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What gaseous substances do humans emit?
Other than CO₂ and Methane what other gases do humans produce or emit?
For example, does  skin decomposition, or aerobic respiration emit any special gases that people don't normally realize or know about.
I ask because of a discovery I made during research is that while being poisonous to the central nervous system, methanol is a natural endogenous compound found in normal, healthy human individuals.
One study found a mean of 4.5 ppm in the exhaled breath of the subjects. https://iopscience.iop.org/article/10.1088/0967-3334/27/7/007

The probably origin of exhaled methane is anaerobic gut bacteria and archaea (yes there are always a few archaea in the gut).

There are 100+ gas phase compounds that come from humans.

Find other information about most of these families of compounds from wiki.
Full list of compounds is here
Methods and findings are here

A few spontaneous comments: 1-octen-3-ol and similar compounds are fungal products from lipid breakdown (probably on the skin) so not directly human metabolites. Also, cymene (and others, I didn't look up the full list) are plant secondary metabolites, i.e. essential oil components presumably from eating plant food with subsequent exhaling and/or pooping. Humans cannot make these themselves.

Oh golly that's interesting. Perhaps that would be a comment for the OP, i.e. that methane is not actually made by the cells of the human body. There are perhaps symbiont funghi on humans according to current research on microbiology.

Yes human skin fungi are mainly from genus Malassezia.

The gases NO, H₂S, CO even have a function in the human body!
Nitric oxide is produced in endothel and neurons as messenger, and in macrophages as cause of nitrosative stress for imprisoned bacteria. Hydrogen sulfide is produced in cysteine catabolism and functions as messenger (only recently discovered). Carbon monoxide appears to act as messenger, too.
http://en.wikipedia.org/wiki/Biological_functions_of_nitric_oxide
http://en.wikipedia.org/wiki/Hydrogen_sulfide#Function_in_the_body
Wu, L; Wang, R (December 2005). "Carbon Monoxide: Endogenous Production, Physiological Functions, and Pharmacological Applications". Pharmacol Rev 57 (4): 585–630. doi:10.1124/pr.57.4.3. PMID 16382109

I always understood that NO, when used as a neurotransmitter / second messenger, is in solution, i.e. not gaseous.

Would these gases be extreated from the body in gasous form?

H₂O is emitted through respiration and perspiration.

A key component of body odour is the volatile thioalcohol, 3-methyl-3-sulfanylhexan-1-ol (3M3SH).  The structure may be visualized as hexan-1-ol  "substituted by a methyl group and a thiol group at position 3 " (pubchem)
A major contributor to the formation of 3M3SH is the  commensal bacterium, Staphylococcus hominis,  which produces it  as a by-product of the metabolism of  (odourless)  Cys-Gly-3M3SH.
Cys-Gly-3M3SH, "an L-cysteinylglycine dipeptide-conjugated alcohol that is secreted onto the surface of the skin by apocrine  glands" (Ref 2), is itself produced as a by-product of human metabolism in sweat glands.
The key enzyme is a cysteine-thiol lyase (C-T lyase), which (among many other interesting attributes), contains pyridoxal phosphate.
References
Ref 1: Structural basis of malodour precursor transport in the human axilla (Minhas et al., 2018)
Ref 2: The molecular basis of thioalcohol production in human body odour  (Rudden et al., 2020; Nature (Scientific Reports))
Ref 3: Know sweat: scientists solve mystery behind body odour (The Guardian, July 2020)
Ref 4: Structure of 3M3SH at pubchem.

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What is Mendelian Randomization, and how is it used to infer causality in epidemiology?
Studies of human traits and diseases are inherently confined to observational studies, known as epidemiological studies. This can make it very hard to determine what actually causes a particular disease or trait, and what is just a by-product or biomarker of the disease.
Mendelian randomization is a statistical technique used by genetic epidemiologists to determine causal effects, for instance whether a biomarker actually influences disease risk, or whether it is just a statistical association.
From basics, how can genetics be used to determine causal associations between traits (such as blood markers) and increased risk of disease, and on what assumptions is this based?

Should it not be trigs ~ SNPs for the triangulation?

Epidemiological studies analyze human observational cohort data to try to statistically link disease risk and biomarkers. For instance, it is know a well know fact that smoking increases the risk of lung cancer, yet before the 1950s it was widely considered otherwise until observational evidence proved irrefutable (Levin, 1950).
Confounding
However, inferring causality using observational data can be tricky. For instance, there is a very strong statistical association between ice-cream sales and risk of drowning in the UK. In this example it is quite clear that the association is confounded - that is, there is a 'hidden' variable we have not accounted for that links the 2. It is easy to infer that ice-cream sales don't increase your risk of drowning, but the 2 are correlated because both ice-cream sales and risk of drowning increase at the same time of year; both are positively correlated with 'nice weather' traits.
In the above example it is easy to say that the relationship is confounded. However, in a hypothetical study on type-2 diabetes that finds higher blood hematocrit (% volume of blood made up of red blood cells) is associated with increased odds of being diabetic, it is less clear whether higher hematocrit increases susceptibility/risk of becoming diabetic, or whether diabetes increases hematocrit. Longitudinal studies that follow the same individuals for many years help determine which came first - this is based on the assumption that if raised hematocrit is associated with increased risk of diabetes 5 years later, it is more likely that hematocrit is causal, and you have a biomarker for diabetes risk (although you still cannot say it is causal, it may just be a proxy for another 'hidden' trait, or the effect may be mediated by another unmeasured marker).
Causality
Genome-wide association (GWA) studies have become increasingly popular since the completion of the human genome project, and there are currently (15th August 2012) 7039 genetic variants (SNPs, single-nucleotide polymorphisms) documented in the catalogue of published GWA studies (www.genome.gov/gwastudies). These SNPs have been robustly associated with traits such as 'risk of type-2 diabetes', or 'blood adiponectin levels', by large genetic epidemiological studies, usually with many thousands of participants sampled.
The key point about SNP associations is that the direction of effect is always known; a particular SNP may affect your adiponectin levels, but adiponectin does not affect the SNPs you inherit.
Mendelian Randomization
In an observational study that finds an association between (for example) circulating triglyceride levels and type-2 diabetes (using fasting glucose and insulin as 'proxies') it is not possible to determine the causal direction; do triglycerides increase risk of diabetes or vice versa? (This is an actual case study by De Silva, et al. 2011).
Using the assumption that the inheritance of SNPs is essentially random (i.e. not affected by the trait you are measuring - in this example triglycerides) it is possible to determine the causal effect of triglycerides on the risk of diabetes. Note: this is only possible for traits (such as serum triglyceride levels) that have SNPs identified by GWA studies.
First of all, you have already calculated the association between triglyceride levels (in this study a 1 standard-deviation (SD) increase in triglycerides was associated, with an Odds Ratio (OR) of 2.68, with type-2 diabetes in almost 10,000 human participants). Because it is a cross-sectional study, only an association is reported, with no causal implications... yet!
Enter the SNPs
Having identified the SNPs that increase triglyceride levels (at the time of the study, 10 were reported in the literature), each participants genotype for these SNPs needs to be measured or inferred (generally this is performed in large cohort studies, which are then used for many different individual projects).
A combined 'score' is then derived for each individual. This is conceptually very simple; if an individual has 4 of the 10 triglyceride raising alleles, they are given a score or 4, and so on. This is complicated by the fact that the SNPs have different effects on the triglycerides, so a 'weighted allele score' is derived instead (simply put, the coefficient of the SNP~triglyceride association is taken into account when adding risk allele scores). By using this kind of score, a 'per allele' estimation of the effects can be estimated;
The association between the triglyceride levels and the allele score is then determined (by linear regression analysis - adjusted for age and sex, as these are known to affect triglycerides). There are actually 2 ways of performing the next analysis, the first of which is conceptually simpler, but the second of which is more robust.
Method 1: the 'triangulation' approach
The two observed associations calculated so far (per-SD trig. levels are associated with diabetes, and the per-allele SNP score are associated with per-SD trig. levels) can then be used to calculate an expected association. This is simply derived by multiplying the 2 coefficients together. Below is the 'triangulation' representation of this;

This expected association between the triglyceride-raising SNPs and risk of diabetes is only true if raised triglycerides increase the risk of diabetes. The observed association between the SNP-score and diabetes can then be calculated. If the expected and observed estimates are similar, then triglycerides raise diabetes risk, and are a causal biomarker. If the observed association is null (or, at least, much smaller), then triglycerides are likely to be secondary.
Method 2: the instrumental variable analysis
This method is for the more hardy statisticians. Instrumental variables are derived variables, and represent an intermediate between a proposed 'cause' and the 'effect', or outcome (in this case the triglyceride raising alleles are proposed to 'cause' diabetes, and this is mediated by the triglyceride levels themselves).
Using a linear regression, the "fitted values" from a model with triglyceride levels as the outcome, and the weighted SNP score as the only independent variable, are extracted. These fitted values represent the variance in the triglycerides that is predicted by the SNP score. We are using the SNPs as the instrument here, as they directly influence the biomarker (exposure, i.e. triglycerides), and are not affected by either the exposure or the outcome (diabetes). The important assumption here is that the effect of the SNPs on the outcome is not confounded, and can only be mediated by the exposure - this is true for genotypes (Lawlor, et al. 2008).
By then determining the association between the derived variable and diabetes, you will know whether the proportion of the triglyceride variance predicted by the SNPs is associated with diabetes - if so, then triglycerides increase the risk of diabetes.
Results
The study performed both types of Mendelian Randomization analysis and found no evidence to suggest a causal association between triglycerides and diabetes phenotypes.
So Mendelian Randomization is a useful tool for inferring causality with biomarkers. It is not necessarily conclusive evidence, but it can help distinguish biomarkers of particular importance and interest (with regard to interventions) from those that are just markers of the disease.

