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Can Nanodiscs be used to study membrane energetics?
Nanodiscs have changed they way we can study the structures, insertion, and functions of transmembrane proteins. Below is an image of a nanodisc bilayer.

The key difference, as far as I can tell, over traditional liposome studies are that one can alter the lipid composition more easily.
Have any studies been achieved on quantifying energetics in these nanodiscs ie energy transfer upon insertion, or stability of the protein in the nanodisc etc?

Do you have any references for these? What peptides are they using to wrap around that disc?

The peptides are "an ideal α-helix" whatever that means... I came across this bibliography in my hunt that also seems to have a few papers on biophysics of proteins in this system. Perhaps one of them explains more about the anatomy of a Nanodisc.

Nanodiscs are very powerful technology for membrane protein invented in Stephen Sligar's lab at the University of Illinois.
A nanodisc is composed of a membrane protein, lipids and two monomers of a "scaffold protein". The most used scaffold protein is an N-terminal truncation of apolipoprotein A-1 (apoA-1), which is the primary protein component of high-density lipoprotein (HDL) particles. But because the size of the scaffold protein defines the size of the nanodisc, membrane proteins with different size could require different type of scaffold protein.
The reconstitution of a membrane protein into a lipid disc is a self assembled process. The reaction consists in mixing the membrane protein solubilised in detergent with lipids (that can be chosen according to the protein) and the scaffold protein. Removal of the detergent (using for example Bio-Beads™ SM-2 Resin) starts the self assembled process and the nanodiscs are created.
Nanodiscs provide an environment where membrane proteins are stable and compatible with aqueous environments in a native-like phospholipid bilayer. The aquesous environment makes membrane proteins compatible with analytical methods that have primarily been limited to investigating soluble proteins. The lipidic environment, that can be chosen and optimised, keeps the membrane protein stable, monodisperse and active. The stoichiometry of the protein can also be controlled.
Compared to nanodiscs, liposome are large, unstable and difficult to prepare with precisely controlled size and stoichiometry.
Several membrane proteins have now been reconstitued into nanodiscs and used for different type of studies, for example for studying ligand affinities  and kinetics, for NMR, for studying protein association state, signalling properties, and ion channels (http://molsense.com/applications/trubind-at-work/ion-channel-inhibitors/).
http://www.sciencedirect.com/science/article/pii/S0014579309007960

A very thorough background to nanodiscs. Perhaps you could expand or provide a reference to the kinetics experiments. I am unclear if you are talking about ligand kinetics, or protein-bilayer kinetics.

Here are some examples: studying the kinetics of ligand binding to membrane protein (http://www.ncbi.nlm.nih.gov/pubmed/25935416, http://www.ncbi.nlm.nih.gov/pubmed/18616345), studying protein-protein interactions (http://www.ncbi.nlm.nih.gov/pubmed/20804721) and studying protein-lipid interactions (http://medx.mech.northwestern.edu/publications/papers/197.pdf). I think nanodiscs are a really powerful technique and as prove there are many really interesting publications around!


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Do genetic modifications exist to enhance the taste of tomatoes?
GM tomatoes seem less tasty. Has there been any research on GMOs with regard to taste?
Can scientists introduce a gene to, let's say, tomatoes, which would enhance the flavour? If not, why?

I'm voting to close this question as off-topic because it is poorly researched.

I think the primary reason commercially available tomatoes have poor flavor has more to do with being bred for hardiness and stability while being shipped and on the shelf. These features were selected in favor of taste and were not added by what most consider "Genetic Modification"

If you trim this question down to the first two sentences I think it becomes quite good. Strong answers should probably consider geraniol synthase etc.

I have a feeling additives would be easier than genetically enhancing taste.

@GoodGravy If you could identify the "flavor genes" that were lost during breeding, you might be able to add them back while maintaining the hardiness. Whether or not this would be cost effective is another issue.

@user137 Flavour is a really really tough one from a genetic perspective. For example sugar content would be the result of several genes and their interactions between one another. Up-regulating some may not revert the flavour. Fibrosity would certainly be a similar story. Of course there is also the more subtle taste factors like metals and complicated carbohydrates which they are struggling with in artificial meat. I agree with your cost summary. Whereas there might be a single gene switch to make the crop drought-proof, taste is far more complex task for a far more trivial outcome.

