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Scrotal temperature and vascularization
The testes (at least in human males) are vascular organs, the vascularization extending even o the interstitial spaces of the seminiferous tubules. The function of the scrotal sac, is to isolate the testes from the abdominal cavity, and provide a lower temperature ($1-2^\circ \text C$ lower than normal body temperature of $37^\circ \text C$).

But since the blood is the main temperature-buffer maintaining the
  normal body temperature and the scrotal (testicular) tissues are
  considerably vascularized, why doesn't the blood flow raise the
  scrotal temperature to normal body temperature?

If the reason is the location of scrotum external to the abdomen, then all limbs and limb-extremities are located in the similar way (and in those cases, heat dissipation would be faster than in case of scrotum due to less surrounding insulation by thighs and clothes), and hence the temperature of all parts except the interior of the head and abdomen must be at considerably lower temperature? Is this the case?

well... fingers and toes do become cold and pale in winters.. :P

The fingers and toes (for example) ARE at lower temperatures than the interior of the torso.  It's why it's so easy to get frostbite on the extremities. As for temperature regulation of the testes, you have to also consider that humans evolved without clothes...i.e. The testes just "hang out" and get lots of airflow, as opposed to modern times, when they are typically under (usually) more than one layer of clothing.
Also, for reference, I took some quick IR temp readings on myself:
Room temp: 19.9C
Fingertip: 27.0C
Palm: 31.8C
Axilla: 34.6C

You didn't include your scrotal temperature, the area of interest here.

I thought it would be odd to use the kitchen thermometer to measure the temperature of my "junk". Next best thing I could come up with was the axilla.

A quick question, why do you think the presence of excessive clothing does not cause increased temperature due to reduced dissipation in all cases?

It probably does reduce heat dissipation in all cases, the reduction dependent on the nature of the clothing.  Scrotal temperature is actually regulated by a couple of methods however: blood flow can be adjusted by changing the flow resistance of the vascular beds, and scrotum itself contains muscles that adjust how closely the testes sit relative to the body.  Take a cold shower, and you will notice the scrotum will pull up snugly against the body. A hot shower and it will hang loosely. Of course, if temperature is not perfectly regulated, it's not the end of the world either.

The lower temperature optimizes sperm production conditions, but a degree here or there will not render one sterile. It may slightly reduce sperm count, but you'd probably be hard pressed to measure a difference. The outside of the body will probably always be about 1C lower than the core body temp, so you aren't looking at a huge alteration in the testicular temperature even if all thermoregulation were stopped by the clothing.

http://www.ncbi.nlm.nih.gov/pubmed/7496419
Apparently special undergarments can reduce male fertility significantly. I'm not sure about how rigorous that particular study is, but I suppose it's something to consider.
I'm not going to attempt to replicate their work, at any rate :D


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Why insects are so small compared to mammals?
I was wondering what biological limitations make the dimension of the insects small compared to the dimension of the mammals. I know in other eras insect were bigger!

Fun: http://science.discovery.com/creatures/10-giant-bugs.htm

@terdon Mothra is that I was searching! Thanks a lot! ;-)

The standard answer to this question is - it's down to the delivery of oxygen to respiring tissues. Insects depend upon a process that is essentially diffusion-driven (via spiracles and the tracheal system) whereas vertebrates have a circulatory system that allows oxygen absorbed in the lungs to be carried via the blood to peripheral tissues. And of course the blood contains specialised carrier cells (red cells) which in turn are packed with haemoglobin which acts as a molecular carrier for oxygen.
The giant dragonflies of the Carboniferous (Meganeura) existed at a time when oxygen levels in the atmosphere were much higher than now (32.5% vs 20.9%), so the diffusion-driven gas exchange system could support a larger body size.

source: Stack Exchange

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Can a plant be programmed to produce any arbitrary object?
I'm wondering if a plant can be programmed (by modifying its genes) to grow into any shape that it's programming instructs.
I understand that a plants genes are its instructions of how to convert the nutrients in the soil into the plant we see. So, instead of producing an apple tree or a giant sequoia, couldn't a plant be programmed to shape itself into a house, desk, or even at a stretch of the imagination, a bioship?
I'm looking for any links, or explanations of why this could/couldn't be done by anyone with more experience than I that could guide my research.
I am hoping that a plant behaves just like any other system. That it has inputs (nutrients, sunlight etc.) a function that describes it (its genes) and an output, the plant that grows. With this in mind, I suppose it would only be a matter of modifying the genes to produce the desired output (with the constraint being the available input).
Thank you,
Paul

It could not fit in a comment...
1) Any material have some physical limitations and living tissue are not exceptions. A plant cannot resist to very high pressure without changing shape for example. Outside of these problems of material sciences I think the major considerations are technical…
2) It is possible to influence the shape of a plant by changing its DNA code, changing its physical and chemical environment (micro or macro environment) or by injecting some substance. Today we might be able to influence the number of branching of certain plants species for example. But we are terribly far from having enough knowledge in biology (in genotype-phenotype map, in biochemical pathway) in order to transform a dandelion into a shopping mall! Even we had already described all chemical pathway linking genes to phenotypic traits, it is immensely more complex to think of which kind of DNA sequence might transform a plant into something that is so different.
I don't know if anyone has ever created a totally synthetic (and not copied from already existing sequences) DNA sequence and could predict the impact it would have on the organism phenotype. Some people were able to create a totally artificial self replicating RNA though.
Note: Some people play with the properties of the DNA molecule in order to create various tissues. This might interest you as well.
One can highly improve this answer by giving some results of studies on synthetic life.

From your comments I understand that theoretically it seems possible to do this sort of thing (apart from the physical strength etc. of a material that a plant can produce from the given resources) but that current understanding is not up to the task. The translation from code to results (genotype to phenotype mapping) is what most interests me and I will look it up. Thanks for the ideas. And if anyone has anything else to add I would love to hear it.

For instance, there is Mycoplasma laboratorium

@Paul I guess the most interesting things you may find are not with the keywords "genotype-phenotype mapping" but rather with "synthetic biology" and "bioengineering". I should have used these words above!

Thanks Remi, I looked up these topics and found many interesting articles, even one about a line detection algorithm using bacteria that respond to light. Anyway, probably the main use I can see for synthetic biology is for recycling, imagine when you want to refurbish your house, instead of knocking down the unwanted part, carting away the debris and building a new section, we could reprogram the genes of the house so that a new section forms out of the existing materials. I think it even though it sounds far-fetched, it's a realistic dream.

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List authors (scientists) by various attribute
Is there an easy way to list authors of biology articles by

University
country
number of published articles
sum of citations of all articles he/she wrote
fields of study (population genetics)
...

For example, I want to know who are the 10 most popular/cited authors in population genetics in UK's universities. How can I achieve such search?
For example, do WebOfKnowledge provide such tools?
Please, let me know if this question should be asked on another SE website.

This is not a direct answer to your question, but you might be interested to check out Scholarometer, which aims to compare top researchers within and across scientific fields. This is based on a standardised h-index with data pulled from google scholar.

I don't think that you can do this systematically in Web of Science but you can do it in an exploratory way.
Initial search: Topic - "population genetics" + Address - UK
sort by Times cited is probably more useful than the default (newest to oldest)
open Authors in sidebar - this is a list of the top 100 names
can refine to the top 10, or whatever
For example consider Brian Charlesworth:

Open an item with his name in the author list (he has some single author items)
Click on the link of his name to see his papers
Click on Create citation report to see charts of published items per year and citations per year

It may be that you know all this.


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Why is fructose not glucose the main energy source of sperm?
The seminal fluid contains fructose as the main energy source for the sperm and not glucose.

Why is fructose and not glucose the primary energy source for these
  sperm, since glucose is the preferred energy source for most other
  tissues?

I am not sure this is a very reliable source but it does seem logical.

@biogirl Not all answers there are reliable, but this one seems to be genuine. But you never can tell without a good reference.

i am not very sure about this but perhaps since glucose is preferred source for other cells, fructose is allotted to sperm so that other cells dont compete for it.. totally a wild guess because I dont know of any other reason.

FYI : Seminal vesicle is the only place in the body where fructose is secreted.

I have been searching this for a while and I got something of an answer, which I am posting below.
There are primarily two advantages of using the polyol pathway and aldose reductase to produce fructose from glucose via sorbitol:-
1) Quoting the book here,

Seminal fluid contains up to 11 mmol/L (200 mg/dl) of free fructose.
  It is the major energy source for the sperm cells in their
  all-important race for the ovum. The advantage of fructose over
  glucose may be that many bacteria, which compete with the sperm cells
  for the available nutrient, prefer glucose to other energy sources.

Thus, to avoid other competitive bacteria and cells from utilising the energy source of sperm, fructose is used as the energy source since the bacteria prefer glucose.
2)Another reason is that the metabolism of sorbitol into fructose is energetically efficient way (no ATP required) as compared to glycolytic conversion of glucose into fructose(G$\rightarrow$G6P$\rightarrow$F6P$\rightarrow$F). Tapping the polyol pathway is energetically preferred to metabolizing fructose produced during glycolysis.This is only to show the possible evolution of the polyol pathway once the advantage of using fructose as the energy source was established. It does not explain why the fructose was advantageuos but just that if it was advantageous, the semen-producers would develop the currently existing machinery to produce fructose as the exclusive source of energy.
Hence, sperms (seminal secretion) contains fructose as the primary energy source and not glucose.

Neither of these arguments make sense to me. Firstly, if there was an advantage to using fructose amongst bacteria that might encounter semen then they would evolve to exploit the niche. I note that E. coli for example will quite happily use fructose as C source. On the second point, I don't see how the fact that you can make fructose from glucose without using ATP is an argument against just using glucose in the first place. In any case I think that arguments from energetic efficiency are rarely convincing.

