text:
Why do only humans commit suicide?
Why is it that only humans commit suicide?
Many animals do 'brave' things to protect their children or family, and some male spiders sacrifice themselves so that they can impregnate the females. However, humans commit suicide without necessarily any intention to help their offspring. Why?

Welcome to Biology.SE. I think the answer is along the lines of Because advanced cognitive abilities

I'm pretty sure some dolphins are smarter than some people who have committed suicide :)

Probably not, although it depends on what one mean by "intelligence". Be careful, I guess some people may feel insulted by what you just said as it almost suggest that one need to be stupid to commit suicide.

I do hope nobody inferred that, I meant nothing of the sort. I do, however, think that 'advanced cognitive abilities' isn't a good explanation in itself. If those abilities make the idea of suicide conceivable, then why is that?

@NeMo Spiders and other primitive animals probably work a lot like robots, taking in input and doing output, and never actually think about what they're doing. The human brain is powerful enough to realize how crappy life can feel and rationalize that suicide is an acceptable solution. We also have the planning ability, knowledge, and technology to follow through with it.

@NeMo Spiders do not know existential crisis :P

First, I think we have to question the idea that only humans commit suicide.  How do you define 'suicide'?  Is the squirrel running out in front of your car committing suicide?

Bees commit suicide

I don't know. Did the squirrel look sad?

Untreated depression is the most common cause of suicide attempts. Many animals are capable of experiencing depression so this is a very complicated question to answer, however there is no documented case of suicide in non-humans. Perhaps cross post to Cognitive.SE and see if they can help out.

@jamesqf I should have tagged you in the above post. This, oddly enough, has been defined in the academic community. The citation explains that although sparse examples exist of deliberate self endangerment, the researchers failed to find an example of non-human species deliberately killing themselves.

This is more of a cognition question than biology IMO.

"7 Cases of Animals that Committed Suicide" and other stories in "Why do only humans commit suicide?", think upon those which are not mere sacrifices. :)

Animals can hurt themselves to save their life for example foxes and tigers falling in traps are known to chew their paws to escape from the trap. Thus one can argue that all living creatures want to live and to escape death as well as losing freedom.
looking at how animals live, an animal can always use its capabilities to escape danger or problems as well as to look for food for survival. In humans who commit suicide sometimes it is because  there is no "exit". for example some people commit suicide due to being unable to pay their loans it is the feeling of distress losing all hope and the fact that it is hard to escape from problems in a human society that lead to this thinking.
so a person doing so is trying to escape the problems instead of facing them. further, the fact that humans have the concept of "responsibility" and being held accountable by members of the society all these lead to this thinking as the only escape

You really ought to back up your answer with sources

I haven't heard of limb gnawing before. It appears it is at least controversial as to whether this has happened, although I haven't looked at either side in any detail.


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Macrophages and what they consume
Do macrophages in the dermis consume any foreign invader to the body, or simply pathogens and cellular debris?  For example, would macrophages consume vitamins or medications injected into the dermal layer of the skin?  Thank you

Macrophages and dendritic cells generally recognize large foreign complexes. They often do this through the recognition of pathogen or damage-associated molecular patterns (PAMPs or DAMPs). Otherwise, cell-mediated pathways involve cyto/chemokines which direct macrophages and the like in the region. We also have to consider, does the compound in question belong in the region, or is it insoluble amongst the tissue? In these cases, the compounds are also degraded/engulfed. So how does the body go about recognizing small, soluble foreign molecules?
These are called haptens. If these small molecules can bind to a larger carrier complex in your body, so that it now looks like there's something foreign, regardless if the initial carrier complex was foreign or not, this may present cause for immunologic reponse (as in the mechanism of urushiol, the toxin in poison ivy). There are certain T-cells, like NKT and γδ T cells, which can "see" these antigens despite their lacking a type I or II MHC presentation.
The short answer is it depends on what the injected drug does once it begins interacting with your physiology. Vitamins, on the other hand, are regularly uptaken by cells for metabolism, and I wouldn't imagine macrophages engulfing exogenous vitamins outside of it's needs on their own.
Source: Cellular and Molecular Immunology, 8th ED.

"... or is it insoluble amongst the tissue..." Would you say, current covid vaccine "nano lipid particles" are insoluble in that sense? By any chance, are your familiar with "lipid particles"? And, please correct: I do think, with "lipids", that they "they turn the other cheek", that they have a non-polar coating, outside. I have heard, by the way, that lipids may become polarized, attracting proteins, in the blood.

Macrophages take up pathogens to present them to other specific immune cells. The things they take up via phagocytosis have specific markers on them called PAMPs (Pathogen Associated Molecular Patterns). Hence they won't phagocytose locally injected substances (until and unless it has a PAMP like region).

This is utmost interesting to me (so long ago you answered) in the framework of Corona crisis and the newly introduced RNA-vaccines. These come in "Nano Lipids". Would you say they are being uptaken by phagocyt.? ( their antigens must be presented by APC for vaccination to be effective). Infering from your answer: Nano lipd particels wouldn't be "deliberately" engulfed and taken up (I like your answer as it alludes to some relationship between receptor uptake and presentation) - nanos would be uptaken through diffusion - not different from any other cell that is no APC?

source: Stack Exchange

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How does the partial pressure of oxygen affect mental focus?
On this Wikipedia page it says that lower oxygen levels can cause light-headedness. Is there evidence that higher partial pressure of oxygen can improve mental focus?

Welcome to Biology.SE. Seems like the whole question is about the definitions of percentage and concentration. This question doesn't appear to be about biology. You probably read a text somewhere which talk about precentage of oxygen in the air and concentration (of oxygen I would assume) in the air that was unclear to you. If this is the case, you should include your passage and explain what you don't understand.

@Remi.b I mean concentration as in focus. If biology.SE isn't the right place for this question, please guide me.

Oh, I got it! Ok. Your question is on-topic! I retracted my close vote. Where did you read that lower concentration in oxygen decreases abilities to focus on a task? It will probably help having the reference.

please rephrase your question, maybe expand a little. Maybe include some reference to what to talk about. Otherwise it is hard to understand

@Remi.b well I thought it was well-known, but when I went looking for it, it was harder to find than anticipated. Edited my question with a source.

Vrwim: I made a pretty significant edit to try to avert the VTC. I think you’re asking about altitude (which is the most common situation where inspired O2 is compromised), but that does not affect the percentage of O2 but rather atmospheric pressure--> partial pressure of O2. I also rephrased it so it seemed clearer to me. Please feel free to roll back whatever you don’t like.

I know that it compromises concentration, I am asking about whether higher levels of oxygen improve concentration.

Vrwim, sorry I misunderstood. I just re-edited, but feel free to restate it however you’d like.

@Susan this is great! Couldn't have said it better myself.

Long term exposure to hyperoxia actually does more harm than good. Though short term hyperoxia is recommended after brain injuries (http://www.medscape.com/viewarticle/741751_3) as a valid clinical strategy.
For lower concentrations of oxygen, there seems to be evidence that cognitive function is improved:

You can read more about that here: Chung, Soon-Cheol, et al. "Effect of 30% oxygen administration on verbal cognitive performance, blood oxygen saturation and heart rate." Applied psychophysiology and biofeedback 31.4 (2006): 281-293.
And also this:

This study aimed to investigate whether inhalation
  of the air with 30% oxygen compared with normal air
  enhances cognitive functioning through increased activation in
  the brain. The verbal and visuospatial tasks were performed
  while brain images were scanned. The results showed that
  there were improvements in performance and also increased
  activation in several brain areas under the condition of 30%
  oxygen. These results suggest that a higher concentration of
  the inhaled oxygen increases the saturation of the blood
  oxygen in the brain, and facilitates cognitive performance.

Taken from: Sohn, Jin-Hun, Soon-Cheol Chung, and Eun-Hye Jang. "30% oxygen inhalation enhances cognitive performance through robust activation in the brain." Journal of physiological anthropology and applied human science 24.1 (2005): 51-53.
Though you have to be a bit careful about oxygen poisoning.

While oxygen is an essential component for living organisms, the
  formation of reactive O2 intermediates is commonplace in aerobically
  metabolizing cells. The stability of a variety of reactive O2
  intermediates varies, but in some instances their interaction with the
  intracellular environment sets the basis for the pathophysiology of
  several disease states.

Adapted from: Cadenas, Enrique. "Biochemistry of oxygen toxicity." Annual review of biochemistry 58.1 (1989): 79-110.

Pathophysiological processes are associated with increased levels of
  hyperoxia-induced reactive O2 species (ROS) which may readily react
  with surrounding biological tissues, damaging lipids, proteins, and
  nucleic acids. Protective antioxidant defenses can become overwhelmed
  with ROS leading to oxidative stress.
The continuous exposure of newborn rats to 70–80 per cent oxygen at atmospheric pressure throughout the first 9 days of life significantly inhibited the growth of the brain which normally occurs during this period of life. The accumulations of DNA, RNA, total protein, and proteolipid protein which accompany brain growth during this period were all approximately proportionately depressed by the oxygen-enriched atmosphere. RNA/DNA and protein/DNA ratios were unaffected. The increase in brain mass in the first week of life reflects mainly cell proliferation, and since the decreased DNA accumulation occurred with no changes in RNA/DNA and protein/DNA ratios, we conclude that the effect of oxygen was to inhibit cellular division. We estimate that the oxygen exposure caused an approximately 7 per cent deficit in the cell population of the brain. These results indicate that the use of elevated concentrations of oxygen may have serious deleterious effects on the growth and development of the brain.

Taken from:* Grave, G. D., C. Kennedy, and L. Sokoloff. "Impairment of growth and development of the rat brain by hyperoxia at atmospheric pressure." Journal of neurochemistry 19.1 (1972): 187-194.*
Also hydrogen peroxide is  produced:

Thus the H2O2 concentration in brains of rats exposed to room air is calculated to be about 7.7 pM, rises 60% when O2 tension is increased to 100% O2, and increases 300% at 3 ATA 100% O2, where symptoms of central nervous system toxicity first become apparent. These studies support the concept that H2O2 is an important mediator of O2-induced injury to the central nervous system.

Taken from: Yusa, T. O. S. H. I. K. O., et al. "Hyperoxia increases H2O2 production by brain in vivo." Journal of Applied Physiology 63.1 (1987): 353-358.
You can also always read the wiki page for oxygen poisoning: http://en.wikipedia.org/wiki/Oxygen_toxicity and also hyperoxia itself: http://en.wikipedia.org/wiki/Hyperoxia

Okay, what do about slightly higher percentages? Like 25-30?

