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Are we more attracted to people of the same ethnicity?
Are humans more attracted to people from their own ethnic groups? I ask this because most of the time people have relationships with people of their own ethnicity, and I wonder if it's purely social, or if there's more behind it.

What do you mean with "own race"? Talking of "human races" is political and not biological backed.

I'm not sure, but would this me more apt on cognitive.SE? The reasons are  underpinned by complex social and historical structures and talking about them in biology rather than sociology doesn't quite make sense. Having said that it might actually be inappropriate on  cognitive.SE too...

I agree with both earlier comments. Once edited to exclude improper terminology it is fit for Cognitive-sciences. I vote to close therefore.

I'm voting to close this question as off-topic because it is a better fit for CogSci, but only after suggested edits.

How would you possibly test out whether the preference was "purely social" or not? Either way, it is an expressed preference. For a not quite scientific but entirely depressing big-data take on this issue, see the famous OKCupid blog post about race and internet dating.

Biologically we are attracted to people that smell similar to our parents but different enough that they aren't close relatives. I'm too lazy this morning to find the paper, however. I might come back to answer later. The rest is more of social convention, and I'm not qualified to answer that. Bill Nye is also quoted as saying "there is no such thing as race scientifically". That being said there still is racism and pressure from parents to marry "your own kind".

I'm voting to close this question as off-topic because race is not a biologically meaningful or accepted concept, and such questions are not on topic http://meta.biology.stackexchange.com/questions/372/why-questions-about-race-do-not-belong-to-biology¨

@AliceD Are psychology questions on-topic in cogsci?

@WYSIWYG - yes - "Cognitive Sciences is [...] cognitive science, psychology, neuroscience, and psychiatry." http://cogsci.stackexchange.com/tour

There is a good chance that there is a relation mediated by oxytocin, a hormone which influences both romantic attraction and distrust against outgroup members. But I don't know its exact mechanism of action (especially the causal direction of oxytocin release, falling in love, and stranger distrust) to post an answer, so I hope somebody with better understanding will post an answer about that.

@rumtscho Oxytocin release will be the molecular mechanism, but it would take a lot of conjecture to tackle sociology using molecular biology!

Edited the word "race" to the more scientifically relevant "ethnic group". I would argue at this point this question can be considered on topic as a behavioral/sociological aspect of biology.

@SolarLunix: If attraction is mediated by smell, how to account for movie stars, Playboy, on-line dating & internet porn, that attraction across a crowded room, et cetera ad infinitum?

@jamesqf (and SolarLinux) - there is more than one contributing factor - smell is one, visual stimuli also affect attraction

@MarchHo Although ethnicity is a less troublesome word, it even less useful here as it's is very plastic and more culturally dependent.

I also think it's necessary to clarify just what's meant by attraction.  If it's purely sexual, that's one thing; marriage is quite different, and friendships different still.  That could explain a lot about those OKCupid numbers: I might be sexually attracted to a person, but not want a relationship unless they shared my views on desireability of hiking, skiing, horses, etc, or my distaste for urban living.

The answer in extreme brief is yes.  Only about 9% of whites and 16% of blacks engage in interracial marriage in the U.S.
But really this deserves a fuller discussion.
The predominant pattern of mate selection in human beings is to marry within their ethnic group.  I say 'ethnic group' rather than 'race' because 'race' has no strong scientific definition. Racial groups as usually understood have about the same genetic variance as the overall species.  I need to point out here that races can have a strong geographical point of origin and thus races can in some cases be genetically identified, but these correlate poorly often with, say health risks or other clinical data.
Because its clear that interracial marriages are on the rise in the US, mating choices are often argued to be socially defined preferences.  Social attitudes affect men and women differently and different cultures too, as tracked by regional differences. Not the least is a limit of opportunities.  Being comfortable with people from other cultures or who simply look different.
Interestingly, Women at least in American studies are much more likely to state a same-race preference (see p17), while men are likely to demur from stating a preference, but act out a bias unconsciously, making both genders equally biased.
As mentioned in the comments by @AliceD, Mating preferences as described in traditional population biology are adapted from availability.  That's to say people don't meet because of ethnic boundaries such as geography, socioeconomic or social strata. Then there are socialized preferences.  I.e. growing up we are imprinted by the sorts of people we see, which can create preferences later in life.
Lastly there may be genetically conveyed preferences. The human race has been spreading out over the past 200,000 years or so.  That's a pretty small amount of time.
There are cases where such mating preferences have evolved.  There are 17 species of arctic penguins, species caused by migration around the globe.  Each species can actually mate with others nearby, but where the penguins met at the far end of the globe, the species do not mate because they have been separated for so long they have diverged.
But speciation events can take millions of years.  Its clear that human beings from the far corners of the globe can produce offspring - we are one species.  It seems as if we are only a fraction of the way towards two neighboring penguin species in the example above.  There doesn't seem to be any evidence that we are genetically predisposed to ethinic self mating.
Only by way of example (not to offend anyone!) you can choose an ethnic group which has divergent behavior and try to get an estimate of how big a role the social context plays.  Looking at the jewish community we see that 47% of jewish people have married out of group in recent statistics.  They are thinly distributed over a wide geographical region and availability is high for out-group marriage.  All this while orthodox jews have a practically non-existent out-group marriage rate. Not judging here, but only for illustrative purposes we can guess that social attitudes and behaviors are playing a large role here.
As and endnote, I'd also like to point out that since whites are about 50% of the US population and african americans are about 10%, the percentages not so disparate as they seem - all things being equal the average person one meets in the US is white.  For caucasians, approximately 50% of the population the average interracial marriage rate with no racial biases is is 50%.  For blacks its 90%.  Again, just using random shuffling for statistical point of view.

There exists a marked geographical segregation of whites and Afro-Americans in at least some cities, like Baltmore. This has its roots in history and the standard of living. Hence other factors may and will likely play a role in partner choice.

yes @AliceD - i thought i'd try to list other factors like geographical, socioeconomic, cultural separation, but the list is quite long.  given all that its not surprising that not a lot of racial intermarriages are happening compared to the rates one would get from at random pairings.  Its a lot like the factors cited in population biology, is it not?  humans are picky for more reasons than animals tho I think..

This answer doesn't really address the biological aspects of the question.

@shigeta - great answer +1

@augurar - the question is not biological (see the comments string under question). Therefore this answer is great.

@augurar of course biology concerns itself with inherited and environmental aspects of behavior.  Sociobiology and Ethology has informed social psychology and behavioral economics, and now literary crit and cultural crit.  They are not as prominent today as biology departments have gone more and more molecular in the past 30 years, which has obscured the previous forms of the science.

The problem here is that attraction is not the same as marriage.  Personally, I've been attracted to - oh, perhaps thousands of women, but haven't to date married any of them.  As far as I can tell, the likelihood of physical attraction is pretty much the same for all races/ethnic groups.  Perhaps if we could find data for casual sex encounters, and correct if for opportunity, we might get a more realistic picture, but I don't think that's possible.

Of course you are right.  Marriage is not a bad statistic to look at because its more easily measured with less slippery subjective critiques.  I'm borrowing from couples research in psychology which only took off when they started using divorce instead of qualitative satisfaction metrics.  Few would argue that attraction is even more broad than marriage, though some might not admit it....

@jamesqf marriage is a good measure to use - while we don't marry everyone we are attracted to, most marriages have some kind of attraction at some point (remember that attraction is more than just a visual thing - biochemical, behavioural, and other biological stimuli are important)

@rg255: As I said above, I think the OP needs to clarify just what sort of attraction is meant.  I'm working on the premise that we're discussing purely sexual attraction.  For marriage, you need not only that sexual attraction, but (in most cases, anyway) shared interests and desire for compatible lifestyles, which makes the set of acceptible marriage partners a very much smaller subset of those to whom we're sexually attracted.

"Lastly there may be genetically conveyed preferences." Do you have a citation for this?

One of the precursors to speciation is that different isolated groups stop mating with each other even if they are exposed to each other.   I'll see if I can find a good link for the penguins, which is a specific example of this phenomenon.

To keep it simple: The term here is assortative mating which describes the tendency of individuals to select reproductive partners based on some arbitrary measure of "likeness" considering the available within-species variation. The "likeness" could be anything from a general evaluation to being based on one specific trait.
This is a evolutionary significant phenomenon, and when it comes to humans there are a lot of factors that seems to be related to human assortative mating, including both race (whatever the term's biological significance) and ethnicity, among other things like height, religion, politics and even altruism. See wiki for ref.
As seen in the article Shigeta liked to, racial belongingness is generally very significant in human assortative mating.
So the concept of choosing "similar" partners as we see in humans is a common biological phenomenon with evolutionary significance, although by what exact mechanisms this selection works in humans and how culturally dependent it is (maybe some assortative selection traits are more or less biologically/culturally founded than others) is probably in need of more research.

But are we really talking about "reproductive partners" here?  Personally, I've always gone to some lengths to ensure that reproduction does not occur - and that's not even considering that it's even going to be a possibility with only a small fraction of those I'm attracted to, because the attraction is so seldom mutual :-(

@jamesqf I think it's quite reasonable to generalize 'attraction' as the selection of productive partners in the context of biology and evolution, especially considering the tags of the question.

But then you're asking a much different question, something like "Do humans generally want to have children with partners of the same ethnic background?"

@jamesqf The OP has tagged his question sexual-selection and evolution, I cannot see that I make any unfounded assumptions about the intentions behind the question even if we can split hairs about sexual attraction not being the same as actual sexual reproduction. I interpret the question as asking about sexual selection - If I'm wrong then the OP is most welcome to correct me and/or clarify his question.

I wouldn't say that differentiating between sexual attraction and reproduction is "splitting hairs".  Certainly not in the context of modern culture, and not in a lot of past ones, either.  (Or among bonobos, perhaps.)  Consider marriages made as economic/political decisions, where the married couple might not even see each other until after the wedding.

