text:
Software recommendation for protein in electric field modeling?
I'd appreciate a suggestion for software to compare models of two proteins in an alternating electric field. The more detail, or perhaps direction towards an existing and similar paper, the better. Right now I'm particularly interested in comparing rotation of the proteins as a result of the field. The first protein, tubulin (1TUB), will be modeled as a dimer while the second, septin (2QAG), will be modeled as a trimer.
The electric field will consist of a 200Khz field at 1.5V/m with a spatial resolution of 300Ax300Ax300A. I believe that I will only need a temporal resolution of 1.5 milliseconds for 10 solutions(?) per cycle for 3 cycles in the 200Khz field (particularly unsure of this part).
In terms of computational power I have access to two 7870 Radeon GPUs, but am also interested in cloud computing if that's not enough.

this is a tough one. Maybe you can help those, who know the field, by specifying temporal and spatial resolution, duration requirement for modeling. Molecular Dynamics approach might help, it has great resolution in t and xyz, but model duration is ~1us, as I remember.

Mathlab has many good toolboxes for many things, although it is not cheap. Good news is that there are alternatives such as Scilab that is totally free.

@aandreev I've done more searches with inclusion of "Molecular Dynamics" and have found GROMACS which, apparently unlike NAMD, allows for an alternating field. This may be the program I'm looking for... http://onlinelibrary.wiley.com/doi/10.1002/anie.200703987/full

@Timmons I am saying, you under-specified your problem. Are you looking for 1ns temporal resolution? Are you interested in how each atom moves? Or you can approximate your molecule as a sphere with uneven charge density? That kind of descriptions might be helpful.

@aandreev ahh, my mistake. I have not "picked" a temporal resolution, but I know that I'm not interested in each atom, rather, how the whole molecule rotates in the alternating electric field. A sphere might work, however, I'd like to eventually to add several of the proteins linked in a filament (realizing that's another level of complexity)

@Timmons - What structure are you using?

As in which protein structure? I'd like to look at the asymmetric units for both, if I understand the question correctly.

MD
Broadly this could be attempted with Molecular Dynamics simulations. As you discovered in your search, one of the most popular softwares is free: GROMACS. This is an atomistic approach to the problem. It also has many electrostatic parameters.
I would point out that generally MD requires careful and robust methods and often an expert lending hand if the results are to be trusted. In order to do MD at the timescales for observing rotating molecules in the field, massive amount of HPC computational time will be required. This is a full blown project to get it to an appropriate level for publication.
This is computationally intensive.
Quick Electrostatics
If a HPC is not available to you then MD may not be an option. If all you want to do is visualise the location of the charge and make qualitative predictions or visualisations, PyMol and ABPS might be a quicker combo. It is also good for quantitative analysis, but obviously is a static model.

Thanks for the additional information. I've actually just come back to this problem after finishing some other work, so your timing is serendipitous.

Your point re: the necessity of an HPC is well taken. I thought my 2 7870 Radeon GPU's might be able to handle it, but your comment suggests otherwise. Do you know whether cloud computing is a viable/affordable option? (https://aws.amazon.com/government-education/scientific-computing/)

@Timmons I've only been a part of three academic groups that were all involved in MD, but they each had their own HPCs and clusters. Whilst I can't see anything obviously wrong with cloud computing, I would email ahead and check that what you want to do is viable.


text:
What are potential side effects of myostatin inhibitors?
Myostatin inhibitors, which are being developed to treat muscle wasting diseases like muscular dystrophy, are likely to be abused by athletes. What are the potential long-term side-effects of taking a myostatin inhibitor? Will it have different side-effects for patients compared to healthy athletes (especially young athletes that are still developing)?  Could there be adverse drug interactions with other common performance enhancing drugs like EPO, hGH, and anabolic steroids?
Sources I've explored (StackExchange is only allowing me to insert two hyperlinks):

New Muscle Drugs Could Be The Next Big Thing In Sports Doping - NPR story by Jon Hamilton
Wikipedia entry on myostatin
Wyeth found one of its drugs to be safe in a clinical trial, but it stopped development because the drug was ineffective at increasing muscle mass.
An internet search mainly brings up dieting and body building forums, which I do not trust.
According to a review by Breitbart et al. 2011, myostatin inhibition does not have negative effects on cardiac tissue.
According to a review by Allen et al. 2012, the increase in muscle mass caused by myostatin inhibition leads to be metabolic health (e.g. lower glucose levels and higher insulin sensitivity).

source: Stack Exchange

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text:
What is the best color for a PPG signal (photoplethysmography)?
What is the best color for a PPG signal?
Red, green, blue or infra-red?
I'm asking because commercial oximeter sensors usually use a pair of red and infra-red LEDs. But recently, I've seen a lot of sensors using green LEDs.
Thanks.

Found the difference in colors :
Longer wavelenght penetrate deeper.
because main artifacts are caused by changes in the blood volume at the measurement site. During movement, the changes in venous blood volume are larger than those in arterial one. While the penetration depth of light is dependent on its wavelength, and green
light do not penetrate as deeply as infrared light. Therefore, the green PPG would include less information from various non-pulsatile sources than the infrared PPG.

I also looked up multi-spectral ppg and from what I read they all give you an exploitable PPG signal, it really depends from what you want to do.
For oximeter you need red and ifr light source. but if ou just want a pulse meter shorter wavelenght are better.
Sources :
http://embc.embs.org/files/2013/3170_FI.pdf
http://www.biofotonika.lu.lv/fileadmin/user_upload/lu_portal/projekti/biofotonika/Publikacijas/A3/Asare_8073_76_Praga.PDF

The reason red and infra-red are used is the difference in absorbance at those two frequencies between oxygenated and unoxygenated hemoglobin. Using the difference in these two frequencies allows the concentration of oxyhemoglobin to be calculated. (The red LED is at the frequency where oxyhemoglobin and hemoglobin have identical absorbance).
As far as I know, there's no reason why a green LED wouldn't work. You would only be able to measure the PPG signal and not the oximetry data (unless you used two different LEDs and did some math).
Absorption curves.

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What exactly are computers used for in DNA sequencing?
I've thoroughly read the Wikipedia article on DNA sequencing and can't get one thing.
There's some hardcore chemistry involved in the process that somehow splits the DNA and then isolates its parts.
Yet DNA sequencing is considered to be a very computationally-intensive process. I don't get what exactly is being computed there - what data comes into computers and what computers compute specifically.
What exactly is being computed there? Where do I get more information on this?

This article goes through computational processes involved in lining up fragments

@jonsca: That looks like we already have "strings" representing the DNA, but how do we get those strings?

It's not the sequencing itself that is computationally particularly intensive, but the assembly of the sequenced fragments into continuous DNA. Also, even a computationally trivial task will take time if repeated billions and billions of times. To get any decent quality sequence, one has to sequence the DNA multiple times (20+). If you have a genome that's 3 billion bases long...

@leonardo That’s easy, in the second case not only do you have less reads (good), you also have longer ones (also good). Even though increasing read length increases computation time, it also increases the quality of the result. In the “worst” case you could just consider those reads as already-created contigs of several smaller overlapping reads.

@leonardo Yes. The extreme case, one read which spans the whole genome. See the pattern? ;-)

Computers are used in several steps of sequencing, from the raw data to finished sequence:
Image processing
Modern sequencers usually use fluorescent labelling of DNA fragments in solution. The fluorescence encodes the different nucleobase (= “base”) types (generally called A, C, G and T). To achieve high throughput, millions of sequencing reactions are performed in parallel in microscopic quantities on a glass chip, and for each micro-reaction, the label needs to be recorded at each step in the reaction.
This means: the sequencer takes progressive digital photographs of the chip containing the sequencing reagent. These photos have differently coloured pixels which need to be told apart and assigned a specific colour value.

As can be seen, this (strongly magnified; the image is < 100 µm across!) image fragment is fuzzy and many of the dots overlap. This makes it hard to determine which colour to assign to which pixel (though more recent versions of the sequencing machine have improved focussing systems, and the image is consequently crisper).
Base calling
One such image is registered for each step of the sequencing process, yielding one image for each base of the fragments. For a fragment of length 75, that’d be 75 images.
Once you have analysed the images, you get colour spectra for each pixel across the images. The spectra for each pixel correspond to one sequence fragment (often called a “read”) and are considered separately. So for each fragment you get such a spectrum:

(This image is generated by an alternative sequencing process called Sanger sequencing but the principle is the same.)
Now you need to decide which base to assign for each position based on the signal (“base calling”). For most positions this is fairly easy but sometimes the signal overlaps or decays significantly. This has to be considered when deciding the base calling quality (i.e. which confidence you assign to your decision for a given base).
Doing this for each read yields up to billions of reads, each representing a short fragment of the original DNA that you sequenced.
Most bioinformatics analysis starts here; that is, the machines emit files containing the short sequence fragments. Now we need to make a sequence from them.
Read mapping and assembly
The key point that allows retrieving the original sequence from these small fragments is the fact that these fragments are (non-uniformly) randomly distributed over the genome, and they are overlapping.
The next step depends on whether you have a similar, already sequenced genome at hand. Often, this is the case. For instance, there is a high-quality “reference sequence” of the human genome and since all the genomic sequences of all humans are ~99.9% identical (depending on how you count), you can simply look where your reads align to the reference.
Read mapping
This is done to search for single changes between the reference and your currently studied genome, for example to detect mutations that lead to diseases.
So all you have to do is to map the reads back to their original location in the reference genome (in blue) and look for differences (such as base pair differences, insertions, deletions, inversions …).

Two points make this hard:

You have got billions (!) of reads, and the reference genome is often several gigabytes large. Even with the fastest thinkable implementation of string search, this would take prohibitively long.

The strings don’t match precisely. First of all, there are of course differences between the genomes – otherwise, you wouldn’t sequence the data at all, you’d already have it! Most of these differences are single base pair differences – SNPs (= single nucleotide polymorphisms) – but there are also larger variations that are much harder to deal with (and they are often ignored in this step).
Furthermore, the sequencing machines aren’t perfect. A lot of things influence the quality, first and foremost the quality of the sample preparation, and minute differences in the chemistry. All this leads to errors in the reads.

