglial activation are beneficial for individuals with a normal, healthy CNS, but in the ageing or diseased brain the response to peripheral infection can be detrimental and increases the rate of cognitive decline. Aged rodents exhibit exaggerated sickness and prolonged neuroinflammation in response to systemic infection […] Older people who contract a bacterial or viral infection or experience trauma postoperatively, also show exaggerated neuroinflammatory responses and are prone to develop delirium, a condition which results in a severe short term cognitive decline and a long term decline in brain function […] Collectively these studies demonstrate that peripheral inflammation can increase the accumulation of two neuropathological hallmarks of AD, further strengthening the hypothesis that inflammation i[s] involved in the underlying pathology. […] Studies from our own laboratory have shown that AD patients with mild cognitive impairment show a fivefold increased rate of cognitive decline when contracting a systemic urinary tract or respiratory tract infection […] Apart from bacterial infection, chronic viral infections have also been linked to increased incidence of neurodegeneration, including cytomegalovirus (CMV). This virus is ubiquitously distributed in the human population, and along with other age-related diseases such as cardiovascular disease and cancer, has been associated with increased risk of developing vascular dementia and AD [66, 67].”

Frailty:

“Frailty is associated with changes to the immune system, importantly the presence of a pro-inflammatory environment and changes to both the innate and adaptive immune system. Some of these changes have been demonstrated to be present before the clinical features of frailty are apparent suggesting the presence of potentially modifiable mechanistic pathways. To date, exercise programme interventions have shown promise in the reversal of frailty and related physical characteristics, but there is no current evidence for successful pharmacological intervention in frailty. […] In practice, acute illness in a frail person results in a disproportionate change in a frail person’s functional ability when faced with a relatively minor physiological stressor, associated with a prolonged recovery time […] Specialist hospital services such as surgery [15], hip fractures [16] and oncology [17] have now begun to recognise frailty as an important predictor of mortality and morbidity.

I should probably mention here that this is another area where there’s an overlap between this book and the biodemography text I’m currently reading; chapter 7 of the latter text is about ‘Indices of Cumulative Deficits’ and covers this kind of stuff in a lot more detail than does this one, including e.g. detailed coverage of relevant statistical properties of one such index. Anyway, back to the coverage:

“Population based studies have demonstrated that the incidence of infection and subsequent mortality is higher in populations of frail people. […] The prevalence of pneumonia in a nursing home population is 30 times higher than the general population [39, 40]. […] The limited data available demonstrates that frailty is associated with a state of chronic inflammation. There is also evidence that inflammageing predates a diagnosis of frailty suggesting a causative role. […] A small number of studies have demonstrated a dysregulation of the innate immune system in frailty. Frail adults have raised white cell and neutrophil count. […] High white cell count can predict frailty at a ten year follow up [70]. […] A recent meta-analysis and four individual systematic reviews have found beneficial evidence of exercise programmes on selected physical and functional ability […] exercise interventions may have no positive effect in operationally defined frail individuals. […] To date there is no clear evidence that pharmacological interventions improve or ameliorate frailty.”

Exercise:

“[A]s we get older the time and intensity at which we exercise is severely reduced. Physical inactivity now accounts for a considerable proportion of age-related disease and mortality. […] Regular exercise has been shown to improve neutrophil microbicidal functions which reduce the risk of infectious disease. Exercise participation is also associated with increased immune cell telomere length, and may be related to improved vaccine responses. The anti-inflammatory effect of regular exercise and negative energy balance is evident by reduced inflammatory immune cell signatures and lower inflammatory cytokine concentrations. […] Reduced physical activity is associated with a positive energy balance leading to increased adiposity and subsequently systemic inflammation [5]. […] Elevated neutrophil counts accompany increased inflammation with age and the increased ratio of neutrophils to lymphocytes is associated with many age-related diseases including cancer [7]. Compared to more active individuals, less active and overweight individuals have higher circulating neutrophil counts [8]. […] little is known about the intensity, duration and type of exercise which can provide benefits to neutrophil function. […] it remains unclear whether exercise and physical activity can override the effects of NK cell dysfunction in the old. […] A considerable number of studies have assessed the effects of acute and chronic exercise on measures of T-cell immunesenescence including T cell subsets, phenotype, proliferation, cytokine production, chemotaxis, and co-stimulatory capacity. […] Taken together exercise appears to promote an anti-inflammatory response which is mediated by altered adipocyte function and improved energy metabolism leading to suppression of pro-inflammatory cytokine production in immune cells.”