@jamesqf Since this is about biology and evolution, I choose to use an approach to this question that is applicable to this general biological domain, and to avoid going into sociology and "modern culture" while still flagging for the issue of cultural dependencies in the answer. And in this context I see limited use of trying to differentiate sexual attraction to sexual reproduction. (Maybe except the odd cases like where dolphins and penguins apparently regularly rapes other species for some kind of stimulation, but that might as well just be instincts gone haywire.)

@jamesqf Arranged marriages sure, but what is the rule and what is the exception here?

The point I'm trying to make here is that in humans (unlike many other species) reproduction is no longer the primary function of sex, otherwise females would come into heat a few times a year, and be unreceptive at other times.  Human sexuality (and bonobo, apparently) has become a form of social bonding & recreation, with only a small fraction of encounters intending to result in offspring.  Therefore reproduction is not a good way to measure human sexual attraction.

@jamesqf I'd argue that reproduction is still very much the primary function of sex even in humans, but that it also have very important secondary functions as well. Even if a human individual might have different priorities. (Although, that human females are receptive throughout their menstrual cycle is easily explained by a little bit of evolutionary game theory in regards of the battle of the sexes and our general human sexual behaviour. I don't see how that would invalidate the significance of mating in sexual activity and attraction). But I think we both have gotten our points across now.

@Alex, this is a good answer.  I do not know why it was downvoted.


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What is the meaning of the "d(...)2" notation when writing a DNA sequence?
When the sequence of a DNA oligo is written as d(CGCTAGCG)2 what is the meaning of the d(...)2?  Why would it not simply be written as CGCTAGCG?
I recognize this sequence is the complement of itself.  Is that what this notation indicates?

Speaking to the author of this publication, they used d(...) to denote a double-stranded sequence and d(...)2 to denote a self complimentary double-stranded sequence.
This publication instead uses the subscript number after the parenthesis to denote the number of repeats in the sequence, e.g., d(AT)10 was used to represent the dsDNA sequence ATATATATATATATATATAT.

Assuming that you were referring to this specific usage of d(CGCTAGCG)2, the image provided for the crystal structure clearly shows that the structure refers to the fact that two strands of deoxy-ribonucleotide oligos were dimerised to each other.
Had the same structure been formed by double-stranded RNA instead, the resultant dsRNA duplex helix would be referred to as r(CGCTAGCG)2

If I understand, the d refers to DNA and the 2 refers to double-stranded, correct?  If I want to refer to triple-stranded DNA would I then use d(...)3, etc?  (The sequence I posted actually came from a study of the middle 8 bp of PDB ID: 1DCV but the structure should be quite similar to PDB ID: 1G7Z and it should not affect the answer.)

@stvn66 If they were 3 independent molecules hydrogen-bonded together, sure.

Can you point to any sort of reference which verifies your description of the notation?  This would allow me to more confidently mark this as the accepted answer.

@stvn66 I assume it is because of standard chemical nomenclature for dimerised molecules, and I don't think there will exist a source that says this for DNA specifically.

Reading this paper, they reference 'd(A)10-d(T)10'.  This seems to indicate the DNA sequence 'AAAAAAAAAA' and its complement.  Therefore, I do not think the '2' refers to it being double stranded, but instead that the molecule have 2 copies of that DNA sequence, since it is the complement of itself.

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For a recombinant pUC19 plasmid with cut sites at Hind III and EcoO109i, does lactose need to be present for the gene of interest to be expressed?
I am growing E. coli transformed with the plasmid pUC19 with an insert of an enzyme in rat brain between the mentioned cut sites (EcoO109i and HindIII). I have isolated and created the recombinant molecule with custom gene synthesis so that the gene encoding for the enzyme easily ligates into the plasmid.
It is after the lac promoter and lac operator, so will I need lactose in the growth media for the enzyme to be made?

Why would you cut out the entire lacZ operon instead of using the MCS? Without any of the machinery expressed from the operon, who knows how well expression of your target gene will work?

@MattDMo lacZ is a single gene, not an operon. The only negative effect I can think of (assuming there are no issues from removing the fragment between lacZ and ampR) is the inability to do blue-white screening with the plasmid.

I would rather use the synthetic analogon IPTG for induction of genes behind a lac promoter, as they are not getting degraded (or at least much slower than lactose) and thus give a much stronger induction signal.
You will need to test the optimal IPTG concentration, but as a starting point I would use and endconcentration of 1mM. If this is not optimal (to much unsoluble protein or not enough), you can change the concentration in either direction. To start, use an overnight culture to inocculate a fresh culture in the morning and follow up the OD600 until it reaches 0.6-0.8 before adding the IPTG.

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How do I test if microbes have survived after dehydration?
I have a solution containing various bacteria and fungi.
My aim is to place solution on filter paper, and wait until it dries.
I then wish to test if the organisms have survived, either on the dried paper, or by rehydrating it (maybe placing the paper into water).
Can someone provide advice on the type of test(s) I should perform to achieve this result?
Preferably the test would be easily sourced (purchase online?) and relatively cheap and easy as I am quite inexperienced in this area.
Thank you in advance for any advice that you can provide.

I think the fungi are not the problem, they are hardy. I used to revive yeast after freeze-drying the poor buggers. Anyway, good question +1

Is there some reason why blotting them onto culture media wouldn't work?

Hi @AliceD and thanks for the feedback. I'm now a bit more optimistic. Regards

Hi @Resonating it may. The drying part is nescessary, which I guess that media would support. Thanks very much for your help.


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Classify chemotherapy drugs?
I'm studying a TCGA dataset trying to find correlations between gene expression and clinical data which might shed light on some pathways. One column of the clinical data provides a list of chemotherapy drugs the patient received and I'm pretty sure there's a wealth of possibility in this column but I don't know anything about how chemotherapy drugs work or what kinds of things it makes sense to classify about them.
I'm making a spread sheet that will have the following columns:
Drug_Name
Number_of_Patients_Received
Mechanism
Associated_Drugs

But I don't know what else is important to know about these drugs. What would you think is important to know to help understand how these drugs might have played an interactive role with the pathways. Also, if you have any recommendations of online resources, I'll appreciate you mentioning them with a short explanation of what I might find there.
But my primary question is else should I look up about these drugs to help me understand their role/impact.

There's this database of genetic/drug interactions, which I think is pretty much exactly what you're looking for.
Probably your best approach is to classify your drugs into a couple different categories of action. Some drugs will have to be in several categories at once(I'm lookin at you anthracyclines) Dose-dependent mutagens(cisplatin), growth-dependent mutagens(fluorouracil), topoisomerase inhibitors, tyrosine kinase inhibitors, microtubule inhibitors, retinoid signaling, immune adjuvants, and then 'misc'.
Alkylating agents and platinum-containing drugs like cisplatin directly damage DNA, while there are other drugs(methotrexate,fluorouracil) destroy or damage DNA by pretending to be nucleotides and breaking things that way.
Anthracyclines bind into DNA strands damaging it, directly create free radicals, disable topoisomerase II, and displace histones.
There are the topoisomerase inhibitors which prevent cell growth by preventing DNA from being unwound for replication.
There is a fairly diverse(chemically) class of 'mitotic inhibitors' that screw up microtubule formation or proper microtubule dissolution, any of which prevents mitosis.
Sunitinib blocks tyrosine kinases, which are involved in blood vessel formation(and nerve formation and some other things). Other tyrosine kinase inhibitors are more specific to vascular endothelial growth factor(VEGF) receptors, but most tyrosine kinases target more or less specifically VEGFR and related receptors. Some are antibodies, some are just small molecules that bind TKRs.
There are a few retinoid-related drugs most of which influence the vitamin A pathways, like tretinoin, bexarotene, etc. Dual use in skin creams.
Immunoadjuvants are designed to activate the immune system to aid in tumor cell destruction. Interferon-alpha, imidazoquinoline, et cetera.
In the miscellaneous category:  Bortezomib, a short peptide that interferes with proper protein degradation. L-asparaginase slows all protein production by destroying asparagine, an amino acid. Some blood cancers respond to corticosteroids, and some ovarian/testicular cancers are treated with sex hormone inhibitors. Nearly any blood-borne poison works as a chemotherapy agent due to the EPR effect, so heavy metals or radioactive isotopes also are sometimes used. Arsenic trioxide, for instance, is just arsenic.
It's also worth looking at the steroids that are given along with cancer treatment(dexamethasone) to mitigate side effects. As regulatory hormones, there's going to be a hefty impact on genetic regulation from regular steroid injections. If nothing else it's a confounding effect you'll need to control for.
The preparations/solvents of chemotherapy agents can also be significant. Cremophor was used to suspend some anticancer drugs, but recent developments imply that might be on its way out.

Thank you, this looks very useful. I expect I might have more questions once I've had a chance to digest everything you've said; do you think its better to post follow-up questions here or as a new question?

Probably depends on the follow-up question you have. Clarification should probably go here, related but distinct questions should be new questions. I'm not a stack exchange protocol expert, so....

Resonating provided a good general classification scheme that could be implemented for categorizing chemotherapeutic agents. Another resource that could be used is the following: http://www.genome.jp/kegg/drug/. This is a pretty good classification of drugs based on ATC resource (http://en.wikipedia.org/wiki/Anatomical_Therapeutic_Chemical_Classification_System). These two resources are good starting points.


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Best way to automatically link Gene Entrez ID with Gene Symbol in TCGA
I am trying to figure out how to link Gene Entrez ID with Gene Symbol, for TCGA dataset.
So far, I have found this ftp directory with Gene info updated daily.
But, for Entrez ID 728661, I have found two different mappings:

here symbol is RP11-345P4.4;
here, and in gene info downloaded from ftp above, symbol is SLC35E2B while RP11-345P4.4 is in the field 'locus tag'.

Since I am a newbie I am bit confused about this thing. And, while I think they can be synonyms, I need to know if there is a best practice to use in this case, and if I need to implement some script on my own to do it automatically.
Thanks

To be sure, if I understand you right: You want to have a list with the Gene ID in one column and the corresponding Gene symbol in the next?