@AlanBoyd The second argument is only to show the possible evolution of the polyol pathway once the advantage of using fructose as the energy source was established. It does not explain why the fructose was advantageuos but just that if it was advantageous, the semen-producers would develop the currently existing machinery to produce fructose as the exclusive source of energy.

@AlanBoyd The first argument, though not substantial in its claims, is the only one I could come up with. If you do know an alternative argument, I would be most happy to consider it and accept it as an answer.

I admit that I have not come up with an answer, but it seems to me that maybe there is something else about fructose that makes it a better choice. Maybe it has some function, other than as a C source, in the fluid. It may be relevant that it is much more soluble than glucose, although the concentration that you quote is way below the solubility limit for either sugar.

If glucose was used in place of fructose it would get used up very quickly as it has a high glycemic index but fructose has low glycemic index and gets used up slowly by the spermatozoa so that they can survive for many days (upto 3) in the females body for successful fertilization.

Thank you for your contribution. Your answer could benefit from some references. Please [edit] to add citations to reliable sources that support the assertions made here. Un-sourced material may be disputed or deleted.


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Use of salt as preservative
I know that salt is used as preservative as it dehydrates microbes. Is there any other advantage like - altering pH or inactivating microbial enzymes ?

salt cannot change pH directly. Dehydration which causes the loss of bulk water can alter the pH and also affect protein activity.

High salt environments are strongly hypertonic and disrupt the osmotic gradients of cells.  Excess salt gets forced in (disrupting function) and cell water gets drawn out.
In simpler terms, it dehydrates them.
This link has more info regarding what is required to live in high salt environments (and by corollary, why organisms without such features die):
http://en.wikipedia.org/wiki/Halophile

Another factor involved is the effect that high salt concentrations have on the 3-D structure of proteins. At higher than "normal" concentrations of salt, many proteins will fold differently and no longer be in their "active" form. Since all biological reactions involve enzymes, which are protein, you can see why this unfolding might be problematic.

Salt draws water out of cells via the process of osmosis. Essentially, water moves across a cell membrane to try to equalize the salinity or concentration of salt on both sides of the membrane. If you add enough salt, too much water will be removed from a cell for it to stay alive or reproduce. Organisms that decay food and cause disease are killed by a high concentration of salt. A concentration of 20% salt will kill bacteria. Lower concentrations inhibit microbial growth, until you get down to the salinity of the cells, which may have the opposite and undesirable effect of providing ideal growing conditions! Salt can also be used when you are preserving a product via fermentation. The salt will inhibit the growth of moulds and yeasts. While some moulds and yeast are certainly needed during the fermentation process, by using small amounts of salt, it is possible to regulate and stabilise this process.
It is only possible to do this though if you are using salt that does not contain ‘free-flowing’ agents or iodine.
Refer to :https://suite.io/rachel-tsoumbakos/xpx288 for details.


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Difference between Medical and Airport scanner radiation
What is the difference between radiation doses of a medical scanner and airport security scanner (X-Ray full body scan)? Is it the same kind of radiation? Does it pose any danger for people who fly often?

What medical scanner are you talking about? An X-Ray machine? Ultrasound? MRI? Immunoscintigraphy? Optical tomography?

I think this question should rather be asked on physics.SE.

@Terdon: Ultrasound and MRI don't use X-rays.

@RamManoharM neither do any of the others. The OP just mentioned "medical scanners" not X-rays, which is one of the reasons why this is a very hard question to answer. As you point out, diffent scanners use different methods.

The type of radiation is quite different in a medical X-ray vs. an airport scanner.
Medical X-rays are high frequency (beyond ultraviolet) radiation, typically on a wavelength of a few angstroms.  While I would emphasize that @Ram is right to point out that there is not very much radiation in a medical X-ray since electronic detectors have been in place over film, the radiation itself is capable of penetrating the entire body and causes ionization.  The body can tolerate a certain amount of this, but that's why we use X-rays - they go through just about anything but bone/minerals and metals.
Airport security scanners emit terahertz radiation, which has a wavelength between microwave and infrared.  This is a very low level of energy per photon.  Terahertz radiation will not penetrate more than a centimeter of light material, which is more suitable to find hidden metal objects (which reflect terawaves).  Relative to X-rays it causes practically no radiation damage.  The intensity of the radiation is also pretty low since these scans are also detected by digital cameras, which are quite sensitive.
Compare the machinery.  X-ray technicians, who might have to take dozens of X-ray exposures a day, usually go behind a protective screen to take a medical x-ray.  The airport body scanners are open to the air and the terahertz radiation spills out into the crowd and the workers stand by it every day.  It has practically no expectation of being hazardous.  This device is pretty good as frequent fliers can have as many scans per year as are needed and health concerns are not significant.
Its always possible we might find some issues with Terahertz radiation in the future, but its hard to imagine it - its safer than standing 2 feet away from a working, closed oven.

As per the American Association of Physicists in Medicine the radiation exposure from full body airport scanners is equivalent to what an individual receives every 1.8 minutes on the ground from natural background radiation or equivalent to every 12 seconds during an airplane flight.
http://www.aapm.org/pubs/reports/RPT_217.pdf
The Back scatter full body scanners at airports use X-rays.


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What will happen if we expose the brain to intermittent light?
If a brain is exposed to an intermittent light source, are specific areas going to fire? If yes, which of them? Is there a experimental data about this effect?

What do you mean? Cutting open a skull and shining light into it? Why would the brain's cells be able to sense light? Even if they did, how could we possibly answer your question without knowing which areas of the brain you were illuminating?

as @terdon said, just shining light on the brain wont make it sense it (unless of course it is a laser of sorts which causes a lesion :P) . You need photoreceptors for sensing light.
In fact I have heard that brain doesnt have mechanoreceptors and nociceptors so someone wont feel that their brain is being cut <read and seen it in the Hannibal (by Thomas Harris): Not a very reliable source, though :P >.

It is thought that there are no active optical receptors in the brain normally, its possible some effect might show up in the future, it would be minor at best.
Shining light into the brain is standard procedure in optogenetics experiments. A subpopulation of neurons is transformed to express an optical receptor to modulate genetic or signalling properties of the cell.
So the reason I say it would be small effect on a native brain is that these experiments do give good results and are run with controls which do not have the optical receptors added, which typically show no change in brain activity.

Channelrhodopsin-2 (ChR2) induces temporally precise blue light-driven activity in rat prelimbic prefrontal cortical neurons. a) In vitro schematic (left) showing blue light delivery and whole-cell patch-clamp recording of light-evoked activity from a fluorescent CaMKllα::ChR2-EYFP expressing pyramidal neuron (right) in an acute brain slice. b) In vivo schematic (left) showing blue light (473 nm) delivery and single-unit recording. (bottom left) Coronal brain slice showing expression of CaMKllα::ChR2-EYFP in the prelimbic region. Light blue arrow shows tip of the optical fiber; black arrow shows tip of the recording electrode (left). White bar, 100 µm. (bottom right) In vivo light recording of prefrontal cortical neuron in a transduced CaMKllα::ChR2-EYFP rat showing light-evoked spiking to 20 Hz delivery of blue light pulses (right). Inset, representative light-evoked single-unit response.

Image taken from the linked Wikipedia page and originally from Baratta et al..

Short answer
Illumination of neurons with light of varying wavelengths can activate them, causing them to fire action potentials. It may, however, not be the photons that activate the neurons per se, but the resulting temperature changes.
Background
In contrast to the comments and original answer, it is possible to activate neurons, other than photoreceptors, with light.
Infrared laser light (~2000 nm) has been demonstrated to activate neurons in the periphery, including neurons in the sciatic and facial nerves and the spiral ganglion cells in the cochlea (Thompson et al., 2014). Interestingly, green laser light in the visible spectrum (532 nm) has also been shown to activate neurons (Wenzel et al., 2009). In the brain, laser light has been shown to activate neurons, including the primary visual cortex and somatosensory cortex. Typically, short (intermittent) bursts of stimulation are used, with exposure times in the millisecond range (Thompson et al., 2014).
The underlying mechanism of laser-activation of neurons is debated. The current evidence points to the possibility that focal illumination of neurons with light may increase the temperature of the cell, which may result in changes in membrane capacitance. Changes in membrane capacitance can cause voltage differences. Hence, lasers may induce action potentials by causing heat-induced depolarizations. Further, it is thought that temperature changes affects transmembrane ion channels and TRPV channels that in turn play a modulatory role in the laser-induced activation of neurons. TRPV channels, for example, include the heat-sensing TRPV1 receptor, that is thought to be activated by laser light-induced temperature changes (Thompson et al., 2014).
Why the fuzz about laser-induced neuron activation? Cochlear and retinal implants restore limited auditory and visual function in the deaf and blind, respectively. In both techniques, electrodes are used to activate spiral ganglion cells (in the inner ear) retinal ganglion cells (in the eye). Electric currents have the tendency to spread laterally through the tissue, preventing focused activation of specific localized regions of the cochlea and retina, respectively. Hence, both approaches suffer from channel-channel interactions, leading to crude stimulation profiles and reduced frequency resolution (cochlear implants) and low spatial-resolution (in case of retinal implants). Lasers, on the other hand, can be made into nearly infinitesimally small rays that have minimal scatter compared to electric current spread. Hence, laser-induced activation may be useful for specifically stimulating small populations of neurons in a certain tissue, opening up great possibilities in neural-prosthetics, such as auditory and visual prosthetics (Rettenmeijer, 2014).
References
- Thompson et al., Curr Mol Imaging (2014); 3(2): 162–77
- Rettenmeijer, Biomed Opt Express (2014) 5(4): 1014–25
- Wenzel et al., J Biomed Opt (2009); 14(4): 044007


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Ribosomes and DNA in chloroplast
Are there multiple copies of circular DNA and membrane bound ribosomes IN CHLOROPLAST ?
Are polysomes found in them ?