@vrwim answer edited.

that is what I was looking for! Thanks! I will look at the references in there and read up on that some more.

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Are there any examples of how to apply evolutionary models?
Apologies if this question it too open-ended; evolutionary biology is not my primary field.
I have been reading a lot about the use of statistical mechanics in analyzing evolutionary dynamics. As an example, consider this paper: http://www.pnas.org/content/102/27/9541.short.
The ideas are pretty straightforward. There is some genotype-fitness landscape and selection drives populations towards maximal fitness. However, genetic drift can pull these populations away from maximal fitness. These two concepts are analogous to minimization of internal energy and maximization of entropy in thermodynamics.
However, most of the papers that develop these models never properly explain how to apply them to observable phenomena. Is it because there is some disconnect between the parameters in the models and nature? For example, many of these models use the effective population size, which is unknown for most species.
I guess my main question is this: are there any demonstrations on how to use these toy models to predict something in nature, or are they simply for theoretical purposes.

The paper you mention basically describes an approach to modelling evolution — so it may be of theoretical interest. Most models are built with an intention of predicting something or explaining some phenomenon in mathematical terms. You can find many studies that do predictions.

You may take a look at this software, aimed at estimate conservation status for declining populations: http://www.cbsg.org/download-vortex

Hypothesis testing in evolutionary biology
Pretty much any paper of empirical evolutionary biology tests hypothesis. If you go on google scholar and look for any empirical paper in the field of evolutionary biology such as "does sexual selection has antagonist effect to natural selection", "evolution of range limits", "evolution of mutational robustness" or "sensory drive speciation" you will find tons of papers that test hypotheses. Many of these hypotheses have been developed either in pure theory papers on in papers that mix some level of theory (no necessary need of complicated modelling) and empirical work. There is nothing in evolutionary biology that is different from the other field of biology or the other fields of science in our ability to test hypotheses.
A tiny bit of history
In the first half of the century most work in evolutionary biology was theoretic. Many hypotheses were left untested. Today, we perform lots of tests and the technics of molecular genetics (sequencing and stuff) help a lot to test hypotheses. Some papers, indeed uses metrics that are very hard to estimate, eventually too hard (too expensive) to be estimated today, it is true and it is an issue, an issue that all sciences know of. We sometimes have to wait for technical advancements before being able to test a hypothesis.
Concerning the paper you mention
From your description of the work they performed in the paper you linked (I haven't read this paper) it sounds like they are describing the concept of shifting balance theory, a process that has been first hypothesize by S. Wrigth in 1932. As said on the wiki article, there is little empirical evidence for shifting balance theory (but you may want to read this and that). The reason is that shifting balance theory is link to the concept of adaptive landscape. Adaptive landscape is what we call the relationship between fitness and phenotype or fitness and genotype. We have descriptions of fitness-phenotype relationship but we are only starting to offer good and complete descriptions fitness-phenotype-genotype relationship. Understanding the link between genotype and fitness is essential for testing shifting balance theory. Shifting balance theory is a logical consequence of the process of drift and selection, both of which are very well known (and well tested). Most of the issue is in the description of how rough is the fitness landscape to know whether shifting balance theory is indeed a thing that occurs in nature or whether it is very unlikely of such process to ever occur.


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How does inbreeding depression cause infertility?
My textbook states that repetitive self-pollination in the plants cause inbreeding depression which causes infertility thus plants develop techniques called outbreeding devices to encourage outcrossing. My question is how does inbreeding depression cause infertility?

One of the main contributors to inbreeding depression are recessive lethal alleles. Suppose you have an individual who has the genotype Aa, where A is the dominant, non-lethal allele and a is the recessive lethal allele. This recessive lethal allele is likely very rare in the population (selection would decrease the frequency of a recessive lethal allele), so our Aa individual can breed with almost any individual without consequence because most other individuals are AA, so the offspring of this individual will be either Aa or AA.
However, if the Aa individual breeds with itself or with a close relative who has the Aa genotype, its offspring will have a chance of inheriting the two aa alleles, which is lethal, decreasing the fitness of the Aa individual that bred with itself (or a close relative). This decrease of fitness resulting from self-breeding will cause selective pressure to outcross instead of self.
It is important to note that the recessive allele doesn't need to be strictly lethal for this effect to happen - if the recessive allele causes infertility (for isntance, failure to produce gametes or something similar) the same effect will happen.
In short, inbreeding can cause detrimental recessive alleles to become paired, causing lethality or infertility.


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Over what range do Thyroid Stimulating Hormone (TSH) levels tend to fluctuate over the course of a day?
I understand that the majority of doctors consider the TSH test "the gold standard" for measuring thyroid function (examples stating that literally are here and here).
The reference range for what is a healthy TSH is under debate, but no matter what range is considered there tends to be a proportionately large difference between the top and bottom (eg 0.3 to 3.0 μIU/mL).
However I've never seen anything indicating over what range TSH varies over the course of a day, or a few days. It's also unclear as to how much error there tends to be with a test result, even if tested from the same sample.
How much fluctuation in a TSH result is normal over the course of a day?

I wrote an answer about the circadian rhythm of TSH for a question over on Health.SE: How does one's TSH tend to vary over the course of a day. That answer also has some information on how this is different in people with hyperthyroidism and hypothyroidism.
The paper Physiological variations in thyroid hormones: physiological and pathophysiological considerations has a section on circadian and seasonal rhythm and a table with some values. Quoting from the paper:

There is a clear circadian variation in circulating TSH concentrations in animals and in humans. TSH concentrations are low during the daytime, increase in the evening, and peak shortly before sleep.  Amounts decline slowly during sleep. Table 3 summarizes studies of mean, nadir, and peak serum

This is table 3:

A chart is available in figure 2 of the paper Modulation of Endocrine Function by Sleep-Wake Homeostasis and Circadian Rhythmicity
As for test error, it's important to note that TSH itself is not used for or at least should not be used for diagnosis of Hypothyroidism and hyperthyroidism. T3 and especially T4 need to be measured.

Heh, only now do I realise that it was actually you asking the question on Health.SE. Oh well, maybe it also helps someone here :-)


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Determining the percentage of the query of an alignment from a BLAST output
Given this BLAST output, how can I determine the percentage of the query sequence in the alignment? I know that it's also referred to as 'query cover' but I can't seem to find anything online that tells me how to calculate this.

I have given it a go and valued it as 145/147, but I'm not too sure on this. Can anybody clarify whether or not this is correct? Any help is highly appreciated

Please don't post images of text. Instead, use the formatting tools and add the text directly into your question. That way, we can copy it and test our answers. Also, since this is essentially a scripting question, please clarify what operating system you are using so we know what type of answer to provide. I assume you realize that your output shows that residues 4 through 146 of a 146aa query are aligned, right?

qcovs and qcovshsp are reported.. In standalone BLAST you can specify the output format too

How can I do it without using BLAST? Just by looking at this output.


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Is the number of stamens always 9 for geranium?
We are studing in class the reproduction of plants.
We start the lesson by observing some components of flowers, it was the Géranium.
First of all we start counting the number of sepals , petals and stamens.
The problem is that I found:
5 sepals
5 petals
9 stamens
and every other student found that the number of stamens was 9 or 8 or 10.
I asked my teacher and he says that he counted the number of stamens many times and he found 9.
Is it really 9? Do the number of stamens is depending of the number of sepals or petals? Is there any relation ?

Are you positive it was a geranium? From what I've seen, geraniums usually have 5 stamens. Marsh St. Johnswort, on the other hand, is a flower that has 9 stamens (http://www.ct-botanical-society.org/galleries/pics_t/triadenumvirg_fl.jpg). In any case, more details would be nice. Were you looking at actual specimens? Where did they come from? Did you collect them from the wild or from a greenhouse? If they came from the wild, then it would be important to know what your location is.

Yes it is the geranium I taked it from school garden , I don't know if it is really 9 stamens , but I heared that stamens have to be a multiple of sepals and petals for exemple if a flower has 3 petals so stamens can be 6 or 9 and if a flower has 5 so stamens are 5 or 10 ... But there is a contradiction with the picture because this flower has 9 stamens but 5 petals ... Is there any other reference about the flower in the picture ?Do really the stamens have to be a multiple of petals ?

The stamens are not always a multiple of the petals, but they usually are. I can write up a more detailed answer in a bit. What country/climate is your school in? It might be useful to know

My school is Nador in Morocco north of africa :)

Its medditeraneen climate


text:
Limited space for animals - how is that bad?
I am writing an English paper wherein I have to appeal for change in the way humans treat wildlife. I was initially given a situation wherein I took a ferry in a certain area and saw a dead baby dolphin.
Along the way, I (it is written in second person) apparently notice how little water is now available for the dolphins (due to some bridge being constructed).
What exactly is bad for dolphins (or animals in general) when they have limited space to roam around?
So far I thought of dolphins hitting boats due to the crowded state of the area in question.

I would consider food supplies too. If you trap a population of dolphins in a small area, and they eat all the available food there, they will begin to starve if they cannot leave to get more food.

The building of a bridge on a river seems like an example of habitat fragmentation. There are a number of reasons why this may be detrimental to the animals.

Reduced resources available. I'm not too familiar with dolphins, but all animals need food, shelter and a place to raise their young. A smaller habitat can't support as many animals due to limited resources.
Isolation from other habitats. The bridge may cut off the dolphins on one side of the bridge from the other side, meaning they can't mate with dolphins on the other side or migrate. The fact that dolphins on one side of the bridge can only mate with the other dolphins that share that size causes a decrease in the genetic variability on both sides of the bridge. Decreased genetic variability means an increase in inbreeding depression and reduced ability to adapt to disease and environmental changes.
The edge effect. This means that areas next to the edge of the habitat (i.e. next to the bridge) will be even less suitable than they were before

Could also be good to mention the psychological effects, especially on more social creatures like dolphins, of being in a confined area.


text:
Neural coupling between Pinsky-Rinzel (reduced Traub) neuron with a Wang-Buzsaki neuron?
I have read two papers related to modeling neurons. The first one is the paper by Pinsky and Rinzel and the second one by Wang and Buzsaki. The first mentioned neuron model is a excitatory neuron based on NMDA and AMPA neurotransmitters. The second one is inhibitory neuron model based on GABA. So I modeled them individually. Now trying to couple such that pre synaptic PR neuron will connect to post synaptic WB neuron which also connects to PR neuron. That is in effect an excitatory-inhibitory coupling with feedback.

Now how would the action potentials of both neuron look like?
Will both oscillate, or only WB neuron alone oscillate?
Is there any specific conditions for the oscillations to occur?