@GoodGravy About the only thing I could think to do would be to compare the genome or transcriptome of poor tasting tomatoes to good tasting ones. This would probably produce a long list of differences to sort through.

@user137 You can see why additives might be simpler :P A simpler way yet would be selectively breeding the better tasting crops for a few generations and see if that sorts things out... Old school, but it doesn't involve a $1,000,000+ project!

source: Stack Exchange

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Understanding F-statistics in population genetics
I am reading the classic Weir and Cockerham 1984 paper about $F_{ST}$ estimation. At the beginning (first page, right column), they define 3 statistics.

$F$ is the correlation of genes within individuals ("inbreeding")
$\theta$ is the correlation of genes of different individuals in the same population ("coancestry")
$f$ is the correlation of genes within individuals within populations.

They also state that the 3 statistics are related by
$$f = (F-\theta)(1-\theta)$$
I don't quite understand those 3 statistics and especially I don't understand why this relationship holds true. Can you help me with that?

I find the 1984 paper to be a bit dense in some places.  I don't have time at the moment to explicate this, but I found Bruce Weir's book very illuminating.  You don't need to read it straight through. There are two or three relevant chapters.  However, for your question, a good population genetics textbook such as Hartl and Clark might be sufficient.

I am a bit wobbly on the subject, but I think the most important bit of information is that they are re-parametrising Wright's (1951) hierarchical analysis of variation, "F-statistics," "hierarchical partitioning of variation," or "population parameters," depending on whom you ask. The parameters correspond as follows (on the bottom of p.1358): Fit=F, Fis=f, Fst=θ.
The relationship arises given some assumptions. Crucially here, if Fis (or f) is a measure of departure from Hardy-Weinberg Principle, and all populations identically depart from HWP, then Fit = 1 - Hi/Ht. It follows that, 1 - Fit = Hi/Ht. As well, we can rewrite this, so that, Hi/Ht = (Hi/Hs)(Hs/Ht).
Together, you can (maybe) see that, 1-Fit = (1−Fis)(1−Fst). Substituting, 1-F = (1-f)(1-θ).
(I realise that this is not a complete answer, but you can rearrange it with some algebra to get the Weir&Cockerham equation, I think).
[Update Oct 25, 2016]: it eventually yields f = (F-θ)/(1-θ). I think the posted question (above) contains a typo--specifically a missing division operator. Perhaps someone missed the stroke on a typewriter in the original paper?

Thanks for the answer. Solving $1-F(1-f)(1-\theta)$ for $f$ gives $f=\frac{\theta-F}{\theta-1}$.

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Cellulose acetate membrane filters vs. glass microfiber filters

At equivalent pore sizes and pressures, which exhibits faster flow rates, cellulose acetate membranes or glass microfiber filters?
I know cellulose acetate exhibits low protein binding, but what about glass microfibers? Would they be suitable when protein recovery is essential?

This might be relevant: how big are the proteins that you want to filter?

@GoodGravy ~270kDa

It would also depend on the pore densities and pressure resistance.
Silica is used for gel filtration chromatography to separate proteins. This can be achieved when proteins do not have affinity to silica, and most of proteins could be analyzed with silica gel. Silica and grass are similar materials. However, glass and silica have negative charges on the surface. Under low ionic strength condition, positive charged proteins or positive charged surface of proteins may bind to glass. Cellulose acetate does not have charge but it is hydrophilic.

Thanks for your answer. I have not been able to find any data on pressure resistance for the filters I'm looking at; do you know if it can be said as a general rule of thumb that glass microfibers typically exhibit faster flow rates than cellulose acetate membranes or vice versa? As in is one typically faster than the other or is it just a crap shoot without knowing the resistance?


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How to gather life-history traits data on bird species?
I am looking for a data base of life history traits for bird species. Is there such data base? If not, what are your advice on how to collect those data? Is there a handy website that list characteristics of birds around the world? I started to gather those data using wikipedia (and the references anytime wikipedia actually have reference) but it is very cumbersome.
The kind of data I am looking for are:

Reproductive age
Average lifespan
Average gestation time
Number of eggs per reproduction event

Welcome to Biology.SE. When reading your question without reading the example, I was rather thinking you were looking for population data (dynamic of population size through time and things like that). But it seems that you are actually interested in life-history measures of different species, is that right? If yes, you might want to change your post to say that you're looking for life-history data (and not population data).