It is best to use the Standard Gene Symbol (HGNC) or Refseq IDs. Some Refseq IDs may be of this form NM_xxxx.x (where x is a number). In such cases remove the . and the following number. Sometimes same transcript can have more than one refseq ids- in such cases use the shorter id (not a universal rule though).

@Chris yes, I want the unique mapping between the two ID, is it possible?

@WYSIWYG Thanks, I think I am going to use the HGNC symbol. Could you please explain, if you know it, why the two symbols differs? HGNC is the standard   for Gene Symbols? Thanks

Do you have this list with the IDs? If so, you can use Biomart from Ensembl to convert it.

Thanks, I will try it as soon as possible. Could you please write that as answer? So if it works I can vote it and accept it..


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How to select genes before log2 ratio on a RNASeq gene expression matrix, based on signal median
I want to transform a TCGA mRNA expression matrix (in linear data format) to log2-ratios and then run a feature (gene) selection, selecting the 1000 most variant genes (genes with higher standard deviation across samples). The workflow is the following:

Select "good" genes before log2 ratio (genes each with median signal at least t in p% of samples);
On selected genes, run log2 ratio, dividing each gene by its median signal and then log2-transforming the result matrix;
Select the 1000 most variant genes along all samples.

How do I select t and p?

Cross posted on biostars: https://www.biostars.org/p/132301/

@DevonRyan biostars is not part of the SE network. It is only cross-posting when posted on different Stack Exchange sites. We can hardly expect people not to poest anywhere else on the internet if they want to post here! We just want to avoid duplicating information across the SE network.

There is no rule for fixing t and p. It depends on the level of stringency you expect. Value of t depends on what is considered to be an active concentration; this need not be same for all genes.
This is an RNAseq data; I don't understand what is the "median" signal you are talking about. For each sample a gene would have a normalized expression value which is typically RPKM (Reads Per Kilobase per Million mapped reads). If you have replicates for each sample then take the mean not the median.
Regarding calculation of log-ratios: Always be careful with this especially in case of zeros. Instead of log ratios you may use some sort of a gain metric:
if
ratio = x/y
then
gain = (x-y)/y
You can also do a principal component analysis on the data and select first n principal components.

Ok for the "median", it was a mistake. I did not understand: "Always be careful with this especially in case of zeros" - what did you mean? Thanks

In case of expression is zero in one sample then log transformation will give you NaN (undefined)


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Alpha wave frequency variance in EEG of single subject over period of a few hours?
I have an Emotiv EPOC (EEG headset: 128 SPS, notch filter @60 Hz, felt saline contacts) I've been playing around with. Over the course of two or three hours using CCA to plot an SNR contour against frequency and time over O1, O2, P7, and P8, I noticed that a strong background signal in the alpha-wave range was existent at ~10 Hz, but then after a while of doing other things and coming back to it the strong background signal at 10 Hz disappeared but a strong background signal at a smidgen over 8 Hz was present instead.
Naturally, this leads me to ask the question: what the heck is going on? Unfortunately, that doesn't make for a good stackexchange question title, so the replacement question:
Can alpha wave frequencies vary within a single subject over the course of a single day? If they can, by how much, and under what conditions?
(if they can't, I have to start figuring out what the heck caused those artifacts...)

What is the impedance of the electrodes?  If you are any higher than about 5-10 kOhms, you're just recording noise and/or EMG artifact from the jaw muscles, etc.

(as an aside, I have never used that product, but most of the "consumer" type headsets are unreliable at best)

The impedance is not listed in the product specs...
I'm doubtful of it being EMG, unless I'm just bad at controlling myself insofar as constantly clenched teeth are concerned (which is possible - I'll try again); the background signal was nearly constant over a period of 8 seconds using a moving window of 2 seconds with window steps of 0.25 seconds.
As for noise... the headset is a fully enclosed wireless unit that transfers data after amplification. Why would such sharp systemic noise move...? I mean, I guess if I had a cell phone doing something right next to my head... Argghhh...

I noticed that a strong background signal in the alpha-wave range was existent at ~10 Hz, but then after a while of doing other things and coming back to it the strong background signal at 10 Hz disappeared but a strong background signal at a smidgen over 8 Hz was present instead.

Alpha-waves (7-13 Hz) are related to a dozen of things. Here is an easy first experiment for you:

Sit still, stare at a wall, eyes open. Remove sources of noise such as TV, radio, etc.
Repeat the same, but now hold your eyes closed.
Record the signals in both conditions for about one minute.
Compare the power spectrums between the two conditions: you will see a big difference in the alpha power, especially at the occipital electrodes.
This is actually a standard test for resting-state analysis.

Back to the actual question: What you noticed is probably about drowsiness. When one gets sleepy, the alpha power starts to increase. You can try this by watching a boring movie.

Can alpha wave frequencies vary within a single subject over the course of a single day? If they can, by how much, and under what conditions?

Yes, because of the varying cognitive load, thoughts, sleepiness, emotions, etc. The brain signals vary a lot.
Also notice that all sorts of artefacts (e.g. movement, cardiac, eye-blinks, biting of teeth) have a dramatic effect on the measured signal. If you blink your eyes it produces a signal that has 100x higher amplitude compared to the signal that comes from your brain. You cannot really say much what causes any differences in the EEG unless you would do research-level recordings with research-level equipment.


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Are the proteolytic enzymes in syconiums and nettles identical to those in rennet?
I would like to compare rennet capability under various conditions. The issue is, I don't want to use calf rennet, but I can't figure out if nettle and syconium, which also coagulate milk, contain regular rennet or other enzyme? If so, what is its name? It's quite important.
Thank you.

Rennet is not an enzyme, but rennin is.
Any protease enzyme should be capable of causing milk to coagulate, or prevent gelatin from setting. Examples such as papain and bromelain are all cysteine proteases, and all of them will cause milk to coagulate.
The example of fig protease that you mentioned is a cysteine protease (no specific name found, referred to in scientific literature as fig protease). Nettles are a highly polyphyletic group, and the specific enzyme cannot be named without a more specific phylogenetic group being identified.


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How does myopia actually work?
This is potentially more or less a question about optics, but I've never been truly satisfied with any explanation that I've been given about Myopia. In fact, I almost can't even believe it. There are a million different versions of this diagram:

I understand that images of things could appear blurry when those things are focused in front of the retina, but then what discriminates between near and far things? Shouldn't everything be blurry? What exactly is different about this image when the thing being viewed is in close proximity to the eye?

The image you gave is not clear because it does not show the fact that the divergence pattern of light from a close object is different from the divergence pattern of light from a distant object.

Light rays from a close object (first image) diverge, and therefore they are in focus for the myopic eye.
Light rays from a distant object (second image) is approximately parallel, and therefore they are not in focus for the myopic eye. The lens corrects for this inability to focus and allows the distant object to become in focus.
Also note that the myopic person is still capable of focusing on close objects despite the lens because the lenses are still capable of dynamic adjustment, but less so than a person with perfect eyesight. You can test this by focusing on a near object, then putting on your glasses. The object should be initially out of focus, and then become in focus as the eye lenses adjust to the distance.
Myopia/presbyopia is fundamentally an issue of the focal range of the eye becoming limited to a specific range. If the ability of the eye lenses to dynamically adjust is further weakened, it may require progressive lenses or bifocals to correct the issue.

This is a great answer! I read originally that it was because of the angle of incidence of the photon and I didn't believe it for the following reason: if a 4m tall statue stands a large distance away from me, it's blurry. If I have a 10cm version of the same statue and I hold it at a distance away from me so it appears the same height as the statue in the distance, it's still sharp. In that scenario, the light from both statues is parallel(or so I thought) but NOW it makes sense, since the incident photons are still going to be at an angle because they're coming from ALL points of the statue.


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Why are bald heads so "shiny"?
Why do males with baldness have  shiny skin on their head, whereas we don't experience such "shiny skin" in other parts of body without hair.

There are products to put on your head to make it more shiny.

I always thought that bald people put some clear polish on there heads

Think of what happens when you don't wash your hair for days. It gets oily and greasy. This oil-like substance on your hair is called sebum and mainly works as a lubricant . The Sebaceous glands responsible for excreting sebum don't stop working when the hair follicles attached to them do. The result is a "shiny" head.

Actually people "shine" there heads on purpose. There are products sold for this.

Some do. Their bodies either doesn't produce enough sebum or they want a more "shiny" look. A slight shiness is natural in any bald head though.

Do bald people not wash their heads frequently?  And why is my chin not shiny if I shave regularly?

@jamesqf Well you have made an excellent point and it has really made me reopen this question. This sebum theory is good but incompletely explains the question.

I have seen many people who keep washing their head as well but still have "shiny" head. It doesn't explain it fully I am afraid.

Washing your head every single day dries it out, hence triggering the excessive production of sebum instead of hindering it.

Skin produces a material called Sebum which is naturally shiny, on skin with hair much of this is absorbed by the hair. In addition the skin over the top of the head is very smooth and fairly taunt because the skin does not undergo much flexion. Smooth surfaces are shiny, skin on a bald head is often smooth and taught (which makes it smoother) which makes reflectivity more even which makes it shinier (glossier) . Smooth taught skin and sebum oils and waxes contribute to being shiny, so even if say your face is not normally shiny the above combination makes the skin on the head shiny. Drying of the skin can also contribute.
Note how people in with uncommonly smooth and hairless skin any large smooth taunt patch (like the thigh or cheek) will also often be shiny (glossy) unless they use makeup.

It’s due to fibrosis. Men with androgenic allopecia suffer from chronic inflammation of the scalp. Male pattern baldness is a poorly understood disease, but what most people don’t know is that it’s an auto immune skin condition. The hormone DHT appears to trigger a cascade of events leading to inflammation and fibrosis This results in a build up of scar tissue, I.e. fibrosis that gives a swollen and shiny look to the scalp. Scalp fibrosis results in reduced blood flow and hair follicles shrinkage and eventual death.  https://perfecthairhealth.com/the-ultimate-hair-loss-flowchart-why-we-lose-our-hair/
You may not realize it, but claims that bald men’s heads are shiny due to sweat and grease is quite insulting to people suffering from an embarassing and irritating auto immune disease. It’s just an uneducated guess in the truest sense. An attempt to explain something without having done any research into the subject. Armpits are usually sweaty and produce oils but are never shiny.