In summary, you need to find the position of billions of small strings in a larger string which is several gigabytes in size. All this data doesn’t even fit into a normal computer’s memory. And you need to account for mismatches between the reads and the genome.
Unfortunately, this still doesn’t yield the complete genome. The main reason is that some regions of the genome are highly repetitive and badly conserved, so that it’s impossible to map reads uniquely to such regions.
As a consequence, you instead end up with distinct, contiguous blocks (“contigs”) of mapped reads. Each contig is a sequence fragment, like reads, but much larger (and hopefully with less errors).
Assembly
Sometimes you want to sequence a new organism so you don’t have a reference sequence to map to. Instead, you need to do a de novo assembly. An assembly can also be used to piece contigs from a mapped reads together (but different algorithms are used).
Again we use the property of the reads that they overlap. If you find two fragments which look like this:
ACGTCGATCGCTAGCCGCATCAGCAAACAACACGCTACAGCCT
ATCCCCAAACAACACGCTACAGCCTGGCGGGGCATAGCACTGG

You can be quite certain that they overlap like this in the genome:
ACGTCGATCGCTAGCCGCATCAGCAAACAACACGCTACAGCCT
                  ATCCCCATTCAACACGCTA-AGCTTGGCGGGGCATACGCACTG

(Notice again that this isn’t a perfect match.)
So now, instead of searching for all the reads in a reference sequencing, you search for head-to-tail correspondences between reads in your collection of billions of reads.
If you compare the mapping of a read to searching a needle in a haystack (an often used analogy), then assembling reads is akin to comparing all the straws in the haystack to each other straw, and putting them in order of similarity.

Think about it like this. Suppose you own a hundred copies of "The Lord of the Rings", a 500000 word novel. Unfortunately, you have those hundred copies in the form of several million tiny scraps of paper, each of which contains about ten sequential words from the novel. Your task is to take those several million scraps of paper and put them in order so that you can read the novel from start to finish. Suppose for example you find the fragment
stab that vile creature, when he had a chance!" "Pity?

You could then search the other several million fragments for a fragment that overlaps this in some way. Perhaps you find
chance!" "Pity? It was Pity that stayed his hand. Pity, and Mercy:

Odds are extremely good that those fragments go together into
stab that vile creature, when he had a chance!"
"Pity? It was Pity that stayed his hand. Pity, and Mercy:

But maybe not! Maybe either (1) there is another fragment of the novel that has chance!" "Pity? that is the correct overlap, or oh, by the way did I mention (2) some of those scraps of paper contain errors, and you have to also detect and eliminate them.
That is an extremely computationally intensive job. DNA assemblers have the same problem: millions upon millions of of tiny scraps of DNA that overlap, that might contain errors, and that need to be sorted into order by analyzing their overlaps and gradually building up short fragments into longer and longer fragments.

Good analogy, but I would say DNA assemblers have the same problem, not sequencers.

@DanielStandage: Thanks -- I am a computer guy, not a biology guy. :-)

@DanielStandage: Would it be correct to change the word "sequencing" with "assembling" in my question and on the charts in the genome.gov page I link to?

@sharptooth I'm not sure you want to replace the word "sequencing" with the word "assembly" in the question. The answer to your question is that sequencing and assembly are closely related, and assembly is the part that requires significant computational power.

In a genome, there are usually billions of base pairs. However, it's impossible to read all of them in one go. The DNA is fragmented, and the sequence of the fragments is determined. Next-generation sequencing techniques are faster and cheaper, but produce only short fragments (say, 100 base pairs, this depends on the technology). It's extremely computationally intensive to put these fragments back together.
More info: Genome sequence assembly primer; intro in Nature Methods

As you mentioned in the question, current sequencing platforms split the genomic DNA into many small pieces which the machine then analyzes. The product of a sequencing experiment is millions or even billions of short "reads"---strings of A, C, G, and T representing the nucleotides of a single fragment of DNA.
The DNA reads in this form aren't particularly useful. The idea in the first place was to determine the sequence of the entire DNA molecule. This is where genome assembly software comes in---to determine the original sequence of the genomic DNA by finding the optimal arrangement of overlapping reads to reconstruct the original DNA sequence.
Computers are crucial at 2 stages of this process---first, in the sequencing experiment itself, the platform must record and interpret fluorescent signals to generate the sequence reads in the first place; and second, very powerful computers are needed to assemble the reads back into a contiguous sequence to recover the original DNA sequence.

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text:
What effects would be caused by exposure to common life forms with opposite enantiomer biology?
Pretend a human had their body "reflected": heart on the wrong side, etc.; but also at the biochemical level: proteins, sugars, cells, DNA, everything.
What would the effects be of that human's exposure to other Earth life?
I assume that quick death from malnutrition would be inevitable, but would there be more immediate life-threatening consequences?
Would anyone who comes into contact with the mirrored person be at risk?
Would reflected bacteria pose an immediate threat to the ecosystem?

The title and the text of the question are pretty different. The answer to the title is that all biological proteins are enantiopure.

I'm going to rephrase it.

dont mix left/right-hand symmetry with anti-matter substances. those would annihilate "normal" matter

I'm not referring to antimatter.  Even if it were a "perfect" reflection it wouldn't be antimatter; only the spins of particles would flip.  (At first I thought the weak force would couple with the wrong-orientation elements, but since it would be an emanation of the same field, there would be no change there either.)

Can the question be reduced to: Can a hypothetical human life form based on D-amino acids survive in our present world? This would, to my opinion, be a rigorous question. Note that the question now is too broad. Also note that folks with partly mirrored organs exist. Until edit I vote to close as too 'broad'.

@AliceD  My question is more specific than your proposal, though.  I don't only mean a human based on D-aminos, I mean a human whose structure exactly matches the reverse: amino acids and proteins, but also simple chemicals like glucose (they would taste the sweetness, but it would provide no energy because it cannot be metabolized).  This is relevant for the "would the bacteria that came with them destroy everything or die" scenario.  I'll try to rephrase the question later today, though.

Please rephrase. It sounds interesting, but as of now it is unclear what you are after. Also, if everything is mirrored, how would you know that glucose will not be metabolized? The issue with hypothetical "what if" questions is that they cannot be answered, only speculated upon. Speculative questions are pretty much always closed. Research on D-amino acids exists afaik and that's why I brought it up as suggestion. Good luck!


text:
What happens after the purification step in Hi-C sequencing?
I am a statistician reading an article on Hi-C, and I am trying to better understand one of the steps in the DNA isolation and sequencing process.
Since I'm a statistician, please try to avoid too much jargon in your answers, and if you must use jargon, please define/explain it.

Specifically, I am confused about the isolation and shearing step.  To explain myself, I will make reference to the following images, labeled 1 through 4.
Image 1)

Image 2)

(source: nature.com)

Image 3)

(source: babraham.ac.uk)

Image 4)

(source: nature.com)

I understand that the DNA is cut up with restriction enzymes, producing a cloud of DNA fragments, some crosslinked and other not. When these pieces are attached with biotin and ligated in a dilute mixture, it is highly likely that these pieces will ligate with themselves, resulting in either; 1) a ring from strands that were NOT crosslinked (as in the third drawing in Image 3 marked "self-ligation"), or 2) figure-eights from strands that ARE crosslinked (as in the fourth drawing in Image 1).
Now, the following is what I find confusing:

The pull-down step draws down the rings containing biotin, but Image 1 and Image 4 make it seem as though only the crosslinked bits are pulled down, and not the solo rings. Don't both have biotin?  And thus would both be drawn into the pulled-down sample?  Or does the pull-down process rely on a molecule having 2 biotins?  If not, can one discern whether a pulled-down piece of DNA is the result of a cross-linked or solo piece of DNA?
Why aren't all of the loose ends of the DNA ligated in Image 2?  Incomplete digestion?  Does this affect the biotin linkage?
My next question.  Assuming that the pull-down process only selects the figure-eights that are crosslinked, then purified (i.e. the cross-linking protein or formaldehyde is removed) forming a ring that is composed of two strands of ligated DNA (as in the upper-right corner of Image 4).  The process then records a genome-wide contact matrix $m_{ij}$, the number of "ligation products" between locus $i$ and locus $j$.  What is a "ligation product"?

Sorry, I know the above is a lot, but I want to understand the nature of the data before I analyze it.  Thank you for your help.

So, after the restriction digestion, the sample consists of DNA fragments with 5' overhangs. These are filled in by Klenow DNA polymerase, adding biotin-dCTP to both ends of each fragment. Then the fragments are ligated under conditions (ie low DNA concentration) such that ligation between non-crosslinked DNA is less likely. This reduced ligation efficiency (which is already lessened by the use of blunt end fragments) means that all crosslinked fragments may not be ligated, which I imagine is what they are showing in Image 2. Any two molecules which become joined by this reaction are called ligation products (ie they are the products of the ligation reaction).
After this step, the sample is treated with T4 DNA polymerase which, as well as having polymerase activity (obviously), also has 3'->5' exonuclease activity. Thus, any free DNA ends which have not been ligated will be trimmed, removing the biotin. After this step, biotin will mostly be present at ligation junctions, and these molecules can be selectively pulled down by the beads. I say mostly because some of these so-called dangling ends will still retain biotin, but these can be distinguished during mapping (see image below).
As for self circles: because they have been ligated, they will contain biotin and be pulled down with the crosslinked DNA. However, since this DNA is from the same genomic locus, they can also be distinguished from valid interacting pairs during mapping:

[source]
Finally, a quick note on the pull-down mechanism. The beads are coated with a protein called streptavidin (or avidin), which just binds to biotin very tightly. Only one biotin need be attached to any molecule (such as DNA) for it to be captured.

Does that answer your questions? Let me know if I can explain something better.

Very helpful, but can tell me what a "ligation product" is?

@Lewkrr, just dissect the word: a ligation product is the product you get after the ligation step in the hic protocol. So basically ligated (glued) pieces of DNA.

@Lewkrr It's in the answer.