@Remi.b Life-history is the term I was looking for

I rewrote your post. If you don't like it,please feel free to rollback. In its current form, I am voting to reopen the post.

I started looking at wikipedia and I even started collecting the life history traits into a spread sheet, but I wanted to see if there was something like this already available, becuase it seemed to be very cumbersome

Yes indeed. And the question seems legit. This is why I cared about improving the format and defining the scope more accurately hoping it would get reopen. I added that you started with wiki to the post.

Have you tried the Ecological Data Wiki? It's an NSF-funded repository of links to ecological databases and datasets:
Main page:
http://ecologicaldata.org
Find data tab, refined to Taxon=Birds, Ecological level=Species:
https://ecologicaldata.org/search?f[0]=taxonomy_vocabulary_2%253Aname%3ABirds&f[1]=taxonomy_vocabulary_6%253Aname%3ASpecies
The 'Avian body size and life history' dataset on the above page looks relevant to your search.

Up until recently, my answer would have been: Find your library's copies of standard reference-works like the Handbook of Australian New Zealand, and Antarctic Birds, Birds of the Western Palearctic, and the Handbook of North American Birds, and prepare to spend a couple of weeks transcribing the details of interest for each species. You still may need to take that approach for some regions.
For Australian birds, most of the key details are now available in digitised form in Garnett et al. (2015). Similar datasets may be available for other regions.


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Is the immune system suppressed during sleep?
Of course we have heard that during stress our immune system is suppressed. This leads me to wonder, does the immune system get suppressed when we are sleeping and why? During both these conditions the body might need to save energy for other biochemical processes, so I wouldn't be surprised that the immune system is suppressed while a person is asleep.

@AliceD is this better?

Nope. 'I' and 'my' should be removed. Importantly, questions should be generalizable and interesting to the community at large. No one can possibly know why your immune system is, or is not suppressed. Again, it is an interesting question, but it needs re-wording.

@AliceD what about this question?: http://biology.stackexchange.com/questions/24555/what-causes-feeling-of-feet-falling-asleep-to-be-delayed/25875#25875

It is a gray area, but that linked question is not a medical issue, it is an observation of a physiological process that is generalizable to something everyone has experienced in their life. Your question is not readily generalizable as you start of with an unsupported personal anecdote that likely not many folks agree upon.

@AliceD I have significantly edited it, now what do you think?

I don't have the time for a full answer, so I may come back, but generally 'no'. There is a difference between hibernation (a reduction in metabolic rate to survive prolonged periods of fasting) and sleeping. Humans do not hibernate, and when we are asleep the body doesn't try to save energy. Our brains go through REM sleep, which is just as active as waking hours, our organs -- especially dermal and muscle tissues -- amp-up their repair mechanisms while sleeping. Given that prolonged poor and/or lack of sleep significantly lengthens illnesses, the immune system should be very active.

During stress glucocorticoids, which are steroid hormones produced by adrenal glands (such as cortisol and corticosterone), are released into the blood-stream. These hormones have anti-inflammatory effects. You may say that they suppress the immune system.
The circadian rhythm, which controls the sleep-wake cycle, also controls glucocorticoid secretion, thereby reinforcing the sleep-wake responses [1]. Sleep deprivation is a form of stress and it causes an elevation in the levels of glucocorticoids. The effect of sleep deprivation could be replicated by adrenectomy (surgical removal of adrenal glands); there is a transcriptional downregulation of several neuroprotective genes [2]. Via glucocorticoid mediated signalling, sleep deprivation also results in inhibition of adult neurogenesis in the hippocampus [3].
Another study says that cytokine (TNFα and IL6) secretion by immune-system cells, are inherently affected by circadian rhythm via a glucocorticoid independent mechanism [4].
Overall, sleep is inversely correlated with reduced inflammation because both the processes are under the control of the circadian clock. Sleep deprivation, as a form of stress, is also correlated with glucocorticoid secretion. It should be understood that sleep doesn't necessarily inhibit glucocorticoid secretion and it would be incorrect to say that reduced inflammatory effects are because of sleep.

So this means that sleep does NOT cause immunosupression?