Interesting answer. Can you please add some references to it?

Welcome. Please refrain from shooting at other answers and focus on writing a good answer by providing sources to your claims.  I've toned down your answer a bit.

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


text:
Y Chromosome in Ovary Cancer Data
I have been analyzing TCGA Ovary Cancer data. In Somatic Mutation data, there is data of mutations in all the chromosomes (1-22 and X), but amazingly, I have found one (just one) row of Y Chromosome mutation as well. What can it mean?
For Reference, I have pasted that row below:
icgc_mutation_id icgc_donor_id project_code chromosome chromosome_start
MU42454          DO28056       OV-US        Y          13500742

chromosome_end chromosome_strand mutation_type
13500742       1                 single base substitution

reference_genome_allele mutated_from_allele mutated_to_allele
G                       G                   A

consequence_type aa_mutation cds_mutation gene_affected   transcript_affected
stop_gained      R194*       580C>T       ENSG00000183704 ENST00000331172

In just one individual you mean? It means that there was a rare recombination event I guess in the father (or grand-father or grand-grand...-father)

Yeah it is for one individual and only one entry: Individual is a 76yo female (deceased)

Does it correspond to a region the is close to the PAR?

Just to expand on what @Remi.b said, there is the possibility that at some point in the past, in a male ancestor, a rare event occurred where a portion of the Y chromosome somehow ended up grafted to an X chromosome (or possibly any one of the others), but didn't cause enough issues/abnormalities to be an issue, or at least noted.

You can find the positions of the PAR on wiki

Its not in PAR of either X or Y

This question was asked more than six years ago.  Since then, the dataset in question has been updated.  I accessed the OV-US project dataset from the ICGC Data Portal, specifically simple_somatic_mutation.open.OV-US.tsv.gz. The 9th field of this file is chromosome.  Counting the occurrence of each chromosome, we see that Y is not represented:
awk -F$'\t' '{print $9}' simple_somatic_mutation.open.OV-US.tsv | sed '1d' | sort -n | uniq -c

  13171 X
  39564 1
  31363 2
  24657 3
  11021 4
  16849 5
  18643 6
  19492 7
  13067 8
  11133 9
  12090 10
  24980 11
  25608 12
   3947 13
  12728 14
  12123 15
  17554 16
  30315 17
   5669 18
  29571 19
   9054 20
   3639 21
   7084 22

Note that icgc_mutation_id MU42454 is only associated with acute myeloid leukaemia in a single donor. Moreover, the search scheme "Donor IS DO28056 AND Mutation Location IS ChrY" yields zero results from the ICGC data browser.
So, it seems that the inclusion of a Y chromosome mutation in the TCGA Ovary Cancer data was a mistake that has since been corrected.

Nice to see the correction


text:
Macrophage pathogen fixation
Overly simplified, macrophages recognise pathogenic patterns and endocytose anything that matches them.
That also works on bacteria, which are quite often very mobile. What if a bacterium was just randomly twitching around when a macrophage recognised it - would it be possible for it to "swim away" (as a reaction to the macrophage or just randomly)? Or do macrophages employ some sort of fixation mechanism to keep anything they recognise close until it is engulfed? Or is engulfment in itself just so quick?

As an alternative explanation I refer to this video.
Crawling Neutrophil Chasing a Bacterium
In addition to a high affinity between the target bacterium and the macrophage, sensing the bacterium will also trigger amoeboid movement to chase down a pathogen.

Do you know how it senses the bacterium? edit: Might put this as a separate question actually...

The explanation one of my tutors offered was this:
In cell recognition, it is very common for interactions between proteins to be based on weak interactions, but a certain degree of binding can trigger a (conformational) change which will instead establish a very strong interaction between the proteins involved.
In other words, if the contact is specific enough to 'count as a recognised pathogen', the interactions will immediately change to strong forces and the pathogen will not be able to move away.


text:
What is meant by "Expression" of Non-Coding RNA?
I was having a look at lncRNAdb and its help says:

The ENCODE project gene annotation list, GENCODE, has predicted that the human genome contains 14,470 lncRNAs whereas only a small proportion of these have been shown to have function.
We have examined thousands of publications in order to find and
  manually annotate lncRNAs that have been shown to be functional by
  overexpression or knockdown experiments.

What is meant by Overexpression of a Non-coding RNA here? Even if you have a look at any lncRNA in the DB, its entry has a column for "Expression":

They queried publications dealing with the lncRNA that studied whether it was functional through over-expression or knockdown. Cells do something measurable in their normal state, that can be observed through microscopy, qPCR, microarray, etc. You use the data as a control which to compare experimental results against.
For an over-expression study, for example, you can transfect your cells with a vector that will transcribe a lot of your ncRNA. Whether the transfection need be stable or transient will depend on a number of other factors. So the methods you used to collect data as a control, apply these to the transfected group. If there are statistically significant changes to anything when you have more of the ncRNA, you can say in part that your ncRNA is functional. It's doing something observable.
The opposite is true for knockdown. You can maybe use CRISPR to do the job but lentiviral transduction with say a doxycycline-inducible promoter is among other possibilities. The goal now is to make the gene, or the expression of the gene go away. You do the same experiment, and see if an absence of the ncRNA does anything to the cell.
Now in the database you're looking at, expression is being used to show you in what tissues has BX118339 been detected? It basically says BX118339 is expressed in testis and the fetal brain.

With many non-coding RNAs, the RNA is the functional endpoint. Therefore, ncRNA "expression" simply refers to the production of that functional component. Similarly to with proteins, this involves looking at differential tissue production of that noncoding RNA (i.e. in which tissues the RNA is produced).
Gene expression is defined in the Oxford Dictionary of Biology as:

The manifestation of the effects of a gene by the production of the
  particular protein, polypeptide, or type of RNA whose synthesis it
  controls.

In other words, gene expression means the same thing whether the functional product is a protein or a noncoding RNA. Of course, there is no translational component to expression of ncRNA.


text:
Degree of complementarity
Apologies in advance for asking bit naive question. I have looked up for this concept a lot but didn't find anything.
The wikipedia (I know its not authentic all the time, but I think works well for definitions atleast) page on Complementarity says:

The degree of complementarity between two nucleic acid strands may vary, from complete complementarity (each nucleotide is across from its opposite) to no complementary (each nucleotide is not across from its opposite) and determines the stability of the sequences to be together

This is quite new concept for me and how come we can have basepairing between opposite nucleotides if there isn't 100% complementarity? Any evidences behind it?

This can happen when DNA separates after melting and then reanneals.
You likely will not see this in chromosomal DNA in the cell, but I would assume it can happen to RNA molecules sometimes as they are single stranded and can self-anneal.
Mostly though, we think about this issue when performing PCR or DNA/RNA hybridization assays. Shorter pieces of DNA will be more likely to bind and anneal incorrectly, especially if the DNA is rapidly cooled after it has been melted.
Rapid cooling does not give the DNA time to "test" the different combinations of complementarity and sometimes can get stuck in a place where there is very little complementarity.
You also need to remember that while A-T G-C hydrogen bonding is favored, bases are able hydrogen bond to other bases, it just makes the double stranded molecule less stable.


text:
Significance of upper-case, lower-case and Ns in UCSC DNA files
I have downloaded human chromosome's data from UCSC FTP. Some part is in small alphabets and some is in large alphabets. Does it show the coding and non-coding region?
Here is an example from the file I just downloaded:
gagaatcccttgaacccgggaggcggagcttgctgtgagccgagatcgca
ccactgcactccagcctgggtgacagagccagactccgtctcaaaataaa
taaataaataaataaataaaAAAGAAAAAGGTTAATACAACATTAAAGAA
CAAGAATTAATATAGCtttttttttttttttttttttttgagacctagtc
tcactctgttgcccaggctggagggcagtggcatgatctcagctcactgc
aacctccgcctcccgggttcaggcgattctcctgcctcagcctcccgaat
aggtgggattacaggcgtgcaccaccatgcctactttttgtatttttagt
agagacggggtttcactatgttggccaggctggtgtcgaactcttgacct
caagtgatctgcctgccttggcctcccaaagtgctgggatgacaggtgtg
agccactgcacctggccTAATATAGCTTTAATAGAAAAGGAGCTAATACA
AGCTTAGAATAATAAACTTTATGCAGACTTACTGAAACAAACAGTCAATG
CAACCTTAACCAAACAGGAATTGATAACAGTTTAATAAAAAAGCTAATAC
CACTTGAATAGAATATATCTTACTACAACCTTCATAAGAAAAACAACTAA
TAcactttgggagaccgaggcaggcagatcacgaggtcaggagatcgaga
ccatggtgaaaccccgcctctactaaaaatacaaaaaaattagcctggca

Second strange thing is to see it beginning with sequence like NNNN. Like here it is:
>chrX
NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
ctaaccctaaccctaaccctaaccctaaccctaaccctCTGaaagtggac
ctatcagcaggatgtgggtgggagcagattagagaataaaagcagactgc

So the question is two fold:

What is the difference between upper and lower case in nucleotide fasta sequences?
What are the repeating N characters denoting?

On the web page that you linked to there is a file named README.txt. I suggest that you read it.

@mdperry I did. But it has nothing related to these N's and lower/upper case

"Repeats from RepeatMasker and Tandem Repeats Finder (with period of 12 or less) are shown in lower case; non-repeating sequence is shown in upper case." This is the relevant passage in the README.txt file. As described in one of the answers, in the IUPAC symbol table for referring to nucleotide bases, where the most common ones are ACGT and U (typically in RNA), the 'N' symbol is used to either represent a base in a sequence motif pattern where any of the 4 may be found, OR in a sequencing run, a base position where the actual base could not be unambiguously determined.