Wikipedia says the number of chloroplast DNA copies (nucleoids) drops from 100 to 15-20 as the chloroplast ages (https://en.wikipedia.org/wiki/Chloroplast_DNA#Nucleoids). No luck looking up membrane-bound ribo-/polysomes. I found lots on protein import into chloroplasts, but no export. My guess is that with many chloroplast genes migrating into the plant genome, protein flux is in rather than out (why would something the whole cell needs be made in just the chloroplast?). If you look at a list of chloroplast DNA genes, it's pretty self-contained; no proteins that would need to be exported.

ribosomes are never membrane bound.. they are adsorbed on ER (in case of RER)

The chloroplast, having multiple circular copies of its chromosome and its own tRNA and ribosome genes more resembles the bacterial systems it is descended from.  See @A.Kennard comment
Polysomes being found in bacteria frequently are single mRNA to which are attached multiple ribosomes.  This paper shows that chloroplast polysomes were observed in the mid 1960s by centrifugal sedimentation.


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Electrophoretic mobility of isozymes
If isozymes are seperated using electrophoresis, which of the following will be the principle of separation ?
A.charge density
B.molecular weight
C.polarity of molecule
I think it should be B.

What class is this for?

for general bio class

(B) is incorrect. I think the answer is (C) but to my mind those other two options are badly worded.
Here is an explanation.
The classic example of isozymes is the case of lactate dehydrogenase.
As you will see if you visit the Wikipedia page the enzyme is a tetramer, and there are two subunit types, the muscle or M type and the heart or H type. Consequently there are 5 isozymes, ranging from M4 to H4 with all possible combinations in between. Different tissues express these two subunit types to different degrees and so are characterised by a different pattern of isozymes.
The M type is encoded by the LDHA gene and the H type by the LDHB gene. I obtained the sequences of the two proteins and analysed them:
LDHA: 332 amino acids; 36.7 kD; pI=8.45
LDHB: 334 amino acids; 36.6 kD; pI=5.93
You can see from this that the ability to separate these isozymes depends upon charge and not molecular weight. That big difference in pI (isoelectric point) means that at neutral pH the LDHA protein will carry a net positive charge while the LDHB protein will be negatively charged. Consequently each tetramer will have a unique overall charge. You can find some details and an image of an electrophoresis result here.
In general, protein electrophoresis is much more sensitive to charge differences than it is to molecular mass except in the special case of SDS-PAGE when the large amount of negatively-charged detergent that binds to the proteins masks their intrinsic charge differences.
Another classic example of this is the ability to separate haemoglobin S in sickle cell patients from haemoglobin A in normal blood. In this case the only difference between the two molecules is a single Glu>Val substitution in two of the monomers in each tetramer.


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How cotranscription events affect splicing?
I want to know how and how often does cootranscription takes place? Can transcription regulators influence splicing during cotranscription?

Can you give some background to why you are asking? Is this a study question or based on your own research?

Co-transcriptional splicing (CTS) is very widespread. Different studies (which are done on different cell types) report different frequencies of CTS. Most of them report a frequency of ~0.8 in different cells except for mouse liver which was reported to have a frequency of 0.45
This is the article that summarizes these different studies.
Can transcription regulators influence splicing during cotranscription?
Yes.. including factors such as nucleosome positions.. this post may be helpful.


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Western blotting questions?
everyone. I've just been introduced to the procedure of Western blotting from my reading, though I'm not entirely sure about some points. I'd appreciate it if someone could help me with this.
What exactly is the function of the blocking buffer?
Why does one need a secondary antibody?
When discussing nitrocellulose membranes, say of a 0.45 5m pore size, what does 5m refer to?

Blocking buffer
Once the proteins in the gel have been transferred to the nitrocellulose membrane it is necessary to coat the rest of the surface of the membrane with an unrelated protein. This is necessary because all proteins will bind non-specifically to the nitrocellulose. Once the membrane has been blocked the only way that antibiody proteins (the probe) will bind to the membrane is via the transferred proteins that they recognise. The usual source of a blocking protein is defatted milk powder, i.e. casein, although some people use (or used to use) bovine serum albumin.
Secondary antibody
In a typical blotting experiment you expose first with a primary antibody directed against the protein of interest, for example this might be an IgG raised in a rabbit. Then in a second step you add a secondary antibody which will bind to the first one - in this case this might be goat ant-rabbit IgG (i.e. anti-rabbit IgG raised in a goat). Why is this done? The secondary antibody is in the form of an antibody-enzyme conjugate, typically with horseradish peoxidase, and the final development of the blot detects the presence of the conjugated enzyme.
Why use a secondary antibody? Two reasons: - firstly, there will sometimes be a degree of amplification if more than one secondary antibody molecule binds each primary antibody molecule; but more important is that it means that it is not necessary to produce an enzyme conjugated form of every primary antibody, which is time-consuming and quite tricky. So in your work you may be studying several different proteins using Westerns, but you will be able to use the same secondary antibody to detect all of them.
Pore size
That should be 0.45 - 0.5 µm. Pore sizes are measured in micrometres.


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Energetics and Products of Pepsin/HCl Protein Digestion
What are the energetics of protein digestion during which the enzyme pepsin is "activated" (whatever that means) by HCl?  I've looked and been unable to find anything like a chemical equation that includes an energy term.
Of course, pepsin, being an enzyme, is not used up in that sought-for equation.  Is HCl used up?  If so, what are the products?  How and in what form is the chlorine removed, assuming the HCl is consumed in the equation?

This is well-explained on the Wikipedia page for pepsin. You are misinterpreting the use of the word activated. The protein is secreted by chief cells in the gastric glands in the form of pepsinogen, an inactive pro- form which has an extra ~40 amino acids at its N-terminus (the propeptide). The propeptide binds at the catalytic site of the enzyme and keeps it inactive. At low pH (this is where the HCl comes in - HCl secreted by parietal cells of the gastric glands acidifies the stomach) the protein is able to cleave off its own propeptide, making it fully active.

So it would appear that the HCl is consumed in a chemical equation that looks something like:
pepsinogen + HCl => pepsin + ?Cl + ?Joules
Moreover, the HCl apparently isn't substantially involved in digesting the protein directly (say, by hydrolyzing it).
Furthermore, pepsin isn't consumed in the chemical equations for digestion (it being an enzyme) so much as it is lost due to normal mechanical effects.  So my question really reduces to filling in the '?' in the prior equation.

The HCl isn't really involved in the reaction at all, except in the sense that it drives the pH down. There may be some binding and/or release of protons during the autocatalytic process. I haven't been able to find anything about the free energy change that accompanies the reaction. Of course the entropy will increase (1 molecule > 2 molecules), so it is even possible that the enthalpy term (your ?joules I assume) would be positive.


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Deciding between chi square and t test
I am always so confused whether to do a chi square test or a t test in the sums given by my biostats teacher. Does anyone have a simple rule to decide this?

You can use a t-test for percents IF you use a binomial distribution for estimating variance.

This is a very subtle question and I encourage you to read the Wikipedia articles on these different subjects (t-test, chi-squared test, p-value, etc) because the authors worked hard to combat common misconceptions about these commonly used statistical tests. Here is a rather oversimplified rule-of-thumb for these different tests:

t-test: Used when you are looking at the means of different populations. For example, you might want to determine whether the difference in the mean gene expression level between treated and untreated cells is different, or if the gene expression level of cells in a certain environment differs from what you would expect in a null hypothesis. Assumptions: You are assuming that the populations you are looking at are normally distributed. The variance of the populations is not known (that would be a Z-test), but it is assumed that the variance of each population is the same. Finally, for the t-test to work, the samples of the data from the two populations are assumed to be independent.
$\chi^2$ test: Several possibilities for this. The most common in biology is the Pearson $\chi^2$ test, which is used when you are looking at categorical data, like the number of pea plants with white or purple flowers and round or wrinkled seeds, and trying to see whether the number of individuals in each category is consistent with some null hypothesis (such as the number in each category you'd expect if the genes for flower color and seed shape are not linked). Assumptions: The data points were randomly and independently gathered from the population, and you have a reasonably large number of samples.

I'd hate to have made a huge mistake, so please edit my answer and/or contribute your own if you think I am completely misrepresenting these topics!

This is correct, though I think the core of it is actually simple. Use chi-square if your predictor and your outcome are both categorical variables (e.g., purple vs. white). Use a t-test if your predictor is categorical and your outcome is continuous (e.g., height, weight, etc). Use correlation or regression if both the predictor and the outcome are continuous.

+1. This is a good account. Out of an abundance of caution--never to be deplored!--you have enunciated overly restrictive assumptions, but they are accurately and well stated. And your remarks about subtleties and misconceptions are to the point: the choice of tests is never as simple as suggested by the preceding comment by@octern, because the nature of the data is one of the least important factors in the decision. The purpose of testing is the primary desideratum; next is a choice of statistical model for the experiment or data; and everything follows from those.