@canadianer  I don't understand how glued molecules translates to something informative in the genome-wide contact matrix, with $m_{ij}$ being the number of "ligation products" between locus i and locus j.  If I have two loci, how does the number of glued-DNA-molecules provide me with any information?  I mean, why bother counting them??

@Lewkrr The point of this experiment is to find distant genetic loci that are interacting and have come in close contact. Loci that are close are cross linked and then ligated together. Loci that are distant and are less likely to be ligated (and any ligation will be random). Thus the extent of interaction between two loci is directly proportional to the amount of ligation products formed between them. Does that make sense?

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"Higher plants" or "vascular plants"?
What is the difference — if any — between "higher plants" and "vascular plants"?
On Wikipedia, "higher plants" redirects to "vascular plants", which seems like an indication that both terms are essentially the same.
Is one of the terms preferred over the other?

The difference is specificity.
Higher plant is kind of a general term which refers to their complexity, but is not a very precise term.  As per Oxford Dictionary:

Plants of relatively complex or advanced characteristics, especially vascular plants (including flowering plants).

Vascular plants, on the other hand, are defined a bit more rigorously:

Any of various plants that have the vascular tissues xylem and phloem. The vascular plants include all seed-bearing plants (the gymnosperms and angiosperms) and the pteridophytes (including the ferns, lycophytes, and horsetails). Also called tracheophyte.

(From The American Heritage® Science Dictionary)

I consider them as synonymous, like Wikipedia:

Vascular plants (from Latin vasculum: duct), also known as Tracheophytes (from the equivalent Greek term trachea) and also higher plants, form a large group of plants

In general I would prefer Tracheophytes or vascular plants, because it make clear and implicit in the name what is the exact classification.
But I would prefer land plants instead of Embryophytes.
In any case I could use higher plants when speaking of phylogeny (or fossil plants), where I don't want to associate the character vascular to a  change, i.e. there is a "before" and a "after" but it is better to leave a blurred zone between the two.


text:
Biofilm microtiter experimental design
We sequenced RNA using RNA-seq from biofilm (attached to bottom) and planktonic growing bacteria (supernatant) in 3 mL microtiter plates.
I'm not sure how is possible to obtain planktonic cells since they may have precipitated. For bacteria growing on the bottom I'm convinced these were derived from initial attached cells and then formed biofilms growing anaerobically (this is a facultative bacteria). However, since this bacteria is not motile, how is possible I obtained planktonic bacterial cells RNA? Maybe was a suttle layer on the surface growing aerobically. Wouldn't them have precipitated after the 36h of the assay?
Specie: Haemophilus parasuis. A facultative anaerobe.


text:
What's the feedback regulation of Thyroid diseases and body temperature?
While going through the feedback regulation, the control of blood glucose level by insulin and glucagon is quite discussed in multiple materials including the textbook. I also understand the basic fever and cold feedback regulation with regards to hypothalumus given in the below picture.

But I am not able to find either a digram or a material that explains the relationship between hypothalamus, pituitary gland and thyroid gland in controling temperature1, specially for those who are suffering from thyroid diseases. e.g. some people with thyroid issues feel too cold or too hot (fingers, hands, legs, skin is quickly cold or hot depending on the external change). Their body seem to fail in maintaining the set point of temperature.
What's the endocrine and feedback regulation behind this1 scenario? What's the hormone pathway? Also in terms of energy, heat and metabolism.


text:
why do chaperones bring protein into mitochondria? why would mitochondria need protein?
Sounds trivial? Please help to sort out. I saw this picture while looking at dehydration reactions and cell revision.

And proceeded to the reference web page.

Protein Folding and Processing in the ER: Various changes occur to proteins in the ER.
  Chaperones and Folding: Polypeptides must assume the correct folding pattern in order to function properly. The correct folding of a protein is mediated by chaperones (they also are proteins--chaperones are abundant in the ER lumen). A completed polypeptide will assume the correct folding pattern spontaneously, however before translation is complete, it could assume an incorrect formation or it could aggregate with other partially made polypeptides. To prevent this, chaperones in the ER (and cytosol) bind to the nascent polypeptide and keep it from interacting with anything until the polypeptide is completely synthesized. (Chaperones bind to polypeptides destined for mitochondria then release them as they pass through the mitochondrial membranes. Chaperones on the inside of mitochondria bind until these polypeptides have completely entered.)

With regards to protein synthesis and folding, this kind of info is not in my textbook. Not in couple of the online open textbooks either. I even search for where do protein fold? to find out if there was a relationship. It doesn't say protein fold in mitochondria and turns out it's a question most scientist are looking for answers.
For whatever protein synthesis I have learnt so far, there was no mentioning of protein strands going into mitochondria but mitochondria as a power houses. What's going on here?
Why would a protein go into a mitochondria? How does it exit and where to?

Almost everything the cell does and every organelle involved proteins, often in the form of enzymes. Mitochondria are no exception.The list of everything proteins do in mitochondria would be absurdly long or incredibly vague.


text:
Rabies in spiders - is it possible?
What if we inject rabies virus into an insect body, like spider or a bee, will it contract rabies as humans, cats, dogs, racoons or bats do?

Rabies is a viral disease that causes inflammation of the brain. It is adapted to the nervous systems of vertebrates particularly mammals. Arthropods are too different for lyssaviruses to survive in their body.

I'd like to ask you a question since I have not enough knowledge of biology and also assuming you'd like to speculate a bit. So the virus enters the body of a spider (let's take a spider as a example) and then what? To put it more metaphorically, the lyssvirus looks around, realizes the host is not a mammal and disappears? I'd like to know how it goes with lyssaviruses, but (to anyone) take it as an invitation to a discussion.


text:
meaning of abbreviations h and m in Na channels
In a voltage-gated sodium channel, there are two gates, one for activation and one for inactivation. They are also known as h- and m- gate. What are these letters standing for?

Are the gates referred to h- and m- in a diagram or as text. I looked up online and nothing seems to mention the gates as those two letters. A citation or picture reference would be great.


text:
Math behind the Genetic Relationship Matrix

The genetic relationship matrix (GRM) can estimate the genetic relationship between two individuals ($j$ and $k$) over $m$ SNPs and $i$ representing a specific SNP. What I don't understand from their equation is why we divide our summation by $m$ (the number of SNPs). $x_{ij}$ is the number of copies of the minor allele for the $j$-th individual in SNP $i$. $p_i$ is the frequency of the minor allele for SNP $i$.

I do not know anything about this GRM but it seems to me it is divided by m to represent the average relationship over all (m) SNPs.

I am not familiar with it either, sources would be very useful. But, this may answer your question, if it does let me know and I will post it as an answer... Summing over $m$ SNPs would give a result for a pair of individuals. But if you want a result which is comparable to other GRM's then you need an average which accounts for the number of SNPs measured, hence $1/m$ times the summation. Otherwise you two GRMs could have different scores purely because of the number of SNPs included.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014363/   This is a link to the paper I am studying hope it helps fill in any missing information you need.

did my comment answer your question (does it make sense?) 2) could you also edit the question to include the article link and explain what each term is e.g. $m$ denotes the number of loci, $i$ is ... $j$ is ... $k$ is.. $x$ is... and $p$ is... The question would be most valuable if it is able to stand alone (not require going to external sites/papers for definitions)

This expression is a mean
$$\frac{1}{m}\sum_{i=1}^m ...$$
($m$ is the number of SNPs) of the ratio
$$\frac{numerator}{denominator}$$
where the numerator is a covariance
$$(a_i-C)(b_i-C)$$
and the denominator is the expected heterozygosity (it is also the variance of binomial distribution with n=2)
$$2p_i(1-p_i)$$
Therefore, it represents how much do two individuals covary $(x_{ij})(x_{ik}-p_i)$ respectively to what is expected on average $2p_i(1-p_i)$ averaged over all SNPs $\frac{1}{m}\sum_{i=1}^m...$, where $m$ is the number of SNPs.
It is a relative measure (relative to the expected heterozygosity) of covariance between each individual (averaged over all SNPs).
Does it help?

Evaluating it this way gives us the average correlation per SNP between two individuals which considering the amount of SNPs it will be a very small value and considering the the correlation will be between -1 and 1 i dont see the intuition behind dividing by the number of SNPs (m). Also does anyone know other methods of estimating the genetic relationship between two individuals?

I am not sure what confuses you with this division by $m$. When you calculated your average grade at school, you added all the grades and divide the whole think by the number of grades. This is what this division is. You add all the relative covariances for each SNPs (relative the expected heterozygosity) and you divide the whole thing by the number of SNPs.

Does it make sense? You might just want to read about the arithmetic mean.

$2p_i$ is the expectation of $SNP_i$:
$$E(SNP_i) = 0 \times (1-p_i)^2 + 1 \times 2p_i(1 - p_i) + 2 \times p_i^2 = 2 p_i$$
$(x_{ij} - 2p_i)(x_{ik} - 2p_i)$ measures how the two SNPs covary. I have no idea why they divide it by $2p_i(1 - p_i)$, but if you leave that one out, you have the plain definition of covariance.
Further readings:
https://en.wikipedia.org/wiki/Expected_value
https://en.wikipedia.org/wiki/Covariance

interesting... can you add a reference to link to your answer that others can read further for more information?

Though the question was posted a long ago, I feel that a clarification could be beneficial.
Heterozygosity / gene diversity
The factor in the denominator is twice the variance of the number of major alleles at site $i$, also known as heterozygosity or gene diversity:
$$
H_i = 2p_i(1-p_i).
$$
What might appear confusing here is the factor $1-p_i$ that is explicitly written instead of traditional $q_i=1-p_i$: $$H=2pq$$ (since the probabilities of minor and major alleles sum to $1$).
Covariance
In statistical terms this factor can be interpreted as twice the variance of the average number of major/minor alleles on the site, which is the motivation for including it in the denominator of the covariance function, as, e.g., when converting covariance to a correlation coefficient. It is necessary however to note that this inclusion can be done in different ways, depending on what statistical aspects one wants to emphasize.