Any speculations, ideas, references (like books, related papers) would be helpful.

NOTE : By action potentials, I mean the neuronal membrane voltage signal which varies with time.

your first link to Pinsky and Rinzel is invalid. Try linking DOI or the abstract page.

@Memming : corrected the link.

By "action potentials" you mean the pattern of firing, not the shape of the action potential itself? Also, what's driving these neurons?

@Memming : I have explained "action potential" too.

are you asking what will happen when these two systems are coupled??

@WYSIWYG : Yes. At least in the simple case considering an excitatory neuronand inhibitory neuron. It can be suggestions, references and so on.

@dexterdev you need to simulate the coupled system for that. For non-linear systems the input-output relationship is not that straightforward (hence non-linear :P). Can you paste both the equations here.

There is no straightforward answer to this.
It completely depends on the properties of the neurons and the network they are in.
I would recommend you to study Central pattern generators. Those are usually small networks that can create a repetitive pattern of action potentials. However, the exact pattern they generate depends on how they are connected (number and type of synapses, network structure,...) and a presumably large number of neuronal properties (like the conductance, the shape of their dendrites, the expression level of various ion channels and all kinds of intracellular messengers/peptides/proteins/molecules). For example, there are "leaky" ion channels that can cause a neuron to spontaneously depolarize (and fire action potentials) regularly without requiring any synaptic input. This means that you can have a regularly firing neuron even without network feedback mechanisms. example
There are many different rhythms in the brain and they are all generated by smaller and/or larger networks of neurons with different properties. Neurons can even participate in multiple different rhythms, depending on the situation (e.g. theta rhythm vs. SPWs in the hippocampus). Thus, even the same neurons in the same network can generate different rhythms.
So, the rhythms that you can build depend on how you tune the neurons' properties and the way they are connected. With just the information you provided (simulated excitatory and inhibitory neurons, but no info on their excitability, network,...) I can't tell you what the resulting rhythm will look like.
I'm afraid I didn't really answer your question, but I hope this was still helpful.


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What is a "NuMA protein"?
I just got out of a lecture on mitosis and this part was incredibly confusing. After searching my textbook it doesn't cover it that well either.
The professor mentioned a NUMA protein that is released after NEBD, and that it helps form more Microtubules. I don't really understand how it does this.
Is it just a protein that sits in the pericentriolar matrix and encourages microtubule nucleation? Or is it a a kinase that deactivates some GAPs that destabilize microtubules?
Also, if you would like, there's also this study question I feel is sort of related: What are two proteins located in the pericentriolar matrix that encourage microtubule nucleation?
I feel like one of them is the y-tubulin ring complex. Would the other be NUMA?
Thanks for reading.

you should ask your professor if you have doubts about a lecture. you can read about NuMA here


text:
Can the Staph streak method be used for culturing Neisseria on blood agar?
What I learned in class:
Normally chocolate agar is needed to grow Haemophilus and Neisseria. The Staph streak technique can be used for culturing Haemophilus on blood agar by allowing the Staph to hemolyze the blood so Haemophilus can grow around it.
My question:
Is there a reason this method couldn't be used for Neisseria as well?

It depends on your species of Neisseria.
N. meningitidis grows on BAP (Blood Agar Plate), with colonies being grey and unpigmented on  appearing round, smooth, moist, glistening, and convex, with a clearly defined edge
You can read more about that here.

N. gonorrhoeae is known not to grow on BAP.
More on that here.


text:
(Genetics) Is a silencer the same as gene silencing (heterochromatin)?
Is silencer the same as gene silencing?
I know that gene silencing refers to those heterochromatin concentrated at the telomeres or centromere. It is also related to methylation. But what about a silencer. Are they the same? Or is silencer an opposite of enhancer (repressor)?

You are a bit confused with your terms here. Remember, when you are talking about natural regulation of a transcriptome, you can control it at the level of the genome, transcriptome and the epigenome.
The Repressor protein is a DNA- or RNA-binding protein that inhibits the expression of one or more genes by binding to the operator or associated silencers. Depending on where it binds, it regulates at the level of the genome or transcriptome.
The Gene Silencer Region, is a DNA sequence in the geneome that  that allows repressor proteins to bind to it. It is usually very near the promoter of the gene that it wants to control. This controls the expression of the gene at the level of the genome to repress the transcription of the gene. There exists a few types (Classc silencers, negative regulators and polycomb response) but lets not get into that).
An example is the regulatory element in the classical Lac operon.

Gene Silencing as you call it is epigenetic in nature. I'll point you towards the wiki as it is quite big and diverse to explain it here:
http://en.wikipedia.org/wiki/Gene_silencing
you can read more about it here:http://www.ncbi.nlm.nih.gov/books/NBK26872/

Never heard of NRE; what is it? Is PRE Polycomb Response Element? or is it negatively regulated and positively regulated. Please expand the abbreviations. Plus, lac operon is not an example of a "silencer". It is just a case of simple transcriptional repression. Silencers, like enhancers operate from a long distance and have more global effects but they function oppositely to enhancers i.e. they reduce the transcriptional activity.

@WYSIWYG Doesn't the Lac operon contain a regulatory element that keeps it off normally (LacI)? Also edited the post for the acronyms.

Yes it has a regulatory element at the promoter that is bound by LacI. These are usual cis elements and are frequently called operators; they only affect the proximal gene. Enhancers and Silencers control a bigger section of chromatin which may include many genes and they also sometimes interact with promoters via DNA looping.

@WYSIWYG Interesting. Never heard of it being called an operator before (was never taught that way) but thats a term. When you say tha Enhancers and Silencers control a larger section of the chromatin, I am assuming that you are saying that the enhancers are not gene specific right (in which case I undertsand)? Or are you saying that enhancers and silencers also control chromatin secondary structures (I have not really looked this up)?

Enhancers and silencers regulate several genes and can interact with their promoters, cause epigenetic changes or both. Both processes lead to a change in chromatin structure.

@WYSIWYG Thanks for the clarification. Do you have a reference for enhancers/silencers altering epigenetics mate ? I would very much like to read up on that. I know epigenetics can cover or uncover enhancers, but I have not heard of it being done other way around.

Many Polycomb/Trithorax responsive elements act as enhancers (that's how I got to know about them initially). Other examples include CTCF, CEBPs, etc. You can find many good reviews on these proteins.

CEBP does not mediate epigenetic modifications but the others that I mentioned, do.


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Biopython - Big Discrepancy Calculating RNA melting Temperature over Literature
I experience big discrepancies when calculating melting temperature of RNA 7-mers with Biopython over values generated by a popular algorithm.
I tried the nearest neighbour algorithm with RNA and salt concentrations as described in a respective paper (thermodynamic table used as in paper below from: Freier et al 1986). Yet, the values largely differ (execute code below to see).
I tried all seven salt correction methods provided by Biopython, still I never get close to the values generated by siRNA design algorithm for the same 7-mers.
Can someone tell me how accurate Biopython's melting temperature nearest neighbour algorithm is? Especially for short oligomers like my 7-mers? Is there maybe something I am implementing wrong? Any suggestions?
Values derived from executing sample input:
http://sidirect2.rnai.jp/
Tm is given for the seed duplex of the guide strand: bases 2-7
Literature:
"Thermodynamic  stability  and  Watson–Crick
base  pairing  in  the  seed  duplex  are  major
determinants  of  the  efficiency  of  the
siRNA-based  off-target  effect"
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2602766/pdf/gkn902.pdf
from Bio.Seq import Seq
from Bio.SeqUtils import MeltingTemp

test_list = [
('GGAUUUG', 21.5),
('CUCAUUG', 18.1),
('CAUAUUC', 8.7),
('UUUGAGU', 19.2),
('UUUUGAG', 12.2),
('GUUUCAA', 14.9),
('AGUUUCG', 19.7),
('GAAGUUU', 13.3)
]

for t in test_list:
    myseq = Seq(t[0])
    tm = MeltingTemp.Tm_NN(myseq, dnac1=100, Na=100,   nn_table=MeltingTemp.RNA_NN1, saltcorr=7)  # NN1 = Freier et al (1986)
    tm = round(tm, 1)  # round to one decimal
    print 'BioPython Tm: ' + str(tm) + '  siDirect Tm: ' + str(t[1])

# Output:
>>> BioPython Tm: 10.1  siDirect Tm: 21.5  # GGAUUUG
>>> BioPython Tm: 8.2  siDirect Tm: 18.1   # CUCAUUG
>>> BioPython Tm: -2.0  siDirect Tm: 8.7   # CAUAUUC
>>> BioPython Tm: 8.2  siDirect Tm: 19.2   # UUUGAGU
>>> BioPython Tm: 1.3  siDirect Tm: 12.2   # UUUUGAG
>>> BioPython Tm: 4.4  siDirect Tm: 14.9   # GUUUCAA
>>> BioPython Tm: 9.0  siDirect Tm: 19.7   # AGUUUCG
>>> BioPython Tm: 2.1  siDirect Tm: 13.3   # GAAGUUU

Question first asked at stack-exchange: https://stackoverflow.com/questions/30037939/biopython-big-discrepancy-calculating-rna-melting-temperature-over-literature

Also asked at Biostars: https://www.biostars.org/p/140873/

What version is your Bio?

The source code for that function is available on Github, there are also some comments in there explaining what methods Biopython uses. The issue might also be that you're comparing apples and oranges, and that your siRNA Tm are not for 7mer parts of an siRNA but something else.

@ dustin: My Biopython is up to date V 1.65. @ WYSIWYG: added outputs for saltcorrection method 7. @ Mad Scientist: Thanks for suggesting the doc, yet I feel drawing conclusions from the included algorithms on my issue is beyond me. I need to think about whether I maybe misunderstood something fundamental, but this I deem quite unlikely.

I was forwarded a PERL script by the researchers behind siDIRECT 2.0. I still do not know why the big discrepancy as both the physical chemistry as well as PERL is beyond me at this point. But at least I was able to integrate that script into the larger frame of my Python program doing melting point calculations. For this concludes the topic without providing an actual answer. Thanks for your contributions.

I still want to see this question answered.

Is the issue open on github?