February 24, 2017 Posted by | Biology, Books, Cancer/oncology, Epidemiology, Immunology, Medicine, Neurology | Leave a comment

Rocks: A very short introduction

I liked the book. Below I have added some sample observations from the book, as well as a collection of links to various topics covered/mentioned in the book.

“To make a variety of rocks, there needs to be a variety of minerals. The Earth has shown a capacity for making an increasing variety of minerals throughout its existence. Life has helped in this [but] [e]ven a dead planet […] can evolve a fine array of minerals and rocks. This is done simply by stretching out the composition of the original homogeneous magma. […] Such stretching of composition would have happened as the magma ocean of the earliest […] Earth cooled and began to solidify at the surface, forming the first crust of this new planet — and the starting point, one might say, of our planet’s rock cycle. When magma cools sufficiently to start to solidify, the first crystals that form do not have the same composition as the overall magma. In a magma of ‘primordial Earth’ type, the first common mineral to form was probably olivine, an iron-and-magnesium-rich silicate. This is a dense mineral, and so it tends to sink. As a consequence the remaining magma becomes richer in elements such as calcium and aluminium. From this, at temperatures of around 1,000°C, the mineral plagioclase feldspar would then crystallize, in a calcium-rich variety termed anorthite. This mineral, being significantly less dense than olivine, would tend to rise to the top of the cooling magma. On the Moon, itself cooling and solidifying after its fiery birth, layers of anorthite crystals several kilometres thick built up as the rock — anorthosite — of that body’s primordial crust. This anorthosite now forms the Moon’s ancient highlands, subsequently pulverized by countless meteorite impacts. This rock type can be found on Earth, too, particularly within ancient terrains. […] Was the Earth’s first surface rock also anorthosite? Probably—but we do not know for sure, as the Earth, a thoroughly active planet throughout its existence, has consumed and obliterated nearly all of the crust that formed in the first several hundred million years of its existence, in a mysterious interval of time that we now call the Hadean Eon. […] The earliest rocks that we know of date from the succeeding Archean Eon.”

“Where plates are pulled apart, then pressure is released at depth, above the ever-opening tectonic rift, for instance beneath the mid-ocean ridge that runs down the centre of the Atlantic Ocean. The pressure release from this crustal stretching triggers decompression melting in the rocks at depth. These deep rocks — peridotite — are dense, being rich in the iron- and magnesium-bearing mineral olivine. Heated to the point at which melting just begins, so that the melt fraction makes up only a few percentage points of the total, those melt droplets are enriched in silica and aluminium relative to the original peridotite. The melt will have a composition such that, when it cools and crystallizes, it will largely be made up of crystals of plagioclase feldspar together with pyroxene. Add a little more silica and quartz begins to appear. With less silica, olivine crystallizes instead of quartz.

The resulting rock is basalt. If there was anything like a universal rock of rocky planet surfaces, it is basalt. On Earth it makes up almost all of the ocean floor bedrock — in other words, the ocean crust, that is, the surface layer, some 10 km thick. Below, there is a boundary called the Mohorovičič Discontinuity (or ‘Moho’ for short)[…]. The Moho separates the crust from the dense peridotitic mantle rock that makes up the bulk of the lithosphere. […] Basalt makes up most of the surface of Venus, Mercury, and Mars […]. On the Moon, the ‘mare’ (‘seas’) are not of water but of basalt. Basalt, or something like it, will certainly be present in large amounts on the surfaces of rocky exoplanets, once we are able to bring them into close enough focus to work out their geology. […] At any one time, ocean floor basalts are the most common rock type on our planet’s surface. But any individual piece of ocean floor is, geologically, only temporary. It is the fate of almost all ocean crust — islands, plateaux, and all — to be destroyed within ocean trenches, sliding down into the Earth along subduction zones, to be recycled within the mantle. From that destruction […] there arise the rocks that make up the most durable component of the Earth’s surface: the continents.”