Nice weather? In the UK? Ridiculous.

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(Rough) Model for DNA evolution in E. coli genes
I need a model of in-gene DNA drift. I'm not interested in bacterial phylogenies alone.
Here is what I understand:

Sequences corresponding to genes have both exons and introns, but in bacteria the proportion of introns is low.
Because I'm talking about genes, sequence changes are not very neutral but subject to selection.

I have checked a few papers linked in Wikipedia about this subject, but I notice that they are tuned for entire sequences, and many of them almost predate the genomic era.
What are some current, canonical sources in this area?

There is nothing like introns in E.coli because there is no splicing in bacteria. What is unclear for me, are you asking about mutations/indels? Then please say so.

Bacterial DNA evolution is like other evolution in the sense that the mutation rates are probably random and biased somewhat by the physical and chemical structure of DNA as well as the presence and activity of DNA repair enzymes.  The mutation protocols for preparing mutant libraries from UV or chemical mutagens are a good place to start I would think - like this reference. Most of this stuff is oriented towards protocols used in the lab, not in the wild.  Like how much UV does E coli get in your gut?  Not much.  Still it does happen and is a typical baseline factor in evolution.
Bacteria also mediate their own adaptation via phage transfer as well as direct lateral gene transfer, chromosomal rearrangement.  The dynamics of this evolution is just as interesting I think as site mutation as the bacteria can use its repair mechanisms and even actively upregulate some adaptation itself, responding to environmental changes.  The fundamental differences between bacterial evolution and say evolution of animals and plants come from the fact that life is relatively cheap - a single well adapted survivor can repopulate very quickly and so that even radical genetic diversity has a benefit even if some of the population dies off every generation from being a bad adaptive experiment.  (BTW animals do this too, but in the short lived haploid gamete phase, not so much as diploid animals).
Well anyway back to bacteria.  Eric Alm's lab at MIT started with his work with operon evolution.  Genes grouped together on a single mRNA leap from one cluster to another across evolution, allowing for coordination of biochemical processes in the cell.  Lately they have been looking more intensely at the evolution of bacterial species, and how the selective environment influences bacterial adaptation.
Its hard to be more specific since I'm not completely clear on your focus and needs here, but this is what I know.

That helps a lot, I just added some papers from the people at MIC to my reading list. My core question was this: what are the differences between DNA stretches under selective pressure and DNA stretches which is garbage... I suspect that because there is not that much garbage in bacterial genomes, I might very well not care that much.

a better question definitely.  Its very hard to tell the difference unfortunately.  Anyone who has actually tried to find a transcription factor binding site knows that in many cases, while the DNA sequence varies substantially and still functions, its pretty hard to tell whether its noise, spacer, or totally functionally important.  I wish I knew the answer.. I'd probably be typing from my office across the hall from Eric Alm...seriously though, that deserves an entire new question.  cant' cram that into a comment.

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Why did life not evolve to use radio?
We use electromagnetic communication everywhere these days. Cell phones, wifi, old-school radio transmissions, television, deep space communication, etc.
I'm curious about some of the possible reasons we have never seen biological systems having evolved to use electromagnetic, i.e. radio, for communication. The one obvious exception to this are organisms that generate their own light, i.e. bioluminescence. Cuttlefish are masters of this, and many other species as well.
It seems like bio-radio could have offered all kinds of evolutionary advantages for animals capable of using it.
Are their basic physical limits in chemistry, or excess energy requirements or something that would basically have made this impossible? Or was this perhaps just something that life never evolved to use, but would otherwise be possible in evolution?

Also, don't forget magnetotactic bacteria

Light is an electromagnetic wave. The only difference is wave length and energy.

What about Sharks?

Life did evolve to use radio, we use it all the time.

See electrocommunication, weakly electric fish and Mormyridae. They use 500 Hz electric sine signals (however, AFAIK using conductance of water, not - electromagnetic waves).

All life generate and absorb EMR, outside of the light spectrum. The fact that there are only a few species known to actively sense or use frequencies above or below the infrared-ultraviolet spectrum, may in part, be due to our inability to recognize those structures.  I would add my own answer here, but perhaps I don't have the reps for it?

Does this answer your question? Why do some bad traits evolve, and good ones don't?

There is a very different mechanism for generation (and detection) of ultraviolet, visible and infrared light vs radio waves.
For the first, it is possible to generate it using chemical reactions (that is, chemiluminescence, bioluminescence) with a typical energy of order of 2 eV (electronovolts). Also, it is easy to detect with similar means - coupling to a bond (e.g. using opsins).
For much longer electromagnetic waves, and much lower energies per photon, such mechanism does not work. There are two reasons:

typical energy levels for molecules (but it can be worked around),
thermal noise has energies (0.025 eV) which are higher than radio wave photon energies (<0.001 eV) (it rules out both controlled creation and detection using molecules).

In other words - radiation which is less energetic than thermal radiation (far infrared) is not suitable for communication using molecular mechanisms, as thermal noise jams transmission (making the sender firing at random and making the receiver being blind by noise way stronger than the signal).
However, one can both transmit, and detect it, using wires. In principle it is possible; however, without good conductors (like metals, not - salt solutions) it is not an easy task (not impossible though).

Why is the thermal noise not a problem in mechanical devices then? Is the only reason the better conductivity of the material?

Thermal noise is devastating for molecular devices, as detectors/emitters (two-level systems) are all the time saturated (so e.g. a photon has the same probability of being absorber and to steal excitation). For macroscopic currents there is no such mechanism and one can easily go beyond the thermal noise (so for animals its only a technical problem of getting good enough conductors and generators of high frequencies). Moreover, AFAIK water absorbs most of radio waves (it's why submarines use sonars, not - radars), so radio communication would work only for land animals.

Wires (like tubules, axons) for electrical signaling seem to be very common in the cell world. Is it actually known that they don't have a radio wave antenna function too?

@LocalFluff Axons are not "wires". They propagate signal due to depolarization, not - conductance.

Okay, I'm lost in this weird universe of microbiochemo whatever. Fascinating stuff. But there are plenty of microtubules around, up to a couple of millimeters long. Life seems to like to make tubes and strings of different kinds. My wild thought is that maybe they could generate and react to radio waves? Some speculate that they might even have quantum mechanic properties. Water is bad for radio, but on the scale of a few cells maybe that doesn't matter much.

Point of fact: Technological EMR absorption, raises the average human body temperature by a significant fraction of a degree, which consequently reduces our caloric requirements. We may not yet have a full enough understanding of all the ways that each part of the EM spectrum may be used, sensed/emitted by life forms. If you stand to too close to a high enough power output radio antenna, in some RF bands, you will "feel the heat". In other cases, electrostatic charges raise the hairs on our skin.

So an interesting follow up question would be, why didn't organisms evolve to incorporate metallic conductors into their body

Because the intermediate stages are not evolutionarily favoured. That's why.
Sound and light perception are useful without any generative capability. An organism with a tiny amount of perception for either of these things has an advantage over those without; and an organism with a tiny amount more has an advantage over those with a tiny bit less. This advantage forms the basis for selection and thus improved sensory capabilities (balanced, of course, by the cost of those capabilities).
Being able to perceive radio on the other hand provides no useful information about the world at low level perception so even if an organism was to randomly mutate so as to detect radiowaves* there would be no selection for this ability, and thus no mechanism to drive the evolution of advanced radio reception. Without the ability to perceive radiowaves there is no possibility of evolving the ability to generate radio signals in a controlled manner.
*-In fact, since radiowaves generally interact very little with organic materials unlike heat, light and sound even this first step of random mutation is much less likely than for sense that have evolved.

I find the "intermediate stages" along with "no useful information about the world" argument more satisfactory than thermal noise argument.
Terrestrial animals preferred visible light to be visible because of Sunlight spectrum. https://en.wikipedia.org/wiki/File:Solar_Spectrum.png
Sound has high attenuation, so it is more fit for local communication without attracting predators from long distance.

How about bats? Their ecolocation is useful without any transmitter.

@DanDascalescu: (1) What do you mean there is no transmitter? Bats create the sound they listen for echoes of and (2) Bats' echolocation works by further evolution of the already-selected-for abilities to hear and make sounds.

@JackAidley, I meant without another transmitting animal.

@Vikas Multicellular organism have many cells which communicate with each other, using many complicated chemical and electrical signals. The first multicellular individual that somehow mutates to use radio could certainly benefit from it. All cells in it would then have the same genes, the same radio channel.

@DanDascalescu It was. many animal can produce ultrasound. Cat for example communicate in ultrasonic we human don't hear normally. Animal other than us mostly nocturnal need ability to find prey and aware of predator. They evolve to hear wider range of sound than us. Then they communicate with that range. And some evolve ability to do echolocation after that

@LocalFluff the problem is "using" radio is not one mutation it is many many mutation because there is no useful amount of ambient radio around. There already is useful amounts of, sound, visible light, and heat.

Actually, electromagnetic communication is used by certain fish, the mormyrids and the gymnotids.  Pulse modulated in the former and amplitude modulated in the latter.
However, the frequencies used are not much greater than 1Khz, which is not what we ordinarily consider to be in the radio frequency spectrum.
There is, too,  another biological species in which the use of the full RF spectrum has evolved. Its activities even extend to the use of the UV and X-ray frequencies.
That species is our own. I am not being flippant here. We must not fall into the trap of considering ourselves as apart from nature.  Contrary to our usual intuitions, technologies have evolved autonomously within the collective imagination of our species.
The broader evolutionary model which supports this contention is outlined, very informally, in "The Goldilocks Effect: What Has Serendipity Ever Done For Us?" , a free download in e-book formats from the "Unusual Perspectives" website.