Comparing Biopython MetlingTemp to other calculators.
I have written the recent version of MeltingTemp in Biopython's SeqUtils.  I have extensively tested the Tm calculations against other programs like MELTING and Primer3Plus and other online Tm calculators with consistent results, thus I'm pretty confident that there is no gross error in the module. The simple answer in this case is: the calculation of siDirect is wrong.
Sources of discrepancy.
One minor thing: Many programs calculate k as k = total Oligo/4. MeltingTemp uses k = Oligo1 - (Oligo2/2). Thus to mimic this behaviour, you have to use
dnac1=50, dnac2=50

instead of
dnac1=100

And now the major thing: Oligo concentration is usually given in nanomolar. However, if you look at the calculation in the Perl script, siDirect seems to use micromolar!
my \$tm = ( 1000 * $dH / ( -10.8 + $dS + 1.987 * log(0.0001/4) ) - 273.15 + 16.6 * log(0.1)/log(10) )

The critical part here is:
log(0.0001/4)

which should be:
log(0.0000001/4)

Thus, you have to pass 1000-fold higher oligo concentrations to Biopython's MeltingTemp to get the same result as siDirect:
>>> from Bio.SeqUtils import MeltingTemp as mt
>>> print mt.Tm_NN('GGAUUUG', dnac1=50000, dnac2=50000, Na=100,
                  nn_table=mt.RNA_NN1, saltcorr=1)
20.1472140567
>>> print mt.Tm_NN('CUCAUUG', dnac1=50000, dnac2=50000, Na=100,
                   nn_table=mt.RNA_NN1, saltcorr=1)
18.1074422939

This is of course wrong if your primer concentration is 100 nM, but that's the result you get from siDirect.

A very insightful and thorough answer, thanks. I can't help but wonder why siDirect went for micro molar.

Thank You for your in-depth answer Markus. Indeed striking that siDirect - one of the most used oligo tool on the web could be so fundamentally wrong. This saves SeqUtils's honour!

I just received a PERL code snippet by the researcher behind  sidirect2 which allows to calculate the Tm. I still do not know the source of the calculation discrepancy as the thermodynamics is beyond me.
I post the code to conclude the question here. The can below can be saved into a .pl file and run from shell with the respective RNA sequence as a string argument.
Code:
#!/usr/bin/perl

# ====================
sub tm_RNA {

# Calculating Tm for RNA-RNA hybrid using nearest neighbour method
#
# usage: $tm = tm_RNA('GGCUGCCAAGAACCUGCAGG') ;

my $seq = lc ($_[0] // '') ;
$seq =~ /^[augc]+$/ or return '' ;

my $dH  = deltaH_RNA($seq) or return '' ;
my $dS  = deltaS_RNA($seq) or return '' ;

my $tm = ( 1000 * $dH / ( -10.8 + $dS + 1.987 * log(0.0001/4) ) - 273.15 + 16.6 * log(0.1)/log(10) ) ;

return sprintf("%.2f", $tm) ;
} ;
    # ====================
    sub deltaH_RNA {  # delta H parameters
    my $seq = lc ($_[0] // '') ;
    $seq =~ /^[augc]+$/ or return '' ;

my %rna_dH = (
    'aa' =>  -6.6,
    'uu' =>  -6.6,
    'au' =>  -5.7,
    'ua' =>  -8.1,
    'ca' => -10.5,
    'ug' => -10.5,
    'cu' =>  -7.6,
    'ag' =>  -7.6,
    'ga' => -13.3,
    'uc' => -13.3,
    'gu' => -10.2,
    'ac' => -10.2,
    'cg' =>  -8.0,
    'gc' => -14.2,
    'gg' => -12.2,
    'cc' => -12.2
) ;

my $sum_dH = 0 ;
    foreach (0..length($seq) - 2){
    my $dinucleotide = substr($seq, $_, 2) ;
     $sum_dH += $rna_dH{$dinucleotide} ;
}

return $sum_dH ;
    } ;
    # ====================
    sub deltaS_RNA {  # delta S parameters
    my $seq = lc ($_[0] // '') ;
    $seq =~ /^[augc]+$/ or return '' ;

my %rna_dS = (
    'aa' => -18.4,
    'uu' => -18.4,
    'au' => -15.5,
    'ua' => -22.6,
    'ca' => -27.8,
    'ug' => -27.8,
    'cu' => -19.2,
    'ag' => -19.2,
    'ga' => -35.5,
    'uc' => -35.5,
    'gu' => -26.2,
    'ac' => -26.2,
    'cg' => -19.4,
    'gc' => -34.9,
    'gg' => -29.7,
    'cc' => -29.7
) ;

my $sum_dS = 0 ;
    foreach (0..length($seq) - 2){
    my $dinucleotide = substr($seq, $_, 2) ;
     $sum_dS += $rna_dS{$dinucleotide} ;
}

return $sum_dS ;
} ;
# ====================
# Calling main method:

my $seq = shift;
    $tm = tm_RNA($seq) ;
    print $tm  # returns value to stdout


text:
Consuming animals by slaughtering vs injecting barbiturates?
In "Least painful way to die" we get an answer ...

Companion animals (e.g., dogs and cats): injected barbiturates are recommended
Laboratory animals (e.g., mice and rats): injected barbiturates are acceptable as are inhaled agents (isoflurane, carbon dioxide).

And assume the butcher guy as soon as he comes to know the animal is gone, he will do the following....

A very sharp knife is used in this process: the point of the knife cuts into the animal's throat and cuts through the dewlap, trachea, esophagus and jugular vein immediately below the jaw-line to allow the blood to flow out.........Animals are bled out before being dressed because it prevents blood from coagulating in the tissues and thus making the meat go rancid. (Source Slaughter Cattle commercial method - Step 6)

Here's my question... What if the animal was killed using 'barbiturates', then is that animal safe for eating by humans or the high levels of 'barbiturates' in that body cause unwanted results in humans too (if they consume it)?

I'm new to biology. :)

even if it is OK, drugs might be too expensive for large-scale food manufacturing

but at the cost of the pain of animals... suppose if people become merciful enough to spend more bucks to eat a hen... then? @aandreev

I know for many antibiotics the animal cannot be slaughtered for consumption within a certain time (some can be upwards of 60 days I think). Even NSAIDs like banamine (flunixin) require a brief withdrawal period (1 week if I remember right). I'd imagine barbituates would also require such restrictions.

"the animal cannot be slaughtered for consumption within a certain time" a little explanation about that and confidence about what you said would make it a good answer in the answer box @canadianer :)

Sure, I'll check some drug bottles for the actual time frames and post an answer when I get a chance. I really can't say anything about barbiturates though.

"cost of the pain of animals" does not exist. Only real alternative is free-range meat gunned down in forest,but it is not as tasty

Barbiturates are narcotics.. they will have even more stringent restrictions compared to NSAIDs and antibiotics.

It is important to note that farm animals are not protected under the animal welfare act

so you mean to say the answer is 'no', it is not at all possible.???. @WYSIWYG

so you mean to say we can play with them anyway we like is it ?? @CactusWoman

Yes and no. To be more specific, there exists a "common farming practices" exemption in the animal welfare act, meaning a practice that is considered a common farming practice, such as using a knife for slaughter, can be performed legally on farm animals when it would be illegal to perform it on, for instance, a dog. So, it's not that you can do whatever you want to farm animals, but rather you can do any common farming practices to them. It is also important to note, as WYSIWYG said, that barbiturates are narcotics and not necessarily safe for consumption in any case.

@servantofWiser I think chemicals can be used to numb an animal after which it is killed by regular slaughtering practices. CO₂ and other gases are used to asphyxiate and make the animal unconscious before it is slaughtered. Inhaled anaesthetics may also be fine because they won't be resident in the body for a long time. But I don't think injected anaesthetics and barbiturates would be a good option. Xenon is a safe anaesthetic but is very costly.

I really appreciate the effort everyone has put in here in helping me sort things out! :)

Actually, slaughter with a sufficiently sharp knife, and sufficiently skilled butcher, is supposedly nearly painless (e.g. Hebrew/Islam ritual slaughter). Think about how you don't notice a paper cut right away, then imagine that you suddenly pass out from blood loss. The reason knife slaughter is often frowned upon is because large operations tend to start cutting corners on the knife and/or butcher.

For many drugs, the animal cannot be slaughtered for consumption within a certain time. Here are the slaughter withdrawal periods for some common drugs when used on cattle. There are also withdrawal periods for lactating dairy cattle, but I won't list them for brevity.

flunixin (NSAID) - 6 days
florfenicol (antibiotic) - 36 days intramuscular, 55 days subcutaneous
penicillin G procaine (antibiotic) - 10-14 days
tilmicosin (antibiotic) - 28 days
oxytetracycline (antibiotic) - 28 days intramuscular, 48 days subcutaneous
enrofloxacin (antibiotic) - 36 days, do not use in veal calves

More importantly for your question, I have found this report on pentobarbital used for euthanizing cattle. It says:

Adequate measures should be taken to ensure that carcasses of animals treated with this product and the by-products of these animals do not enter the food chain and are not used for human or animal consumption.

Barbiturates are more dangerous than many other drugs and, furthermore, dead animals cannot metabolize them and they can be quite heat stable (ie won't readily decompose during cooking).

seriously! you answered this? Cool! ......I know a simple 'thanks' is not sufficient, but still, a simple 'thanks'. :)

@servantofWiser You're welcome.


text:
Some kind of better oxygen-transport protein or something... Can you help me find it?
I remember reading about artificial or man-made, oxygen-transport protein that is somehow an improvement on hemoglobin, and that it is possibly immune to sickle-cell anemia, or something... But I forgot where I found it and what exactly it is...
Can you guys help me find it?

Is the protein HP7?

Seems like it is HP7. It would be immune to sickle cell anaemia because the disease happens because of a mutation in haemoglobin; any substitute for the diseased haemoglobin would work, including normal haemoglobin. For curing sickle cell anaemia you would need to anyway express it in RBCs. But the group who reported this protein never claimed that it is better than haemoglobin [1 and 2].

So, is there any oxygen transport protein that is better than hemoglobin? ... I remember reading about this where it said that it is an example where nature doesn't do the best it can, but just adequately... ... Looking that up now...

@Malandy Nothing other than HP7 turns up when I google "synthetic/artificial oxygen carrying protein". I have not heard of any protein being better than Hb. Fetal haemoglobin binds oxygen with slightly higher affinity and activation of fetal Hb in sickle cell patients is an ongoing therapeutic research.


text:
Linkage Disequilibrium in GWAS
I see a lot of studies on GWAS not filter out for linkage disequilibrium or even make the slightest mention of it. Why is this the case? I would imagine that you want to keep SNPs that are only in linkage equilibrium to promote independence (and besides, without it an additive model makes no sense)

One possible reason to not filter for linkage disequilibrium is to produce peaks/hits with multiple SNPs, thereby clearly indicating a region of a causal genotype. For example, in the manhattan plot below, each point represents a SNP, the higher it is in the plot the more it relates to the phenotype.  We see that in each of the peaks multiple points are significant due to linkage disequilibrium.  If instead we had pre-filtered all of these points out then a peak has a greater choice of representing some type of error in data collection, since there is some probability that there is some error in each SNP, but not much chance of the error occurring in all the SNPs of one peak.