Additional Info
T-test
As A.Kennard said t-test is applied when the random variable is normally distributed. How to know what is normally distributed is a relevant question. Regular measures which suffer some random error of measurement are normally distributed. The mean values estimated from different samples (the experiment that generates that sample may have any distribution) follow normal distribution. For e.g mean time interval of a radioactive decay- the interval itself is exponentially distributed but the mean of the mean decay interval will be normally distributed. You can reason that it is again an error of measurement that leads to variation in the mean value calculated in different samples. This is called the central limit theorem.
A normal distribution has two parameters- mean and variance i.e. you need to know these values beforehand to construct a normal distribution. A uniform distribution has no parameters- that doesn't mean that uniformly distributed samples have no mean or variance (in this case mean and variance are sample properties not distribution parameters).
A t-test or z-test is done to see if a sample is a representative of a given normal distribution. That again means that the calculated mean and variance are equivalent to the corresponding distribution parameters. In case of z-test you know the population variance (distribution parameter). You may ask how can anyone possibly know the population variance beforehand. An example is a case in which you already know the error rate of your measuring device (may be provided by the manufacturer or interpreted from its design).
$\chi^2$ test
There are several variants of the $\chi^2$ test. But what is common between them is that they refer to $\chi^2$ distribution. Variances, which are always positive cannot be a normally distributed. These follow $\chi^2$ distribution. The F-test for variances uses the ratio of the $\chi^2$ statistic of the two random variables denoting variances. Even in the Pearson $\chi^2$ test, the test statistic is a sum of squares which makes it always positive.
In fact this $\chi^2$ distribution is also used in t-test. As . Kennard said, one of the assumptions of t-test is that the population variance is unknown but assumed to be equal. Since population variance is unknown it has to be estimated from the sample. Like the case with all estimates you dont have a fixed value but a range of acceptable values falling in some confidence intervals. T-distribution is basically an average of several normal distributions with variance values falling in the allowed confidence interval of a $\chi^2$ distribution.
It is not necessary that categorical data are to be tested by $\chi^2$ test. Coin toss experiment gives rise to a categorical but it can be tested against a binomial distribution. So $\chi^2$ test can be used for categorical data but it is not the only test.
Bottomline: a statistic tested by a $\chi^2$ test has $\chi^2$ distribution as its sampling distribution. That statistic should be a square/sum of squares- something that can never possibly have a negative value. Perhaps that is why it is called $\chi$ squared.

Its true that T-test are used when your dependent variable is Numeric and Chi-Square test is used when you are analysis categrical variable. But how about this:
You have a categorical response (0,1) to a campaign. 1 who bought the product and 0 who did not. If you sum up the responses in your Test group and Control group and devide them by thier respective population size, lets say you get something like this - .23% response rate in Test Group and .01% response rate in Control Group.
Can't you use T-Test to see if these reponse rates are different? If yes, then let me remind that these variables were categorical (0,1) but we still used them as numeric.
All I want to say is that if we are comparing response rates or percentages, then T-Tests can be used irrespective if the dependent variable is character or numeric.
Sachin

No, you can't compare just two numbers (.23 vs. .01) with a t-test.


text:
Is AT or GC content important in electrophoresis?
Will it make a difference in running speed if we run samples of same no of bases but different AT - GC content ?

Can we get a little more information on the DNA?   Is it linear, cut, or nicked?  Is it an uncut plasmid or chromosome?  In cut linear DNA I don't think you would see a difference, but you could certainly see differences in the migration of the super-coil.

@AtlLED why? Are you thinking about mechanical things like long stretches of As that will make it bend or something else?

Nucleotide does affect the persistent length of the DNA.. but it is not just the length I guess; it is also affected by the sequence pattern.

@AtlLED It is linear double stranded dna.

It does make a difference on polyacrylamide. A and C are faster while G and T are slower.
Image from publication:

The "standards" are AAA/TTT/GGG/CCC molecules.

nice find - i think that proves the point!

Practically speaking I have never seen a difference, but the agarose gels usually used in the lab do not have the highest resolution. There is one paper showing that the form (A- or B- form) and the GC-content play a role since DNA molecules of higher GC content are less flexible and therefore have more problems getting through the agarose matrix. Interesting paper, but probably not very relevant in the lab (under "related publications" on the right side of the PubMed site you can find more on this topic):

Mechanical Properties of High-G⋅C Content DNA with A-Type
Base-Stacking

There is however a technique, which makes use of the different GC content of DNA samples. It is called "Denaturing Gradient Gel Electrophoresis" and makes use of gradients (either chemical or temperature) to denaturate DNA on the gel. Depending on the GC content, this happens earlier or later, which influences the migration pattern (denaturated DNA is much bigger and slower). See here for more details.

if you run a circular plasmid on an agarose gel you can see more than one band due to different ways the plasmid dna can twist.  In extreme circumstances I could see it making a difference.  Usually it would not.

Ok, I was assuming we compare doublestranded DNA of approximately the same size with different base compositions. In the case of the plasmid the different shapes (coiled, supercoiled, relaxed) make the difference.

hey Chris I was just supporting what you were saying - that if you had an odd collection of bases it might bend up like a supercoiled plasmid and would be visible on a gel.


text:
Protein purification methods
Which of the following methods would yield the most purified protein fraction?
A. Salt precipitation
B. Charge separation
C. Affinity purification
The first one is the most commonly used, is it the best ?

The choice of strategy for protein purification of course varies by application.  As @A.Kennart points out, affinity chromatography is the most popular right now.  It can be expensive.  If you have an expression system for a recombinant protein its more common than not to add a poly-Histdine tag on the N or C terminus or some other protein fusion.  So there are cases where you won't be able to use it.
For instance if the protein you are looking for is not coming from a recombinant source / is not sequenced, it can get expensive and time consuming - getting an antibody made requires that you have a somewhat pure prep of the protein in the first place.
The most common reason we still use all these methods are because different levels of purity and different raw materials show up in different contexts.
In the case where the protein has never been isolated before or you have a sample (like a cell sample rich in a particular protein) then salt and sizing columns are very useful.  Salt precipitation is useful when you have a lot of sample and you need to make some rough cuts.  Even if the gene is well characterized, it may depend on expression in its native organ.
I've prepped protein using ammonium sulfate precipitation from homogenized whole tissue for instance.  That's great - the result got rid of well over 90% of the material, but the outcome has hundreds of bands on the gel.  In this case an affinity column was used next; immobilized conconavalin A would bind to the glycosides on the target product.  If I had put the whole tissue homogenate I would have destroyed the affinity column by stuffing it full of meaty gunk.
Charge affinity columns with salt gradients are often still needed after affinity chromatography. Some folks don't need pure protein, but affinity chromatography (esp his tags) will still have many bands left on a protein gel and those contaminants always leave your experiment in question.

thanks :) my first tech job involved collecting adrenal glands at a slaughterhouse.  they did not smell or look good - no way you'd put that on a $300 column!  Salt and centrifuge. was the only way to clean that up.

Well, I see that this is the accepted answer, and I don't disagree with any of it, but it isn't really the answer to the question that was posed. In terms of a purification factor (e.g. increase in specific activity of an enzyme), affinity purification will win out every time.

In my protein purification experience, affinity purification is the most selective purification strategy. This makes sense because affinity tends to be very specific, and only one protein should in theory be preferentially separated by the column. Think how specific the antibody-antigen interaction is compared to the differences in, say, charge between proteins (which could be very slight). When I was purifying proteins I would pretty much always start with affinity purification. If more purity was desired after the affinity purification step, I could perform salt precipitation or charge separation on the affinity-purified sample, which had already been cleared of major contaminants/nuisance proteins by affinity purification.

The fact that all of the tagging methods used for purification of proteins expressed from cloned genes (his tags, FLAG tags etc.) are designed to facilitate affinity purification approaches underscores this answer.

If you stick with only one technique you get high yield but low purity. With each additional step you lose some sample, but gain on purity. The whole process is a trade off and the answer depends on your purpose of purification.
Affinity chromatography is somewhat different from other techniques you listed. Most people tend to choose it as the most effective one. It is based on specific high affinity binding. The problem arises when you start eluting the bound protein. The usual practice is using low pH and compensating it with high pH just after elution. There is a high probability that drastic pH changes are going to disrupt protein activity/native conformation. Another possibility is immobilized metal affinity chromatography if your protein is expressed recombinantly, but that is not the case usually and it was explained in detail in previous answers.
My approach would be to start with salt precipitation to remove bulk proteins. Proceed by ion exchange chromatography (depending on the properties of the target protein, optimal if its pI differs greatly from other proteins in the solution, but possible otherwise with some difficulties). The final purification step should be size-exclusion chromatography for further purification. This is only a template of the most commonly used purification approach. Therefore, the exact purification procedure depends on your target protein, but I recommend this strategy as a starting point.

To answer your question directly, affinity chromatography would yield the most purified protein fraction, but that would be a simple answer to a really complex question.

If possible please add some references to that users can look them up if needed.


text:
Niche overlap Index
I wanted to use the compana() function from the adehabitat package in R. I don't really understand the meaning of the data in the example they provide (from aebisher et al., 1993).
The two main arguments of the compana function are:

compana(used, avail, other.arguments…)

used
  a matrix or a data frame describing the percentage of use of habitats (in columns) by animals (in rows).
avail
  a matrix or a data frame describing the percentage of availability of habitats (in columns) by animals (in rows).

What does...

"[..] a data frame describing the percentage of availability of habitats by animals"

mean ?
By 'Availability of a habitat', I guess it means the proportions of these habitats that were not used. Isn't it? But then, how can this be categorized into different species?
I guess I am missing something easy in the functioning of this statistical tool!