In particular, the equation given in the question, taken from this paper (referenced in the comments), is the covariance of a standartized genotype matrix, defined as
$$
W_{ij} = \frac{x_{ij}-2p_i}{\sqrt{2p_i(1-p_i)}}.
$$
In other words the standartization is done prior to calculating the covariance
$$
cov_{jk} = \frac{1}{m}\sum_i W_{ij}W_{ik}.
$$

This is however different from a more traditional definition of Genetic relationship matrix, as in VanRaden's work, where such division is done after calculating the covariance:
$$
G_{jk} = \frac{\frac{1}{m}\sum_i(x_{ij}-2p_i)(x_{ik}-2p_i)}{\frac{1}{m}\sum_i 2p_i(1-p_i)}
=
\frac{\sum_i(x_{ij}-2p_i)(x_{ik}-2p_i)}{\sum_i 2p_i(1-p_i)}.
$$
Finally, as a word of caution, let me mention that $x_{ij}, x_{ik}$ in these expressions can take only values $0$ or $1$.

The matrix gives you an estimate of the average linear relationship between any two individuals genomes, it's essentially taking the average of the betas (like linear regression betas) across each locus. One of the formulas for 'beta' is covariance divided by the sample variance, which is exactly what is happening. Each locus beta predicts the state of a person's genome at that locus from another person's genome at the same locus. Taking the average of these betas across the entire genome gives you a coefficient that can be thought of as a measure of how well we can predict one person's genome from another.


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What causes skewed lanes in a DNA gel electrophoresis experiment?
In gel electrophoresis, what causes effects like these (see column 11 in the first one, and column 6 in the second)?

(These images were samples that I took from an online activity we did for class)

These are sequencing gels (in the cases here even radioactive ones) - they are run a lot longer than ordinary agarose gels and they are made from polyacrylamide. Im my experience, the most likely cause for skewing of lanes (not only bands) are samples, which still contain too much salts from the PCR reactions. This can also happen to only a few bands as seen here. Overall these are still pretty nice and clean gels, I have seen much worse.
Other reasons which cause uneven gels are cases where the acrylamide is not mixed good enough (causing the gel to have different concentration and thus causing different running conditions), uneven warming (although this was usually preventing by the gel heating system) and leaky gels where the electric field was not completely even.

Could please elaborate on why too much salt from PCR can lead to this ? Thanks!

The different salt concentrations cause different conditions for the currents which flow through the gel. When the conditions are not uniform, then the field is not uniform, which causes the transported nucleic acids to not move uniform. We could call it a "disturbance in the force" :-)

@Chris's suggestion is very possible, high salt characteristically shows this towards the end of the gel. But there are additional suspects. This looks like it's just an agarose gel, Correct? I've seen a few things cause this including the gel not being level, the gel shifting during during it's run the percentage of agarose not being uniform and the temperature jacking up towards the end. If you do it again keep an eye on your amperage during the second hour or so.
But first, try doing a quick PCR cleanup on a column (be sure to get one that will not loose your smaller fragments) or alternatively a phenol extraction followed by an ETOH precipitation will purify your reaction; and the force will be with you.


text:
Family tree for edible plants?
I am looking for a family tree for plants, particularly veg / herbs / fruit.
Something similar to:
If it could be slightly less technical than all the Latin names too :). The aim is to easily find out what plants are related such as Cabbage, Broccoli, sprouts, Cauliflower (all Brassicaceae). Currently having to look them all up on Wikipedia which is quite painful.
Another example, but a plant version.

By a Family tree, do you mean a phylogenetic tree (http://en.wikipedia.org/wiki/Phylogenetic_tree) or a "classification" tree?

I very much doubt that you will find any sort of family tree for edible plants, because a plant that is edible may be closely related to another that is poisonous.  The common example is the Solonaceae family https://en.wikipedia.org/wiki/Solanaceae which contains familiar foods such as potatos, tomatos, and peppers,  along with datura, mandrake, and deadly nightshade.  Indeed, some parts of plants may be edible while others are poisonous, as with potatos and the aboveground parts of the plant.

A tree of edible and non-edible would be the complete tree. Remove some elements and you have a partial tree, of edible plants. The accepted answer has already covered this, but thanks all the same!

Basically just search "thing you want" and "phylogeny" and you'll find a million results on Google.  For you, I might recommend the Botanist in the Kitchen blog, which has a whole page on the subject and has assembled this phylogeny, including many, many others.  It's pretty impressive!

Yes, it is definetely the best solution. For detailed information concerning the phylogeny of any taxon, the best website to my knowledge is http://tolweb.org/tree/ but it will not be good looking like any picture you wil find with a simple google search. There is also this site http://www.onezoom.org/ but I am not sure it is complete.

I hadn't heard of http://onezoom.org before, that's pretty neat.

+1 for bringing my attention to Botanist in the Kitchen – what a great site!

Thanks, I think this is pretty much what I am looking for. Botanist in the kitchen is great too. onezoom.org seems good but have not found a plant section as yet.

Beware that tolweb is not very accurate: from what I understood, they lack the funding necessary to update it. So it doesn't reflect the state of the art in terms of phylogeny.


text:
Which grows slower: the thumb nail or the pinky nail?
I notice that thumb nails and pinky nails grow slower than the 3 other fingers in the middle. But which one is slower, the thumb nail or the pinky nail?

This isn't at all my experience - it seems this may vary. I'm sure apparent growth also depends on "usage".

Biology Stack Exchange is a question and answer site for biology researchers, academics, and students. This question is of freak-show triviality. I have voted to close it.

@David There are many papers on the subject. Bean wrote 4 papers on fingernail growth that are considered important medical findings.

Which one is slower? "Important medical findings"? Mr Bean?

Your question would be better if you summarized relevant said research. It's also probably prudent not to call other people sssholes.

I did some research and found out that it is actually the pinky that grows the slowest. See the decadal reports of Bean on fingernail growth or:
Gupta, Gita Rani, et al. "Human nail growth pattern and medicolegal aspect." (2005).

So how are you going to use this information? If you really think it is worthy of anyone's attention, cite references it in a way that someone else can consult them. You need proper journal etc details with a link of some sort.


text:
Weinman AIF and High temporal resolution AIF difference
I'm dealing with MRI DCE maps. Actually I need to find such parameters as the transfer constant $K^{trans}$ and rate constant $k^{ep}$ according to the two-compartment model.
Mathematically, this dynamic system can be described by the following equation:
$$C_t(t)=DK^{trans} \sum\limits_{i=1}^2 a_i [exp(-k^{ep}t) - exp(-m_it)]/[m_i-k_{ep}]$$
Now if I have contrast agent concentration $C_t(t)$ time series from experiment I can extract transfer constant $K^{trans}$, and rate constant $k^{ep}$ by least square fitting. The only problem is that I need to know $a_i$ and $m_i$ constants. For this purpose I use the next formula for blood plasma concentration: $$c_p(t)=D \sum\limits_{i=1}^2 a_i exp(-m_it)$$
However I can't find the relation between the blood plasma concentration: $c_p(t)$ and $c_t(t)$. In the next article:
 Geoff J.M. Parker et. al. "Experimentally-Derived Functional Form for a Population-Averaged High-Temporal-Resolution Arterial Input Function for Dynamic Contrast-Enhanced MRI"
I have found that biexponential function $c_p(t)$ is used to describe AIF (arterial input function). It's presented on plot as Weinmann AIF.

QUESTIONS:
1) How Weinman AIF and High temporal resolution AIF can describe the same processes, actually from mathematical point of view it's two completely different functions?
2) Do I really can find $a_i$ and $m_i$ parameters by fitting AIF curve with $C_p(t)$ function?
P.S. I'm just a programmer so I completely unaware in the field of biology and any help is appreciated.


text:
why did capacity of regeneration decreased in evolution?
When we consider trend of evolution the ability of organisms to regenerate decreased as the complexity of body structure increased.

acetabularia-can regenerate whole body even if 1/100th part of body is left
small pieces of sponges can grow complete individual.
hydra-interstitial cells are totipotent.
planarians-neoblasts are omnipotent
crustaceans-heteromorphosis.
lizard-regenaratetail..........what is the reason you expect for this ?

Evolution made life more and more complex. The more complex the "bodyplan" of an organism became, the more different tissues and cell types it has, the more difficult it is to regrow them.
For example a sponge has about 3 different cell types. If you take a small piece of a sponge, these 3 cell types well still be present and once they grow they can form a whole new sponge.
The more complex you go, the more different cell types you need, and thus the harder it becomes for a few cells to represent a full organism.
It also has to do with symmetry. Sponges have none, cnidarians have radial symmetry, mammals bilateral. To put it simple: if you slice a piece out of a jellyfish like you would out of a pie, it's easy to imagine it can 'close' itself with new tissue coming out of the border tissue, because it's the same tissue as the one you removed. If however you cut of a foot of a human, the cells of the leg won't produce new foot cells, because they are (a lot of different) 'leg cells'.
As for the lizards, they can "regenerate" a tail but it is never the same tail as before, it will always be shorter than the one they lost. Also the tail is still pretty 'simple' (if you can put it that way). If you would cut of a lizards leg it would not regrow...
I hope this has somewhat answered your question!

Well, you right. Still some lizard can grow limbs too if I remember it right

Also, cancer. Larger, more complex animals have to balance regenerative ability with the danger of uncontrolled proliferation. The more stem cells and stem-like cells you have floating around, the more likely one of them is to become immortal in a way you don't want it to. I would elaborate more, but I think the original post is a homework question anyway.

Seriously? I get voted down just for not elaborating more? I thought the answer was useful as it stood...I just figured the OP might not be needing it now that it's been two days. Sigh.


text:
If our skin is burned by the sun what causes the red color of the skin?
Mostly it is not a good sign we are burned because too much UV light has damaged our skin and increases skincancers.
But the red color we get after a few ours, what is the exact cause of it and why is it red? Is that our blood we see or other changes in skincells or our immunesystem reacting?
And why does it takes a while before it gets red?