I am unclear why not pruning for linkage equilibrium would prevent an additive, or any model from working.  After all a GWAS is just a series of regressions between the genotypes of one SNP and one phenotype.  In this single SNP frame of reference the LD does not affect the calculation of the betas.  Zooming out, all of the significant SNPs should group together because of the LD, thereby indicating only a few regions and not causing an increase in the false error rate.


text:
How does frame-shift mutation not absolutely ruin the organism?
I'm a bit curious as to how frame-shift mutation works. If you shift one amino acid towards another. How does this not affect the entire chromosome? Wouldn't this mean that the organism would be absolutely "scrambled" as it would essentially be "missing" a whole chromosome...Or having a chromosome that did all kinds of wrong things at least?
Thanks.

have you read at least wiki article on frame-shift mutations? Basic structure of genome is blocks of (Promoter:gene) type sequences. So what happens inside gene sequence is isolated for that gene only

For transcription, the cell uses different signals from start- or stop- codons to determine the start and end of a transcript (see this wiki page). Codons (and hence frameshifts) are only relevant at translation of the mRNA to protein. Frameshift mutations occur in coding sequence, so the promoter and termination signals for transcription are unaffected.
Genes which are further downstream will be transcribed normally from their own promoter and their coding sequence is still in-frame with their own start-codon.
When a frameshift occurs in coding sequence, the genetic code downstream can indeed be considered 'scrambled', much alike random nucleotide sequence. In random sequence, three nucleotides have a 3/64 chance of being a stop codon. So one would not expect translation to go on beyond about 20 amino acids from the mutation. Additionally, non-sense mediated mRNA decay will detect these premature stop codons and destroy the mRNA.

The untranslated region (UTR) isn't translated by the ribosomes (hence its name), and therefore contains no reading frames, because it is never "read."  Without a frame, the term "frameshift" mutation does not have a clear sense.  Perhaps you mean a small indel in the 5'-UTR could affect the translation initiation site? By definition a frameshift can only occur in the region of the mRNA downstream of the initiation codon and upstream of the termination codon. Well, in the DNA that encodes that region of the mRNA.

Also, any single frameshift (i,e., downstream of the ATG) HAS to throw the STOP codon out of frame.  There is nothing special about proximity to the translational termination signal (e.g., "the terminal exon").

terdon, my first point is about transcription, not translation

Oh good grief. I was really on fire that day. I also had a stupid error in my own answer. Sorry about that, Sleepses, @mdperry, comment deleted.

First of all, for a frame shift mutation to occur nucleotides (not amino acids) are either deleted or inserted into the sequence.  Read about details on this wiki page. Here's an image about frame shifts.
On panel A a new stop codon is formed  (TAG is trasncribed to UAG that is serves as stop codon during translation) thus the result is an abnormally short protein.
On panel B the codon 37 is swapped to a G thus the TA from the codon 38 is shifted left by two resulting in the new codon GTA.
On panel C exactly 3 nuleotides are deleted thus codon boundaries do not change, but a codon and the amino acid encoded by it is completely deleted.
On panel D 4 bases are deleted thus the T form codon 171 and the C from 172 are merged with the G from 170.

source: the linked wiki page.
It does not affect the choromosome in the way you describe because genes are separated and are independently transcribed (with some simplification). Every gene has its own translation  start that determines the correct frame in that gene If you think of a chormosome as a chapter of a book and the genes are paragraphs, you can see that deleting letters or words may scrable one paragraph, but the others are not affected by this because they have their own boundaries.
EDIT:
During translation the ribosome reads the mRNA in the form of triplets. When a single nucleotide is deleted then the whole sequence from that point shifts to the left by one thus the first letter of the subsequent triplet gets mixed into the frame where the mutation happened (the same goes for insertion but the sequence is shifted to the right). This causes the change in the meaning of the triplet, and can ruin stop codons that would affect the translation of the mRNA but not the transcription of the gene to mRNA.It is important to understand that transcription is a countinous process and the DNA is transcribed from base to base not in triplets, and that transcriptions stop is not encoded in the from of stop codon.

So the stop and start codons are not frame shifted?

I've edited my answer I hope it helps.

Okay so when does the RNA polymerase stop then? I thought it would start coding for a gene at a start codon and stop at a stop codon?

As I indicated in my answer codons only play role and make sense in terms of translation. Genes have promoters and transcription start sites to initiate transcription and transcription termination signals and polyA signals to indicate the end of transcription. All these signals are longer than 3 basepairs. I suggest reading this wiki page.http://en.wikipedia.org/wiki/Gene this covers many basic things

No, it wouldn't. In order to understand why, you must understand that

Genes are translated independently of one another. The ribosome starts translating at a start codon (ATG) and stops at one of the three stop codons (TAG,TAA and TGA). Whether another gene has a frameshift mutation or not, does not affect the current gene in any way. The ribosome will always1 start at the ATG and stop at the STOP codon.
Genes make up only a tiny portion of the total DNA, especially in eukaryotic organisms. In humans, for example, genes are ~5% of the total DNA.
The vast majority of DNA falls in intergenic regions that do not code for protein. For example:
  gene1             INTERGENIC REGION               gene2
---------|----------------------------------------|---------
ATGCCGTAGgcgtatagcgatatcgcatcgctatcgatacgctdcggcgcgATGCACTGA

While a frameshift might throw the first gene out of sync, the second will not be affected. The intergenic regions are not translated, so frameshifts are irrelevant to them.

So, a frameshift will only affect the gene it occurs in.

1 There are some exceptions to this, some alternative start codons but they are not really relevant to the discussion here.

A frameshift will affect all downstream coding sequence, regardless of what exon the mutation occurred in.

@canadianer no, it will only affect the downstream sequence of its own exon. The splicing signals are not codons and won't be affected unless the frameshift occurs in the acceptor or donor sequences themselves. How would a frameshift affect a different exon otherwise? Remember that we're discussing frameshifts that happen at the DNA level. Naturally, post transcriptional modifications/mutations are a whole different ballgame.

The reading frame of the first exon defines the reading frame for all subsequent exons since they are literally ligated together into one long coding sequence. If you have two exons, say AUGGUA and AGUUGA, they are spliced together and translated to get the peptide Met-Val-Ser. If there's an insertion in the first exon such that it becomes AUGAGUA, the peptide, after splicing with the second exon, becomes Met-Ser-Lys-Leu-… Thus the frameshift in the first exon changes the reading frame of all downstream exons.

@canadianer you're absolutely right, of course. I have no idea what I was thinking of.  I leave genomics for 5 years and I forget the basics! Answer edited,  thanks for pointing it out and my apologies for being dense.

Haha no worries. I'm sure you'll be able to return the favour for me someday ;)


text:
Antibodies stored in freezer instead of fridge
Someone from my lab put the box of antibodies in the freezer instead the fridge.  Can I hope that they will still work?

Depending on the type of protein and cryoprotectant (eg glycerol), freeze-thaw cycles may or may not damage proteins.

No way to tell until you try them! If they're in glycerol you have a good chance. We normally store our antibodies in the freezer.

@anael Have you read manual for that antibodies? Unless they are home-made, there should be one available. If home-made, then ask person who made them.

Most IgGs should be fine when stored in the freezer.

Freezing at -20 or -80 in small aliquots is the optimal storage
  condition. Ali-quoting minimizes damage due to freezing and thawing,
  as well as contamination introduced by pipetting from a single vial
  multiple times. Aliquots are to be frozen and thawed once, with any
  remainder kept at 4 degrees C.
Enzyme-conjugated antibodies, should not be frozen at all and should
  instead be kept at 4 degrees C. Freezing and thawing will reduce
  enzymatic activity in addition to affecting the antibody binding
  capacity.
Antibodies of the IgG3 isotype are unique in their tendency to form
  aggregates upon thawing and should always be stored at 4 degrees C

Personally, I store antibodies I use at -20 (they are in PBS), and they work fine for the first freeze thaw cycle. I have never tried more than one freeze thaw, so cant comment on that.
http://www.abcam.com/ps/pdf/protocols/antibody_storage.pdf

You don't specify which temperature of freezer you're talking about. Antibody can be stored at -80 for long time, but needs to avoid freeze and thaw process. Some people store aliquot of antibody in -80. Another way is to dilute antibody in Glycerol, make 50% dilution. You need to mix well, and then store at -20.


text:
What will happen to transcription if histone acetyltransferases are removed from a eukaryotic cell?
If HATs are destroyed in a eukaryotic cell, how will gene expression level be affected? Will all gene expression be affected or just be slowed down and most gene will still be expressed?

There are many HATs, but I would expect many of them to be lethal knockouts, for example this.

Yes as March Ho said there are several HATs. Hat1 knockout is lethal [Ref]


text:
Genetic notations
Genetic testing revealed these two mutations (hypothetically):
IVS11+6G>A and IVS11-4G＞A
Could you please explain every part of this notation, especially "+" and "-" signs.

have you tried googling first? if not, this query yields a useful page: Nomenclature for the description of sequence variations. Quote: 88+1G>T (alternatively IVS2+1G>T) denotes the G to T substitution at nucleotide +1of intron 2, relative to the cDNA positioned between nucleotides 88 and 89

These describe mutations (SNPs) in introns.
IVS stands for intervening sequence (which is another term for intron).
The number following IVS specifies which intron the mutation occurs in (intron 1 follows exon 1, etc).
The +n or -n, where n is an integer, gives the location of the nucleotide. +n counts 3' from the preceding exon whereas -n counts 5' from the following exon. I think that the first and last nucleotide of each exon is given position 0 such that, for example, +1 refers to the first nucleotide of the intron and -1 refers to the last.
X>Y, where X and Y are nucleotides, describe what mutation has occurred.