@TanMath Yes it is the opposite actually- immune system may be activated during sleep. But I wanted to refrain from saying that. Because such a strong statement cannot be made.


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Blood pressure measurement
When measuring your blood pressure in your arm, when you hear the sound to measure the systolic, is that sound NOT the heartbeat?

what do you define by "heart beat"? The sound of the heart beating or the rate of the heart beating?

The sound in question is called the Korotkoff sound:

If the pressure is dropped to a level equal to that of the patient's
  systolic blood pressure, the first Korotkoff sound will be heard. As
  the pressure in the cuff is the same as the pressure produced by the
  heart, some blood will be able to pass through the upper arm when the
  pressure in the artery rises during systole. This blood flows in
  spurts as the pressure in the artery rises above the pressure in the
  cuff and then drops back down beyond the cuffed region, resulting in
  turbulence that produces an audible sound.

I'm glad you found that info. I wasn't sure if the question was asking about the sound of the heart or some artificial sound cuffs make.

@mattkaeo blood pressure + measurement = Korotkoff sounds, this what the question is about...


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Is it theoretically possible to restore the Tiger population to 100,000?
The current global Tiger population is roughly 5100 according to Wikipedia. With habitat destruction and poaching continuing and no effective improvement in conservation, most likely the Tiger population will continue to decrease. I'm wondering what is the theoretical maximum population to which the Tiger species could be restored. Suppose poaching and habitat destruction stopped today and conservation efforts were increased drastically, so the only factors limiting population growth are the natural dynamics between the species and its prey. Could we then restore the global population of tigers to its pre-20th century level of 100,000?

Just FYI: to go from a population size of 5100 to a population size of 100,000 in 85 years, each female has to have on average 1.036 offsprings. This calculation assumed no stochasticity. variance in number of babies among females would bring this number up by a little bit.

How did you calculate that.

$5100 * x^{85} = 100000$ and solving for x. Of course it also assumes constant growth. But I think that it gives a feeling that if we manage to have growing populations, then whether and threshold of is at 50000 or at 250000 does not matter so much. Of course, whether each female will keep reproducing as much depend on whether the carrying capacity is high enough, and typically a logistic model might be a better fit than this exponential model.

Yes that is not a rigorous calculation.

Well, it is. It just makes a bunch of assumptions as it is super simplistic. As said, you can use more complicated models but you'll have to define other parameters such as the carrying capacity or the environmental change through time for examples.

Yeah that is what I meant. Anyways 1.036 offspring is low for tigers anyways, so your calculation shows that its not an unreasonable goal at least theoretically.

The question is interesting but I think there are tons of parameters involved and to answer this question one would need knowledge in a whole lot of different field. Generally, speaking I am afraid that biology (and geography and sociology as all those things are in interplay in the question) is rarely advanced enough to be able to answer this kind of question. We can make predictions about what should and should not be done, we can observe the beneficial impact of conservation efforts but we're not good enough to make the predictions you're looking for.

I think more interesting question would be what the minimum is (caused by outbred selection).

It is possible any day. Given time, their population will increase exponentially. But, we need co-operation fo people which can't be achieved easily