The FTP download files are documented on the UCSC site (from which they also may be downloaded from a web browser). The page for the human genome is http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/. I don't know which files you downloaded, but I quote three of the descriptions:

hg38.2bit - contains the complete human/hg38 genome sequence
      in the 2bit file format.  Repeats from RepeatMasker and Tandem Repeats
      Finder (with period of 12 or less) are shown in lower case; non-repeating
      sequence is shown in upper case.
hg38.fa.gz - "Soft-masked" assembly sequence in one file.
      Repeats from RepeatMasker and Tandem Repeats Finder (with period
      of 12 or less) are shown in lower case; non-repeating sequence is
      shown in upper case.
hg38.fa.masked.gz - "Hard-masked" assembly sequence in one file.
      Repeats are masked by capital Ns; non-repeating sequence is shown in
      upper case.

It would appear that the number of Ns corresponds to the number (perhaps an estimate) of bases the identity of which are not known. This is suggested by the following on the general downloads FAQ page at https://genome.ucsc.edu/FAQ/FAQdownloads.html.

N characters at beginning of human chr22
Question:  "When I download human chr22 from your web site, the
  unzipped file contains only N's."
Response: There is a large block of N's at the beginning and end of
  chr22. Search for an A to bypass the initial group of N's.

Difference between UCSC and GenBank files
If you download the human chromosome data from GenBank you will not find the lower-case characters (i.e. they are not a standard feature). The reason they are in the UCSC files is because these files are the ones used within their Genome Browser, which displays these repeats, as illustrated below.

Lowercase letters indicate repeat-masked regions. N's represent gaps.
See:
https://groups.google.com/a/soe.ucsc.edu/d/msg/genome/S4Sx8UdJAwM/tLTpVVzdhFMJ

How is the number of N's determined ?

In my experience, a run of N's, like 30 or 50, or 100, used to indicate a gap between two mapped contigs, but nowadays, with NGS sequencers I think they usually represent a base that could not be determined unambiguously, and thus for the purposes of length measurement we know the Number of bases in the fragment, but not the actual sequence of every base.

Sequence in caps are usually regions of interest, such as exons. N in the DNA alphabet refers to "unknown nucleotide" It can refer to any of A/T/C/G when the actual underlying base is unknown.

The IUPAC Nomenclature symbol table for RNA and DNA nucleotide sequences (via Wikipedia)

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


text:
Apple-seed-sized black bug, not very leggy - (Canada)
Can anyone identify this bug:

I'm in Toronto (Canada), 9th floor of a highrise building.  Most of the apartment is wall-to-wall carpeting, though the kitchen, entrance, and washroom are tiled.

We're going to need a better picture.

Darn.  I killed it and flushed it.  :(

Can you describe it? How does it move? Where's the head in this picture? Maybe even label the picture you have.

Sorry, I can't.  I'm at the age where progessive lenses are needed.  I was actually relying on the photograph to be able to see the details better.  At least it doesn't look like a bed bug, but I am curious what it is.


text:
Why a particular species of bacteria give rise to particular type of colony?
Bacterial colony varies in form, elevation, margin, opacity, chromogenesis etc. What gives definite character to a colony and what is the source of all the diversity? Is the reason similar to that of structure of salt crystal (i.e basic elements connect in a very definite way with each other)?
UPDATE (clarification)
On this page, it is observed that colonies can take various shapes, as if they have some internal structure, so that for example, form, elevation and margin differ and can even be described as having a particular shape.
Assuming that bacteria do not have any form of social organization or specification, what factors cause their colonies to have a particular shape, and that shape is well-defined for each species?
Also, for example, this picture on this website clearly shows a bacteria colony that is like ice crystals. If bacteria colony has no inner structure, what makes the bacteria to follow such a crystal-like pattern? Why not a shapeless goo, as one would imagine?

It all depends on the gene expression of the component cells, and their interactions between each other.

Different bacteria types are different species of animals. And different animals can have anything different, so there's no surprise to that. For example, it is obvious that cats have different lives than ants or monkeys, and so on.

@noncom The question is not why bacteria is the way it is rather why it has a distinctive colony? Why is it not random or different in each time?

So you mean that different bacteria form some sort of a macrostructure and that macrostructure is different per bacteria type?  Also, you ask how the form and structure of that colony is influenced by properties of a particular bacteria species? And ask if that formation is akin to how molecules form a crystal, likening bacteria to crystal molecules?

@noncom exactly!

This should not have been closed.

To me, this has gone from an unclear question to a broad question.

@GoodGravy is it hard to sum up the reasons for at least one of the relevant cases? Would it take more than a single paragraph to at least make an overview approach to the answer? It does not seem like if the reason for why bacteria colonies form their shapes is known, it would take more than a normal length answer to make an excursion into that.

@noncom Gauging an appropriate level of detail is tricky. I could simply say: "different environmental stresses causes different gene expression causes different proteins, and the proteins cause the cells to assemble in different ways. Each species reacts differently to their environment (and some will do the same thing in any environment)." A good question in my opinion would ask about a particular species and why it adopts a certain shape.

@GoodGravy I agree, and at the same time what you're saying is actually a rather appropriate answer to the question (imho). The authors main question was if this phenomenon is related to how crystals grow. The growth of a crystal is defined by molecular and atomic properties of the compound, and in case with bacteria you have specified the focus on gene expression and protein workings. Can they be likened to each other? That's a subject for furhter research of course. Also, I agree that asking on a particular case would be easier to answer precisely.


text:
Does a microwave oven disinfect food?
Imagine I am preparing food -- just about to put it into a microwave oven --  and some of it falls on the floor. Assuming it got some bacteria or other organisms (viruses?) on it, will the microwave processing disinfect it? What about worm eggs? As I understand, any living organism, given enough exposure to microwaves, degenerates. Is this true?

That amount of usual pathogen does not matter unless you clean up the floor with food... Yes microwave will disinfect the food, it is used for pasteurization.

http://cooking.stackexchange.com/questions/32055/why-microwaves-do-not-kill-harmful-bacteria-as-well-as-boiling

A little off topic, but I'll share anyway I have some old artifacts of wood with evidence of active worms. After putting them in a 1200 watt oven for 10-12 minutes all evidence of wood worms disappeared, that was several years ago. My assumption was that the water in the live worms was heated to a lethal temp. Obviously size was a constraint, although a commercial oven should be easy to find. Not for painted objects probably.

Microwave ovens can indeed kill bacteria in food by heating them to high temperatures. For example, this article found that microwave heating could kill all of the Salmonella bacteria in a chicken thigh sample:

The effect of microwave heating on Salmonella Enteritidis inoculated
  on fresh chicken was investigated using a microwave oven (800 w) to
  determine the destruction of Salmonella Enteritidis isolated from
  chicken carcasses, in relation to the time of heating at two power
  settings: high (power level 10) and medium (power level 6); The
  relationship between heating time and temperature was also been
  studied. The destruction was 6.4 log cycles at time 95 sec for the
  high power level, and 5 log cycles at time 140 sec for medium power
  setting. After 110 sec for higher power level, no survival of
  Salmonella Enteritidis was detected in samples (100g), but at 140 sec
  for medium power level, these food pathogens were still present.

However, the main issue is balancing the amount of microwave exposure. Heating the food for too long will result in the food becoming dehydrated or burned, and heating it for too short a period of time will result in food poisoning.
This paper shows that insufficient microwave exposure can and will lead to food poisoning if there is a large amount of bacteria inside the food:

Leftover samples of a savoury rice dish consumed by all six ill
  persons contained 6×103/gm Salmonella enteritidis PT4. The rice salad
  comprised boiled rice, raw carrots, eggs, cheese and curry powder. The
  curry powder and remainder of the pack of six eggs were negative on
  microbiological analysis. The rice dish had been prepared by heating
  in a 500 W microwave oven with a rotating turntable on full power for
  5 min.

So in essence, microwaves don't kill bacteria / viruses. Heating does.

I'm no expert, and this is more of a physics answer rather than biology. The microwaves will not reach all of the food. This is why you get hot and cold spots. The rotating plate mitigates this to some extent, but not all of the food will receive a blast. However heat will slowly spread throughout the food. If the contaminated bits are hot enough for long enough it should be OK, but I wouldn't rely on this.

Yeah, the warming of the food is extremily non-uniform, but I thought that at least the surface area gets enough radiation. And still - how much does it take to kill a bacteria? A colony of them? Is that heat that can kill them, or do microwaves affect them? ...and I bet if I asked this on Physics SE, they'd said, this one is for Biology :D.. Somehow my questions always fall in between SE sites :/ funny thing..

Not even the surface will be covered. The microwaves don't have a problem penetrating the surface, the problem is due to the wavelength only some areas get their molecules excited by the waves. This explains it better than I can: https://what-if.xkcd.com/131/

Can you provide citations to back up your claim?

Keep in mind that microwaves kill organisms via heat, and in particular, heating mainly water.  This is contrast to ionizing radiation, for example as obtained from the cobalt used in commercial food radiation.
If the pathogen does not contain, or is not sufficiently adjacent to liquid water, or is not sensitive to heat, it may persist.
As an explanation, a microwave generates heat because microwave oven peak frequency emissions (~2.45 x 10^9 Hz) (reference) are able to excite water molecules.  The physics of that are actually quite complex, but at a high level, water doesn't really exist appreciably as H20.  Rather, it is better described at STP as HOH (hydrogen hydroxide) or more accurately, H3O+ OH- (hydronium hydroxide).  All those ionic and van der Waals' forces (reference) allow water to have a high specific heat (i.e. a small bit of water can contain a lot of heat).  That water heat (via predominantly conduction) heats the rest of the food.
The importance of this is that in order for a microwave oven to sterilize anything, including food, both of the following must be true:

The target must be raised to a sufficiently high temperature.
That temperature must be maintained long enough to adequately disrupt the homeostasis or (in the case of a virus or prion) structure of the pathogen.