IVS11+6G>A means a G to A transition at position +6 of intron 11
IVS11-4G＞A means a G to A transition at position -4 of intron 11


text:
Why some parts of the human body have immune privilege?
Why have the eye and CNS have immune privilege? Why does the body not develop tolerance against their tissue and instead risk their damage in case an accidental immune cell infiltration?
Wiki:

...in immune privileged sites, tissue grafts can survive for extended periods of time without rejection occurring

i added quote from wiki and a link, please feel free to remove it if you feel so. but I would encourage you to read that wiki page on immune tolerance

The immune privilege is happening for brain, CNS, eye, testis and the uterus. All these tissues (or structures) share that they cannot be regenerated once they get damaged by an overshooting immune reaction. Infections in the eye followed by a strong immune response often lead to the loss of the eye, in rodents loss if immune tolerance against the fetus leads to abortion.
So basically this is a protective mode for these tissues to protect them from a stong immune reaction. See the references for some more details (especially on how this works).
References:

See no evil, hear no evil, do no evil: the  lessons of immune
privilege
What is immune privilege (not)?
Immune privilege or privileged immunity?
Immune Privilege and the Philosophy of Immunology

Each immune privileged tissue has a unique function. The eye must protect the light path and signals that stimulate the retina, and photoreceptors to preserve vision. The testis has to protect the sperm as they proceed to the epididymis where they mature. The maternal reproductive tract has to protect its eggs both before and after fertilization and thus has developed specialized mechanisms to modify the body’s response to foreign antigens. These unique challenges require different solutions and lead to unique immune privilege mechanisms. However, although microenvironment and the stromal cells that carry out the particular function may vary between tissues, and consequently the mechanisms that promote regulation may be unique to that cell or tissue, the goal is the same: limit collateral damage to preserve tissue integrity and maintain homeostasis to the extent possible, without compromising host defense.


text:
Identifying the origin of replication of an unannotated *E. coli* plasmid
I have attempted a few searches for a list of origins of replication for plasmids in E. coli, but I was only able to find a list of origins, but not their individual sequences. The available plasmid maps are often extremely vague on where exactly do the origins start or end, and in any case it would be extremely tedious to collate a list of origins in order to determine the exact origin of replication of a plasmid.
Is there a database of origins of replication available anywhere that would allow one to perform BLAST searches on the plasmid sequence in order to obtain the compatability group of the plasmid, in cases where the plasmid's origin of replication is not specified?

What do you mean by "unknown"? Is that naturally generated outside labs? Adgene shows positions of functional sequences and actual sequences.

Plasmapper Is quite good a recognizing common origins of replication. You will have to look up compatibility yourself. It is what I usually use when confronted with unnannotated plasmids.

There must be some such information, because at the lab we are using the SnapGene software, and it is able to recognize the Ori sequences (and all other sequences). Since it is proprietary software, I doubt they would disclose their list, but others might have the same information.
If you really need to find your origin for your plasmid, you could try the demo. Good luck!

Try this. I would suggest OriDB also, because it's precisely what you asked for. Getting the sequences out for easy blasting might be a chore, depending.

OriDB is not what I asked for, since they don't have E. coli in their organism database. http://pombe.oridb.org/organisms.php

That's why I didn't list it first. It appears to be the only database of Ori sequences, so it's a pity it is so small.

Go this URL
And click pBR322 origin in the map. Then, the corresponding sequence will be highlighted.
2248-2867: pBR322_origin
2201    cccttaacgt  gagttttcgt  tccactgagc  gtcagacccc  gtagaaaaga  2250
2251    tcaaaggatc    ttcttgagat  cctttttttc  tgcgcgtaat  ctgctgcttg    2300
2301    caaacaaaaa    aaccaccgct  accagcggtg  gtttgtttgc  cggatcaaga    2350
2351    gctaccaact    ctttttccga  aggtaactgg  cttcagcaga  gcgcagatac    2400
2401    caaatactgt    ccttctagtg  tagccgtagt  taggccacca  cttcaagaac    2450
2451    tctgtagcac    cgcctacata  cctcgctctg  ctaatcctgt  taccagtggc    2500
2501    tgctgccagt    ggcgataagt  cgtgtcttac  cgggttggac  tcaagacgat    2550
2551    agttaccgga    taaggcgcag  cggtcgggct  gaacgggggg  ttcgtgcaca    2600
2601    cagcccagct    tggagcgaac  gacctacacc  gaactgagat  acctacagcg    2650
2651    tgagctatga    gaaagcgcca  cgcttcccga  agggagaaag  gcggacaggt    2700
2701    atccggtaag    cggcagggtc  ggaacaggag  agcgcacgag  ggagcttcca    2750
2751    gggggaaacg    cctggtatct  ttatagtcct  gtcgggtttc  gccacctctg    2800
2801    acttgagcgt    cgatttttgt  gatgctcgtc  aggggggcgg  agcctatgga  2850
2851    aaaacgccag    caac
You can find various ori sequences from this site.

I already know how to do this. I specifically stated that "and in any case it would be extremely tedious to collate a list of origins in order to determine the exact origin of replication of a plasmid. " and therefore asked if there was a database which solves this problem.

It's just four or five in total, isn't it? pMB, modifiedpMB, R6K, p15A, pSC101, modified colE1. If you get a plasmid somebody constructed for recombinant DNA experiments, probably the ori is one of these. If you get plasmids from natural resources, you might not find complete match by alignment, but you could find a similar sequence among them. Or are there more types of ori out there?

I mean once you get those 6 types of sequence, you can blast by yourself. You do not need to search plasmid from the URL I show. Did I miss something?


text:
Are there fishes with a double circulatory system?
Our courseware materials say there are such fishes, but my Internet searches suggests fishes only have a single circulatory system.

Can you please put a little more effort into your question?

It seems that lungfish display the beginnings of a double circulatory system:


text:
Trying to understand the big picture behind DNA sequencing, alignment and searching
I'm about to start a bioinformatics research project but I haven't any biological background.
I know my project is in regards to a performance analysis of DNA sequencing and searching "weapons" like Hadoop, Apache Spark and Apache Flink - so I've spent the last couple of days trying to put together the "DNA picture" before I get started with the programming stuff.
My understanding of the situation is that:

Next-generation sequencing (NGS) techniques are used to efficiently provide reads of DNA (conversions from real, physical DNA to something that can be read and analysed), however today's most practical methods provide reads that are short and in disorder.
Reads tell us which nucleotides, labeled one of ACGT, occur in sequence. Different nucleotides or values to take their place may exist, like N or X. Reads could range from 50 to 1000s of nucleotides in length depending on the method of sequencing.
You can find historical raw read data in various places online, including the Sequence Read Archive (SRA). The same website contains lots of other DNA/bio related information. Reads are commonly stored in .fasta files which follow a simple and practical standard. A single file could contain a very small or very large read or sequence.
Reads are then provided to DNA alignment programs like bowtie which will place them back in the correct order. The algorithms could use a template sequence to align them, or run "de novo" (without a template). The result of these alignments have also been indexed online however for the purpose of my studies I'll probably be aligning them myself.
Once aligned (or maybe during alignment), nucleotide differences from a template sequence can be programmatically found or searched for, with crossbow for instance. Note that many other tasks can too be performed - not only this search. If a particular substitution occurs in more than 1% of a what I think is called a "genome's" population, then it is called a Single-nucleotide Polymorphism (SNP or snip). Most SNPs have two alleles, or two different recorded nucleotides (like either G or C), but more than two is possible.
SNPs can be studied and mapped to various conditions or characteristics. A particular nucleotide could be responsible for part of one's emotional tendencies, reaction to particular medicines, or maybe anything, so a particular SNP could make a big difference.

What am I missing/what did I get wrong?

Did you find an exemplary/representative application that highlight usage of Spark or Flink (Hadoop MapReduce is dying AFAIK)?

@Phil It appears as though Flink has yet to gain significant traction, but Spark is well on its way. This is observable as a rough trend in table 1 of my draft literature review (https://bilalakil.github.io/mphil/assets/literature-review.pdf) - there you can also see that Hadoop isn't quite dying yet. Again, that's a very rough form of measurement, however it should be fairly indicative of trend. I've yet to describe such a explary/representative use of Spark thus far.

Here's a quick summary of a few mis-hits in your otherwise good analysis:
Not many bioinformatics applications use Hadoop, Apache Spark or Apache Flink. In fact, I have never heard of the Apache Spark and Flink tools, and I've seen only 2 people use Hadoop to process alignment files.

Reads are not "converted" real, physical DNA. They are representations of the signals from the molecules making up the DNA, as read by sequencing machines.
A,T,C,G are molecules making up DNA. N refers to "any of A,T,C,G", which translates to either unknown or ambiguous.
Reads are stored in FASTQ files. Sequences are stored in FASTA files. Reads include sequence as well as quality information, so, FASTA+QUAL=FASTQ
Re-ordering is a crude but approximate way to think about it. Remember, this process involves overlaps while reordering seldom does. Overlaps are crucial to the process of assembly/alignment. You are correct about the alignment to reference sequence and de novo assembly parts.
This is correct, though statistical models are applied to account for differences that are not necessarily significant variants (such as for sequencing errors)
Yes, variants, which include SNPs, can be correlated to differences in phenotype (traits). Emotional tendencies is something a bit too advanced, think of something more basic like diabetes or eye color.

The picture you have here is of a regular NGS analysis pipeline. This involves alignment/assembly, variant calling and biological analysis with relevant hypotheses. The assembly/alignment is the most computationally expensive part, and we use either HPC clusters or scalable cloud services such as AWS to get this done.
You should definitely talk to a biologist that has some computational experience to gain insight into the reason behind our analyses. Once you understand the motivations, your contribution will be more relevant and helpful to the community.

Mind my naivety, but if A, G, C and T are molecules, then what's a nucleotide?

@BilalAkil Nucleotides are a type of molecule. A, G, C, and T are both molecules and nucleotides

A nucleotide is a phosphate, pentose sugar, and a nitrogenous base in the most general sense. The A/C/G/T are derived from the type of nitrogenous base affixed to the sugar-phosphate.

@BilalAkil I wished to avoid as much biology as possible, hence the term "molecule". a nucleotide is a biological molecule with a sugar (ribose/deoxyribose) and a nitrogenous base (adenosine, guanosine, cytosine, thymidine) attached to it with a phosphate bond. The first part determines if the nucleotide is a part of DNA or RNA, and the second determines the base. DNA is a macromolecule, a super large molecule build from smaller constituent molecules :)

RAM's answer is very good, I'll just add on the computational side, short reads are error prone.  That's important to account for when aligning or assembling.  The reads themselves can just be inaccurate, which we detect by having multiple reads overlapping by a lot; we assume that stray discrepancies seen in only a single read at a position are errors.  Also, if a reference genome is not quite close enough to the sample, reads might be misaligned.
The genomes of many organisms also have repetitive elements which can make it hard to correctly align reads, and hard to make an accurate reference genome.
And note that there can be many more discrepancies other than single nucleotide substitutions, though anything more involved can be challenging to detect with short read data alone.  And most traits that are actually interesting to study are polygenic, so it's not exactly easy to say that one difference in DNA causes a detectable difference in phenotype.