One can run that in R to get more info:
install.packages("adehabitat")
require(adehabitat)
?compana
# The following code comes from the example provided in the help page
data(pheasant)
pheasant$mcp
pheasant$studyarea
compana(pheasant$mcp, pheasant$studyarea,
                   test = "parametric")

Not 100% sure, but this would be my guess:
"used" would be the percent of time spent in a certain habitat, while "avail" is the amount of that habitat that specific animal encounters in its life.  This example explains what I mean:
Say you have some sort of ground squirrel that lives in rocky outcrop(R).  Next to the hole is a field (F), and across the field are some trees(T) with squirrel food in them.  The squirrel spends all of its time in these three habitats, but it doesn't like the field much because its exposed and can be attacked by predators, so when it goes out for food it runs quickly across the field, gets food, and runs back.  Say it spends 60% of its time at home, 10% of its time running through the field, and 30% of its time in the trees eating.  Those give you your "used" numbers.  But, the relative proportion of the habitats that the squirrel ever occupies (relative size), are very different.  By area, the field is 75%, the trees are 15%, and the rocks are 10%.  In this example, your "used" matrix would look like this:
used
            R    F    T
Squirrel  0.6  0.1  0.3

and your "avail" matrix would look like this:
avail
            R    F    T
Squirrel  0.1  0.75  0.15

With this information you can do a statistics to test whether the squirrel randomly picks habitats, or prefers certain ones.  If it randomly picked habitats, you'd expect it's "used" percentages to look like its "avail" percentages, i.e. it spends ~75% of its time in the field because it just wanders around aimlessly between rocks, field and trees.  But since you observe a pretty sizable increase in time spent in low-availability habitats, you are likely to see a statistically significant preference for certain habitats (rocks for sure, and maybe trees).
Then, if you have multiple species, this program probably tests to see how similar two species' patterns of habitat preference and avoidance are, and uses that to define niche overlap.  The "avail" could be the same if they both travel through the same rocks, field and trees, but if another species has a larger foraging area, its F and T percentages in the "avail" matrix will go up relative to the R.
Again, not totally certain about this interpretation, but it seems to make sense to me from the R documentation.


text:
Hardy Weinberg Equilibrium problem

In a small tribal population, the frequencies of two alleles A and a
  at a particular locus were 0.3 and 0.7, respectively. However, not all
  the individuals with genotype aa could live up to the reproductive age
  and the relative fitness of this genotype was found to be 0.5. The
  remaining genotypes had a relative fitness of 1. What is the expected
  percentage of heterozygotes among newborns in the next generation?

My answer is : 37 % but the answer given 43.52 %
I did the following : First I assumed the population to be of 100 individuals and then I counted the no of individuals with respective genotypes. Then I assumed that each person leaves 1 offspring except the aa ones which leave 0.5 offsprings and then I calculated the new genotype frequency from this.

Are you saying that f(A)=0.3 and f(a)=0.7?

@fileunderwater yes.

Your reasoning is incorrect, because once you have the number of individuals surviving after the 50% mortality you can't just give them all one identical offspring, you have to mate them with one another.
However, I can't get the answer given, instead I get 47.9%
Here is my reasoning:

f(A)=0.3
f(a)=0.7

calculate genotype frequencies:

AA = 0.32 = 0.09
aa = 0.72 = 0.49
Aa = 2 * 0.3 * 0.7 = 0.42

(these add to 1 as expected)
use 1000 individuals for nice round numbers

number AA = 90
number aa = 490
number Aa = 420

Now apply 50% mortality in aa individuals:

number AA => 90
number aa => 245
number Aa => 420

now calculate new allele frequencies in mating population:

f(A) = (90 + 90 + 420)/1510 = 600/1510
f(a) = (245 + 245 + 420)/1510 = 910/1510

calculate proportion of heterozygote offspring

= 2 * f(A) * f(a)
= (1200 * 910)/(1510 * 1510)
= 0.479

I hope someone can see where I'm going wrong.

I got 47.89% when only working with frequencies, so your answer seems right. My feeling was also that both answers were incorrect, therefore my comment to the question.

thanks @fileunderwater, I've been tearing my hair out over this! You should post your calculation - I expect it is more elegant than mine.

This is pretty much what I was getting as well, and I couldn't figure out how to get the 43.52%.

This is basically the same solution as @AlanBoyd's answer, but since he asked me to, I will post my solution as well (also a reason to try out $\mathcal{MathJax}$ a bit more).
Assuming:
$f(A)=p=0.3, \\f(a)=q=0.7$
and the standard formulas:
$f(AA)=p^2\\
f(Aa)=2pq\\
f(aa)=q^2$
gives these genotypes in the parental generation (G1), before and after selection (using the fitness vector $w$):
$$
\begin{array}{c|cccc}
\text{genotype} & \text{freq G1} & w & \text{freq breed.pop} & \text{freq breed. rescaled}\\
\hline
AA & 0.09 & 1 & f(AA)\cdot w_{AA}=0.09 & 0.119\\
Aa & 0.42 & 1 & f(Aa)\cdot w_{Aa}=0.42 & 0.556 \\
aa & 0.49 & 0.5 & f(aa)\cdot w_{aa}=0.245 & 0.325\\
\text{sum} & 1 & & 0.755 & 1
\end{array}
$$
The genotype frequencies in the breeding population (rescaled so that they sum to one) are then used to calculate allele frequencies as:
$$f(A)=f(AA)+\frac{f(Aa)}{2} = 0.397\\
f(a)=f(aa)+\frac{f(Aa)}{2} = 0.603$$
These are then used to calculate the genotype frequencies in the next generation (G2), using the same HW-equations as in the beginning:
$$\begin{align}
f(AA)&=0.397^2=0.158 \\
f(Aa)&=2\cdot0.397\cdot0.603=0.479 \\
f(aa)&=0.603^2=0.363 \\
\end{align}
$$
I do not understand where the given answer $f(Aa)=0.435$ is coming from.


text:
What significance does the moon have for the development of life on earth, and for earth's ecology?
Planets like Mercury and Venus do not have moons, and they do not appear to harbor (higher) life (forms). How significant is it for the emergence of life on a planet that it has a moon? How does it affect the planet's ecosystem, especially seasons and periods?

This question is too broad, and to a large extent off-topic for Bio-SE.

This question appears to be off-topic because it is about astronomy, not biology.

"our" moon certainly affects tides and so the animals living near the sea shore like barnacles have to face both high tide and low tide during which they become dry.

Out of curiosity, has this been posted on Astronomy SE? The moderators might take it, although I believe it's a duplicate of something.

Pluto does, in fact, have moons.

The part of the question which asks about the ecosystem is utterly on topic, and also interesting. If I'm not mistaken there are theories which attribute the genesis of life to the moon (via tide pools).

Another factor is that the moon stabilizes the earth's rotational axis. Without the moon the axis would significantly tilt over geological periods of time, causing large-scale climate change. So this is an interesting question straddling astronomy and biology. I'd vote to re-open, but I can't.

This question is indeed off topic here but the reason these planets don't have moons is pure chance. Different moons have different origins, some could be meteorites or other drifting bodies that were captured by the planet's gravity, others could be pieces of the planet itself that were spun off before it solidified or from an impact event. Others could be mass ejected from the sun which was captured by the planet and so on and so forth. Whether any of these events occurred in a planet's history is down to chance.
As for ecosystems, since the only planet we know of that has one is the Earth, we have no idea how other ecosystems would be affected by the presence or absence of a moon. The absence of tides would be the most obvious effect but you could also get tidal effects if you have an erratic orbit around the sun or if you have other large bodies close enough to affect you.


text:
What species is this rabbit-like animal?
This image was included in something I was browsing through.
It reminds me of my lion head rabbit, but the head shape doesn't match.
Is this some kind of Siberian wild rabbit?

Google image search yielded a lot of references to wise bunny

This does indeed seem to be a viscacha, which are actually found in South America (not Siberia). Their morphological similarity to rabbits is simply a case of convergent evolution, as these animals are in the rodent order (Rodentia) [whearas rabbits are order Lagomorpha].
Viscacha consist of two genera (Lagidium and Lagostomus). However, your image much more closely resembles one of the 3 Lagidium species in a morphological sense, and your mountainous background also supports Lagidium as these are considered "mountain viscacha."
Which mountain viscacha you have pictured is difficult to say (and I'm certainly no expert). If we knew location of the original image, we'd be abl to narrow down easier. From a morphological sense, Wikipedia claims that the two most common mountainous species can be differentiated because the northern viscacha (Lagidium peruanum) is grey/brown on its back while the southern viscacha (Lagidium viscacia) has redder-colored fur.

However, the individual Wikipedia pages for each species suggest that the northern species has a "cream or pale grey" belly while the southern species has "yellowish-grey upperparts [and] paler underparts."

Given this [somewhat unclear] information, I'm still not certain on species. If I had to guess between these two I'd guess your image shows a northern viscacha (Lagidium peruanum).

 Source: BBC Planet Earth II

hmm.. interest... who am i kidding.. they're soooo cuuuuuute!

Thanks to @Satwik's tip about google image search, I was able to figure out that the animal in question is a Viscacha. Though the source answer is thanks to reddit, so take that as you will.

Viscachas or vizcachas are rodents of two genera (Lagidium and Lagostomus) in the family Chinchillidae. They are closely related to chinchillas, and look similar to rabbits, apart from their longer tails

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


text:
Do men have a higher genetic variance than women?
I've heard that with the  distribution of our genome women have less variation on the bell curve than men.
Is there any basis for this? It was my understanding that women have more genetic variation than men due to having two X chromosomes and there being more variation on gene expression within the X chromosomes.
I've also seen a theory that with traits linked to genes on the X-chromosome in women being averaged across the genetic variants found on each of a woman's two X-chromosomes, this would reduce the likelihood of extreme traits (men have just one X-chromosome, precluding this averaging process).
Is there any biological possibility for men to have more genetic variation than women, and if so what would be a potential reason for it?

This question is very interesting and I would love to have a reference to an article that provides such evidence.
Decomposing your question:

Is it true that there is more genetic variance among males than among females (in humans I guess)
If the answer to (1) is yes then: Why is it so?

I am trying to address these two points below
1. Is it true that there is more genetic variance among males than among females ?
There are different ways of understanding your question 1. Do you want to talk about…

Allele richness
Heterozygosity
Polymorphism
Part of the genetic variance in phenotypic variance (heritability). I don't think it is what you meant though!