Thanks for the info about mormyrids and gymnotids. I'll do some further reading on them. I am only interested in biologically-generated radio communication, which was where my question was directed. One of the key differences between technology and biology is that the former is not inherently inheritable. A flock of birds that communicate via radio will pass that ability to their young and is innate. Human speech capability is passed innately to our young. The knowledge to build technology is sociological, not biological.

You miss my point. The evolution of technology cannot be dismissed as "sociological". If examined carefully is ti an inevitable outcome of biology.  It is so very easy to fall into that anthropocentric trap.  The mechanisms of transmission are, of course, not identical for the various phases of the overall life process.

this is a good answer - adaptation could, given proper conditions, create radiosensitive organisms I'm sure.

I just found a research about possibility of organism with loop DNA (Mostly bacteria) could use there DNA as antenna to transmit and receive radio wave around 1kHz
http://www.wired.com/2011/04/bacterial-radio/
But as other said. Communication mostly evolve from sensory organ. So the radio wave has too much noise and could not give useful information about situation. They don't selectively evolve to the point that they could be used to communicate
But the bacteria has inherently possibility from start. So they may actually do some communication

That's really interesting! Thanks for the update.

A quick comparison between light and sound vs. Radio

Light: Wavelength 380 nm -740 nm
Sound: 17 mm - 17 m
Radio: 1mm - 10e5 km

From the Planck relation, the energy of a wave is inversely proportional to the wavelength. As a result light is stronger than sound which is stronger than FM radio which is stronger than AM radio. Very likely, the energy density provided by radio is far too weak to have meaningful signal processing.
However, there are some uses in the radio frequency. Bat echolocation occurs at a frequency of 14,000 to 100,000 Hz which is well within the radio frequency.

You don't need a lot of energy to communicate anything. Anyway, radio and wifi works, so it's not a good argument.

@PiotrMigdal, I agree with that counterargument. However, electronic forms of communication via Radio and wifi have the luxury of being amplified. Yes, signaling cascades exists but certainly are not optimal compared to higher frequency channels. Your answer is certainly better.

A good point with amplification. For radio you want sine-wave amplifiers, not cascade amplifiers, so here there may be a problem as well. However, the first step is to have good wires...

echolocation uses sound not light. the mechanism for detecting sound is basically the same regardless of frequency while light has to change significantly across that difference. the detection of sound can lead to the detection of high frequency sound but an eye cannot be made to detect radio without significant changes at every level.


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Where can I find a list of diseases and their incidence?
Say I am studying a particular disease and I know that its incidence is 0.8 per 100000 children below the age of 18, how would I find the incidence of a whole bunch of other pediatric diseases (or even not just limited to pediatrics)?
Essentially I want to find diseases that would be familiar to a medical audience and provide an example of a disease with an incidence of about 0.8 per 100000, another one that is one tenth the incidence and another one that is ten times the incidence.

About diseases in general, that would concern the health dept. of the respective country government, and the govt. is the most probable source for such numbers.
However, orpha.net has two reports with numbers concerning hereditary diseases, have a look at http://www.orpha.net/consor/cgi-bin/Education_Home.php?lng=EN#REPORT_RARE_DISEASES

Pretty good. The list details prevalence. I would prefer incidence but if prevalence is all I can get then I will settle for prevalence.

The Center for Disease Control is a great source for disease statistics. I didn't find a chart with common disease incidence on their website, but if you search for a specific disease you should be able to find the incidence.
http://www.cdc.gov/nchs/

Nice resource but not for what I am trying to find. I specifically am looking for a listing of incidences across many disease all in one place. I could search for any disease in pubmed (or even better I could start my search from MESH headings) and specify that I am looking for statistics or incidence.

A couple resources that might be of use:
If you subscribe to it (annoyingly, I cannot get at my copy), the American Academy of Pediatric's Red Book may contain incidence figures. I don't recall, but it contains a remarkable amount of information in it.
For viral diseases, most if not all entries in Fields Virology have an Epidemiology section, and many of these touch on the incidence rate, especially for more common diseases.
In addition to the CDC (listed in another answer), take a look at the Agency for Healthcare Quality Research (AHRQ)'s Healthcare Cost and Utilization Project (HCUP) data. While this will only give you incidence in terms of hospitalizations rather than all cases, it may be potentially useful.
Beyond that, I'd suggest looking for particular papers, or search for "DISEASE OF INTEREST" meta-analysis in Google Scholar or PubMed for overall incidence figures.
Additionally, a useful formula:
Prevalence = Incidence * Duration. Which in turn means Prevalence/Duration = Incidence. If you can find those two pieces of information, you can calculate the incidence for what you want.
There's also a way to extract incidence from a mathematical modeling paper about a disease which reports an R0 figure, but that may be a touch technical given your stated desires.


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Optimization of annealing temperature
I am trying to optimize annealing temperature for some primer pairs. I have tried optimization using cDNA, genomic DNA, Taq polymerase, phusion polymerase etc., but every time I am either getting non-specific amplification, a band of larger size than expected or no amplification. Every time I am getting a primer-dimer. Can anybody help me optimize the annealing temperature?

Posting the primer sequences and what genome they are designed against would be helpful.  Furthermore, have you double checked that your primers are in the correct orientation?

I've heard of people using single stand binding protein to reduce primer dimers.

Most PCR enzymes come with a manual (which can also be found online) that has a troubleshooting section, often dealing exactly with you problem. It may be more fruitful to first follow these steps, and include the outcome in your question.

I'm going to give this question a blind attempt, assuming you are talking about PCR (since you are talking about genomic DNA and cDNA) and not RT-PCR or qPCR, since I know very little about them. This answer is not necessarily optimisation for annealing but checking some basics and potential solutions. In my work I had to do a lot of site directed mutagenesis and genotyping and there are some important rules you have to follow in your primer design. First try to keep your primer length between 25-30 for sequencing (at least thats what I do) and for site-directed mutagenesis between 25-45 since anything above 45 bases increases the likelihood of secondary structure formation. Primer melting temperature (Tm) should be >=78 oC (Tm= 81.5 + 0.41(%GC)-675/N [with N being primer length in bases]). Also primers should have a minimum CG content of 40% and should ideally start and end with a C or G. So check your primer CG% since if its too high then it will make a few problems including increased Tm and possibility of primer-dimer formation due to CG triple hydrogen binding. This document has some nice information (http://www.genomics.agilent.com/files/Manual/210518.pdf)
In my opinion if all else fails and you are still struggling with your primers, then re-design a new primer set, if possible. Now getting back to the annealing temperature issue, in my experience for cDNA, annealing temp of 50 oC works best and for genomic DNA, annealing temp of 55 oC works best, which is about the range most people work with and I have so far had no problems. I'm more than happy to share my genomic DNA PCR cycle information with you if you wish through an edit.
Hope this helps!


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Given ATP synthase's structure, how can 3.33 protons ultimately synthesize one and only one ATP?
I am familiar with the structure and function of ATP synthase, but one small detail doesn't seem to make sense. It also happens to be a detail that seems very hard to express.

Depending on the organism, there can be from 10 to 14 c-subunits in the c-ring of ATP synthase. For practicality/consistency's sake, let's assume 10.
Every c-subunit is protonated (from the cytoplasmic side of the mitochondrion) and rotated almost 360 degrees within the lipid membrane before it is ultimately deprotonated, which releases a proton into the matrix. Thus, since the c-ring is 10 subunits big (in this case), 10 protons are being introduced to the matrix every 360 degree turn, which mean that 1 proton = a 36 degree turn.
These protons allow for the gamma-subunit to be turned at the inside of the hexamer (of 3 alpha-subunits and 3 catalytic beta-subunits) found below the rotor. The b-subunits are thus located 120 degrees apart. Every 120 degrees precisely, that beta-subunit changes shape, dictated by the rotation of the gamma-subunit, and enters one of the three states that ultimately catalyze one ATP from ADP and inorganic phosphate (since only one of the three states actually forms ATP). Since the gamma-subunit catalyses the formation of 1 ATP every 120 degrees, a full 360 degree rotation would yield 3 ATPs.
This is where things go awry in my mind. Within 360 degrees, 10 protons and 3 ATPs are made. Thus, that makes (10/3) protons per ATP, or 3.33 protons/ATP. When the gamma-subunit receives a proton, it rotates 36 degrees, but ATP formation only happens at precisely 120 degrees, and the only common interval between 36 and 120 is 360 (when 3 as opposed to 1 ATPs are made)!
EDIT: The gamma-subunit must rotate in exactly 120 degree increments, because we observe it do so, as shown here.

More detailed analysis in the presence of lower concentrations of ATP revealed that the γ subunit rotates in 120-degree increments, with each step corresponding to the hydrolysis of a single ATP molecule.

Logically, this shouldn't happen though, since the closest increments of 36 to 120 degrees are 108 and 144.
To summarize: How can 3.33 protons ultimately synthesize one, and only one ATP, knowing that a literal third of a proton doesn't exist (excluding quarks) and that increments of 36 degrees can never add up to exactly 120 degrees?

References

“A Proton Gradient Powers the Synthesis of ATP - Biochemistry - NCBI Bookshelf”, n.d. http://www.ncbi.nlm.nih.gov/books/NBK22388/.

It's probably not that the rotation has to stop on 120° but rather pass 120°? That's the impression I got from the snapshot animation of tagged subunits anyway.

I've added another section to the question which addresses this comment, thanks Armatus.

I think the point is that the rotation of the c subunits is not directly linked to the gamma rotor's movement. As far as I recall, the stator locks the rotor until sufficient energy has built up, then it will let the rotor move 120° in one stroke (kind of like a spring snapping I suppose), stirring the next a or b subunit. BCM has been a while so I'll check up on that and come back with ref tomorrow :)

Never heard that before, sounds interesting. :)

Ok according to my lecture (whose information probably came from Alberts MBotC), the axle rotation is necessary to push the newly formed ATP off the beta units, so it does sound to me like the rotor could stop "in front" of the next b-ATP to be "pushed off" until enough energy accumulates, then flips, pushing it off, and rotating the full 120° to the next b-unit. The article you linked refers to 120° steps when hydrolysing ATP, i.e. the "reverse" rotation.