Thank you. I did not wish to delve deep into the complexities we face. Pathogenic Single Nucleotide Variants in single-gene diseases are the easiest example of mutations with a phenotypical effect, aren't they? Note to OP: 1. Pathogenic, 2. single nucleotide, 3. single-gene 4. diseases, 5.phenotypical effect are the points of variability, each with at least 4 other entities that can take their place - the combinatorics give you the possible categories. Imagine the research when most of what we do it try and assign these categories to thousands of variants!


text:
How to conceptualize the action potential?
In my AP Biology class, we were taught that action potentials are not electrical impulses in the same way current travels through a wire. Rather, we were taught that action potentials are changing concentration gradients of sodium and potassium ions.
However, when I looked into modeling action potentials I saw capacitances, inductances, and currents--properties associated with electrical circuits.
My question: action potentials--are they electrical currents in the classical sense? If so, what is the charge that is flowing (is sodium and potassium ions?), and how is the overall charge neutrality of the body maintained?

A changing concentration gradient of Na+ and K+ means that these ions are flowing and thus there is a flow of charge, which is the definition of electric current. However, as opposed to a wire, this current is largely travelling perpendicularly to the direction of the impulse.

@canadianer good point about signal direction and currents. But signal -- membrane depolarization -- goes along axon/whatever

@aandreev What?

@canadianer current flows normal to membrane surface, but signal travels parallel. that is due to nature of ion channels (e.g. inactive phase of Na-channels)

@aaaaaa Yes, I said that.

First of all, electric current is defined as movement of charges, $I=\frac{dQ}{dt}$. In electronics that you see around that is not very useful definition, because electrons move much slower than signals, that is changing electric field. Speed of electric field propagation reaches speed of light, whereas electrons move at 1%-30% or $c$.
In biology, especially regarding action potential, only movement of charges is important, not propagation of electric field, probably because conductance is much smaller than in copper wires. that is signal propagates not through interaction between charges, but via intermediate agent/amplifier -- ion channels.
Charge in body is more or less constant, because action potentials are cyclic: after depolarization membrane comes back to -70mV or something. There is no flow of charge out of the body.

Short answer
Action potentials are mediated by electric currents and can be modeled by electronic circuits.
Background
An electric current is the flow of charge. Therefore, action potentials are mediated by current flow. However, action potentials are mediated by the flow of ions across the membrane (Fig. 1), whereas current flow in electric circuits is typically mediated by the flow of electrons (Fig. 2).

Fig. 1. Action potential propagation. Source: Chen, Carnegie Mellon.
The action potential propagates in the direction of the dendrite. However, the underlying current flow is perpendicular to the axonal membrane and is mediated by ion channels. These ion channels are activated through the sensing of the electrical field. For example, voltage-operated Na+ channels are activated by a small depolarizing potential, while voltage-gated potassium channels open during the later phase of the action potential by a large depolarization.
Whatever the differences, an axon can be well modeled by a parallel resistor-capacitor circuit powered by the membrane potential. The electrochemical properties of the cell membrane makes it equivalent to a circuit with a set of resistors (which are voltage-dependent), batteries (whose voltage depends on ion concentration differences), and a capacitor all connected in parallel (see Fig. 2).

Fig. 2. Electronic circuit representation of the axon. Source: Yale University.
As to your question on charge balance; neutrality is maintained, because Na+ and K+ flow are going in opposite directions and neutralize the net flux of charge.


text:
Influence of eating at late night on sleep
I have often heard the saying that one shouldn't eat a mountain of food because blood will go to the stomach and it'll be difficult to sleep well because the stomach can't rest at night.
I see that the blood goes to the stomach when you eat something to digest the food. Have any studies been done on whether it does actually affect the quality of sleeping?

Who exactly is the 'you' here?  I do (though I don't eat a mountain of food at any time); other people may or may not.  Depends on the individual.

I do not have any scientific information about your question but, I want to share my experience, last night I ate a cup of soup an hour before I slept and I had several nightmares during night and when I woke up, I did not felt good

According to Crispim et al 2011, caloric intake late at night is correlated negatively with sleep quality:

We conclude that food intake during the nocturnal period is correlated with negative effects on the sleep quality of healthy individuals. Indeed, food intake near the sleeping period (dinner and late night snack) was negatively associated with sleep quality variables.

The paper is an interesting read. Here's their figure 1 which shows the correlation between sleep latency (how long it takes to fall asleep) and nocturnal caloric intake:

Thick line: men
Thin line: women
Basically, it shows that people who eat more at night take longer to fall asleep, and other results show that it's also correlated negatively with other sleep quality indicators.

Really interesting, I would never have expected that huge difference between men and women


text:
What does this mutation notation mean: MAP2K41161fs*12
I received a report that had a mutation notated as: "MAP2K41161fs*12". I'm used to seeing this sort of thing: BRAF(V600E) or BRAF(T1799TA). Can someone tell me how to interpret "MAP2K41161fs*12" ?? Is it a deletion? An insertion? A translocation? Or What?

Can you post a link to the paper? MAP2K is the protein. fs means frame shift and *12 gives the length of the new reading frame (ie the number of codons up to and including new stop codon). Usually before fs they give the first mutated amino acid, so I'm not sure what the number 41161 means (no protein is that long).

Or the protein is MAP2K4 and the number is 1161, but I still don't know what that means since MAP2K4 is 399 aa…

Oh my....duh! Of course I should have figured out that "fs" means frameshift. But I'm used to seeing just the ins or del. I've now looked at a couple of online pages describing this notation, but they refer to the length of the new reading frame (just as you did). This seems like an odd thing to provide...doesn't the new reading frame go on until the next stop codon or complementary indel? And you'd have to provide the terminal event (X or indel) anyway, right? Weird. Anyway, thanks! I can figure out the rest from this hint.

fs means frame shift and *12 gives the length of the new reading frame (ie the number of codons up to and including new stop codon). Usually before fs they give the first mutated amino acid, so I'm not sure what the number 41161 means (but apparently the OP does).


text:
Mechanism behind negative conductance of ion channels
I am struggling to understand negative conductance shown on I-V curves on ion channels. Mechanistically, negative conductance means that inward (or outward) current increases when voltage across membrane decreases. I-V curves of such a channel have both positive and negative slopes. How it is achieved by ion-channels?
For example, this is in addition to the example posted in the answer by aandreev.

Thanks for the question. I am afraid I do not entirely get your question. How is what achieved? Or specifically, where does "it" refer to in your concluding sentence?

@AliceD. Sorry for the confusion. I've made some edits. A figure added to question shows one mechanism by which negative conductance is obtained.

With this figure you cannot really say with surety that slope reverses direction.  More replicates and data points are required to prove that.

This is the figure OP talks about:

Negative Conductance Caused by Entry of Sodium and Cesium Ions into the Potassium Channels of Squid Axons, Francisco Bezanill, Clay M. Armstrong (1972).
Short answer is that in non-linear mode of ion channel operation other ions start passing through it (channel loses specificity). Cumulative effect (because of different concentrations of different ions) is negative total conductance, if you don't measure currents of specific ions.

The experiment described in the linked article Bazanilla & Armstrong (1972) is about a voltage clamp experiment in squid axon. Voltage clamping basically means that the potential difference across the axonal membrane can be set at will by an external artificial electronic power source.
NMDA receptors are channels that conduct positively charged ions (mainly Na+). If the voltage is clamped at negative potentials under physiological conditions, positive current will be drawn inward. This is generally plotted as a negative current, as shown in your picture in your post. If, however, the voltage is made more positive, current will reverse at a certain point, and positive currents will be measured by the electrodes. This positive current is characterized by an outward flow of positive ions through the NMDA receptor.
The sign of the current is arbitrary, but the most important point to make is that most channels allow current to pass both ways. There are rectifier channels, however, that will only allow one-way traffic. Rectifiers are more exceptions than the rule.
Linked reference
- Bazanilla & Armstrong, J Gen Physiol (1972); 60: 588-608


text:
How to compute the regression of individual fitness on individual phenotype
Consider a population structured in groups of two individuals. Individuals' interactions follow an additive prisoner's dilemma:
\begin{array}{c |c |c|}
& C & D \\
\hline
\text{Cooperate (} C \text{)} & b -c & -c \\
\hline
\text{Defect (} D\text{)} & b & 0 \\
\hline
\end{array}
where $b$ is the benefit and $c$ is the cost. The payoffs are to the player on the left. I need to calculate  the regression of individual fitness on individual phenotype, $\beta (w_i, p_i)$, where $p_i = 0$ if $i$ defects and $p_i = 1$ if $i$ cooperates. Note that I need to calculate the regression within groups rather than between groups.
I thought that the right way of calculating $\beta(w_i,p_i)$ would be to vary the strategy of the focal individual while keeping the strategy of the other individual constant (my thought was that the other individual becomes the environment for the focal individual). Because switching to cooperation causes an individual to lose $-c$ in fitness (if we keep the strategy of the other individual constant), I thought that $\beta(w_i,p_i) = -c$. But, to my surprise, $\beta(w_i,p_i) = -b -c$. To show why, McElreath & Boyd ("Mathematical Models of Social Evolution", p. 242) draw the following graph:

Let $V(X|Y)$ be the payoff of individual $X$ when it interacts with $Y$ (in other words, $X$ is the focal individual; $Y$ is the other individual). I understand that McElreath and Boyd calculated the regression coefficient by computing $V(C|D) - V(D|C)$ --- as opposed to $V(C|C) - V(D|C)$, where you keep the strategy of the other individual constant. My question is why this is the right way of calculating $\beta(w_i,p_i)$.

I totally forgot to say what $V$ was. I fixed this now. Thanks for pointing it out.

If I understood your second question correctly, I want to calculate the regression of fitness on phenotype. The example only deals with two phenotypes, cooperate and defect; and the population only contain two individuals.

@Remi.b : I rewrote the question and added a graph explaining the example. Hope it's clearer now.