And these predominantly depends on water being present (though some fats and sugars can also efficiently absorb microwaves).  If there is insufficient liquid water present, the microwaves would inefficiently heat the dry food, and as a consequence could fail to achieve a sufficient kill temperature.  (In contrast to liquid water, ice has a much lower resonance with microwaves, which is why the defrost cycle of a microwave uses lower energy and switches on and off to allow heat conduction to even things out and minimize hot and cold spots).
Some pathogens would not likely be killed by a consumer microwave oven:

Bacteria which form spores often require very high heat, above the boiling point of water, so in a microwave, those spores (e.g. Bacillus anthracis, Clostridium botulinum) would be unlikely to reach those temperatures (as additional microwave energy would convert the water to steam at STP, rather than raise its temperature).
Prions are also notoriously difficult to destroy.  In fact, when a patient with CJD has neurosurgery, typical hospital policy is to throw away the entire surgical instrument tray, because there is no adequate sterilization procedure available, period.  (You're unlikely to find prions in your kitchen unless your name is Hannibal Lecter, but I include them for completeness.)

Finally, there are two main routes by which pathogens in food make you ill.  The first is an active pathogen (e.g. live bacteria or replicating virus) acting within your body.
The second mode is that of preformed enterotoxins. While the OP asked about food on the floor (presumably of short duration), the title is directed to disinfecting food.  From a food safety perspective, many common bacteria (most classically Bacillus cereus) produce toxins which persist even after they have been killed and the toxins can survive high heat.  Microwave radiation and the expected heat produced would not destroy these molecules, and would not render the food safe.  This would also apply for many chemical substances potentially present on a typical kitchen floor.
All that said, in 2014 microbiology research done at Aston University suggested that there is actually a decreased risk of eating food dropped on the floor if you follow the five-second rule.

Yes, you are right. If the food just fall on the floor and you microwave it, maybe that could kill most of bacteria or disinfect virus. bacteria are living organism, normally can't tolerate over 100 ℃, and virus' protein coats are not stable too.
However! maybe there are Spores which are generated from gram positive bacteria, the power of microwave or oven can't disinfect all of them. And also, the toxic chemicals on the floor, which might from your floor cleaners or be generated by bacteria, maybe microwave or oven are not strong enough to denature them. In biological lab, we use autoclave, 121℃ and 1.2 kg / cm² pressure over 15 mins, to kill bacteria spores. And it can't clean toxic chemicals. To be noted, there is not pressure function in normal housse using oven and microwave!

What do you mean by "spot"? Did you mean "spores"?


text:
How does the brain manifest and realize the intention to resist sleep?
The question title says it all. What happens in my brain when I resist sleep? I would appreciate explanations on neurotransmitter changes, what areas of the brain activate to resist sleep, what can an EEG tell us about that condition, are there any phases to that condition..
Switching to sleep mode is very complex and automatic process and it takes over and changes the functions of the whole brain (and the whole body also), so..
What actually does the brain do to not fall asleep?


text:
Identify the fluff that I have found in tea
I have just opened a new pack of a black Pu'er tea that I've bought at the local store selling it by weight, and found a piece of fluff in there. To me it looks much like some kind of a bird fluff. It might also actually be a plant fluff, but somehow I doubt it. Probably it was white originally, but being so long in contact with the tea made it a bit browner.
Of course it spoiled all the impression of the tea. Now I wonder if I should drink it, or even keep buying at the same store. The worst thing is that their supplier is one of the major suppliers of Chinese tea in our country, and this is not the first time I find something hairy in it. The last time it was a human hair.
The questions:

Please help identify what it is (I don't mean a particular species of whatever it is, but at least what is it? Bird fluff, plant fluff, synthetic fluff?)
Would you still drink your tea if you'd find such things in it? Like, should it be safe, or is there any risk in contracting some terrible infection or a virus in case if it comes from a bird?

Here are some photos that I've made:

It also very much looks like this picture I've found in the internet, but failed to find a proper description of what is it actually (the most bold one was that its a swan fluff, but I can't imagine how a swan would come in contact with the tea):

It definitely appears to be a down feather. Identifying a specific species would almost certainly require a specialized lab to examine it. I don't think your tea supplier has good quality control.

It's kind of hard to tell from the picture, but maybe it's a variety of Milkweed seed-pod that got into the tea?


text:
Can ncRNA convert to coding RNA/mRNA by mutations?
DNA has transcribed into RNA (Non-coding). Can this RNA mutate and become a Protein-Coding one/mRNA? Have there been any such instances reported by scientists?

The ncRNA will be transcribed from nuclear genomic DNA.  Therefore the mutagenic events would take place in the DNA that codes for that ncRNA gene.  It has nothing to do with the relative chemical stability of the different types of nucleic acids.  There are viruses with RNA genomes, that do not require a DNA intermediate for replication, like Q-Beta, which codes for its own RNA-based RNA polymerase (as I recall).  Are you familiar with the so-called "Central Dogma" of molecular biology? DNA makes RNA. RNA makes Protein (or in this case, the RNA does not code for a protein).

@mdperry Let me rephrase it: Can a ncRNA (transcribed product from DNA) convert into a mRNA? By mutation, I mean mutation in RNA, not in the DNA which makes that RNA (as it has already transcribed now)

If you had a gene that mutated its start codon or something, so that it was still transcribed, but the RNA could not be translated, that could be considered ncRNA. If that mutant gene had another mutation that fixed the original error, it might be translated again and become a coding RNA. But mRNAs typically need a start codon, kozak sequence, etc. These features would be hard to find on a ncRNA that didn't originate from a mutated gene.

@user137 Thanks! I got you! Yeah it seems highly unlikely to have that much modifications/mutations to ncRNA. You made an interesting statement in start of your comment. Can you please explain it further (start codon or something)?

Answers

Possibly, but with such a low frequency as to be unimportant and undetectable. (Monkeys with typewriters producing Shakespeare’s Hamlet comes to mind.)
No. Because it would be extremely difficult to detect, it would seem to be of no importance if it did occur at a very low frequency (you make no suggestion of why it would be of interest) and therefore there would be no reason for anyone to try.

Why would it be difficult to detect?
The difference between mutations that occur in DNA and any that might occur in RNA, is that a mutation in DNA can give rise to a population (e.g. of bacteria, or offspring) all of which carry the mutation. Thus, even though the chance of the particular mutation may be very low (say 1 in a million or less), a situation can arise in which the whole population is producing the RNA transcribed from the mutated gene and the protein encoded by the RNA. If RNA mutation occurs with the same frequency and managed to change a non-coding RNA (perhaps a pseudogene with a frameshift) to a coding RNA, only 1 in a million transcripts would be changed. Therefore if one sequenced the RNA the incidence of such changed RNA would be too low to detect, or if detected would be below the error margin in such experimental work.
Why has nobody looked for it?
Experimental science costs time and money. Scientists explore theories that have an importance and probability of success that attracts competitive funding. If there is no particular reason to look for something and the chances of seeing it are minute, nobody will waste their time on it, and they would certainly not get funded to do so.


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Why does NAD+ become reduced if it gains a hydrogen proton?
I've heard that $\ce{NAD^+}$ gains a Hydrogen proton during glycolysis and the Krebs cycle and becomes reduced to $\ce{NADH}$. However, isn't reduction when a molecule receives an electron?
Maybe I've been misinformed? Does $\ce{NADH}$ gain a Hydrogen electron, not a proton?

You are correct that reduction is simply a gain of electrons. This results in a decrease in oxidation number.
You know that NAD+ is reduced by this process because it starts off with a positive charge (+1) and ends up with a neutral charge (0).
The reducing agent that is donating the electrons is the hydrogen. More correctly, the electrons come from the hydride (H-).
The hydride is represented by 2 electrons on this redox diagram:

As you can see, the reduction reaction transfers a proton (H+) and hydride electrons (H- or 2e-) to NAD+. One electron goes to the nitrogen, and the other to the carbon where the proton binds. More details on the redox reaction of NAD can be found here.

Hydride anions do not exist in biochemical reactions.


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Relative microRNA comparison from from TCGA data?
I have a conceptual question that I was hoping someone could answer.
Can I say that microRNA A is expressed x-fold greater than microRNA B directly from the TCGA miRseq data?  Can I do this after normalizing the data?  Does it matter if I use RSEM or RPKM values.  It seems to me that it should be legitimate in any case since microRNAs are approximately the same length, but maybe I am overlooking something.
For example, I am following a paper published in Nature Communications entitled "Identification of a pan-cancer oncogenic microRNA superfamily anchored by a central core seed motif".  The authors download the data and collapse isoform reads to a single read count using the reads.  They say they used the reads per million microRNAs mapped, which establishes each microRNA read count as a fraction of the total microRNA population.  The authors then do upper quartile normalization which they say is important because a subset of microRNAs (miR-143 in particular) contributes so significantly to the total read count.  In the text, the authors appear to use the resulting values to do a direct comparison between microRNAs.
I definitely want the collapsed isoforms, and I think it makes sense to do the normalization.  However, I would like to say that a particular microRNA is expressed x-fold higher than another.  Can I do this from the collapsed and normalized data?
If this has already been answered, I apologize.  I could not find it.  Thanks.

Small RNA sequencing is good for a preliminary screen because as with most high throughput experiments, the sample size is less and therefore inter-sample comparisons may not be accurate.
For miRNA expression calculation, I use mirdeep2's quantifier script with a slight modification. The script basically aligns the reads to known pre-miRNAs and finds if they are aligning to the annotated mature miRNA region (some window of partial mapping into non-mature region is allowed and can be set to zero as well). It uses bowtie for alignment.
What I generally do is, instead of running bowtie in the -v mode (as set in mirdeep), I run it in the -n alignment mode by making a small modification in the script. The -n mode lets you define a seed region, and number of mismatches in seed and non-seed regions. I set seed length to 10 with 0 seed mismatches and around 2 non-seed mismatches.
To calculate reads per million (RPM) I normalize it with number of unique mappings to genome, with same alignment parameters (except --norc). As per this study an RPM of 100 is biologically relevant.

Can I say that microRNA A is expressed x-fold greater than microRNA B?