Therefore, the question is slightly unclear and I don't know of any paper who address any of these questions anyway. So... I don't know!
2. Assuming (1) is true, then Why is it so?
There are many possible explanations for such observation (assuming this pattern has been observed). Below I give some hypothesis I can think of.

There might have a higher selection (survival differential) among females than males. In a consequence, genetic variance among young females is higher than among older females. In total, genetic variance among females would be lower to that of males.
There might have a very high variance on the Y-chromosom but not on the X-chromosom. This might for example be a consequence of the arrest of recombination that occur on the Y-chromosom. As a consequence of this arrest of recombination, selection hardly purge deleterious mutations (see Muller's Ratchet). See Shigeta's answer for a more detailed overview of this point. See shigeta's answer for more information.
Males might migrate more. If one look at the sex-specific genetic variance in a population (and not on the world-wide metapopulation), this variance might be greater for the sex that migrate more because different genetic background might be brought from other neighbor populations.
Higher selection within mitochondria in females very early in life (in the egg for example) resulting in a decreased mitochondrial genetic variance in females. This is maybe a bit far-fetched.
There might have other hypotheses for species for which sex of an individual is not determined by sex-chromosomes. (see Sex Determination System)
Depending on what you mean in your question, I guess we might find many other hypotheses when thinking of migration, different fitness landscape (different optimum), different selection intensity.
We might as well think of genetic variance of gender-specific gametes (especially for haplon-diplontic species for example)
This evidence (assuming it has been observed) was a False Positive!

Note:
@terdon provided a very good explanation (which was deleted) of why males might cause most of the genetic diversity but as @terdon and I discussed (in the comments that were also deleted) this is not an explanation for greater diversity among males. The reason is that the variation is created via mutation in the testis and the ovary. The fact that most mutations happen in males does not influence the sex-specific diversity because males sire as many male offsprings than female offsprings (so do females!).

what was the explanation that @terdon offered?  I wish it wasn't deleted.  It would seem that if there are more mutations in males (source??), there would be greater genetic variance in males, whether the mutations are in germ or somatic cells.

As @terdon and I agreed after discussion, the argument was flawed (that's why it was deleted). It is not because males transmit more new mutations to their offspring that there will be a greater genetic diversity among males as males make as many males than females make males.

Can it please be retrieved so I can read it?

@sterid High rep users (I don't know how much rep it takes) can see deleted posts. I therefore, copy-pasted this old post into a new answer for you with a very bold warning!

This is an interesting question and we should probably have a statistical geneticist around, so further answers may show up better than this one.  So I'm also assuming the question is: Is the rate of variation on the X,Y and autosomal chromosomes different?  Also assuming XY male heterogamy as humans have - different sex chromosomes will give different results between male/female.
The main distinction being - if males absorb more mutations on X as well as Y, then it would be nigh on impossible to see this phenomenon statistically since those chromosomes get passed on to females immediately.
Its important to remember that some of the X and Y regions do recombine even in men in the pseudoautosomal regions. The Y also maintains itself with a self-recombination event in meiosis.  So the message here applies to the SDR (sex determining regions) of the X and Y chromosomes - other regions are quite possibly not different than the other chromosomes.
Anyway I've found this modeling paper "Patterns of Neutral Genetic Variation on Recombining Sex Chromosomes" which estimates and compares the relative coalescence time for variations on the autosomal, X and Y regions.
In any case the models in the paper says 'yes they are different and Y non-autosomal regions accumulate variants faster'.   The coalescence time is the amount of time for a variation to spread into the general population on one of these 3 types of regions.
In cases where there is neutral selectional pressure "Close to the SDR, expected coalescence times are shorter on Y chromosomes and longer on X chromosomes than for
autosomal sites."
Which is to say speeds for incorporating variations are Y > X > autosomal
Note that the paper goes on to say that when there are more than one competing versions of the Y chromosome in a population (which seems likely to be the case), then the spread of variations on the Y goes to a normal autosomal rate.
So its likely that this effect is small overall.
It has been documented that some Y chromosomes are very competitive though.

WARNING!
This post contains a logical error (see the added section on comments) and the post was deleted by its owner (@terdon). I copy-pasted this deleted post here by request of @sterid who wanted to see it.
Please do not up or down vote!
Yes, this is  actually true. The following is taken from the introduction of the classic human genome paper[1]:

The mutation rate is about twice as high in male as in female meiosis, showing that most mutation occurs in males.

This actually makes a lot of sense from an evolutionary point of view. Primates, like many animals, are species where the male competes for the female. This means that the onus is on the male to convince the female that he is the best father for her offspring. This gives rise to some fascinating and strange behaviors:

Mutations are one of the ways that genetic diversity can be generated. Genetic diversity, in turn, makes it likely that someone or other will manage to impress the female, that some male is born faster, smarter or stronger (or whatever it is the females of his species value) than his peers. Diversity is also important as it increases the chances that a species will survive a catastrophic change in it's environment. The more random stuff you have floating around, the likelier it is that one of them will do just what it is you happen to need at the moment.
Now let's imagine a mutation whose result is a slight increase in the rate of mutation. This will cause in a net gain of diversity. As long as the increase in mutation rate is not great enough to start causing other problems, the increase in diversity will be advantageous and the mutation in question will be selected for. This is a horrible simplification, but I hope you get the general drift.
So, in humans, the males generate greater diversity but it is the females who chose which new traits are favored. In other words, men push evolution but women drive.

Lander et al Nature 409, 860-921 (15 February 2001) | doi:10.1038/35057062;

Discussion between Remi.b and terdon explaining the logical issues of the post
Remi.b: t explains why men create greater genetic diversity. I think the OP was asking if it is true that, among men, the genetic variance is higher than among females. I don't this question got answered. Or maybe I didn't get the link between differential of "diversity creation" between sexes and difference in genetic variance (or diversity?) between the sexes.
terdon: @Remi.b My logic is: men have a higher rate of mutation => more mutations occur in men => men will have more differences in their DNA compared to other men. This means they will have a greater genetic diversity since the higher rate of mutation will result in a more varied group of genotypes.
Remi.b: Yes but mutations occur in the testis when reproducing. And men sire male and female offspring that both carry the same amount of mutations. Therefore the diversity should be the same. What point am I missing in your speech? The only possible explanation to me would imply differential selection early in life in male and in females. The sex that is under stronger selection has a lower genetic variance.
terdon: @Remi.b ah, you make a very good point. Looks like it is my reasoning that is missing something and not your understanding of it :). I will edit my answer as soon as I get the chance.


text:
Are Asians more genetically homogenous than other races?
I've heard that Asians (I'm not entirely sure which subgroup was being referred to) tend to be more genetically homogenous than other races, with people of African lineage being on the other end of the spectrum being the least homogenous.
Is there any truth in the claim that Asians or a specific subcategory such as South East Asians have less genetic variance than other races?
I've seen papers like Extreme mtDNA homogeneity in continental Asian populations for example, but I don't understand enough to know if that answers my question.
If Asians have less genetic variance, would that lead to less variance in phenotypes?

I think you'll find that Africans are the most genetically heterogeneous.

@AlanBoyd That's interesting...do you have a source? I remembered reading it was Caucasians which given how far Caucasians have spread and evolved independently would make sense to me...

This Wikipedia page is a good source http://en.wikipedia.org/wiki/Human_genetic_variation The argument is that during the out-of-Africa migrations only a small sample of the total genetic variation left Africa. Those who remained inherited all of the existing variation. Detailed information at the WP page.

I will side-step the question of 'race', as you seem to be using it (like most people) in the sense that has no scientific basis, but the answer to your question goes something like this:
The amount of genetic variation in a given population is mostly the function of the geographical distance from Africa. Roughly, populations in Africa are most genetically variable as they are the oldest (they have been there for the longest time in human evolutionary history and therefore there was a long time for mutations to accumulate in their genomes). Because all the other populations in the world have originated from these original African populations, they have a subset of that original variation - they are less variable. The further you get from Africa the less variation remains (a very simplistic scenario: a group of Africans left and settled down in the Middle East; then a group of Middle Easterners went further up to Europe and further east to India; then the Indians moved further north and east etc. etc.; in each stage, only a subset of the previous variation would be travelling along).
A famous image depicting this phenomenon is in Ramachandran, S. et al. (2005). Support from the relationship of genetic and geographic distance in human populations for a serial founder effect originating in Africa. Proceedings of the National Academy of Sciences of the United States of America, 102(44), 15942–15947. doi:10.1073/pnas.0507611102 figure 1B

On the Y axis you have a measure of genetic relatedness (more relatedness = less variation) and on the X axis you have rough geographic distance that takes into account major geographic barriers (like seas etc.). Green is Eurasia and blue is Australia and Oceania. Red is variation within the groups (so, Europeans only, Australians only etc.) The variation  between the populations explained by this geographic distance is close to 80% - which is huge. If I get this right, it means that most of the genetic differences between people in different populations are a simple consequence of living outside Africa. And the variation within each population is tiny (12%) compared to the between population.
This is a simplified view, because we now know that there were other species of humans that lived when our ancestors left Africa, and that thay had sex together as our ancestors spread around the globe, so a small portion of the others' DNA is still present in us today. Also this picture may be slightly different depending on what region of the genome you're looking at. But the major picture stands.
Going back to your question - "Asians" (populations with ancestry in east Asia) are on average more genetically homogenous than "Australians", "Americans", and "Africans".
EDIT: A paper I like that should explain all this much better: Barbujani, G., & Colonna, V. (2010). Human genome diversity: frequently asked questions. Trends in genetics : TIG, 26(7), 285–295. doi:10.1016/j.tig.2010.04.002 link

a sensible , scientific answer to a sensitive question!

Thankyou, a very interesting answer! I disagree that race has no scientific basis as I think that's being overly PC. Could you clarify if on your last point when you say "Australians" or "Americans" you mean Caucasians, or if you actually mean Australians or Americans?