But there seem to be some not understood discrepancies anyway. My lecturer mentions that plastids' ATP synthase has 14 c-units but appears to require only 2 protons per ATP (6 per turn but 10 units?).

Armatus is right the axle/stalk acts more like a spring than a rigid shaft. It means a single ATP production does not correspond to exactly 120 degrees as the shaft is twisted then snaps into a new position, I always like to think of it working like an old fashioned trip-hammer. Plus there are several variations of ATP synthase that affect things like rigidity of the shaft or whether the rotor can turn backwards. One variation is whether each rotor subunit actually has a slot for a proton or if the subunits are the same size. http://www.sciencedirect.com/science/article/pii/S0014579301023006

@Armatus, remember that not every one of these subunits may actually have a place to slot a proton. They are not all identical, some are just filler. For some bacteria out of 12 subunits only 8 actually have a site for a proton.

I might be taking a bit of a simplistic view on this, but I'm not sure what the confusion is.
The ring of 10 c subunits constitute a motor which uses a proton gradient to drive the rotation of the FoFc synthase.
 (image reference is a nice lecture on ATP synthase).
The motor of c subunits has a 10 fold symmetry.  Each subunit releases a proton as it drives a 36 degree movement of the gamma subunit shaft.  The sixfold symmetry of the ATP FoF1 synthease is completely separate from this, and every 360 degree rotation, three ATPs are generated.  As long as the c motor keeps spinning more ATP is created.
This question is similar to asking why the gear ratios of a bicycle or a car are 3:2 or 5:3.   The symmetry of the synthase and the c subunit are independent except that the torque generated by the c subunit motor needs to be enough to drive the ATP synthase.  If the proton gradient were stronger it could well be 7 protons per full rotation and not 10. Think of the two as gears and you might get the picture?

In a normal chemical reaction you have to have integer numbers. 3 H+ react with 1 ADP and so on. The beauty of this system is that the electrochemical energy of the proton gradient is transferred physically not chemically to the ADP+P to form ATP. This is why you can break the integer rule that dominates most of biochemistry. The flow of H+ deforms the rotor proteins, they later transfer this energy to their substrates, and fuse ADP and phosphate generating the higher energy ATP compound.

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


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Why do rod cells not recover after bleaching upon exposure to bright light?
I'm confused as to why, when exposed to bright light, rod cells cease to function.
I understand that exposure to bright light bleaches all of the rhodopsin, but...so what?
Why doesn't the bleached product (opsonin/retinal) simply get re-synthesised back into rhodopsin as it does in dim light, and therefore continue the cycle and enable impulses?

In rods, the pigment Rhodopsin can be thought of as a receptor protein with a pre-bound chemical agonist. The receptor protein is called Opsin, and the pre-bound agonist is called retinal. The absorption of light causes a change in the conformation of retinal so that it activates the opsin, causing bleaching (changing the wavelengths absorbed by rhodopsin). The bleaching of rhodopsin stimulates a G-protein called transducin in the membrane, which activates phosphodiesterase, which in turn breaks down cGMP that is normally present in the cytoplasm of the rod and is necessary for the continued operation of the sodium channel sending the neural signal.
In the dark, the channel is open and the Rods are active. In bright light, due to the amplification from cascade effects, all of the channels will be closed, stopping the Rods from sending their normal signals until cGMP levels are able to compensate.
My guess is that the bleaching ceases fairly quickly and the proteins return to their original conforms shortly, but it's the cGMP levels (remember, cGMP is destroyed, so any new cGMP replacing the old must be synthesized first) that take a long time to recover as the signal to destroy the cGMP initiates a cascade that results in strong amplifications.

A good guess but you need some kind of a reference for it


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Is "computational biology" different from "bioinformatics"?
Are "computational biology" and "bioinformatics" simply different terms for the same thing or is there a real difference?

Probably better to ask them directly :) http://www.biostars.org/
Actually there are few questions going into that direction already: http://www.biostars.org/search/?q=computational+biology

Thank you! I'm glad that someone recognizes that there is a difference http://meta.biology.stackexchange.com/a/169/389

@Chirag And between the answers and comments, we're lucky enough to have 4 alternative definitions for everyone to choose from!

@bobthejoe I don't think anyone has suggested there is no differences, but getting the community to agree on exactly what those differences are is an exercise in futility.

@Chirag Why did you roll back Daniel's edit, it was a more grammatically correct version?

@jonsca The idea is to ask not just what is the difference but also to ask whether the is a difference in first place...

@jonsca I made it unambiguous :)

@Chirag It was more the sentence structure I was taking issue with, which wasn't that big of a deal, I was more curious as to why you decided to roll it back.  The "biology" versus "chemistry" issue wasn't as bothersome.  Anyway, I think it's fine now.  One of your answers still uses the term "computational biology", but I think it all still makes sense.

Wait...when did this question become about chemistry?

@DanielStandage replaced biology with chemistry by mistake... my bad.

I found this post by Russ Altman quite good.  Below is his opinion about the two similar but distinct fields:

Computational biology: the study of biology using computational
  techniques.  The goal is to learn new biology, knowledge about living
  sytems.  It is about science.
Bioinformatics: the creation of tools (algorithms, databases) that
  solve problems.  The goal is to build useful tools that work on
  biological data.  It is about engineering.

Just as a note:
This is just one persons opinion and I have heard many other definitions for both of these terms.  For example, one person I know mentioned that he believes computational biology is concerned with very theoretical research such as NP-hardness (ie. articles published in the Journal of Computational Biology). Other people think that bioinformatics is an applied field that is essentially using already published tools.

This is perhaps the most closest you can ever get to the definition of this field. :)

As informative as this answer is, keep in mind that many (talented) scientists will disagree, or at least have a slightly different answer.

Would bioinformatics not be more about the use of tools to solve statistical problems than the creation of such tools?

I don’t think this definition is commonly agreed on. Where we work, most people are self-proclaimed bioinformaticians (heck, we even have it in the institute’s name) but according to this answer they’d be classified as computational biologists. They may also do the latter (because the topics are usually intrinsically linked) but the focus is definitely biological study, not development of tools.

@KonradRudolph: Again the goal of my answer was not to give a concrete definition hence why I gave an additional note showing other people have varying opinions on what they are.  The definitions are going to be very institute / lab specific depending on what their research topics and goals are.

I think this question is on topic here, although yes you would definitely get a lot of answers  at BioStars. But consider this from the bioinformatics tag wiki on this site.

Bioinformatics is a broad field that interfaces a variety of life
  science disciplines (biology, genetics, biochemistry, biophysics, etc)
  with a variety of quantitative sciences (mathematics, statistics,
  computer science, engineering, etc). Bioinformatics techniques
  typically involve developing and applying software and algorithms to
  computationally intensive biological questions, such as those common
  in structural biology, genomics, sequence analysis, and systems
  biology.
Some scientists draw a distinction between the term bioinformatics and
  computational biology. While these areas indeed broad and diverse,
  these distinctions in terms are not consistent or well-defined.

Case in point: @GWW's answer cites two different definitions, while another has already been suggested in response to his answer (as a comment). More definitions are sure to come from additional answers, comments, and edits. None of these definitions are necessarily wrong, but in the same way none are "right" as there is no objective way to determine which of the definitions is "better" than the others. If you were to ask 5 experts in the field, you are likely to get 5 different definitions.

There must be an option to choose multiple answers :)

@Chirag Well, unfortunately there are many "good" answers to this question...which is kind of the point.

+1 but I shall contest that the formation of that tag was contested (by myself) http://meta.biology.stackexchange.com/q/168/389

Computational Biology - usually involves creating a model (software or other logic) where you attempt to shed light on some process of biology by building information flows using known physics/chemistry/biology --- (how to cells divide ? -> build a biomechanic model of mitotic spindles ...) --- You are building up complexity in your model to simulate biology to ever increasing levels of accuracy
Bioinformatics --> measure biology then analyze the data --- biology already has the complexity --- You are attempting to understand this complexity by identifying patterns in measurement data
Both of these fields drive research efforts to create better sensors used to measure biological processing

I think there is no general agreement on what is computational biology and what is bioinformatics, so the answers are bound to vary, depending on the field and the background of the person. Let me list a few options:
They are the same
There is a good reason to think that these terms are used interchangeably, particularly in the academic environment. Moreover, while the two terms co-exist in English, in some other languages/countries only one of them exists, used to convey both meanings: e.g., in French one can say bioinformatique/bioinformaticien(ne), but there is no translation for computational biology.
Computational biology is a method, bioinformatics is a tool
Some people, more prone to treating the figures of language literally, would say that computational biology is about mathematical approach to biology, whereas bioinformatics is doing these computations in a computer. This approach seems to lack nuance and broad knwoledge, but it is surprisingly common even among the experts.
Bioinformatics is about sequencing
If one looks for a job outside of academia, bioinformatician in a job description usually means somebody who can process sequencing data, i.e., trim, clean, assemble/map genomes, calculate the differential expression, etc. Biostatistician usually describes someone involved in experiment/study design and the subsequent data processing. Computational biologist occasionally surfaces as a term in job descriptions coming from big research labs, meaning that the job description is rather vague - these are usually R&D jobs.
These are two broad and overlapping domains of knwoledge
As I mentioned in the beginning, people working in a narrow domain would tend to equate the two terms. However the range of computational disciplines related to biology is very broad: some of them are more naturally associated with bioinformatics than the others. The following list is definitely incomplete, but should give the idea:

Processing and analyzing sequencing data
Protein and RNA structure modeling
Biostatistics (study design, analysis of non-sequencing data, etc.)
Population genetics
Epidemiology
Mathematical ecology

To summarize: there is no definitive answer to the question, but there is a lot to learn by exploring it.