Sunburn causes damages in the skin which further triggers an immune reaction.
This immune reaction causes a dilation of the capillaries in the skin to allow a higher transport rate towards the inflammation site. This dilation and the higher blood flow in the area causes the "redness" of inflamed tissues. See the image for illustration (from here):

The delay in the appearance (for more severe cases also the swelling) is caused by the time it takes for the body to react and to develop an immune response. For further information you can look here and here.


text:
Tiny huntsman found in Czechia?

I found this spider today and I'm curious to know what it was.
Discovered today at 14:27 CEST, about 200 meters from Hamr pond, Prague, Czech Republic. It may have been carried all the way from the pond.
Body length cca 5mm (cf. photo #2 - reference object is a 5 CZK coin, 23mm in diameter according to Wikipedia)
Initially swept off my shoulder (white T-shirt), to which it succeeded in attaching, and began descending on a thread. After it was placed on a small ledge, it began climbing the adjoining wall. After some convincing to stay in place (using my finger as an obstacle and a little physical contact with its legs), it lifted its four front legs, so I left it alone.
I didn't manage to get good sight of its eye pattern, but it did appear to be consistent with the eye pattern of huntsman spiders - the sides of its face were black with a brighter region between. I was unable to determine the quality of its eyesight either, though it did not object to placing the coin nearby to it.
Wikipedia page on huntsmen living in Europe lists nine species, but none of those that had photos included matched in appearance.

The spider in question is actually Xysticus Cristatus, also known as the common crab spider.
From Wikipedia:

The adult female reaches a length of 8mm and the smaller male 5mm. The colour varies from light cream, dark brown to greyish with a dark triangle on the carapace which ends in sharp, defined point. The abdomen has a broad middle band fringed by two darker rows of triangular marks which extends back to about two thirds of the carapace length from the posterior row of eyes. ... Active adults have been recorded from February to December, with a peak of male activity in May and June. ... The species has a Palearctic distribution, being found throughout Europe, including Iceland.

Arachnology.cz provides several photos of the species. The image below is a perfect match.
Copyright by Rudolf Macek; published at https://www.arachnology.cz/druh/xysticus-cristatus-800.html; page viewed in May 2019


text:
How to get conservation of vaccine candidates between hundreds of bacterial strains?
We have identified vaccine candidates via an ex-vivo RNA-seq approach. Next step would be to perform conservation of these candidates (about 20) between multiple bacterial strains (about 200). I would like to know your suggestions to perform this step.
From my bioinformatic experience, it could be possible to start with a first rough screening using blastp or blastx against a multiple strain database, and more accurate approaches would be then performed to validate this results.
Please let me know your suggestions and possible references you might consider helpful to answer this question.
Thanks for your help. Best regards, Bernardo

It may help to specify how sophisticated your computational techniques are. The answer will be very different depending on whether you want to use websites and black-box software, or if you are able to do this on your own computer using custom-made scripts/programs.

Hi adam, I prefer command-line tools, I have medium programming experience. I would like to combine already available tools with possible on-the-go scripts if needed.

This website will allow you to blast all sequenced microbial genomes:
http://www.ncbi.nlm.nih.gov/sutils/genom_table.cgi
Recovering the full gene sequence might be a bit of a hassle, since BLAST will only allow you to recover the region that matched your query. You may need to set up your own pipeline for this. You may also be able to identify a "protein family" or "cluster of orthologous genes" using the BLAST results.
Once you have the full sequence, you can perform a multiple sequence alignment with CLUSTAL, Muscle, or another program. This will help you to identify the conserved regions.

Hi adam. The 200 genomes have not been published yet, so I would have to use local BLAST in any case. I will avoid to recover full gene sequence going through BLASTP. Thanks for the CLUSTAL, Muscle tools, they will be useful for a latter step. I think the question now is to think how to get yes/no table, that is, presence/absence of each vaccine candidate in each of the 200 bacterial strains.


text:
New GO terms after TopGO enrichment?
I ran an enrichment analysis with custom annotations using TopGO and surprisingly I obtained new GO terms inside the significant GO terms.
Is that  possible? The only reason I can imagine this is possible is because TopGO adds new GO terms based on the input GO terms, since GO ontology is structured hierarchically.
Please let me know if you need more details about the experiment.
Code:
https://drive.google.com/file/d/0B8-ZAuZe8jldbjNuTHhNeXRVX00/edit?usp=sharing
TopGO output:
https://drive.google.com/file/d/0B8-ZAuZe8jldZnlYUzlxZ3psMWc/edit?usp=sharing
Thanks, Bernardo

These new GO terms seem to be the parents of the already GO terms present in my original dataset.

You seem to have answered your own question with that comment. Is everything clear now? Do you understand why the parents appear?

These new GO terms seem to be the parents of the already GO terms present in my original dataset.

Looking over your code I see that you are using the Fisher exact test (FET). Several assumptions of the FET are not meet when testing gene sets by GO annotation (cite). If this is for a school project FET is good enough. However, if this data set is for publication I would recommend using a different analysis. I don't know what your data looks like and the questions your trying to answer, but I would start by looking at these: Review, Camera (Wu 2012), ROAST (Wu 2010), and the first article I linked to.


text:
Blast a sequence against multiple databases
I would like to BLASTP a list of protein sequences in fasta format against multiple protein databases. Since I'm only interested in the first hit of each database, it is possible that BLAST result would need to be parsed.
Do you know if this strategy is a good one, or you suggest me others?
Thanks for your help. Best regards, Bernardo

"Good" for what? If you're asking whether your approach with parsing is good, I'd say parsing should be performed for most of purposes any-case. If you want just one hit, there is a parameter 'max_target_seqs': see manual

We have identified vaccine candidates via an ex-vivo RNA-seq approach. Next step would be to perform conservation of these candidates (about 20) between multiple bacterial strains (about 200). I would like to know if it's a good idea to start with a BLASTP or go to more advanced strategies.

Are you willing to set up standalone blast on your computer, and run a script on it? It's actually pretty straight-forward once you get to that level (using BioPython or BioPerl)

I do this all the time if you use stand alone blast. It's a simple flag you need to pass.

To scan multiple databases:-

Download all the databases that you intend to match against (all databases will have a download option)
Download standalone BLAST executables from NCBI
Pool all the sequences from the different databases and make a BLAST database using the program makeblastdb (it is better to check for redundant sequences and remove them for improved performance)
Run blastp with desired parameters against this database
The first hit wont mean much. Use cutoffs on E-value or Bit-Scores or precentage identity

Now, if you vaccine targets a protein it is good to study the protein sequence conservation. BLAST isn't a really good option for studying conservation by comparing different orthologs. Use tools for multiple sequence alignment like ClustalW or T-COFFEE. In my opinion it is also better to use BLOSUM for scoring the alignments compared to PAM.
It might also be important to look at the protein structures. You can try VAST if possible.

Hi, maybe I didn't explained myself well. I already know that my vaccine targets a protein. My question is how to get yes/no table for 200 bacterial strains. ClustalW, T-COFFEE and VAST seem to be a latter step, but thanks for this. :) If I merge all the 200 bacterial genomes in the same database, would it be easier to parse later? that is, when checking presence/absence for each bacterial strain?

If it's a species with already sequenced genome and you use a genome mapping approach, you probably get gene id's "automatically" during transcriptome assembly.
If it's a de novo approach, and all you get is a list of sequences, KAAS would help you to identify orthologous genes already known in other bacterial genomes (there you'll find even predefined subsets of genomes to blast against, 'for Prokaryotes' being one of them).

They bacterial genomes have not been published yet. I've been  used a genome-based RNA-seq approach.


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Relationship between mN and mg in vessel contraction studies?
What is the relationship between mN and mg as the units involved to measure the changes in contraction?  A tool most widely used is an instrument called myograph. In these exoeriments, either units of mg or mN is used without any mention of why. Note that the first two examples use an experimental tool called a myograph - used to measure the force produced by a muscle (in this case muscular blood vessels) when under contraction. Both examples mention tension. Wether these myograph experiments are completely different i'm unsure.

This example shows force as 'mN' from this paper Fig 1A a.
Those who are interested the caption is as follows
"Effects of Li+-PSS and NMDG-PSS in PGF2α-preconstricted pulmonary arteries. Intrapulmonary arteries (IPA) were first exposed to 80 mM K+, and endothelial function was assessed by relaxation of PGF2α contractions (10 μM) to acetylcholine (10 μM). Following a period of recovery, IPA were preconstricted to 10–15% 80K using PGF2α, and then physiological saline solution (PSS) was replaced by Li+-PSS in the continuing presence of the same concentration of PGF2α in endothelium-intact IPA"

This example shows units as 'g'. note that some examples from other papers also use units of mg essentiall x1000 similar to other unit conversions from
this paper Fig 1a.
The caption is pretty much the same as 1. Particularly thromboxane A2 (TXA2) is a pharmacological agent affecting a set of ion channels:
"Requirement of thromboxane A2 (TXA2) for hypoxic pulmonary vasoconstriction (HPV) in rat pulmonary arteries (PAs). A: hypoxia alone (3% PO2) did not induce contraction of PA (left). In the presence of 10 nM U46619, hypoxia induced a strong contraction equivalent with the response to high K+ (80K, right)"

For a third example click here. It does interestingly state "The PA rings were stretched to a predetermined optimal passive tension of 750 mg". As such mg (or g) is an alternative representation of a unit of force as presumed. Figure 1 in this link again demonstrates mg as an y-axis unit of measurement under myograph experiment

1kg weight = 9.8 N but I am not sure if that is what they mean here.

Ick, unlabeled axes. Do you have a description of the figure from the text? Something like 'In figure A we describe...' would help a lot. g doesn't seem like a unit of force, but I'm not sure what else they could be measuring. Perhaps it's literally a gram weight?

@woosah: Can you post a little bit bigger (2nd) picture?

@DevashishDas
I added some more examples and made images bigger. Plus links to articles

@JeremyKemball I added some more notes and caption and also links to articles. If you think I should write more on this question let me know I'll change it

@WYSIWYG May be you're onto something. As mentioned above which i noticed in example 3: "The PA rings were stretched to a predetermined optimal passive tension of 750 mg". Clearly its a unit of force. 750mg is approximately 7350 mN? That sounds too big here though if my calculation are correct. As you said don't know what they mean, seems unusual to use that formula anyway

Something looks off. $1^{st}$ is N/m or Tension or unit force exerted on the artery walls. Whereas $2^{nd}$ one is actually change in mass of thromboxane $A_2$ ($TXA_2$) with time. Can you clarify that?