Answering the second part of the question: I mean people of Australian or American ancestry. The first part: I encourage you to read that Barbujani paper. When people say "race" they tend to mean a social construct that classifies people into groups based on physical appearance (skin color, hair, eyes etc.). You cannot divide people like that based on the DNA, because the variation is continuous and all division of it will be arbitrary. It's like seeing a rainbow: can you tell me exactly where the yellow ends and orange begins?

The reason I think it's being overly PC to assume race has no biological basis is because we have evidence to the contrary. When people say Caucasians or Asians or Black, we genrally know what the people being referred to look alike. That aside, there are different medical issues effecting different races. Race is the subgrouping of people with common characteristics, which we know exists in biology. That's all it has to be, unless you make it into something more. Koreans all have genetic characteristics in common that Caucasians lack, and vice versa. No?

I think your answer would be a lot more useful if You didn't compare races to nationalities, especially nationalities only established in the last half century. Would you say the Asian Race is more genetically homogenous than the Caucasian Race? - http://en.wikipedia.org/wiki/Caucasian_race and http://en.wikipedia.org/wiki/Mongoloid_race

@SonnyOrdell No. I agree that when people say Asians or Black, we generally know what they mean - because we tend to think in general features of appearance. This may be particularly true in the US, where the people of many greatly different ancestries live together (imagine picking only "full" colours from the rainbow). But the gradients are there - in physical appearence and in DNA. And "what people look alike" is just adaptation to the environment (ie skin color is an adaptation to uv radiation). Anwyay, this comment section is probably not a best place for a complex discussion...

@yotiao would you be interested in taking it to chat? My point I guess is that the phenotypes we associate with cultural constructs have a basis in DNA..gradients are there, but I don't see why that takes away from it. If we study ancient human remains we can see the remains, can we not?

I'm still interested in how Asian genetic homogeneity compares to Caucasian genetic homogeneity, outside of Americans and Australians.

Fine with chat - but I don't know how, I've never used it :-) Give me some time to find relevant papers and we can talk.

@yotiao I've been busy, sorry for the late reply. You still down to chat?

@SonnyOrdell I am, probably later today. My local time is GMT. I think we may do it there: http://chat.stackexchange.com/rooms/11814/genetic-variation-in-humans

Don't worry, caucasians all look the same to Asians too. This is because of the cross-race effect: we are able to effectively distinguish people we are more familiar with.
But as for the most genetically diverse race, this article might be of interest: "Sub-Saharan Africans have greater genetic diversity than other populations." The research paper the quotation is based on is found here.
To be honest, I don't see the point of this question. The answer depends on what classifies under a race: Do Asians include Indians? What qualifies as Caucasian? etc.
The only thing that can be gained by exploring race-specific genetic distinctions is if you want to promote some racial supremist attitude like that guy with the toothbrush mustache.

I'm not really interested in the tired of argument of whether or not race is a valid construct, and I don't see it as relevant to the question. For most people outside of the UK, Asians refers to the peoples of the Asian continent..Chinese, Japanese, Koreans, Thai etc..

The point of the question is that I was curious about it and wanted a detailed answer after being unable to find one myself...isn't that part of why sites like this exist? The cross-race effect is interesting, but doesn't address the question. I'm not asking about the most genetically diverse race, and I think it's naive to say exploring race-specific genetic distinctions is only useful for promoting racial supremacy.


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Gel electrophoresis question
Leena is a molecular biology student. She purifies two fragments of DNA, 800 and 300 base pairs long. These were obtained from a plasmid after digesting it with HindIII. Each of these fragments has a single EcoRI recognition site.
Leena wants to join these two fragments to get a 1.1kb gene as shown in Figure 7.1. She suspects that this gene has a unique protein-coding sequence.
She, therefore, mixes the two fragments in the presence of excess DNA ligase in an appropriate buffer and incubates the mixture. She removes an aliquot (a small part of the reaction mixture) after 30 minutes and loads it on an agarose gel to check the results. She is surprised to find many bands along with the expected 1.1kb band  in the gel.

FIG 7.1

Leena is interested in the 1.1kb fragment shown in Figure 7.1. Hence, she elutes the 1.1kb fragment from the gel shown in Figure 7.2 and subjects part of this sample to HindIII digestion. She obtains the expected pattern with two bands, 800 and 300 base pairs long. To confirm its restriction map, she subjects the remaining sample to complete EcoRI digestion. Which pattern of bands would she obtain?

The answer given is this and I understand why this is the answer and other options given in the question(not posted here) are definitely not the answer but I do not understand why there are no 0.4kb fragments, 1.1kb fragments here.

@A. Kennard beat me to it. Here is my answer, assuming complete digestion. The two HindIII fragments can ligate to give four different products as shown below. I have marked one end of each HindIII fragment to help in seeing polarity, and I have left the ligated junction as a gap to help see what is going on. I don't understand the point about the 1 kb band.

Nice graphics! I had a typo in my response, I meant to say that if you assume the digestion is incomplete, in addition to 0.4 kb and 1.1 kb bands, it should be possible by not cutting at one cut site to get an extra 1kb band not present in the complete digestion case.

You are being too smart for this question ;) If you assume that the enzyme is perfect, you've optimized the amount of template, and you leave the reaction for a really long time then there will be complete digestion. If you assume complete digestion, then there won't be any 1.1kb fragments. Furthermore, if you work out all the possible orientations of the 800bp and 300bp that were ligated together (each piece can be in the orientation shown and the opposite orientation) and you force an EcoRI cut at every available EcoRI site, then you won't have any 0.4 kb fragments from imcomplete digestion, either.
In reality, I've never been able to optimize my reaction conditions to get complete digestion of my template, and I always have some residual template around, so it's good your thinking that way for "real-world" preparation.
If you do not assume incomplete digestion, in addition to the 1.1kb and 0.4kb bands missing from this gel, I calculate that there should be a 1kb band as well.

Edit: I goofed up the last paragraph. It should read: "If you do not assume complete digestion, in addition to the 1.1kb and 0.4kb bands missing from the complete digestion gel, I calculate that there should be a 1kb band as well.

I agree with @Alan Boyd. Even I do not understand why 1kb should be there ?

I had a typo. I meant to say "if you do not assume complete digestion." Look at any of the possible restriction products that @Alan Boyd has displayed. If you get 800bp and 200bp products (or 700 and 300, or 900 and 100) with compete digestion, you would get a 1kb product with incomplete digestion. Sorry for the confusion!


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Cytoplasmic determinants in amphibians
If cytoplasmic determinants are distributed before fertilisation then how can an amphibian's embryo, whose dorsal side is determined after fertilisation develop ?


text:
Alternatives to CFU plating for measuring number of viable cells?
I am hoping to measure growth rates of a bacterial culture in several growth conditions. I am concerned that these growth conditions may cause cell death, which would lead to a decreased correspondence between optical density and number of live cells, so I want to independently measure the number of live cells. I understand that plating serial dilutions of a culture and counting the number of colonies to calculate the Colony Forming Units (CFU) per mL is the gold standard for measuring the number of viable cells. However, I have seven different conditions I want to test in parallel, and making 3+ plates for each condition (to do serial dilutions) and taking measurements at several points in the growth curve leads to a lot of plates. In general this method seems more labor intensive than what I need. I am mostly doing this as a sanity check, so it seems like a lot of work just for a sanity check.
My question is: are there any alternative methods to CFU plating for measuring cell viability, given that I am doing a lot of experiments in parallel? I don't have access to a FACS machine, so cell sorting won't work. I have read this question on measuring live cells in algae. I'm not sure that a hemocytometer is the right alternative for my particular high-throughput application (though you could convince me otherwise). The best answer to this question would compare from experience the precision, accuracy, sensitivity, and ease of use of this alternative method with CFU plating.
Thanks!

Even small cell counters are available. Stain cells with trypan-blue and you can count viable cells. I think invitrogen supplies this machine.

I've only used it for mammalian work, but the Cedex HiRes Analyzer from Roche is pretty sweet. From their docs you can analyze particles from 1-90 μm in size (the cells I work with are about 20 μm), and there are a ton of configuration options depending on your needs. As long as your cells can be stained with Trypan Blue (it's been a long time since I've done bacterial work, pardon my ignorance) to look at viability, then this machine should work quite well. When set to Maximum (i.e., the analysis takes as long as possible), I get results in less than 4 minutes per sample - and this includes about a minute for the sample to settle in the flow cell. If you lower the sensitivity, you can get results faster.
Basically the way it works is you take 300 μl of your liquid culture (diluted if you think the concentration is too high), and add it to a sample cup. Put all your cups in the tray, fill out the sample info and analysis options in the MultiRun screen, and hit Start. The machine includes all the reagents onboard, so it mixes the Trypan Blue with your samples, then injects the proper amount into a flow cell. The sample is allowed to settle, then a high-resolution image is taken of the entire length of the flow cell. Then, depending on your sensitivity settings, a certain number of frames are taken from the full-length image and analyzed. An average count is then generated based on all the images, and your final numbers are spit out. You can then print or save a report of the cell counts and viability.
The software comes with some analysis tools as well, so if you keep multiple cultures going over time with different growth conditions, you can aggregate the data for each one and generate graphs of cell counts, viability, cell size, etc. - there are at least 10 or 20 parameters to choose from. All the image analysis options are configurable, so if you look at the results and think the machine is picking too many dead cells, or not enough, you can change the parameters to your liking, and save the options as a custom analysis type.
One option I like that would beat the pants off CFU-based counting (besides the drastically-reduced time) is the fact that the machine can detect aggregation of the cells. One issue with CFUs is that it's hard sometimes to know that a certain colony really arose from just one parent cell - if you let the colonies grow long enough you may be able to tell based on size, but it's a tough call. Since the Cedex is looking at the cells themselves, it doesn't matter if they're aggregated or not, as they'll all get counted (assuming the aggregation particle doesn't contain a huge amount of cells, like 50+, that can't be resolved by the software due to occlusion of the image).
I don't know how much these guys cost, so depending on your budget you may not be in the market for one, but if you're going to be doing a lot of counting then one of these is invaluable. I'm sure other companies make similar instruments, but I haven't used them, so I don't know how they compare to the HiRes. I do know that the person at my company that was responsible for buying the HiRes is very good about researching possible alternatives, so this one is probably as close to the top of the line as we could find, for the needs that we have.
Good luck with your counting!