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What role does a protein's size have on protein-protein interactions?
Protein-protein interactions are when two or more proteins bind together, possibly for some important biological function.  Recently, I'm starting to look more into proteins, and in particular, networks related to proteins, such as protein-protein interaction networks.  I found myself thinking about the following question:

Question:  Does the relative size of two proteins significantly affect how likely they are to interact?  Does it affect how they interact?

It would be plausible for me to download a protein-protein interaction network, and calculate their relative size from the proteins' PDB files.  However, I predict that the results from such a computation would not offer much insight into why (or why not) the protein sizes affect binding.

Do you mean size in 3D space or mass?

I meant in 3D space, although, now that you mention it, it would also be interesting to consider mass instead.

Protein interactions occur mostly (if not all) through residues that are on the "surface" and exposed to the milieu in which they exist, be it cytoplasmic or extracellular. So, a naive thought would be to guess that a greater surface area means a greater swathe of exposed regions and probable interacting parts. One protein can bind many others, even simultaneously, through different such interaction surfaces. However, other factors also determine whether a given set of two proteins interact, most important being where they localize in a given cell. If one protein is nuclear but the other on the plasma membrane, it is highly unlikely that they ever interact even if thermodynamics highly favors complex formation. Interaction between two proteins also depends on their respective conformations, which can further be regulated by events such as post translational modifications (eg., phosphorylation by a kinase), bound nucleotide state (eg., GTP vs GDP in a G protein). Last, scaffolds can also sequester and organize protein clusters together spatially and increase the probability of them interacting. All put together, I think it is highly unlikely that just looking at 3D surface area of a protein will say much about its "interactability" with other proteins.

@gkadam is correct and quite exhaustive. There are a lot of things that can affect protein binding, and many of them have little to do with the protein itself, but instead where it is and what else it is doing.
All other things being equal, smaller proteins will form networks more quickly than larger ones for kinetic reasons.
Size differences don't affect how likely two proteins are to interact by itself. Big proteins and little ones interact all the time.

Sir what are the things that effect protein binding? Is there an exhaustive list of it??

If there is it would be a medium size textbook. pH, temperature, saltiness, conformational changes, adaptor molecules, and the identity of the proteins are the big contributors, but there are many many more.


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What are the white dots on the tree in this photo?
Today in Kongens Have, I noticed a lot of white dots on bark of trees. There are plenty of them, and they were usually on 'bottom' side of branches. Does anyone know what this could be?
I took a photo:

I suspect some pupa of insects, but it is just a quess.

I'm not 100% sure, but I think they look like scale insects (Coccoidea). In particular it looks at bit like a hermaphrodite cottony cushion scale insect (Icerya purchasi)...

The white fluffy thing underneath the insects is the ootheca (egg case). The mature insect migrates to the main trunk of its host tree and attaches to the bark. It then secretes the cottony sack between itself and the bark. Hundreds of eggs are laid into the case, which eventually hatches and the nymphs disperse to the tips of branches where they feed on leaves. As they get older they gradually move first to twigs, then to branches and finally back to the main trunk to breed again.


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Circulation through the liver in light of drug metabolism
I have a lingering question which stems from an answer that I gave to What hydrolyses aspirin within the digestive tract and blood stream?
When a drug or any other substance is absorbed into the bloodstream in the stomach or small intestine, it ultimately passes through the hepatic portal vein and into the liver sinusoids, where it is processed by hepatocytes and introduced into the general circulation via the vena cava.  In terms of metabolism, this is what causes a "first-pass" effect for drugs that are ingested.
For drugs that are delivered either by intravenous, intramuscular, or sub-lingually (as in the other Biology.SE question), this first-pass effect is avoided, and the drug is introduced into the general circulation without being metabolized by the liver first.
Even though the first pass is avoided, the blood in the body still makes its way back through the liver eventually via the hepatic artery, which is a branch off of the celiac artery.
The issue I still have is, does the incoming blood from the hepatic artery merge with the blood from the hepatic portal vein?  If not, does the blood from the hepatic artery still interact with the hepatocytes in some way? (it makes sense that it does, and I have also read that one of the main functions of the hepatic artery was to deliver blood supply for the liver's metabolic needs)  If this is not the case, where in the body would these drugs that were introduced via IV, etc., be metabolized?

Yes, the blood from the hepatic artery (proper) and the portal vein mix in the sinusoids of the liver. The hepatic vein supplies about 75% of the blood to the liver, and the hepatic artery the remaining 25%. Because the portal vein provides such a large part of the blood supply to the liver, then any disease that causes the blood to build up can cause portal hypertension.
The hepatic artery carries oxygen-rich blood from the heart. The portal vein is part of the portal system and connects the capillary beds of the gastrointestinal tract to those of the liver. Because of the larger volume through the portal vein, I think that each vessel carries about half the oxygen supply to the liver.
For drugs introduced through an injection, for example, metabolism occurs throughout the circulatory system and in the liver. Remember that it's all the same blood supply, but the first-pass effect just refers to the blood that goes to the liver before entering the systemic circulation (by which it can travel to its target).


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What negative effects can pinhole glasses have on the body?
While shopping in the local supermarket yesterday, I was surprised to find this display on one of the shelves.

I also realize that I should have taken a pamphlet with me, as well as a picture of the boxes for the glasses, as seen in the picture to read more about it.
This answer at Skeptics.SE shows that there are in fact probably no long-term benefits of wearing pinhole glasses, but my question is...
What potential harm/side effects can be caused in the short and long-term from wearing this type of eyewear?

They forgot the point: "make you look like an idiot"

Did you try them on by any chance?

@Luke, I didn't. I had a feeling that they were just an alternative healing scam, and so didn't bother.

Probably not many side effects except in specific instances, but no benefits either. My guess is that there are potential side effects because these glasses reduce the amount of light that the eye receives and restrict the visual field. Therefore, the restricted visual field would be bad for activities such as driving, in which you need your peripheral vision. And the reduced light makes objects appear dimmer, so it is harder to see with them at night. I also imagine (just a guess) that they would be bad for very young infants, who are almost always born without perfect vision and require some visual stimulation to correct.
There are probably many reasons why optometrists use a pinhole occluder for diagnostic purposes, but never prescribe it as treatment...

interesting answer, well reasoned, but do you have any references (e.g. for the infants being born without perfect vision)? I agree that these could definitely be argued to reduce your field of vision, and thus be unsafe for driving, but if you can find a study into this that would be ideal

A good reference by someone who studies child vision is here, and a less technical one is here. The infant brain is highly plastic. Completely patching the eye (24/7) will cause form-deprivation myopia, but interestingly, patching a few hours a day (in young children) can correct myopia. But I don't know how pinhole glasses will affect them. And as for studies, well, everything I can find online sounds like a scam.

Oops, I meant that patching in children can correct amblyopia.

I believe I saw a documentary (years ago) that stated that these glasses are illega for sale to the public.
Wikipedia says making any medical claim at all is illegal. This is the result of a legal settlement made with the Federal Government.
They can cause blindness when worn regularly outside as Sunwear.

It would be better if you could provide some citations on your claims.


text:
Does body mass have a bearing on reflex speed?
A reflex is an unconscious action in response to some specific stimulus, e.g., blinking an eye, or pulling the hand away from a hot pin
I know from school biology, and reading online that withdrawal reflexes are quick because

CNS is involved
Danger receptors send impulses to prevent inhibition of flexion

When a person is extremely obese, do these flexion impulses take longer?
Is a withdrawal reflex in an obese person noticeably slower than in case of a reasonably fit person?

Do you have some reason to suspect that it would be slower?

Merely speculating. An obese person probably does not generate extra nerves/muscles to support their extra mass. Rather any existing nerves may be compressed, and pre-obesity muscles might be distended(sp?) by the added mass already/

No, what would weight have to do it? An obese person has more fat than a person of a normal weight, but fat isn't innervated by nerves (or motor neurons, at least). What about very muscular people? In this case, they actually have more muscles that require innervation, and this comes from increased branching at the nerve terminal.
Since you mentioned muscles, let's focus on motor neurons (or motoneurons). Remember that the cell body of motor neurons is actually in the spinal cord. In the case of alpha motor neurons, which innervate skeletal muscle, the cell body is in the ventral horn. What you think of as a "nerve [bundle]" is really just a collection of cell axons. The axons can regrow if cut in the middle, but you don't grow more axons very easily.
Additionally, a spinal reflex is very quick because it involves the CNS, but not the brain. Look at the example of the patellar (knee-jerk) reflex. The stretch is sensed and transmitted to the spinal cord, causing contraction of the quadriceps while simultaneously inhibiting the antagonist muscle, the hamstrings.

I went into this with the same assumptions that jello did, but I found two studies that had some interesting results.
Isojärvi (2010) found that in obese and under-exercised adults, physical fitness level predicted, among other things, a significant proportion of the variance of nerve conduction velocity and F-wave latency.  So, these individuals may theoretically have a slower response to noxious stimuli.
While Type II diabetes is not always a result of obesity, there is often a high correlation between the two.  Oltman (2005) found that, in "Zucker diabetic fatty" rats (an animal model of Type II diabetes), motor nerve conduction velocity in the sciatic was significantly slowed.  This effect, too, would affect the rate with which an individual could withdraw a limb from a noxious stimuli.
So, I don't think that anatomical changes in the bodies of individuals who are obese would have any effect, but clearly there are physiological changes in the neurons of these individuals.

References:
Isojärvi H, Keinänen-Kiukaanniemi S, Kallio M, Kaikkonen K, Jämsä T, Korpelainen J, Korpelainen R. (2010). Exercise and fitness are related to peripheral nervous system function in overweight adults. Med Sci Sports Exerc. 42(7):1241-5.
Oltman CL, Coppey LJ, Gellett JS, Davidson EP, Lund DD, Yorek MA. (2005).  Progression of vascular and neural dysfunction in sciatic nerves of Zucker diabetic fatty and Zucker rats. Am J Physiol Endocrinol Metab. 289(1):E113-22.