Yes, if the read counts are clearly different. EM and other likelihood models calculate a confidence interval for read counts or RP(K)M and if two RNAs do not have overlapping intervals then they can be said to be expressed in different levels. I haven't used likelihood models for miRNAs though, but I guess it is fine because:

The search space is highly reduced.
Search is stringent.
Isoforms are collapsed.

This effectively eliminates the possibility of dubious reads. You can set an RPM difference of 100 to call two miRNAs as differentially expressed, as 100 is the lower limit of biological significance (as previously mentioned).
Most relevant miRNAs have comparable GC content; so there may not be such a great sequencing bias (moreover the read itself is quite small).

I would be very careful in making such a claim from sequencing data. There are well known biases in sequencing due to things like GC composition and hexamer composition. Because of this, it's possible to compare relative levels of some species across treatments but tricky to do so within a given sample or samples. One way around this is to perform qPCR using absolute quantification on a few samples and then compare that to sequencing results. Granted, you're not going to have the same samples as TCGA or exactly identical library prep, but it'll be a reasonable starting point. You could then use that to calibrate how reliable the relative levels might be within the TCGA dataset.

Thanks Devon.  If there were no sequencing biases, do you think this with be a legitimate claim with isoform-collapsed and normalized data.  I have qPCR data on cell lines (though I used relative quantification and not absolute quantification), so I wanted to compare clinical data to that.  As an aside, I have been comparing the fold-change from microRNA A versus small RNA RNU6B to the fold-change from microRNA B versus small RNA RNU6B to determine fold change of microRNA to microRNA B.  I am wondering if your suggestion to use absolute quantification suggests that you don't think this is ok?

authors: Дима Боргояков


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Are some non-coding RNA spliced?
Are some non-coding RNA spliced like mRNA? I tried to find some information but I don't find anything...

yes yes - many of them are spliced out of mRNA too

You should check out Howald C, et al[1].  This is one of the many recent papers tied to the ENCODE data. They've used RT-PCR to amplify exon-exon junctions and then sequenced the results. Supplemental table 2 shows 3076 validated exon-exon junctions in putative processed transcripts which, in the main body of the paper may be sub-classified as:

Non-coding, 3prime_overlapping_ncrna, Ambiguous_orf, Antisense,
  LincRNA, ncRNA_host, Retained_intron, Sense_intronic,
  Sense_overlapping, Processed transcript.

Howald C, Tanzer A, Chrast J, Kokocinski F, Derrien T, Walters N, Gonzalez JM, Frankish A, Aken BL, Hourlier T, Vogel JH, White S, Searle S, Harrow J, Hubbard TJ, Guigó R, Reymond A. 2012. Combining RT-PCR-seq and RNA-seq to catalog all genic elements encoded in the human genome. Genome Research, 22: 1698–1710, doi:10.1101/gr.134478.111.

Yes, check out HOTAIR (in human), as well as cyrano and megamind in zebrafish -- they are all spliced.

Do you call ribosomal RNA non-coding? rRNA is spliced both in prokaryotes and eukaryotes.

can you give some reference, please?

Sure. Some general information on pre-tRNA and pre-rRNA processing from Molecular Cell Biology: http://www.ncbi.nlm.nih.gov/books/NBK21729/

And of course rRNA is non-coding. Coding refers  specifically to sequences coding for protein.

One of my teachers considered all "functional" RNA coding.


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TCGA data, and bioinformatics design questions for SNP/ mirna analysis
It's my first time posting to this forum but was looking for some help on the data aspects of this project.
My tools of choice would be in python/R .
Goal: I'm looking to create a disease specific profile of just SNPs and SNPs in miRNAs and miRNA target sites.
PART 1: TCGA
My first step is using TCGA data which lists a ton of harmful mutations in a LOH .txt format. I'd like to be able to map those mutations to SNPs or genes or miRNAs  (whatever entities they belong to). The TCGA datasheet is here.
Example data is here. for breast cancer. I guess I can use this miRNA and mRNA data as well.
Questions here:

How to decipher the LOH data to figure out if it's meaningful and where it maps?
Which Tools to use for mapping and what formats for final data ? Fasta ?

miRNA/Targets and SNPs
Next up is getting cancer specific miRNAs and mRNAs and mapping SNPs to them?
I'm assuming using dbSNP or Sanger miRNA databases to get miRNA/targets and seed sequences.
I'm a bit lost as how to combine all these pieces of information, what formats to use for output (linked to individual pieces) and which tools if any to use to gather all this data using python. This tool is useful tool mirdsnp.

I think you might be missing a few links - I don't see your TCGA datasheet and I can't tell whether the last sentence is meant to have a link or not.

Hi I edited it.
I believe I was only allowed one link so hopefully it's fixed.. here is the data sheet
I guess my issue is understanding how to make sense of the LOH data and how to map it in a meaningful way.. The second is how to incorporate the miR and miRsnp related data.
Thanks for catching the missing links. If I wasn't clear enough or didn't provide enough information let me know and i'll clarify as much as I can

I can't comment on the science, but as far as the analysis goes, Biopython has great tools for working with FASTA-formatted data: see SeqRecord

You can take a look at this tutorial to understand TCGA MAF files. And you can find a list of TCGA MAF files containing mutations mapped to genes and miRNA at https://www.synapse.org/#!Search:syn1710680

Thanks for the answers. @dd3 I was planning on using SeqRecord. Though I'm not sure how to add features to SNPs to link them to associated miRNAs or targets etc..

those are great links. How did you deduce the MAF. I'm looking at SNP and expression miRNA profiling for level 3 [https://tcga-data.nci.nih.gov/tcga/tcgaDataType.jsp data]. for where I found that information and it mentions LOH and MAGE-TAB. Here is another [http://www.broadinstitute.org/collaboration/gcc/data/data-types link] .. So i guess for this particular part I'm confused about data format, and meaning of the data :) . For the rest it's how to manipulate miRNAs and then batch search with python/biopython. Thanks again.

Affter finding this [https://wiki.nci.nih.gov/display/TCGA/Accessing+MAF+files link] it seems to me that level 2 files are MAF files. Level three are .txt something else.

@prussiap: I'm not familiar with the file formats, but could you use Python dictionaries, using SNP IDs as keys?

That is the method I am using so far. This does remove duplicates but I still would like to do a mass download of positions and annotations for each SNP.

@prussiap: Level 2 is processed data and Level 3 is interpreted/validated data. Check the TCGA Data Level Classification page for more details.


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Mapping a mutation to known SNP, 3' UTR, miR
I've parsed out a very large TCGA cancer ssm (single mutation file) file to give me the essential information.
The ssm is in the following format:
['Gene name', 'Ensembl Gene ID', 'Chromosome', 'Chromosome start', 'Cancer Type']
['NTRK1', 'ENSG00000198400', '1','156849827', 'Prostate Adenocarcinoma (TCGA, US)']

From there I would like to grab each mutation and :

Map the chromosomal position to a known SNP (rs something output).
See if this snp is found in a 3'UTR
See if this snp is found in a miRNA
Missense or sense mutation
Any relevant genbank etc.. ids

I'd like to do this Python (I think BioPython is suited for this) for downstream applications.

What is the question? It would also help if you gave the format of the file you would like to process.

I've already parse the format for this:
code   ['Gene name', 'Ensembl Gene ID', 'Chromosome', 'Chromosome start', 'Cancer Type'] ['NTRK1', 'ENSG00000198400','1', '156849827', 'Prostate Adenocarcinoma (TCGA, US)'] code
First line is features, second is an example of what's in it. aka. gene name, ensemble gene id, chromosome position and cancer type..
I would like to use this information (any or all) to locate if these SNPs occur in any miRNAs or any 3'UTRs predominantly.

You can also post on biostars.org for bioinformatics related questions.

In order:

Unfortunately, there's no easy way to batch query with only location. You could look up SNPs within genes here.  (You could find the gene a SNP is located in by searching an annotated human genome file for the position.)
You can figure out whether it's in 3'UTR by comparing to a list of human 3' UTRs. The UCSC genome browser page here will help: change the region to "genome", output format to "BED", click "get output", and then filter for 3' UTRs.
You can figure out if it's in a miRNA by comparing chr/start positions to the database available here.
You could use the chromosomal locations and compare to the human genome sequence.  There is probably a better way to do this, but the FASTA for each chromosome is available here.
You could use the SNP IDs to map to Genbank accessions, as per the instructions here.

You don't need BioPython for the first three steps, since it's just parsing each line and comparing values. BioPython is useful for working with FASTA files (so part 4).  All in all, this is mostly reduced to a programming question with all of these resources (basically, you need split each string to get the appropriate numbers/IDs and then compare).

So I've managed to wrangle mutations and their corresponding 3'UTRs . I still don't see a way to determine if that mutation in the UTR is in a miRNA target site ? Are there any thoughts on this?

@prussiap: your original question asked to find if a mutation was in miR -- to find if a mutation is a target of a miR, the best I know is DIANA. Unfortunately, the lookup goes the other way: if you look up a miR, you can find predicted target sites: http://diana.cslab.ece.ntua.gr/


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Small non-coding RNA (sRNA) vs. micro RNA (miRNA)?
What are the differences between micro RNAs (miRNA) and small non-coding RNAs (sRNA)? Are these two terms used interchangeably?

miRNA are one member of the small non-coding RNA family.
"Small non-coding" is a pretty broad term that encompasses microRNA and short interfering RNA, among other regulatory RNA species. The key word is 'regulatory'; each type of small non-coding RNA works by binding complementary sequences to exert some sort of regulatory control over gene expression. Someone more knowledgable might correct or add to this, but I believe that a key difference between the various types of regulatory RNA hinges on which other factors they recruit to their site of action.
Structural differences between these RNA are a little harder to characterize. This paper, for instance, discusses how miRNA have been found to play contradictory roles in gene regulation; repressing some genes, up-regulating others. The precise mechanisms behind this are an active topic of investigation, but a lot seems to depend, as I mentioned earlier, on which other proteins they recruit and this in turn, seems to be influenced by things like cell type and developmental stage, among others. Here's another paper , that goes into some of the sequence-based structural features of miRNA. I know that this one is free, but the first one might be behind a paywall. If it is, contact me & I can send you a pdf.
I don't hear the terms used interchangeably, however, at least not in the sense of someone saying "small non-coding RNA" when specifically talking about miRNA.