Thanks for the detailed, well-explained answer! I'll have to check whether Trypan Blue works for bacteria; from my initial investigation it seems so. I'm a bit leery about buying a whole machine to do this, but at some point if I want to beat tedious (and cheap) CFU counting I'm going to have to get real and buy something; if it could be done better than CFU with similar technology, it would have been done before!

A company called HemoGenix offers a CFU alternative/replacement assay. It's called HALO PCA (progenitor cell alternative). We have used it with great success. It does, however, require an instrument. We use a bioluminescence plate reader, but it looks like they also have kits for fluorescence and absorbance readout.

Welcome to Biology.SE! A little more detail about the procedure would be very helpful.


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Improving throughput of CFU plating?
In a separate question I've described my general experimental setup where I need to measure the number of live cells in a growing bacterial culture in a fairly rapid and high-throughput manner. In that question I'm asking if there are any alternatives to the gold standard of CFU plating for making this measurement. As I wrote that question, it occurred to me that if I could just make CFU plating easier to do with lots of samples (esp. using fewer plates), then this method could work just as well. So my question here is if there is a way to make CFU serial dilution plating easier when I have to measure the number of live cells in 7+ different growth conditions every hour of growth?
Thanks!

see my answer on your previous question - I'm not sure you'll be able to get much more high-throughput than that :)

Thanks, @MattDMo. I agree that the Cedex machine is more high-throughput, but I am looking for any neat tricks people have tried to make CFU plating more economical. For example, one idea would be to put agar in each well of a [12 well plate][1] and do 12 samples at a time; then the question would be if people have tried this and the reduction in available area for counting made the CFU counting significantly less reliable. Or something like that. [1]:http://globebio.com/plates/multiwell-plates/suspension-culture-sterile-multiwell-plates/12-well-cell-culture-plate-with-lid-sterile.html


text:
Stability of helical strands of DNA?
The DNA molecules contain phosphate groups involved in $3'\rightarrow 5'$phosphodiester linkages. These groups, in the bonded state with deoxyribose, contain 1 negative charges ($\ce{3'-PO4^{-}-5'}$). This means that the two strands are very highly negatively charged. What than stabilizes the double helix, preventing the natural electrostatic repulsion between the two strands?
[I am assuming that the phosphate groups are dissociated under physiological pH, correct me if I am wrong.]

There is indeed a repulsive force between the phosphates in the DNA backbone. (The pK of the phosphate is very low so it will be ionised at all physiologically-relevant pH.) This is why DNA behaves like a stiff rod over short distances. However cations in solution will help to shield some of this repulsion. This is why DNA melting temperature decreases as the ionic strength of the buffer is reduced (by increasing the repulsive force relative to the favourable energy term coming from the base-pairing).
When DNA is condensed these repulsive forces are masked by cations like spermine, and of course by histones.

Careless typo.Corrected in the question. :)


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Why are transmembrane proteins difficult to crystallise?
I know that in vivo there are a lot fewer transmembranous proteins in general, and that they are produced at a lower rate than their free counterparts. This is mainly because transmembrane proteins are only required in a 2D space on the membrane rather than a 3D cytoplasmic or extracellular space. This (again, very broadly speaking) means that the probability they will interact with their target is higher.
I also know that this is one of the reasons that producing crystals for X-ray crystallography is notoriously difficult for transmembrane proteins. What are the other reasons that make transmembrane proteins typically tough crystallisation candidates?
What specific part of the crystallisation process yields such poor success rates of transmembrane protein structure elucidation?

high hydrophobicity is one factor

There are several factors that make obtaining crystal structures from membrane proteins more difficult.  In brief, nearly every stage of obtaining the structure via crystallography is more difficult.
First: protein expression.  Large amounts of pure, well-folded protein are required and this is much more difficult to achieve than it is with a soluble protein. Since membrane proteins are bound within a membrane, the mechanisms to translate the peptide into the membrane and to fold the protein in the membrane are different.  They may involved folding factors which are only available in a particular compartment of the cell (making bacteria impossible as a system of expression).  In high amounts in the cell, their hydrophobicity might tend to result in clumps of unfolded protein instead of gobs of membrane-associated protein.
Next: Purifying the protein is more difficult.  The expressing cells are usually broken open in the presence of detergent to get the proteins to float around as individual proteins in detergent micelles.   The wrong detergents might break up the membrane protein and it may lose its fold - the concentration of detergent must be carefully managed and kept at optimal levels or the micelles might break up and the protein will be ruined.
Crystallizing the protein is quite a bit more difficult too.  Membrane proteins in detergent micelles which may or may not be charged themselves look like oily blobs with hopefully a domain or two of folded proteins sticking out of them.  Compared to a soluble protein with a nice ordered surface in every direction instead of a detergent micelle which might undergo a phase change at high concentration or by the addition of a salt or change in pH, membrane proteins take a task that might take thousands of trials and adds new dimensions to worry about.
The proteins are 2D-like, but the crystals still have to be 3D for crystallography to work usually.  Protein crystals are small, but those derived from membrane proteins - which tend to organize into planes like the membranes they inhabit - are often thin, which can make them too delicate and small to get a good set of data even from synchrotron beams.  As a result, the crystals are commonly too small or thin to use at first, requiring extensive optimization after the first crystal are found.
In a few cases some membrane proteins have been solved with 2D crystals using electron microscopy on crystalline arrays of porins and rhodopsins in membranes. That was a ton of work but they were the first membrane protein structures by years and years.
Not to make all this sound impossible - once you have crystals, they can usually be improved; there are good starting points to crystallization and purification with detergents.  It's just that a process which can already take quite a bit of time (years) and can sometimes end in frustration takes even longer and is less certain with a membrane protein.

.. i have to say it doesn't matter so much anymore - cryo EM will do most of these structures...


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Reformulation of Hamilton's rule
Who (and in which article) was the first to reformulate Hamilton's rule using the letters $B$ and $C$?. See below comments on this reformulation.
Hamilton, in his 1964's article gave a mathematical formulation to explain the direction of social traits. Below is its formulation. Note: Hamilton used quite a complicated formulation, I hope I am not misunderstanding its meaning. Please let me know if it is the case.
$$R\cdot\frac{dw(x,y,z)}{dx}>\frac{dw(x,y,z)}{dy}$$ where $R$ is the coefficient of relatedness which can itself be expressed as a correlation between the variables $x$ and $y$. $w(x,y,z)$ is the fitness function of a focal individual expressing trait $x$ and interacting with an individual draw from a subpopulation expressing trait $y$ (expected value of the probability distribution of trait expression of individuals in the subpopulation) in a population expressing trait $z$ (expected value again).
Most often this formula is expressed in the following form:
$$RB>C$$
$B$ and $C$ are called cost and benefit of the social trait. I believe that $B$ and $C$ are often not understood as a consequence of a change of the social trait but rather as a cost and benefit of carrying the social trait (compare to not carrying it).

Interesting question but since B and C basically strip the idea of a lot of mathematical content, isn't it possible that the first use was, say, in a graduate text for non-mathematicians to give a sort of quick idea? That would be hard to track down I bet.

@daniel Yes that's possible indeed! One thing that got me interested into this question is that $B$ and $C$ are usually conceptually understood a bit differently than $\frac{dw(x,y,z)}{dx}$ and $\frac{dw(x,y,z)}{dy}$ and I am wondering how the first guy has somehow argued for this new understanding of Hamilton's rule.

Tracing it backwards this was the earliest reference I found via google scholar, it's from 1988 and uses the $r B C$ notation in the format we are used to.

Hamilton's rule states that for a social
  action to be favored under natural selection, rb - c > 0, where c is the cost to the
  actor in terms of the effect on (usually a reduction in) his expected number of
  future offspring, bis the benefit to the recipient in terms of the effect on (usually
  an increase in) his expected number of future offspring, and r is the relatedness of
  the actor to the recipient

However, I've traced back a bit further by going through the references and found this Michod paper from 1978 where they defined terms $b_i$ and $c_i$ as the

"fitness effect of genotype $i$'s behaviour on others"

and,

"fitness effect of genotype $i$'s behaviour on self"

respectively, which was the first such notation I could find which could be interpreted as $b$ = benefit via the increased fitness of others and $c$ as a cost to self.
As far as I can tell this leaves a 14 year gap between Hamilton's long version and the beginnings of the current notation by Michod in 1978, with the first "rB - c > 0" type of notation appearing in 1988. Obviously my search is not exhaustive - many papers from this period will be hard to find and text search (e.g. using "rB" as a search term) - but it's a good start. If I find an older reference any time I will update.

(I will try to examine the papers further to try to find both "r = relatedness, b = benefit to recipient, c = cost to provider" and "altruism will evolve if rB>C" types of statements, but that will have to wait - It's quite laborious, even this much took over an hour, and I have a long list of jobs to do by Friday!)

Nice work. Thanks a lot Robert! Michod in his abstract talk about kin selection theory as the cost-benefit rule as if it was obvious to other people that kin selection theory is a cost-benefit rule. I should check Hamilton's paper to see if he himself used the word cost and benefit.