That's really interesting, thanks. I didn't realize there would be such measurable changes, even in the larger nerves. Diabetic neuropathy causes serious problems for people, initially in the hands and feet, due to their distance from the brain. But apparently excess weight can cause negative physiological changes to nerves.

@jello Yes, I learned a few things I didn't know.  My mention of you wasn't a critique in any way, it was more saying that you'd already covered 95% of it, so I wouldn't rehash the info.

I didn't take it that way, no worries :)


text:
Short-term Lamarckism in asexual single cell organisms
I was reading through the Karr et al. (2012) whole-cell computational model. One of the things they did was to induce single-gene disruptions in their model. They observed several to be fatal, but:

In some cases (Figure 6B, fifth column), the time required for the levels of specific proteins to fall to lethal levels was greater than one generation (Figures 6C and 6D).

As far as I understand this is because when a single-cell divides, daughters get not only get a copy of the mother DNA, but also have their initial levels of proteins and RNA set to those of their mother (or similar, with some statistical fluctuation).
To me this screams of Lamarkism: if an organism during its lifetime came in contact with an environment that caused a greater expression of some protein that in had at birth, its children will also have a higher initial expression of the same protein. In other words, the trait of "level of this protein" seems to be being passed down in a Lamarkian way. Is my understanding correct, or am I missing the point?
If my understanding is correct, then what are some standard methods to account for this short-term Lamarkism in mathematical models of evolution?

Notes

I am primarily interested in mathematical (or other formal) treatments of this. I have a background in mathematics and some work in mathematical modeling for population biology and evolutionary game theory. I have no background in biochemistry or microbiology. I would appreciate answers or references that cater to this awkward background but I am not adverse to plowing through some microbio if it is for something awesome.

Similar partial arguments can be made for non-single-cell and sexual organism by considering hormone expressions of the mother during pregnancy, as in this answer. I am satisfied with an explanation for asexual single-cell organisms, but bonus points if it can also say something about non-single-cell and/or sexual organisms.

Follow up question on modeling the mechanism behind this: Macromolecule levels in daughter cells after fission

The distinction between Lamarckism or not doesn’t make sense for single-cell organisms: the cells acquire features all the time and pass those on to their daughter cells – literally Lamarckism. (Disclaimer: Lamarck of course neither thought of single-cell organisms nor did he imagine a feasible mechanism by which his theory could be made to work.)

Thanks @KonradRudolph, but to clarify from a naive modelers perspective: if I was making a model of the interaction of single-cell organisms learning and evolution then it would be reasonable to have the organism pass on its learned strategy (which would hypothetically be encoded in something like the levels of various proteins) to its offspring?

It does, by necessity: the mother cell splits off into two daughter cells which each “inherit” some of the material from the mother cell. Since there is no “erasing” step going on during mitosis, this means that daughter cells inherit the proteins of the parent. Now, whether this information is sustained across generations is a different question: proteins in particular have a half-life so after some time all this learned “information” is gone unless it’s encoded in the genome or as epigenetic information.

Thanks for the explanation @KonradRudolph, if you have more insights I'd love to hear them on G+. I might ask a separate question about how to best model how the proteins are distributed among the daughter cells.

@KonradRudolph: Looks like you've more or less answered the question; would you mind turning your comments into an actual answer?

This phenomenon is well known and can be observed in several species. In fact, if you look at the time it takes for E. coli to change its transcription program in order to react to the environment (signal->transcription->translation), you will find it can be longer than its ~20 minute doubling time. You can indeed think of it as a form of Lamarckism. However, it is part of a field that has been booming in the last ~10 years, known as epigenetics.
Epigenetics is a field of research that deals with hereditary information that is passed by non-genetic mechanisms, i.e. not in the DNA sequence. This includes several mechanisms including passing of proteins/RNA, chemical modifications of DNA (methylation) and chromatin (histone modification). It turns out that epigenetic effects can be observed in virtually all species (including human) and affected phenotypes can be significant (leading to disease, for example).
This is a very interesting field of research and is very wide, so I would recommend reading a bit. Specifically regarding protein/RNA passing, I believe this is somewhat less deeply studied, perhaps due to the fact that the effect decreases exponentially (due to dilution), can be limited by protein/RNA stability and some measurement difficulties. However, I think I recall mathematical treatment of this in Prof. Uri Alon's book. You can also catch his systems biology course online on youtube, which I think touches on some of this material.

Just to clarify: The most well-known examples of epigenetics are chromatin (DNA/histone) modifications, but there are several other mechanisms that can pass hereditary information, often along several generations. Any such mechanism is considered epigenetic. Passing protein/RNA is one such mechanism and it is certainly being studied by epigenetics researchers.

I would say not, though you might find occasional cases where it is important.
Lamarckism could certainly apply to single celled organisms, but it would still tend to be associated with DNA.  As originally described by Lamarck, the traits cited would be retained through the offspring's lifetime, potentially being passed on to later generations.
Protein half lives vary quite a bit, but usually in bacteria they are about an hour and can change faster than that.  The changes accorded to Lamarckism should at least persistant for most of a lifetime.  If this is Lamarckism then all embryonic stem cells have Larmarckian inheritance... at least for a few hours.
On the other hand... This is Lamarckism in the sense that historically Lamarck was sometimes observing physiological adaptation.  One of his classic examples was how a blacksmith's muscles would become large from working hard.  The Blacksmith's children who also did this work would also become muscular.  That has little to do with inheritance though.


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Would a plant survive if it was watered using hard-water?
Hard water is water with high mineral/salt content. I'm told that a potted plant watered with a salt solution dries out sooner or later. Is this true?
If so, would a plant survive if watered using hard-water?

It would depend on the content of the hard-water. If the water contained heavier metals like lead or radioactive elements like tritium (Hydrogen-3), the plant would most likely die. Most land plants cannot survive when watered with massive amounts of salt water as the salt would absorb the water from the leaves.

I'm sorry, I do not understand. Do you mean the flow would be backwards out of the root - or would the salt in the water accumulate at some location/s within the plant and absorb the fluid locally?

The salt in the leaves would accumulate  like any other mineral would. However, the salt would attract the water molecules to them and prevent them from carrying out their job (transporting other nutrients throughout the leaves and stem), thus robbing the plant of necessary nutrients like phosphate and nitrogen. Does that answer your question? I'm not an expert on herbology unfortunately.

Hard water normally refers to water with high concentrations of dissolved calcium or magnesium and consequently has a slightly alkaline pH. Plants which are adapted to growing on limestone or similar alkaline substrates (calcicoles) will be fine with hard water, whereas those plants which are specific to acidic soils (e.g. carnivorous plants) will likely die slowly.

Tap water contains chlorides and fluorides and many hard tap waters contain high levels of calcium making it too alkaline for carnivorous plants.

Most plants are tolerant of a fairly wide range of pH and mineral levels and will cope fine with hard water.
Salt water is another issue and again depends on the plant. Most plants will eventually be killed by a buildup of salts in the soil and the plant tissue but halophytes (around 2% of all plants species) from coastal and desert environments can handle high levels of salt.


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Text/resource with information on all skeletal muscles and their motor units
Something analogous to an encyclopedia on baseball players with a list of all thier stats would be ideal.
I'm not looking for just generic muscle names, locations and illustrations.
Good answers would include links to resources, textbooks etc. which include stats for most or all of the following of every skeletal muscle in the human anatomy:

Language of origin/LOO definition
Labeled by ? shape, location, size etc.
Type of Shape (i.e., fusiform, pennate, bipennate, multipennate, convergent, parrellel etc)
Angle(s) of pennation (if applicable)
# of motor neuron innervations
# of proprioceptors in muscle
# of fibers
Length of muscle
Length of fibers
Typical fuel system reliance (determined off of biomechanical placement and architecture..CPr, oxidative, etc.)
Biomechanical function (structure of corresponding bone/tendon anchors and corresponding movement manipulation)

I'm not inclined to think that there is a "Compendium of All Muscle Data" out there, unfortunately.  I'm looking forward to an answer as well if there is.  Much of the insertion point, etc. data can probably be found in any medical anatomy text, but the fiber/fusiform/spindle/motor unit data may not be readily available for humans in one place.  I know Robert E. Burke at the NIH has devoted an entire successful career to exploring the motor unit anatomy and physiology of the cat preparation.  A pubmed search should give you a number of papers in that regard.

You can try Biomechanical models used in software frameworks such as OpenSim.
Here is an example of a combined upper and lower body model in XML: ULB_Project.


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Computational/mathematical models for predicting phenotype from genotype
Karr, Sanghvi, et al. (2012) propose a whole-cell computational model for predicting phenotype from genotype in Mycoplasma genitalium. Their model simulates myriad cell processes such as DNA replication, RNA transcription and regulation, protein synthesis, metabolism and cell division at the molecular level over the life-cycle of the organism.
They achieve this by combining many existing mathematical and computational models into one piece of software. The article suggests that this is the first comprehensive whole-cell model at the molecular level in detail. They cite previous molecular-level models for their sub-modules, but don't go on to discuss more coarse-grained genotype-to-phenotype models. Their model is exciting, but the 10 hours of simulation on a top-end computer cluster to follow the life-cycle of a single organism is unreasonable for someone who wants to study evolutionary processes.
Are there prior reasonably accurate computational (or even better, analytic) genotype-to-phenotype models? If there are, what are some of the best coarse-grained genotype-to-phenotype models for commonly studied organisms such as E.Coli?