@DevashishDas I wouldn't even know fully but that force represented in the graphs are 'resultant' forces ins response to various stimulus'. The 2nd graph is also change in contraction from a myograph experiment (I included some caption). Thromboxane A2 (TXA2) is just a pharmacological agent used in some experiments to affect certain channels. TXA2 is an example of a stimulus during a typical myograph experiment along with introducing hypoxia or change in ionic concentrations (e.g. 80K+ Fig 2 in question).

@DevashishDas Maybe there are different myograph experiments hence different units due to different types of contractile force. I just found out there  are several set ups of a myograph whose sole aim is to measure contraction in pieces of muscle (in my case pertaining to blood vessels but apply to anything muscular).
Two types of experiments I just come across are wire and pressure myographs

The difference between above two units is a matter of metric systems used:
The newton (symbol: N) is the International System of Units (SI) derived unit of force. One newton is the force needed to accelerate one kilogram of mass at the rate of one metre per second squared.
Another system:  kilogram (gram, milligram)-force
The kilogram-force (kgf or kgF) is a gravitational metric unit of force. It is equal to the magnitude of the force exerted by one kilogram of mass in a 9.80665 m/s2 gravitational field (standard gravity, a conventional value approximating the average magnitude of gravity on Earth). Therefore one kilogram-force is by definition equal to 9.80665 N. Similarly, a gram-force is 9.80665 mN, and a milligram-force is 9.80665 µN.
Kilogram-force is a non-standard unit and does not comply with the SI Metric System.
See:
http://www.sengpielaudio.com/ConvForce.htm


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Are human bodies programmed to die?
Following from this question: What is the evolutionary advantage of death?:
Is there any evidence that human bodies have systemic self-destruction built into their developmental program? I'm not talking about the cell death response, which I know is an important part of growth, development and cancer prevention.
I've read some things about telomere-shortening but don't know if this is a cause or an effect.

From a certain point of view you could argue that our bodies have an inherently limited lifespan;

Telomeres are extensions to the end of chromosomes that prevent damage or loss of genetic information during cell division. Telomeres are not replaced (in normal cells), which gives rise to a replicative lifespan; the number of times a cell can divide before permenantly leaving the cell cycle (senescence).

This is generally viewed as an anti-cancer mechanism to protect against errors creeping in to the genome through many cell divisions. In order to become cancerous, a cell must first overcome its replicative lifespan [ref.]. This is achieved by activating the (normally inactive) telomerase enzyme that extends the telomeres - embryonic stem cells are one of the few cell types that normally express this enzyme.

There are other ways in which you could argue that our lifespans are fundamentally limited, but it is important to note that the objective is not 'to die', but to increase fitness (in a Darwinian sense) earlier in life. This is known as antagonistic pleiotropy; when an advantageous trait early in life is disadvantageous later in life.
Telomere shortening is just one example of antagonistic pleiotropy (protects against cancer when younger, but limits the number of times your cells can divide).
Other traits that inherently limit lifespan include;

Neurons (as a rule) do not replicate, and last for your whole life time. This certainly excludes them from replicate senescence, however it means they are highly susceptible to 'wear and tear'; oxidative stress is a natural by-product of respiration, and the vast majority of damage done by these species (e.g. reactive oxygen species) is repaired by the cell, however some will always remain unchecked, and eventually this leads to neurological dysfunction and cognitive decline. Without intervention this is inevitable in each individual (the rate of aging differs between people, but aging and age-dependent diseases are disorders are a natural part of living).
The same is true for cardiac and smooth muscle - whilst much repair can be carried out, it is inevitable that damage will creep in over a life time, and thus the vast majority of human age-related deaths are due to cardiac problems in one way or another.

So there is no 'programmed' limit to life span, in that we have not evolved to die, but our bodies are inherently limited by the systems that have evolved. Life expectancy a few thousand years ago was ~20 years (if you lived beyond infancy!), whereas now in the developed world this is ~80 years, so our bodies can already survive way beyond our 'natural' life span, and thus we now succumb to age-related disease. Evolution has spent millions of years giving us every possible advantage that leads to reproductive success. Natural selection of traits beyond reproduction are secondary to those beforehand, and thus we have fundamentally limited life spans.

There is an argument for an evolutionary advantage of limited lifespan. This seems at first counter-intuitive, until you consider that natural selection does not act on individuals, but genes. It is proposed to be advantageous (in some circumstances) for an organism to have a shorter life span, as this increases the turn-over of individuals in that population. This in turn increases their evolvability - clearly advantageous for the gene(s) influencing this trait if it increases the likelihood of successful reproduction and thus passing on that gene/allele/trait...
I like this hypothesis, and can see that natural selection may favour it. I think mice are great examples here; they have much shorter lifespans than us, yet they 'age' (biologically) the same (cardiac problems, diabetes, cancer) but at a faster rate to give a higher population turnover). In a high mortality environment, the most adaptable animals will be more successful.
However, I think this is likely to be secondary to the pressures on other survival traits that  more directly increase the chances of successful reproduction.

Great answer, this makes several things clear which I didn't fully grasp before. However, what evidence is there for 'population-level' natural selection (the last section of your answer)? It sounds like the sort of 'group selection' explanation which has been disproved by the modern gene-centred understanding of evolution.

@RichardSmith I'll try to find where I read that last section, but I don't see how group selection can been disproven in that manner. Evolution acts on the genes, not on individuals. If it is advantageous for a group of genes (e.g. the genome of a species) to have a high turnover, giving greater evolvability (i.e. more chance of survival in changing conditions) then it will come at the 'cost' (shorter life) to the individual.

That's exactly what I meant, natural selection is acting on the gene, not the population. It only matters whether any given mutation affecting evolvability leads to more copies of the gene containing it. There is no such thing as group selection, it's a relic of the Modern Synthesis of the 50s and 60s. I think perhaps you were unintentionally using group selection language but you were describing an effect of gene selection. The population doesn't care about the turn-over of its members - it's just an arbitrary group.

So, if something is only advantageous at the population level, it won't be selected for, which is why this sentence doesn't make sense: "This in turns increases their evolvability - clearly advantageous at the population, not individual, level."

@RichardSmith right OK, I see where you're coming from, but not how to re-word what I am saying; that a trait that increases the relative (strictly Darwinian) 'fitness' of a species can be selected for at the 'cost' of a shorter life to an individual organism. This may be disadvantageous in some terms, because they will have less time to reproduce, however higher population turnover will be advantageous in fast-changing conditions.

All you need to do is remove the idea of fitness of a species (or population, or any group) - there is no such thing. Short lifespan would evolve where the risk of death is so high that the best reproductive strategy for the genes of an organism is to make their host reproduce furiously as early as possible in its life. For any given risk of death by accident, disease, etc., there is a corresponding best strategy for investing resources in reproduction, even if doing so puts an upper limit on the lifespan of the organism.

That's because the upper limit is not under selection, because the organism will most likely die anyway before it is reached. So the selection pressure is to ensure the available resources are most efficiently invested in reproduction before the average age of death by accident.

Also I realised I didn't help reword your last idea... In a highly fluctuating environment, there is a high risk that the offspring of any individual will be poorly suited to the environment. Therefore a selection pressure would develop for either individuals with a short generation time (who invested heavily in early reproduction), or individuals with higher rates of mutation. Which one prevailed would depend on the constraints of the organism. In general, K-selected spp. would experience the first pressure, and r-selected spp. the second.

@RichardSmith thanks, that definitely helped. I've changed my answer accordingly - if you feel it is still mis-leading/wrong, don't hesitate to tell me (goes with any of my answers naturally).

I'm not very knowledgeable about evolutionary biology, so I'm going to answer this from the perspective of molecular/cell biology.
The short answer is: No, there isn't any evidence that I know of for a built-in "death".
The long answer:

Telomeres are shortened over repeated copying of chromosomes (so as cells divide repeatedly, the telomeres shorten). Shorter telomeres make chromosomes more unstable, and can lead to some of the symptoms of aging (like limiting organ regeneration). But this is more a side effect of humans not having the most efficient telomerase ever.

In the end, while shortened telomeres contribute to limiting the number of times a cell can replicate (Hayflick limit), we also have stem cells that can then replace those senescent cells. (This is also true in the brain and heart: stem cells are present in the heart and even in highly restricted areas of the brain, and produce neurons throughout life: https://pubmed.ncbi.nlm.nih.gov/10834848/.) So the telomere problem alone doesn't predetermine death.

Other factors that contribute to aging can be divided into two categories, genetic factors and accumulation of damage.

But in both of those groups, the cause isn't that genetically we're programmed to die at a certain time. It is always a case of the systems used to repair or regenerate failing after a time: DNA mutation repair becoming incapable of keeping up with the rate of mutations; failure of clonal deletion leading to autoimmunity; inhibition of autophagy by mTOR leading to accumlation of old and damaged cell parts; failure of senescent cells to undergo apoptosis.
There are many other hallmarks of aging, but the common thread we see is not that there is a set program leading to an organism's death, but rather a failure (through accumulation of damage) of the systems that keep us alive.

In Richard Dawkin's book "The Selfish Gene" it seems that there is a programmed purposeful death of the body. The genes are in control.  They seek out a mate to produce offspring that will be even better suited to pass it's genes on again.  The meaning of life as we have seen and observed it is really for the genes to perpetuate their existance into the future.  Once passed on the body that did the passing is of little use.  The more complex the organism like a human mates may be selected by life span of parents and relatives.  There doesn't seen to be any Newtonian law that states a cell cannot just keep replicating and essentially avoid aging.  Our bodies are doing that for a purpose.  One we just don't understand.

Welcome to Biology.SE! I hope the response to this post site won't prevent you from coming back. ——— You are more likely to get a positive response to your answers if you avoid speculation and provide supporting references (preferably from primary literature) for your assertions. Without this your answer is indistinguishable from opinion, which is likely why this answer is getting downvotes. ——— You may also wish to take the [tour] and then consult the [help] pages starting with [Answer] effectively on this site. Thank you!