I am not sure I understood the question. Let me know if this helps.
Case: N=2, freq=0.5
Let's assume that the frequency of those who cooperate is 0.5. The slope of the regression line (which R.squared is equal to 1 as we have as many data point than degrees of freedom) is by definition $\frac{\Delta w}{\Delta p}$. You defined $\Delta p = 1$. What is $\Delta w$ then?
Well, given that one cooperate and the other defect (as indicated in the fact that the frequency of cooperators is 0.5), then one will have the payoff of a cooperator that is facing a defector and the other one will have the payoff of a defector facing a cooperator. The cooperator has a fitness of $w_0 + b$ and the defector has a fitness of $w_0 - c$. I think you may misread the payoff matrix. To know the fitness of someone with strategy $i$ when facing someone with strategy $j$, you look that payoff at row $i$ and column $j$.
Therefore, $\Delta w = (w_0 - c) - (w_0 + b) = - (b+c) = -b - c$, and so the slope is  $\frac{\Delta w}{\Delta p}=\frac{-b-c}{1} = - b -c$
Case: N>2, freq=0.5
In such case the slope of the regression will necessarily be lower than $-b-c$ because some cooperators will meet other cooperators, increasing their fitness (=highering the points on the right hand side of your graph) and some defectors will meet other defectors, decreasing their fitness (=lowering the points on the left hand side of your graph)
At equilibrium
The only stable equilibrium is when the frequency of cooperators is 0. In such case, there is no variance and we cannot even talk about regression.
Conclusion
The slope is not necessarily $-b-c$. It is $-b-c$ for the very special case where we have two individuals, one cooperate, the other one defects. In any other scenario the slope $s$ of the regression is lower. More specifically, the slope $s$ of the regression for any $N$ and for any $freq$ is $0 =< s < -( b+c)$

In a case in which the frequency of cooperators should be 0.5, I can see why the regression coefficient has to be $-b -c$. My question arose because McElreath and Boyd derive the regression coefficient without making any assumptions of the frequency of cooperators in a group. If we assume that we can have any frequency of cooperators in a group (0, 0.5, and 1), it seems that the regression coefficient can have value $-c$.

I agree with you that $-b-c$ is not the slope of the regression in any case. Yes, in some cases the slope of the regression can be -c. I did not calculate the slope of the regression for any $N$ and frequency but it probably is not very hard I guess. See edit.

Here is my mistake, I think.  $w_i$ can be written in terms of $p_i$ in the following way (note that $p_i$ can only assume two values, 0 and 1):
$w_i (0) = w_0 + kb$
$w_i (1) = w_0 -c + (k-1)b$
where $w_0$ is the baseline fitness and $k$ is the number of altruists in the group (so, $k$ can be 0, 1, or 2). When we are calculating $\beta (w_i, p_i)$ the question we are trying to answer is "for a given group in the population, what is the regression coefficient $\beta (w_i, p_i)$?". This implies that $k$ must be constant because, otherwise, we would be considering individuals from different groups. Accordingly,
$\beta(w_i, p_i) = w_i(1) - w_i(0)$
$\beta (w_i, p_i) = -b -c$
For $\beta(w_i, p_i)$ to be equal to $-c$ we would need to consider the payoff of cooperators from different groups.

Following up on the equation Remi.b suggested in a comment below, we can write the regression coefficient to a population structured in groups of $n$ individuals. More specifically,
$w_i(0) = w_0 + [k/(n-1)] b$
$w_i (1) = w_0 -c + [(k-1)/(n-1)] b$
With this definitions, we get
$\beta (w_i, p_i) = w_i(1) - w_1 (0)$
$\beta (w_i, p_i) = -c - b/(n-1)$

$b$ and $c$ doesn't have the standard definition as given by Hamilton. The fitness of an individual who cooperates is usually computed as $w_c = w_0 + x PO_{cc} + (1-x)PO_{cd}$, where $PO_{cd}$ is the payoff of an individual who cooperates when meeting an individual who defects and $x$ is the frequency of cooperators in the population.

My impression is that, in the example above, your equation yields $w_c = w_0 + x (b-c) + (1-x)(-c) = w_0 + xb - c$. The chance of a cooperator meeting another cooperator will be $(k-1)/(2-1)=(k-1)$, which gives the equation I wrote. Am I missing something?


text:
How could I identify whether given RNA-seq data is paired end or single end
I need to have a RNA-Seq dataset and therefore, I've visited the following site NCBI-geo C. Elegans
In the Supplemantary file part, I clicked the SRP/SRP051/SRP051702  ftp and downloaded sra file. Then I need to convert it to fastq file format. For this purpose I've heard that there is a sra-toolkit and within it there is  fastq-dump exacutable. However in order to use it, first of all I have to figure out whether my sra file contains paired-end or single-end read data. So my question is that how could I know the type of reads in the dataset (in the first link) ?
Without any information, I used --split_files flag of fastq-dump exacutable and it generated two 14 Gb  (both of them  are exactly 14.346.367.840 bytes) files and their name are  SRR1741330_1.fastq and SRR1741330_2.fastq Does it mean my dataset is paired end ?
As an another question (different but related with part 1). In the SRR1741330_1.fastq file, at some lines, sequences contains different characters such as CCCFFFFFGFHHHGJJJJI#1?FEIGGI... Before looking into these files, I have thought that these sequence lines should only contain the letters of A,G,T and C . What are these F,H,J,I,#,? etc..
My questions might be trivial and unmeaningful but since I'm a totally new person in this area, I could not understand them.
EDIT
New question
I wonder one more thing, since its related with previous part of my question I asked it here instead of creating new question. After I generate fastq file with dump_fastq executable, all the sequences are length of 50. Does it because of parameters of the dump_fastq executable or its related with original sra file? In other words, could I increase this length?

To find out whether a dataset was paired-end or single-end, go to SRA, click on a run, and look under "Library". Paired-end datasets will typically have "Layout: paired". Note that people don't always mark this correctly, which causes no end of headaches.
Regarding lines like "CCCFFFFFGFHHHGJJJJI#1?FEIGGI", that's the quality score line. Look at the fastq article in wikipedia for more details.
Edit: Regarding the updated portion of your question, no you can not increase this length. The sequences produced by Illumina machines are a fixed length and that length (or whatever length was submitted) is what you're getting. As a general rule, you'll get whatever sequence was updated (unless you forget the --split3 (or whatever it's called) option).

@Devon_Ryan thanks for your help, by mean go to SRA, I think you refer to sra file that I downloaded from the link I provide above,right?

Okey, now I found it, it is in the browser

Exactly, I mean the webpage for a run on the SRA website. The same information is typically available in GEO too (click on one of the samples and read the "Data Processing" section), but it's typically a bit faster to just go to SRA.

@Devon_Ryan thanks again, could you look at my question again, I've updated  it

I've updated my reply. The short answer is that you're getting what was uploaded, which is typically all that was sequenced.

@Devon_Ryan thanks for the last time : ) So, as I understood from your updated answer, rna-seq data was supplied by the length of 50 base pairs read then.

@stackunderflow Yes, exactly.


text:
Who is organizing the heartbeat and its roles in the body?
Throughout the decades we heard that the heart is the only muscle whose nervous system is not acting. So who is he responsible for organising the heartbeat and its roles?

The Sinoatrial Node is a ganglion (clump of nerves) attached to the heart, which regulates heartbeat independently from the brain (http://en.wikipedia.org/wiki/Sinoatrial_node).
Heart rate can also be influenced by hormonal responses produced by the autonomic nervous system. To quote Wikipedia:

While heart rhythm is regulated entirely by the sinoatrial node under
  normal conditions, heart rate is regulated by sympathetic and
  parasympathetic input to the sinoatrial node. The accelerans nerve
  provides sympathetic input to the heart by releasing norepinephrine
  onto the cells of the sinoatrial node, and the vagus nerve provides
  parasympathetic input to the heart by releasing acetylcholine onto
  sinoatrial node cells.

Source: en.wikipedia.org/wiki/Heart_rate#Physiology

THANK YOU for the answer ! But what about autonomic bervous system ?

Ah yes, of course, there's that too. I updated my answer.


text:
Is Sanger sequencing still used in labs, and therefore worth learning?
If iI were to have access to funds for research, would learning this technique be a boon for me? Or are next-gen sequencing methods all the range now? My knowledge of both are limited.

Sanger sequencing is still used in the labs today - and not only on the side. Next-generation sequencing has its strength when it comes to sequencing very large amounts of DNA (basically whole genomes or exomes).
Sanger sequencing is used when you want to sequence smaller regions or portions of a genome/plasmid. Typical read length is (depeding on the machine and the skills of the operator) 600-900bp. This is enough to verify site directed mutagenesis, the presence of inserts you cloned as well that they are correct (if the insert is longer you will typically nest the single sequence reactions to cover the whole sequence) or look for a polymorphism. There are endless possibilities.
Additionally it is relatively cheap to sequence single samples (which is not really working on the next-gen machines), one sample comes between 8 and 10$ (at least the last time I sent this out). And you don't even have to do it yourself as there are a lot of service providers available. The technique itself is not hard to learn either.

i have additional question, not related to OP's. Do people perform sanger in-house nowadays, or either send samples to company or centralized facility (as in big research centers or universities)?

It depends on a number of things: First, you need a sequencer or at least access to one. If this is the case, people often sequence themselves, as this is more flexible (and you can use much less chemistry than ABI recommends :-) ). Additionally, if you want to sequence yourself, you need at least a minimum number of samples per month, otherwise the sequencing kits (minimum 100 reactions) are too expensive to simply rot in the freezer. Big units often have centralized facilities, but this also depends on their scope.

Moreover the error rate in Sanger sequencing is less than many NGS. I know of a recent genome sequencing paper where they used Sanger sequencing.

No its not Depends on the scale you are workin on

The dideoxy termination method of DNA sequencing (often called Sanger
sequencing after the technique’s inventor, Fred Sanger) has been the
workhorse of pretty much every molecular biology lab for the last 30
years. However, over the last few years the method has been
increasingly supplanted by so-called next-generation sequencing
technologies, which allow incredibly rapid generation of large amounts
of sequence data.
Sanger sequencing is still widely used for
small-scale experiments and for “finishing” regions that can’t be
easily sequenced by next-gen platforms (e.g. highly repetitive DNA),
but most people see next-gen as the future of genomics.

The article also addresses issues on read length and cost between NGS and Sanger.

For Sanger sequencing these reads are routinely 800-1000 base pairs long; next-gen methods produce much larger quantities of sequence, but in the form of much smaller reads (the two best-performing platforms generate 35-75 base pair reads, while a third, lower-throughput platform can manage 400 base pairs).
Read length is absolutely crucial when it comes to assembling accurate sequence, especially for genomes as complex and repetitive as the human genome. If a repetitive region is much longer than a platform’s read length, it can’t really be accurately assembled – so human genomes sequenced with current next-gen platforms actually consist of hundreds of thousands of accurately sequenced fragments interspersed by gaps....

Taken from: http://www.wired.com/2009/01/sanger-sequencing-is-not-dead/