The Karr et al. paper attempts to capture most of the details in their model by combining features from the genome, transcriptome, proteome, and metabolome. This work heavily builds off of the coarse-grained models that you ask of especially on the work from Bernhard Palsson from which Markus Covert did his training. The answer to your question rests entirely on the type of question that you seek and what you want the model to do.
For the most part, most of your questions can be answered using COBRA, the COnstratins Based Reconstruction and Analysis Toolbox. You can get a good idea of what genotypes can be knocked out and see how it affects the phenotype as long as the phenotype is a known pathway that gets affected by that gene and you don't care about temporal and dynamic information.
There is also the E-Cell Project for E. coli. I personally don't know much about it but it has created some basic models for E. coli and that may be good enough.
If you want to build your own models, you should check out BiGG where all of the large-scale reconstructions exists. A good chunk of the code is stored at the Palsson lab website where you can attempt to use COBRA and start generating your own hypotheses.

The questions I seek are ones of micro and macro-evolution with more realistic models of genotype-to-phenotype mapping. I come from a background in EGT where the distinction is virtually non-existance. Your answer is very helpful, but I don't understand how it is about more coarse-grained models, aren't these models also at the molecular level?

I assume EGT is evolutionary game theory?

@ArtemKaznatcheev, I'm not entirely sure how you would be able to achieve any sort of genotype-phenotype mapping without having some molecular element in the modeling. Clearly, I misunderstood your question.

@ArtemKaznatcheev the paper you cited describes a model that uses many different molecular phenotypes to build a 'model' of whether cellular functions are affected by gene perturbations. Bob has cited several viable options for you to do a simplified model in E.coli, however if these do not help in answering your question then it may need re-phrasing.

@Luke in the question I ask what are some of the best coarse-grained genotype-to-phenotype by coarse-grained I mean more coarse-grained that the paper. In other words, something that doesn't go as far into the details as tracking at the molecular level. If no one suggests such a model then after a few days (and some more extensive search on my part), I will accept Bob's answer.

@ArtemKaznatcheev Ok, but I don't really see how you could quantify the effect you are having on the outcome without making a specific change at the molecular level. What is it you would actually like to measure, and what it is you would actually like to 'alter' to see an effect? Thanks!

@ArtemKaznatcheev, For a coarse-gained approach, I would still suggest looking at the molecular models and then placing constraints around key pathways using FBA. For the most part, a lot of those components can be treated as a "black box".


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Cell proliferation limit and senescence of embryonic stem cells and fibroblasts
I am trying to understand the importance of proliferation limits and cell senescence. In particular, I would like to compare the proliferation limit of Embryonic Stem cells (ES) and fibroblasts (which would be trans-differentiated) and describe how this difference plays a role when thinking about engineering organs, for example a heart transplant.
How do ES cells and fibroblasts compare with regards to the Hayflick limit?

You are right but this is  Not a homework question.

You may want to change the wording a bit, then as compare the proliferation limit and describe how makes it sound like it came from an assignment, it will probably get more attention :) I will delete my other comment then.

Telomeres (caps on the ends of chromosomes that are gradually shortened during each cell division) determine the maximum number of times a cell can divide, known as the 'Hayflick limit'. Cells that are able to express telomerase, an enzyme capable of extending the telomeres, can divide indefinitely.
There are various types of stem cell in a body which replenish the 'pool' of cells in the tissue once they reach the end of their useful lifespan. Each stem cell expresses telomerase to lengthen the telomeres after a round of cell division. The inhibition of telomerase in somatic (normal) cells is a major 'challenge' for cancerous cells - in order to achieve uninhibited proliferation, they must express telomerase. Thus, there are many molecular similarities between stem cells and cancerous cells (well, with regards to telomerase, anyway!) [ref].
Fibroblasts are somatic cells - they are the 'normal' cells that make up the vast majority of organsisms. The only cells not known as somatic are gametes/germ cells and stem cells. They thus do not express telomerase, and have a replicative lifespan, also known as a Hayflick limit.
With regard to your question about engineering organs, there are many resident stem cells in tissues (and thus organs) that 'top-up' the somatic cells when required. As organisms age, this pool of stem cells becomes depleted [ref]. Artificially engineering an organ such as the heart (I assume this is what you mean) would be inherently difficult, as many vascular cells are not replaced, but can last a whole lifetime - this is also true for many neuronal cells. So replicative lifespan is not necessarily related to lifespan!


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How to compute properties of peptides ?
I have been tasked with writing a program for computing properties of a give set of peptides. These peptides are given as 1-letter amino acid sequences and I need to compute the following :

Length of peptide
Number of Each Amino Acid
Percent composition of each amino acid
Molecular weight
Net charge of peptide
Positive charge
Negative charge
Isoelectric point (pI)
Hydropathicity
Percent polar amino acids
Percent positive amino acids
Percent negative amino acids
Percent hydrophobic amino acids
Hydrophobicity
Lipophilicity
Amphiphilicity
Water-Octanol Partition Coefficient
Steric Bulk
Side chain bulk
Net donated hydrogen bonds
Percent \alpha helix
Percent random coil
Percent \beta sheet

While some of these properties are self explanatory ( eg. size, num. of amino acids, percentage of amino acids. ) and easy to compute. Other properties ( like Molecular weight, Net. charge, Positive charge, Hydorphobicity etc ) have been difficult for me.
I donot have Chemistry or Biology background and hence have found these difficult to compute. I would be appreciative if someone could point me in the correct direction ( I have already been through Wikipedia ) containing methods to compute the above mentioned properties or to a standard text which would explain the above mentioned properties and also provide methods to compute them. Thank you all.

Biopython and the other bio-programming languages typically have examples of how to do this kind of thing.
For example here is some python code for calculating some of these:
http://biopython.org/w/index.php?title=ProtParam&redirect=no
Many of the propensity scales are in this database:
http://www.genome.jp/aaindex/
And there are also biojava classes for accessing these. Essentially you will need to know what these physicochemical properties are and get access to a scale for converting the letter to a numerical value.

That's quite a laundry list, and I doubt someone is going to sit down and give you hints for all of them. Note that some of the properties (like percent alpha helix) rely on prediction method (secondary structure prediction, in this case). "Net donated hydrogen bonds" sounds like it makes sense only for a given complex with a solved 3D structure.
Nonetheless, you will probably find some of the stats implemented in EMBOSS, for example in the pepstats app.

Thank you for your reply. It has been helpful but would you happen to know of any reference text which goes over these properties ?

The introductory chapter of Proteins by Thomas Creighton (http://books.google.com/books/about/Proteins.html?id=hu8T_kI1LrkC) would be a great place to start. It was my Bible for introductory biochemistry in college!


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Is there such thing as "biological cycles" of activity/performance/etc in mammals?
I'm looking for some information about the cycles of increased/decreased activity, mental performance or endurance that are related to a mammal's age and the time of a season.
First of all, I'm not sure what such cycles would be called. I know of circadian rhythm, which is an ~24 hour cycle. The other cycle I've heard about was circa-septan (7 days?). What keywords should I use to search for cycles that may have period of a month to several months?
The second question is - are there are episodes of increased activity/performance over a mammal's lifetime? I can think of two example - mating rituals may involve fighting and hibernation involves almost no movement at all.  Are there additional events that may last more than a month?
The final question is if the age comes into equation of increased/decreased activity. Lets say a younger rat an and older rat are put into the same maze, would their ability to "solve" the maze be related to age?
This question is rather broad. Maybe there are some keywords or a science branch that deals with these questions and I can refine my search?
I appreciate your input!

I think you have already answered your question of “are there such cycles?” – with that in mind, could you maybe make the question more directed to a single point that can be answered?

You could look for "seasonal rhythms" or "infradian rhythms"

This "might" be considered as an answer to at least on of your questions. http://en.wikipedia.org/wiki/Seasonal_affective_disorder. I don't have the time now, but will likely have a full answer later tonight.

With regard to episodes of increased activity/performance over a mammal's lifetime, I can add migration to breeding/mating and hibernation or torpor. Birds of course are better known for migration, but some mammals, notably in Africa migrate over an extended time.


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Is sperm contagious?
A person may have a cold or any other disease that is an airborne-infection.
Is their sperm also contagious? Should the person take any precautions?

Most airborne infectious pathogens (including flu and common cold) will not be sexually transmissible.

Had to laugh at the title. Yes, sperm may indeed be confer other sperm-bearing life-forms so I’d say that it’s contagious.

Why would a pathogen NEED to be airborne to be contagious?

I don't think it need to be airborne, but I'm interested in airborne diseases.

@KonradRudolph: Also, sperm can't reproduce without invading, and interfering with the genomes of other human cells. So perhaps they could be classified as some kind of ultra complex virus?

Spiderman killed Mary Jane with his radioactive sperm so yes :P

I'm unsure about the use of the word "contagious" here. If you would describe it as an organism "catching something" from another only once, this answer applies (vaguely perhaps).
One example of "contagious" sperm: sea urchins spray their sperm (called milt) into the surrounding water, so that it can hopefully fertilize roe, which are fish eggs. The eggs are also expulsed for some species, but for others they stay on the surface of the female sea urchin, as this Wikipedia article explains.

The gonads are lined with muscles underneath the peritoneum, and these allow the animal to squeeze its gametes through the duct and into the surrounding sea water where fertilization takes place.
  ...
In most cases, the eggs float freely in the sea, but some species hold onto them with their spines, affording them a greater degree of protection.

Thus, it's entirely possible for a female sea urchin to be bombarded by free-floating sperm, to ultimately fertilize the eggs on its surface. I don't know if one would strictly consider this a contagion, but it's what I initially thought of after reading the question.

When referring to sexually transmitted disease however, sperm in itself is not contagious, as it would require the pathogen to "tag along with it" in whichever environment (whether it be airborne or aquatic). Also, if considering impregnation as "contagious", physical contact would certainly be required.
In short: For humans, no. For sea urchins (and likely other aquatic animals), sort of.