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Which brain regions are D1 dopamine receptors expressed, and which brain regions are D2 dopamine receptors expressed?
This is a follow-up question to If D1 receptors stimulate adenylate cyclase (through GPCRs) and D2 receptors inhibit it, then why do mutations in both have similar effects?.
As a further question - I'd like to ask: do D1 dopamine receptors have the same (excitatory) effect everywhere in the brain? And do D2 dopamine receptors have the same (inhibitory) effect everywhere in the brain?

As to your main question, I imagine the Paul Allan Brain Atlas has what you're looking for. In 3D even.

Human Brain Map
D1 Receptor
D2 Receptor
Also comes in mouse flavored.

Offhand, I cannot be of assistance for the second question.

To your second question, from what I remember the activity of each receptor is the same, it is the balance of the frequency of activating and inhibitory receptors that summate temporally and spatially that give the overall effect.


text:
Is forced cell growth related to apoptosis?
Could an instance of forced cellular growth cause some cells to have their self-destruct mechanisms to malfunction or 'turn off'thus preventing apoptosis?

What do you mean by "forced cell growth"? Cancer? Or something different?

It is trivial to assume that forced growth will overcome apoptosis which is what you see in the immortalized cell lines and in cancer.
But the interesting thing is that there are instances in which forced growth causes apoptosis. The classic example is of the brain in which expression of SV40 t-antigen to make cells proliferative actually caused loss of tissue [1,2].
Knockdown of Aif (apoptosis inducing factor) also causes neurodegeneration and thymic hypoplasia by causing increase in oxidative damage.


text:
Virus titre and copy number of integration
If I need to preform a genome-wide high throughput screen using pooled shRNA library infection followed by deconvolution by deep sequencing, how would I determine the optimal infection rate at which the majority of cells only be infected with single copy of shRNA? Thanks!

Do you mean infection, transduction, or transfection?  If you mean infection, what virus are you using?  Several viruses you can use an MOI of 1 to get that result, but others wouldn't produce it with an MOI of 10 (good example of the later is RSV).  Presumably you are using a lenti system?

I also don't know what cells you're using.  If your lab has never done lenti infections before, I recommend pLKO.1-puro optimization (or an analog) before trying shRNA library.  That should give you a ball park MOI, but a good guess is between .5-3

Yes I am using Lentivirus in human melanocytes. I need to get MOI of 1(one copy per cells) for majority cells for the purpose of later sequencing. I just need to determine the infection rate to achieve MOI of 1(is there asomewhat methematical model for this?)

Oh, are you wanting to know how to titer lentivirus to get the molecules or TU per mL? I didn't get that from your question.  Calculating MOI is easy with the titer information.  You have the number of particles/PFU/FFU/ELISA/RT-PCR readout per mL of virus stock.  You then take the amount of virus you added to your cells (in what ever units) and divide by the total cell count.  Perhaps I'm still not clear on what you want, but transduction optimization and titering are different things.

I did lentivirus infection before using pLKO system and I know the  infection rate judging by GFP or puroR. However, we don't know the copy numbers of virus in each cell. The copy number of virus per cell is important when we are doing a pooled library screen-I don't want  cells infected with  copies of virus carry different genes. My questions is, by either mathematical  or experimental approach, how we determine the optimal amount of virus added to cells so that the majority of cells will only carry one copy of virus while we still have a  reasonable infection rate. Thanks!


text:
How to reduce/eliminate cell clumping in suspension CHO cells, without using an anti clumping agent?
what is the best way to eliminate clumping suspension cells used for transfection. Anti clumping agents interfere transfection and hence can't be used. Though maintaining the cell density low helps, but doesn't help in the long run.

changing the media can help - for example, chemically-defined media designed specifically for protein expression might work better than media containing serum, which can promote the expression of cell adhesion proteins

Thanks for the reply. I am currently using invitrogen's CHO S SFM which, though not completely defined chemically, is serum free.

I had clumping issues with HepG2 cells for a long time, I had to split them every 2 days and trypsanize them for 30-45 minutes at 37C to really break them up. Then I would split them at 2 or 3 to 1 ratios so that they formed a more uniform distribution. Less density seemed to cause more clumping, probably because each cell grew into a clump. These aren't CHO cells, but might be helpful.

After looking around for a bit, I came across a few potential solutions however they are not a definitive answer to your question and requires testing under your experimental conditions to see which works best.
This page contained one excellent suggestion and that is to select for cells that do not clump during the passage, by pooling the cells in 15ml of media in a tube and letting the clumped cells to settle down by gravity for 5 minutes and afterwards taking the top 1ml and passaging it and after 20 passages using the exact same technique, you should be able to get single cell suspension. The main caveat is the heavy selection for non-clumped cells, which might harbour certain properties that might be undesirable for the procedure you are interested in so it might be nice to do some RNA expression profiling to observe the differences and perhaps even publish it! Amongst the suggestions on this page included using different concentrations of Mn2+, Mg2+, and Ca2+ in your (custom?) media since cell adhesion is regulated by integrins, cadherins, and selections and in general, Mn2+ increases ligand affinity, Mg2+ promotes adhesion to cells, and Ca2+ decreases cell adhesion as referened here. You can also try heparin, as tested by this paper on CHO cells and it impotent to note that in the study, heparin did not reducing cell growth, antibody secretion, and antigen-binding activity although I'm unsure as to whether heparin would be classified as an anti-clumping agent and whether it interferes with the ability of the cells to get transfected but that can be simple tested by a GFP transfection and looking at the transfection efficiency of the heparin treated and control cells treated with whatever heparin is dissolved in.


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Can nosebleeds cause death?
Can epistaxis or nosebleeds be a cause of death?

if u have a bleeding disorder, then yes..https://www.hemophilia.org/Bleeding-Disorders/Types-of-Bleeding-Disorders

Can you explain a little bit?

Great image choice.

I have heard of it happening:  A small kid is hit in a pretty minor traffic accident.  The impact caused a nosebleed and knocked him on his back.  The impact with the ground knocked him out.  Head injury--they waited for the paramedics.  The kid drowned.

Well if you are driving at the time and have a car crush due to thinking about the nosebleed...

Yes, it can, but it is extremely rare.

... nosebleeds are rarely fatal, accounting for only 4 of the 2.4 million deaths in the U.S. in 1999 [1].

The main issue is that epistaxis can be a sign of potentially fatal diseases:

The instances in which nosebleed is potentially fatal are those in which there is a history of recent head injury, severe arteriosclerotic cardiovascular disease or an underlying vascular tumor in the nasal chambers. Fatal nasal bleeding has not been reported in children [2].

Here are some causes which can lead to fatal complications if left untreated [3]:

Leukaemia
Hypertension
Coagulation disorders
Hepatitis
Head injury
Myelodysplastic syndromes - Epistaxis

Death by massive hemorrhage can occur when epistaxis is the result of a ruptured aneurysm [4, 5, 6]:

An instance of fatal epistaxis is reported in a patient with an unsuspected aneurysm of the infraclinoid portion of the internal carotid artery. There was no known history of trauma. The aneurysm was subsequently detected on an old X-ray film. Epistaxis from an aneurysm at this site is nearly always preceded by significant head trauma and is associated with cranial nerve palsies, a syndrome with a high mortality. Epistaxis from rupture of a non-traumatic aneurysm is very rare [4].

References:

Work Table I. Deaths from each cause by 5-year age groups, race and sex: US, 1999 Page 1922. U.S. Centers for Disease Control Published 2001-05-11. Via Wikipedia contributors, "Nosebleed," Wikipedia, The Free Encyclopedia, http://en.wikipedia.org/w/index.php?title=Nosebleed&oldid=617613780 (accessed July 21, 2014).
QUINN FB. Fatal epistaxis. Calif Med. 1961 Feb;94:88-92. PubMed PMID: 13738727.
Right Diagnosis (tm). Epistaxis Deaths. Available from http://www.rightdiagnosis.com/symptoms/epistaxis/deaths.htm (accessed 21.07.2014)
HORNIBROOK, J. and RHODE, J. C. (1981), FATAL EPISTAXIS FROM AN ANEURYSM OF THE INTRACRANIAL INTERNAL CAROTID ARTERY. Aust. N.Z. J. Surg., 51: 206–208. doi: 10.1111/j.1445-2197.1981.tb05942.x
P. N. Pathak (1972). Epistaxis - due to ruptured aneurysm of the internal carotid artery. The Journal of Laryngology & Otology, 86, pp 395-397. doi:10.1017/S0022215100075423.
Urso-Baiarda F, Saravanappa N, Courteney-Harris R. An unusual cause of massive fatal epistaxis. Emerg Med J. 2004 Mar;21(2):266. PubMed PMID: 14988378.

Any injury, that results in external bleeding can lead to death, since it is a breach in the body's defenses and an entry point for pathogens.
Explanation:
When you have nose bleeding the blood must be coming from somewhere. Usually from inside your body.
That means there is a hole in your body which is big enough for blood to stream out.
That in return also means that through the same hole things can get back inside your body.
For example any kind of virus or bacteria.
That does not mean every bleeding is potentially lethal. But depending on the environment the body is in the chance of infection is certainly greater with a lesion (hole in the circulatory system) than without it.

Good Answer, Can you explain a little bit?

Thanks. Was that sufficiently explained?


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Is there some genetic variance underlying music appreciation?
Is there any research done on the genetic variance for Music appreciation?
If not, why is there no genetic variance for this trait?

It is important to understand what genetic reason behind .. means. It usually makes much more sense to ask Is there some genetic variance underlying music appreciation?. You may want to have a look to this and/or this post. You may, then eventually want to edit your question.

@Remi.b: Sounds better!

I wouldn't vote to close but I think your question deserves a little editing. Is there some genetic variance underlying music appreciation? Not gene variance. Is there any research done on the gene responsible for Music appreciation? This assumes there is only one gene. I'd suggest to stick to the formulation using genetic variance. I consider it is a detail but that might be important to you given that someone voted to close! What do you mean If not, is there any reason its impossible to predict? Does it mean If not, why is there no genetic variance for this trait??