So sRNA may include miRNA, but not the other way around?

@becko That's right.

Small non-coding RNAs are NOT generally abbreviated as sRNA but as sncRNA, if anything. I say "if anything" because the "small" part is subjective and just a portmanteau descriptor. The straight answer to your question is as Forest has written: miRNAs are a specific type of small non-coding RNA, some of which appear to function in the regulation of gene expression — others remain to be characterized. Another problem with the 'small' epithet is that miRNAs may only be 20 nt in length, but are formed from pre-miRNAs of up to 1000 nt.
Other categories of small non-coding RNAs include:
tRNA (transfer RNA) - originally abbreviated sRNA (for soluble RNA) - involved in protein biosynthesis.
5S rRNA - a component of the ribosome
7SL RNA - a component of the signal recognition particle involved in recognizing the signal peptide of secreted and membrane proteins.
snoRNA (small nucleolar RNA) many of which act as guide RNAs in ribosomal RNA modification.
snRNA (small nuclear RNA) many of which are involved in the splicesome, the ribonucleoprotein complex which removes introns from precursor mRNA (these have names like U1, U6 etc.)
SINE RNA: RNA transcribed from the short interspersed nuclear repeats (SINEs - Alu sequences in humans) which are thought to be 7SL signal recognition particle pseudogenes.
And, no doubt, there are some I (and science) have missed.

Maybe not in general, but I have seen some papers where small non-coding RNA is abbreviated sRNA.

No doubt. But not standard, and not to be encouraged.

@David small-RNAs is a commonly used term and nowadays is more or less becoming standard but the abbreviation as "sRNA" is probably not (in contrast with lncRNA). Generally, "non-coding" is not included in these references. So sRNA when used, would mean small non-coding RNA (sncRNA is hardly ever used). In any case the definition of a small noncoding RNA is also quite loose; RNA <200nt. There are many other inconsistencies too. As of now miRNA in itself is a neatly defined term and any classification would only cause confusion.


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How TCGA CNV values are calculated?
When I download CNV SNP array data for ovarian cancer from TCGA data portal, I see some very small numbers like -5, -6 in the "Segment_Mean" column of the segmentation data files. I am very new to genetics, and I don't understand how that much deletion can occur in the gene. We have 2 copies of each gene in our normal cells, and my intuition tells that the minimum loss for a gene in a cell can be -2 (both copies of that gene are lost). So, could someone please explain shortly how this -5, -6 copy number losses are computed?


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TCGA gene expression data are missing matched normal
I'm trying to use the TCGA data portal to get gene expression data for cancer tissues, but I'm not sure what "Tumor matched normal" means.
It is unclear to me if the values are already compared to a control (non cancerous) tissue or there is another sample which gives the healthy tissue values. Assuming it is the latter, I don't see these samples anywhere.
This is a screenshot of my filter
http://www.clipular.com/c/5452873408184320.png?k=rlA9jy3Kb2U47JT7GrXBTzdsln4
And here is a picture of the result (only TNs appear).

These values are not already normalised to normal tissue controls. That a tumour is marked as tumour with matched normal doesn't mean that all of the platforms used by the TCGA (Expression, CNV, protein and methylation) have normal tissues available - in most cases Tumours with Matched Normals are annotated thusly because mutation calling was performed using tumour vs normal comparisons.
If you are looking for normal tissue controls for your given platform type (RPPA in your screenshot) you will find them labelled NT or N. If you don't see any that are labelled with those descriptions it means normal tissue samples have not been processed using that platform.
The TCGA doesn't do loads of normal tissue analysis for anything other than mutation calling because the project is focused on classifying tumours into subgroups using unsupervised approaches, primarily, and therefore all comparisons that get heavily used are tumour vs tumour after allocation to subtypes.

The TCGA Project has been collecting primary tumors and matched normals per patient per cancer indication and has been exome (WXS) profiling those samples, and genome (WGS) profiling a subset of them. In those cases, the primary tumor is tissue collected from the primary tumor and the matched normal is usually a blood samples, but could be derived from a buccal swab or some other source. As you can imagine, it would make less sense in terms of expression to assay matched control that derives from another tissue type such as blood.
For the expression data, the majority of the data comes from RNA-Seq and array-based profiling of the primary tumor data, and in some cases where adjacent normal tissue was collected and submitted, the TCGA has also profiled those samples. These are probably what you are seeing with your TN filter, and generally why you are having trouble finding equivalent amounts of matched normals as compared to primary tumor samples for the expression projects.
Incidentally, the company I work for has recently launched a free genomics data portal for the research community called GeenPool Reference. As part of it, we have deposited all of the publicly available TCGA RNA-Seq. The data along with all the associated clinical metadata is readily available for navigating and data mining. You'll easily be able to figure out the quantity of samples, and their matched pairs. If you're interested, you can take a look at the data projects available in GenePool Reference here: http://www.stationxinc.com/reference-library
~Sandeep


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Horizontal Gene Transfer
I understand the different ways bacteria can undergo horizontal gene transfer (transformation, transduction (phages), conjugation (plasmids)).
Is there an experimental method to tell how a specific gene got into the host (during evolution)? For example if a gene A got into host X from species Y, is it possible to know how gene A was transmitted?

I don't understand your question. What exactly are you trying to determine/distinguish?
Would you mind re-wording your question into a clearer form?

@Soph - I've edited your question, to avoid closing the question. You can revert my editing, if you wish.

Very interesting question. My wild-guess is maybe yes by looking at the DNA regions around the gene. You would probably get some base pairs from the vector yet I am not sure how many and if this would be sufficient to map that back to a plasmid or a phage. Will look that up.

@cagliari2005 - I was thinking the same. Intehration to the host genome requires homology si most likely there are some remaining eytra sequeneces around the integrated gene.

If you're looking at evolutionary timescales, then the only available source of information is the target organism's genome sequence. At least some of the methods of horizontal gene transfer you mention leave a distinctive signature in the genome. For example, retrovirus particles that have become incorporated into the human genome are easily identified by their local DNA sequence.
So in terms of an experiment, you would sequence your specific gene of interest as well as the surrounding local sequence (say, 10,000 base pairs downstream and upstream of your gene). In at least some cases you would be able to say for sure "this gene arose from retroviral incorporation", whereas in others it would be more ambiguous.
References:
Belshaw, R. et al. Long-term reinfection of the human genome by endogenous retroviruses. PNAS (2004)


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Which came first in hominid evolution: language or bipedalism?
I suspect both language-use and upright-posture have anatomical consequences. So does the fossil record show which came first?
NOTE: To the folks who marked this question "homework": This is certainly not an actual homework question. I have been an accomplished biologist for over 30 years and I am genuinely interested in the evidence for the timing of these critical human attributes. If you feel that the answer is so trivially obvious, that I should have been able to find out with only modest research, could you please leave a comment explaining, as if to a child, exactly how it is so obvious?

Question, although short, is in my opinion valid.

@Christopher Bruns - "homework" is defined here as a question with no research effort made to find an answer. If you hover on the downvote button, a message comes up: This question does not show any research effort. The homework close reason is explained: unless you have shown your attempt at an answer. That's all we're asking. As to defending it by claiming your accomplishments, this is the internet, remember. I can claim to be a Nobel Prize winner. If I post a question that is one sentence, without any explanation or support, it's still a poorly researched question.

@Christopher Bruns- I suggest you modify your question by editing in some useful information, and nominate it for reopening, or post a reopen request in meta

Thank you @anongoodnurse for your thoughtful and helpful comments. I think I will allow this question to fall into closed status and die, then later reask it, next time demonstrating more evidence of research.

Homework question doesn't mean the question is homework. They key statement to that closed reason is unless you have shown your attempt at an answer. I think the close reason shouldn't say homework at all but just say you need to show work so it dosnt cause people to debate that a problem isn't actually homework.

This article by Philip Lieberman On the subcortical bases of the evolution of language, page 22, he asserts the neurons associated with language may have developed based on the demands of bipedal posture. While there's no concrete data to support when we see the first languages, here we see evidence of arches (and presumably bipedal posture) in Australopithecus afarensis. So there are still some dots that need connecting.

OK, so if that one A. afarensis toe bone is convincing, then bipedalism is at least 2.5 million years old. Aren't there also skeletal changes in the skull and neck to allow our modified larynx position, so we could bracket the timing of spoken language development too?


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Eye strain using 3D screens
I don't know if this is a physics or a biology question but this might be better orientated around the element of how the human eye works in a 3D visual situation than physics.
Given the rise of 3D virtual reality headsets such as the Oculus Rift. I have a question about eye strain relating to these.
Often on a computer, eye strain comes from staring at the monitor for too long of which is the same continuous distance that your eyes have to focus on... thus causing computer vision syndrome (as well as lack of blinking). Meaning you should look at an object far away every X amount of minutes to rest the eyes.
How ever in a virtual headset, although the monitor is much closer, it is in 3D (or a fake 3D). So i wondered, does the eye muscles adjust based on this trick of fake 3D. Or will your eyes still focus on the fixed distance to the screen display regardless of the fake 3D perception, meaning you still get eye strain either way?
If you imagine looking through a 3D VR Headset, staring at a 3D object in the distance, is that going to create the same change in muscle adjustment in the eye as looking at a physical object in the real world of the same distance.
Essentially - 3D VR headsets could reduce the amount of eye strain. Because the eye is busy adjusting all the time for these "fake" distance illusions, rather than forced to focus in one way for a long length of time such as a 2D monitor. It might help people who struggle with computer vision syndrome perhaps?
Has any studies been done on this?

I think all illusions are based on certain drawbacks of our own eyes.

Most likely but thats a rather different topic entirely :P