I don't think your core question can be answered exactly, namely "...the theoretical maximum population to which the Tiger species could be restored", since there are too many variables to consider. However, the comment from @Remi.b is apt, in the sense that it shows that population growth rate per se (or reproductive rate) should not be the limiting factor. The bottleneck will probably be some concept of carrying capacity (the number of tigers that the current tiger habitats can sustain). This number will naturally depend on many factors, for instance whether all hunting of tigers and their prey is removed from the remaining habitats.
You could arrive at a ballpark estimate of the current "theoretical" maximum population size by finding the area of suitable tiger habitat and dividing this by the average size of tiger territories. However, the size of tiger territories differ quite a bit between males and females and between areas with different prey densities, and probably also between different tiger subspecies (which will covary with average prey density to some extent - Siberian tigers generally live in habitats with lower prey densities). Looking at estimates from the Smithsonian and wikipedia, territory sizes seems to range between 20 to 510 km2, with maybe even larger male territories in Siberia (so quite a big range). The IUCN Red list assessment of tigers state that the current tiger range is to 1.185 million km2 (from Sanderson et al. 2006 as "Tiger Conservation Landscapes"). It is however unclear whether this number corresponds to the total range extent or to the actual amount of suitable habitat (probably the former though). Nevertheless, lets say that the average territory size is 265km2 (probably way to high on average), that the estimated area from IUCN is correct, and lets assume that territories never overlap (which they do; male territories overlap with several female territories and female territories can also overlap to some extent). In that case, we will arrive at an estimated theoretical popultion size of ~4500 tigers. If we instead use a more modest territory size of 40km2 we will arrive at a population size of ~30000 tigers. Since territories can overlap, these estimates should be biased low, given their other assumptions.
Sanderson et al. (2006) however also includes a summary of "Effective Potential Habitat" of tigers, which sum to 5.65 million km2. If this number is used to calculate the estimated theoretical popultion size you will arrive at ~21300 or 141000 tigers, using the same territory sizes as above. However, this completely ignores issues such as landscape connectivity, and that tigers are completely missing from large parts of their former distribution (these factors, and others, were used to define the "Tiger Conservation Landscapes"). Also note that much of the potential habitat lies in low-productivity areas (Russia and parts of China), where territory sizes are relatively large.
Another perspective would be to use the historical population size pre-20th century and the amount of decrease in tiger habitat to estimate a theoretical population size. I don't know anything about the historical population size, but let's say that your 100000 is accurate (source?). Dinerstein et al (2007) state that current tiger ranges correspond to 7% of historical ranges. Using only these numbers, you could arrive at a current theoretical population size of 7000 tigers (100000*0.07), assuming that the tiger population should directly mirror the amount of available habitat. However, the current 7% of historical ranges include range contractions due to hunting, so this estimate should be biased low (maybe strongly so) with respect to the possible population size that could be obtained if all hunting stopped.
These calculations should show that the current Tiger populations could probably be increased quite a bit with strong and effective conservation efforts (in your words: if "poaching and habitat destruction stopped today and conservation efforts were increased drastically"), but that 100000 will probably be extremely hard to obtain, given that so much of the historical range has been lost. With reintroduction programmes and a long timeframe it doesn't appear to be impossible though, but this is ignoring potential future problems such as increased tiger-human conflicts. Also note that I don't know much about Tiger biology, that these are merely rough back-of-an-envelope calculations, and an expert could certainly give you a more detailed and accurate answer.
References:

Dinerstein et al. 2007. The Fate of Wild Tigers. Bioscience 57 (6): 508-514. doi: 10.1641/B570608
Sanderson et al. 2006. Setting Priorities for the Conservation and Recovery of Wild Tigers: 2005-2015. The Technical Assessment. WCS, WWF, Smithsonian, and NFWF-STF, New York – Washington, D.C.
The Smithsonian: Tigers
Wikipedia: Tiger
IUCN Red List: Panthera tigris

What about the bottle neck effect? Increasing the population size won't increase the genetic diversity, won't it possibly lead to tigers that are not fit?

@SolarLunix For individual populations/subspecies inbreeding/genetic diversity might an issue, but for the species as a whole (if pooled) it is probably not a big problem. Overall, I don't think inbreeding is seen as a big problem in wild tiger populations at the moment. Either way, I don't think it makes much difference for this particular question, which is speculative and other factors are likely to be much more important.

but pooling the tiger population would be difficult wouldn't it? It would involve moving tigers to new habitats and hoping that that group of tigers will accept the new tiger rather than shun or kill them. According to my evolution teacher they are already going through a bottle neck effect, and if it's anything like the wolves we read about on the isolated island it could be quite detrimental to the species as a whole.

@SolarLunix My point isn't that all populations can be pooled, but that inbreeding is probably not the main factor to consider when answering this question. I agree that all populations cannot simply be pooled per se, and genetic effects might be an issue for some subpopulations. However, I doubt that inbreeding is the main problem for the survival of tigers as a species or for trying to restore tigers to much larger populations (but I might be mistaken). Also, a bottleneck is one thing (they are by definition going through one), and effects of inbreeding on population growth is another thing.


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Do High Triglycerides Invalidate Tests for Infections?
Blood donation websites tell you to avoid eating fats a few days before the donation, because when there is a lot of fat in the blood, they can't test for infectious diseases and must discard the blood.
Does that mean that if you have, say, high triglycerides, the result of an infection disease blood test will be invalid? Say you test syphilis, HIV, etc. If at the same time you get a result of high triglycerides, does that mean the results of infectious diseases can't be trusted?