Yes, it looks much better to me! Well.. I personally have no idea! The only think I can say is that such studies in humans are likely to be biased due to the culture and assortative mating.

One can perhaps look at extremes: compare Beethoven (or any other musical genius) with lets say a great mathematician who has no appreciation for music. (That way you have normalized for basic intelligence). But these are just opinions.

Or otherwise if it is just about sound patterns (harmonics), their production and perception then the genomes of a songbird and a chicken can be compared.

@WYSIWYG: Mathematician and Music is actually kind of wrong as both need same region of brain might have some correlation: http://www.ucl.ac.uk/news/news-articles/0214/13022014-Mathematical-beauty-activates-same-brain-region-as-great-art-Zeki

So wouldn't that just mean that music appreciation is correlated with general IQ ??

@WYSIWYG: I don't think so. Still I found this http://www.psmag.com/navigation/books-and-culture/classical-music-linked-to-high-intelligence-27959/

See that is what I was saying. There are no conclusive studies and this question is tending to "primarily opinion based". Did you consider the option of comparing songbird and chicken?

Yeah, this is definitely on topic.

In 2013,
Dr. Liisa Ukkola-Vuoti, University of Helsinki, Finland, done a detailed GWCNV(Genome-Wide Copy Number Variation) Analysis of certain group of people for musical creativity and aptitude.
Genome-Wide Copy Number Variation Analysis in Extended Families and Unrelated Individuals Characterized for Musical Aptitude and Creativity in Music

They did a Genome Wide Copy Number Variations (CNVs) in five extended
pedigrees and in 172 unrelated subjects characterized for musical
aptitude and creative functions in music
Muscial Aptitude is taken as Sum of Scores of Auditory structuring ability, Seashores test for pitch and for time. Along with data on creativity in music was surveyed using a web-based questionnaire.
Several CNVRs containing genes that affect neurodevelopment, learning
and memory were detected.
A deletion at 5q31.1 covering the protocadherin-α gene cluster (Pcdha 1-9) was found co-segregating with low music test scores (COMB) in both sample sets. Pcdha is involved in neural migration, differentiation and synaptogenesis.
Creativity in music was found to co-segregate with a duplication covering glucose mutarotase gene (GALM) at 2p22. GALM has influence on serotonin
release and membrane trafficking of the human serotonin transporter.
Genes related to serotonergic systems have been shown to associate not only with psychiatric disorders but also with creativity and music perception.
Both, Pcdha and GALM, are related to the serotonergic systems influencing cognitive and motor functions, important for music perception and practice.
A 1.3 Mb duplication was identified in a subject with low COMB scores in the region previously linked with absolute pitch (AP) at 8q24.
No differences in the CNV burden was detected among the high/low music test scores or creative/non-creative groups.

In summary, CNVs and genes found in this study are related to
  cognitive functions. Our result suggests new candidate genes for music
  perception related traits and supports the previous results from AP
  study.

Source:
[1] Genome-Wide Copy Number Variation Analysis in Extended Families and Unrelated Individuals Characterized for Musical Aptitude and Creativity in Music
[2] Musical Aptitude Is Associated with AVPR1A-Haplotypes

Interesting study, thank you! It is worth noting that probably these two traits (creativity and aptitude) may be highly correlated with IQ.

@Remi.b: Yes they are. Thanks for not closing this question.


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How thick is the residue from a fingerprint?
I am trying to work out the mass added to a small glass sphere when it is picked up with bare fingers.

Wouldn't that depend a lot on your skin type, how moist your skin is, what stuff you have touched before,... ?

Yes, but there must be an average. I am weighing stuff to an accuracy of a microgram

Figure in the range of a 25 micron cube of oil perhaps.

The average thickness of a latent fingerprint is .1 micron.
Or, <10 micrograms.
https://www.ojp.gov/pdffiles1/nij/225327.pdf

Presumably that would be for a full print from the first joint of the finger (i.e. the bit that they do the fingerprint ID from), and would decrease with less contact area and increase with more area covered. I wonder how big the "small glass spheres" OP is using are?


text:
Is there a difference between Luria Broth and Lysogeny Broth?
Is there a difference between Luria Bertani and Luria Broth? Or are they both the same thing? Is it necessary to autoclave LB medium after it has been made?

Although both names are quite common, both are wrong. In the original paper from 1951 Bertani was studying the lysogeny in E.coli, hence he called his media for this purpose "Lysogeny Broth" or LB in short. In the subsequent decades this name was transformed to "Luria Bertani" or "Luria Broth", which is incorrect. See the references 1 and 2 for more details.
For the second question: It is absolutely necessary to autoclave the media after preparation to avoid the contamination and growth of microorganisms which are present in our environment. Only making the media sterile makes sure that we can work with defined microorganisms.
References:

Studies on Lysogenesis I.
The Limitations of LB Medium


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what is the modern, molecular explanation of independent assortment of alleles?
in case it isn't obvious i'll begin by stating that i'm a layman, trying to understand something about this subject from reading books. the books i've looked at so far give murky and confusing accounts of meiosis (example: a schaum's outline refers to chromosomes that are "identical, but not the same"), and they always seem to duck the questions i most want answered. what i'm reaching for right now is a coherent, schematic understanding of meiosis. i'm aware that the real world is messier than that, but there clearly is a simple schema that accounts for the most typical cases of meiosis in an approximative fashion. in particular, there must be some way of deriving the approximate correctness of mendelian independent assortment of alleles from the molecular process of recombination.
here's what i think i can make out so far: if two chromosomes x1...xn and y1...yn get paired together then some number of interstices (spaces between successive loci) get picked and each chromosome gets broken at each interstice. after crossover each of the two resulting chromosomes have x-segments alternating with y-segments. for example if only one interstice is picked, and it's between the k-th and (k+1)-th loci, then the chromosomes that are there after crossover are x[1]...x[k] y[k+1]...y[n] and y[1]...y[k] x[k+1]...x[n]. it also seems clear that all interstices are equally likely to get picked. have i got at least this much right? and if so, how many interstices get picked?
also, if anyone knows a book that might suit my needs i would obviously be glad to hear about it. thanks if you can help
peace
stm

Homologous recombination does not occur randomly at every locus and thus independent assortment really only holds for genes that are far apart or on different chromosomes.

Parts of your general idea are correct, others need some refinement. To skip ahead to the conclusion: I'm going to recommend that you read up about linkage disequilibrium. Everything else is background information that will hopefully help you understand why linkage disequilibrium is what I think you're looking for.
Background: Meiosis Basics
Firstly, in the bare principle, meiosis is quite simple. Let's consider meiosis in a human cell, i.e. the division of a primary spermatocyte (the only other example is division of primary oocytes). That's this one we're talking about: http://en.wikipedia.org/wiki/File:Figure_28_01_04.jpg In simple terms, the ready-to-develop primary spermatocyte contains 46 chromosomes, two (2n) each of chromosomes 1-22, an X and a Y chromosome. Two chromosomes of the same type (e.g. "chromosome 4") are called homologues. Homologues contain different versions (= alleles) of the same genes. During Meiosis I, homologues align in pairs, recombine, and then separate into two new cells (which are now 1n). The following Meiosis II is basically a mitosis, meaning that the two (identical apart from previous recombinations) chromatids of the chromosomes are separated and packaged into separate cells.
Recombination and Interstices
During the alignment of homologues in Meiosis I, the attraction between the two chromosomes depends on their homology - i.e. similarities in DNA sequences that end up being close to each other in this setting. This attraction is on the molecular level, so the tightly-condensed neighbouring DNA double-helices actually physically stick to each other because the polarities/charges across relatively large stretches of the molecules complement each other. I don't think how this exactly works is well understood yet, but what is clear is that homologues attract each other at this point because they contain mostly the same DNA sequence, apart from small differences (which make the different alleles).
How exactly recombination occurs at this point isn't entirely clear either on the molecular level (http://en.wikipedia.org/wiki/Homologous_recombination#In_eukaryotes). Just to get an idea of how it could work: while the two chromosomes are aligned, it's not difficult for them to actually tangle up and break, leaving the repair machinery with the potential to fuse the wrong chromosome ends together. The breaking and exchange might also be a programmed process with dedicated cell machinery (quite probably so I think personally).
In any case, not all locations are equally likely to recombine. It depends on levels of homology (i.e. "stickiness") between individual stretches of DNA and other unknown factors. Just in the same way, the number of break points in each chromosome isn't pre-determined.
Mendelian Inheritance
First of all, the concept of loci is arbitrary and has no molecular basis. A locus is simply "a place on a chromosome" and could refer to an individual base(-pair) or a whole stretch of DNA. The concept is however useful when talking about inheritance, as it illustrates the principle of linkage, or rather linkage disequlibrium.
As far as I am aware, it comes from a time before we began thinking in chromosomes. One could study whether there was linkage between two traits (or more generally loci), i.e. a higher-than-random chance to inherit the two together. We now know that linkage simply means that the two loci are on the same chromosome (for example, chromosome 6). Thus, if no linkage exists, the chance to inherit both loci together is no larger than expected by random Mendelian inheritance (which makes sense as there is no molecular mechanism that makes offspring e.g. more likely than 50/50 to inherit chromosome 6 and 19 together from the same parent). If linkage exists , the loci are on the same chromosome, and any case where the two are not inherited together must be caused by recombination.
The idea of linkage disequlibrium is that loci that are close to each other on a chromosome are more likely to remain together (and be inherited together) than far-apart loci. This is obviously due to the increased chance of a recombination event occurring between them with increased distance. This actually seems to be what you are looking for as a pointer for where to go next, so I recommend using your preferred internet search engine to learn more about that :)

some number of interstices (spaces between successive loci) get picked
  and each chromosome gets broken at each interstice.

That number is usually around 1.  It's not common to have a lot more than that per chromosome.

It also seems clear that all interstices are equally likely to get
  picked.

No, there certainly are "hot spots" that are more likely to be cross-over junctions than other sequences.