“Basaltic magmas are a common starting point for many other kinds of igneous rocks, through the mechanism of fractional crystallization […]. Remove the early-formed crystals from the melt, and the remaining melt will evolve chemically, usually in the direction of increasing proportions of silica and aluminium, and decreasing amounts of iron and magnesium. These magmas will therefore produce intermediate rocks such as andesites and diorites in the finely and coarsely crystalline varieties, respectively; and then more evolved silica-rich rocks such as rhyolites (fine), microgranites (medium), and granites (coarse). […] Granites themselves can evolve a little further, especially at the late stages of crystallization of large bodies of granite magma. The final magmas are often water-rich ones that contain many of the incompatible elements (such as thorium, uranium, and lithium), so called because they are difficult to fit within the molecular frameworks of the common igneous minerals. From these final ‘sweated-out’ magmas there can crystallize a coarsely crystalline rock known as pegmatite — famous because it contains a wide variety of minerals (of the ~4,500 minerals officially recognized on Earth […] some 500 have been recognized in pegmatites).”

“The less oxygen there is [at the area of deposition], the more the organic matter is preserved into the rock record, and it is where the seawater itself, by the sea floor, has little or no oxygen that some of the great carbon stores form. As animals cannot live in these conditions, organic-rich mud can accumulate quietly and undisturbed, layer by layer, here and there entombing the skeleton of some larger planktonic organism that has fallen in from the sunlit, oxygenated waters high above. It is these kinds of sediments that […] generate[d] the oil and gas that currently power our civilization. […] If sedimentary layers have not been buried too deeply, they can remain as soft muds or loose sands for millions of years — sometimes even for hundreds of millions of years. However, most buried sedimentary layers, sooner or later, harden and turn into rock, under the combined effects of increasing heat and pressure (as they become buried ever deeper under subsequent layers of sediment) and of changes in chemical environment. […] As rocks become buried ever deeper, they become progressively changed. At some stage, they begin to change their character and depart from the condition of sedimentary strata. At this point, usually beginning several kilometres below the surface, buried igneous rocks begin to transform too. The process of metamorphism has started, and may progress until those original strata become quite unrecognizable.”

“Frozen water is a mineral, and this mineral can make up a rock, both on Earth and, very commonly, on distant planets, moons, and comets […]. On Earth today, there are large deposits of ice strata on the cold polar regions of Antarctica and Greenland, with smaller amounts in mountain glaciers […]. These ice strata, the compressed remains of annual snowfalls, have simply piled up, one above the other, over time; on Antarctica, they reach almost 5 km in thickness and at their base are about a million years old. […] The ice cannot pile up for ever, however: as the pressure builds up it begins to behave plastically and to slowly flow downslope, eventually melting or, on reaching the sea, breaking off as icebergs. As the ice mass moves, it scrapes away at the underlying rock and soil, shearing these together to form a mixed deposit of mud, sand, pebbles, and characteristic striated (ice-scratched) cobbles and boulders […] termed a glacial till. Glacial tills, if found in the ancient rock record (where, hardened, they are referred to as tillites), are a sure clue to the former presence of ice.”

“At first approximation, the mantle is made of solid rock and is not […] a seething mass of magma that the fragile crust threatens to founder into. This solidity is maintained despite temperatures that, towards the base of the mantle, are of the order of 3,000°C — temperatures that would very easily melt rock at the surface. It is the immense pressures deep in the Earth, increasing more or less in step with temperature, that keep the mantle rock in solid form. In more detail, the solid rock of the mantle may include greater or lesser (but usually lesser) amounts of melted material, which locally can gather to produce magma chambers […] Nevertheless, the mantle rock is not solid in the sense that we might imagine at the surface: it is mobile, and much of it is slowly moving plastically, taking long journeys that, over many millions of years, may encompass the entire thickness of the mantle (the kinds of speeds estimated are comparable to those at which tectonic plates move, of a few centimetres a year). These are the movements that drive plate tectonics and that, in turn, are driven by the variation in temperature (and therefore density) from the contact region with the hot core, to the cooler regions of the upper mantle.”