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DNA modifications other than 5-mC/5-hmC/5-fC/5-caC in vertebrate genomes?
Other than 5-Methylcytosine and the more recently discovered 5-Hydroxymethyl, 5-formil and 5-carboxylcytosine DNA modifications found in DNA sequences, what are other DNA modifications present in vertebrate genomes? There is apparently a whole bunch of modifications that can be found in prokaryotic and eukaryotic genomes, but I would like to know which ones have been found relevant in vertebrate/mammalian genomes of the eukaryotic ones.

There can be other modifications resulting from DNA damage (oxidising agents etc). Those may include Xanthine and Hypoxanthine (caused by chemical deamination of guanine and adenine).
There are no other known modifications resulting because of a physiological process.


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Are mature trees as susceptible to mutation from exposure to radiation as seedlings?
If a plant, say a ficus or a teak, as a sapling were exposed to radiation, and a tree of the same species were present at the same distance, which of the two would be more likely to mutate?

Aren't they all trees in the end, anyway?

Either is an ecosystem in itself.

Hehe, yeah, but I'm saying if you want to compare trees and plants you should have a true exemplar of each.

Erm )+: I don't have an example ...

Don't worry about it, then, it was just a suggestion.  See if you get any answers with it as is.

All trees are plants, I'll rephrase the question. Please let me know if it doesn't meet your intentions.

Ionizing radiation is harmful because of damage to genes. It's thus far more damaging, to everything, earlier in the development cycle; down to the point of the very first cell, where relatively small doses of radiation can render the potential organism non-viable (like killing the cell outright). Random mutations all over a tree are unlikely to amount to much

Ben raises a good point about this question actually - @Everyone are you interested in the effect of radiation on a single cell sampled from a large tree (compared to a single cell in a sapling), or on the organisms as a whole? If it is the whole organisms, Ben has the correct answer I believe; mutations (and thus radiation) will be less of a problem to a plant once it is larger, as any cutting of the plant could go on to form a new tree; each individual cell becomes less important. (See http://biology.stackexchange.com/questions/2055/do-trees-age-on-a-microscopic-level/ out of interest)

Depending on what plant/tree is concerned, I could also imagine things like bark absorbing some of the radiation  being emitted.

@Luke: The overall effect and any inheritable changes were what I had in mind.

@LanceLafontaine: Visualizing the bark as a radiation shield is a great idea! Anybody know whether it is possible/tested?


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Why does this illusion work?
This is another image I found on Google+

All lines are absolutely straight, parallel and perpendicular but why does it appear to have a curvature?

Related: How does this illusion work?

I think this question is a better fit for CogSci.SE than biology. Even in the answer, both references are to the psychology literature.

The lines are NOT perpendicular. You can see this by noting that the left side spans 12.5 squares but the right only 12.

Like these questions :) Many of these illusions come from Prof. Akiyoshi Kitaoka, a japanese Psychologist and expert for Gestalt Psychology. On his website you'll find some more fascinating illusions and questions to ask here ;)
The illusion above is named Cafe Wall illusion and the newest model to explain those illusions is the contrast-polarity model. Short explanation from his webpage:

The paper explained it better to me:

Kitaoka, Pinna, and Brelstaff (2004) proposed a phenomenal model to
  explain the Café Wall illusion, which stressed the importance of
  contrast polarities of a solid square and its adjacent line segment.
  When a dark/light square is accompanied by a dark/light line segment,
  the apparent tilt is the direction of contraction of  the square angle
  (Figure 18a,b). In contrast, when a dark/ light square is accompanied
  by a light /dark line segment, the apparent tilt is the direction of
  expansion of the square angle (Figure 18c,d).

This explains why you perceive a tilt. If you position the smaller squares now in distinct edges of the big squares, you can achieve 2- and 3-dimenional illusions. Here you see a increasing of the tilt due to more smaller squares:

Here you can see that the positioning of the smaller squares is critical to achieve the 3D effect of the orignial bulge effect in your question:

Notice that Gestalt Psychology is a non-reductionistic theory approach and investigates mainly the phenomenology and underlying Gestalt Laws of visual perception. How these Gestalt Laws developed on a deeper level is a question of neurobiological evolution similar to, "why have some species of apes color-vision and some not". The ellipses in the explaining picture above show you, that our cognitive visual machine somehow tries to group divided objects (square and line of same contrast/brightness) in one line and we see a tilt. I'm guessing here, but this is probably due to cognitive brain algorithm that saves things and objects we see and perceive mainly by countor and shapes, rather than pixel by pixel like a computer and digital camera do it, which of course don't perceive any tilt or 3D illusion in any of those trick images :)
Read the papers for more explanations and examples, not behind a paywall:
Apparent contraction of edge angles, A Kitaoks, Perception, 1998
Tilt illusions after Oyama (1960): A review. Kitaoka, A,  Japanese Psychological Research, 49, 7-19.

Very nice answer.


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Is there a correlation between incidence of type 1 diabetes and vitiligo?
Does the data indicate that if you have one, the probability of you having the other is higher than that of someone who doesn't have the one?

Is there a particular reason for you to suspect that this might be the case?

This article ( http://www.ncbi.nlm.nih.gov/pubmed/16477262 ) talks about co-occurrence of type 1 diabetes with other autoimmune diseases, but not the autoimmune disease vitiligo.

It has been associated with polymorphisms with the CD4 gene, which is usually implicated in type I diabetes. The wikipedia article for vitiligo also mentions studies for the NALP1 gene. NALP1 is expressed at high levels in T cells and Langerhan cells, white blood cells that are involved in skin autoimmunity.

References to papers? If there isn't one on the wiki page with the statements, I would be less than happy to vote this up. Also, if CD4 mutations been found to cause both diseases, is there any observational evidence (epidemiology) you have found whilst reading? It does seem likely they are related as they are both autoimmune diseases of the T-cells, but they may have independent mechanisms of action via CD4 (etc), so disease incidence may not be correlated.


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Macromolecule levels in daughter cells after fission
When a prokaryote undergoes binary fission, how are the non-DNA macromolecules distributed between the two daughter cells? This is motivated by comments on a previous question and a G+ discussion. I am primarily interested in explicit mathematical models.

Naive model
For DNA, each copy is attached to a different part of the cell membrane, to ensure each of the daughters receives a copy. As far as I understand, there isn't such specific mechanism in place for other macromolecules. If we are allowed to assume that non-DNA macromolecules are distributed uniformly through the parent cell than this would mean the two daughter cells would receive half the proteins within statistical fluctuation. This has a specific meaning: if there are $N$ molecules of type A in the parent, then the difference in number of type A molecules in the two daughters after fission will be on the order of $\sqrt{N}$.
However, this model might be wrong. For instance, as the new cell-wall is being formed it could have in it specific pumps that either balance the protein levels below chance (producing an average difference lower than $\sqrt{N}$) or to create asymmetries higher than chance. Altenrnatively, the initial distribution of macromolecules in the parent could be far from symmetric for some other reasons. Which is the case?
If the naive model is correct, is there experimental evidence explicitly showing this?

blog post discussion of how the cytoplasm can't just be modeled as a homogeneous mixture.

Many years ago I worked in the field of bacterial chemotaxis, and your question brought this paper to mind:

JL Spudich & DE Koshland Jr. (1976) Non-genetic individuality: chance in the single cell. Nature 262:467-471.

The authors report an analysis of the chemotactic behaviour of individual bacterial cells from a single homogeneous culture. They find marked differences between individuals, and their favoured model is what they refer to as a Poissonian variation in the inheritance of key proteins that are present in small numbers.
According to ISI Web of Science this paper has been cited 241 times - presumably at least some of those papers might be about other more recent examples of this type of phenomenon.

This is a great starting point, thank you! I will search forward from here.

Incidentally, as a counter example, there is a lot of good work showing that mRNAs are actively moved around during bud formation in yeast. This paper is probably a good place to start (in the middle of the story!).
K. A. Shepard et al. (2003) Widespread cytoplasmic mRNA transport in yeast: Identification of 22 bud-localized transcripts using DNA microarray analysis. Proc Natl Acad Sci U S A. 100:11429–11434.
doi:  10.1073/pnas.2033246100


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Black bumps on the cut surface of an avocado
I cut an underripe avocado in half and put half of it, without the pit but with the peel, in the refrigerator. A day later, I see very small black bumps along the cut surface. What are they? What causes them?

If this is off-topic here (which I doubt), please consider migration to [cooking.se] in lieu of outright closure.

That avocado does not look underripe to me...

This is on-topic, the answer is about plant anantomy

@nico, the parts closer to the pit were too hard to be palatable (to me), so either it was underripe or I like avocados overripe.

It looks to me (although I'd want to use a microscope to check) like the black dots are xylem. When you cut the fruit, you've severed the xylem and also exposed the flat surface. Three main things have then happened:

The increased surface area has led to the 'fleshy' part of the fruit contracting as the cells dehydrate.
The stiffer, lignified xylem tubes have retained their structure and have not contracted with the rest of the fruit, so they've become exposed.
The exposed xylem have blackened due to oxidation - this eventually happens to all the flesh but, again speculating here, the oil in the fleshy tissue might protect it from oxidation longer than the exposed xylem which don't have the oil coating.


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Drosophila reference genome
Does anyone know the details about which line they are using to sequence as the Drosophila melanogaster reference genome?

Did you check the genome publication

As far as I know they just used a generic D. melanogaster lab strain.

does this help? http://www.fruitfly.org/annot/release3.html#strain

That was surprisingly buried.
I found this in a paper describing genome build 3 - See "Materials and Methods".  I imagine that this is consistent through to the current build.  In any case it should get you started.
"Sequencing templates were made from P1, BAC and WGS DNA libraries using the D. melanogaster strain yellow (y1); cinnabar (cn1) brown (bw1) speck (sp1)."