“The outer core will not transmit certain types of seismic waves, which indicates that it is molten. […] Even farther into the interior, at the heart of the Earth, this metal magma becomes rock once more, albeit a rock that is mostly crystalline iron and nickel. However, it was not always so. The core used to be liquid throughout and then, some time ago, it began to crystallize into iron-nickel rock. Quite when this happened has been widely debated, with estimates ranging from over three billion years ago to about half a billion years ago. The inner core has now grown to something like 2,400 km across. Even allowing for the huge spans of geological time involved, this implies estimated rates of solidification that are impressive in real time — of some thousands of tons of molten metal crystallizing into solid form per second.”

“Rocks are made out of minerals, and those minerals are not a constant of the universe. A little like biological organisms, they have evolved and diversified through time. As the minerals have evolved, so have the rocks that they make up. […] The pattern of evolution of minerals was vividly outlined by Robert Hazen and his colleagues in what is now a classic paper published in 2008. They noted that in the depths of outer space, interstellar dust, as analysed by the astronomers’ spectroscopes, seems to be built of only about a dozen minerals […] Their component elements were forged in supernova explosions, and these minerals condensed among the matter and radiation that streamed out from these stellar outbursts. […] the number of minerals on the new Earth [shortly after formation was] about 500 (while the smaller, largely dry Moon has about 350). Plate tectonics began, with its attendant processes of subduction, mountain building, and metamorphism. The number of minerals rose to about 1,500 on a planet that may still have been biologically dead. […] The origin and spread of life at first did little to increase the number of mineral species, but once oxygen-producing photosynthesis started, then there was a great leap in mineral diversity as, for each mineral, various forms of oxide and hydroxide could crystallize. After this step, about two and a half billion years ago, there were over 4,000 minerals, most of them vanishingly rare. Since then, there may have been a slight increase in their numbers, associated with such events as the appearance and radiation of metazoan animals and plants […] Humans have begun to modify the chemistry and mineralogy of the Earth’s surface, and this has included the manufacture of many new types of mineral. […] Human-made minerals are produced in laboratories and factories around the world, with many new forms appearing every year. […] Materials sciences databases now being compiled suggest that more than 50,000 solid, inorganic, crystalline species have been created in the laboratory.”

Some links of interest:

Rock. Presolar grains. Silicate minerals. Silicon–oxygen tetrahedron. Quartz. Olivine. Feldspar. Mica. Jean-Baptiste Biot. Meteoritics. Achondrite/Chondrite/Chondrule. Carbonaceous chondrite. Iron–nickel alloy. Widmanstätten pattern. Giant-impact hypothesis (in the book this is not framed as a hypothesis nor is it explicitly referred to as the GIH; it’s just taken to be the correct account of what happened back then – US). Alfred Wegener. Arthur Holmes. Plate tectonics. Lithosphere. Asthenosphere. Fractional Melting (couldn’t find a wiki link about this exact topic; the MIT link is quite technical – sorry). Hotspot (geology). Fractional crystallization. Metastability. Devitrification. Porphyry (geology). Phenocryst. Thin section. Neptunism. Pyroclastic flow. Ignimbrite. Pumice. Igneous rock. Sedimentary rock. Weathering. Slab (geology). Clay minerals. Conglomerate (geology). BrecciaAeolian processes. Hummocky cross-stratification. Ralph Alger Bagnold. Montmorillonite. Limestone. Ooid. Carbonate platform. Turbidite. Desert varnish. Evaporite. Law of Superposition. Stratigraphy. Pressure solution. Compaction (geology). Recrystallization (geology). Cleavage (geology). Phyllite. Aluminosilicate. Gneiss. Rock cycle. Ultramafic rock. Serpentinite. Pressure-Temperature-time paths. Hornfels. Impactite. Ophiolite. Xenolith. Kimberlite. Transition zone (Earth). Mantle convection. Mantle plume. Core–mantle boundary. Post-perovskite. Earth’s inner core. Inge Lehmann. Stromatolites. Banded iron formations. Microbial mat. Quorum sensing. Cambrian explosion. Bioturbation. Biostratigraphy. Coral reef. Radiolaria. Carbonate compensation depth. Paleosol. Bone bed. Coprolite. Allan Hills 84001. Tharsis. Pedestal crater. Mineraloid. Concrete.

February 19, 2017 Posted by | Biology, Books, Geology | Leave a comment