You might want to look at this article about how Lipemia causes interference with some tests.


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What is the difference between options protein and replication in the NCBI database?
After checking the NCBI help page, I am still unclear about the difference between protein and replication interactions for HIV.
http://www.ncbi.nlm.nih.gov/genome/viruses/retroviruses/hiv-1/interactions/

I'm not entirely sure what you're asking. What is your specific question? That help page seems clear enough.

What is the difference between options protein and replication

I've suggested a pretty major edit. Feel free to role back, but I think the question is much clearer now.

These edits provided by users are good, perhaps the OP could add what their interpretation of the two terms are so it is easier to identify where the misunderstanding is.

what is OP?????

@Rishika You are OP=Original Poster i.e. the person who starts the thread. In SE this just means the asker.

Interactions denote protein-protein interactions, which means physical association between proteins. By nature, these networks/graphs are undirected.
Replication interactions (actually a not very good term) denote gene regulatory interactions that affect HIV replication. These sets also include the regulatory effects of HIV genes on host genes (and hence the terminology is unsuitable). These networks are directed as well as signed (positive or negative interactions i.e. activation or inhibition respectively).
You can see for yourself, from the dropdown boxes and the interaction lists in this page.

So its basically just PPI not leading to virus replication versus PPI that lead to virus replication??

@Rishika in the PPI networks the final outcome is not considered. The "replication" interactions are more about proteins regulating transcription (which happen basically via protein-DNA interactions).


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Where’s the flaw in my proposed TB Treatment?
Ten years ago, I emailed a prominent lung specialist with my suggestion for a treatment for Tuberculosis. His lack of response led me to believe that the idea had no merit whatsoever – but I had no idea what the failings might be. I still don’t and I’m hoping someone will enlighten me as to why this is not worth exploring.
The idea is based upon the fact that Mycobacterium Tuberculosis is aerobic – in fact Wikipedia states it “requires high levels of oxygen”. I’d heard that the TB “Sanatoriums” used to practice the treatment of breaking ribs on one side to collapse that lung and “rest” it. It seemed to me that this resting was actually starving the bacterium of Oxygen and killing the infestation.
My suggestion is that we take advantage of having two lungs by inserting a pair of breathing tubes into the respiratory tract and into the head of the two bronchi: it may be necessary to use ultrasound or other imaging to accurately position the tubes.
We feed pure Nitrogen into one tube, and 40/60 Oxygen/Nitrogen into the other – double Oxygen concentration. The patient therefore receives all their Oxygen requirements through one lung. After some period of time, when we judge that the bacteria in the “starved” lung are all dead, we switch the supplies to the 2 tubes and treat the other lung.
Can anyone see why this idea is so flawed?

The proposal is very flawed as pointed out by WYSIWYG, but I generally would like to see more questions like this.

also in tubeculosis, the mycobacteria go into tubecules and can remain in a dormant state for prolonged periods of time.  Its unlikely this will kill those protected and dormant bugs.

Your main flaw is forgetting that HUMAN cells require oxygen. See https://xkcd.com/1217/ for details.

High oxygen concentration can be deleterious; it can induce oxidative
damage.
The systemic blood circulation would supply oxygen to the
"oxygen-deprived" lung.
Moreover, Mycobacteria can survive in anaerobic conditions.

And what if both lungs are infected? There are many flaws in the proposed therapy.
Adding this point from one of the comments below:
There are several cases of extrapulmonary tuberculosis and in these cases treating just the lung would not make sense.

That link is interesting - so possibly they can survive indefinitely without Oxygen - that might explain his attitude. I don't think the oxidative damage side of it appears to be a major factor given that it will only be in one lung for a limited time period...? I deal with treating both lungs in the  text of the question. Yes, the derived lung would be supplied with Oxygen via the circulation - but I assume the bacteria receive their oxygen from the air in the lung - not via the blood supply...?

@Lefty "I assume the bacteria receive their oxygen from the air in the lung - not via the blood supply" : not true. In fact Mycobacteria reside inside the lysosomes of macrophages and there are several examples of extrapulmonary tuberculosis.

just one more question then sorry. Do you think the whole rib-cracking thing was ineffective then - given that "resting" the lung wouldn't have any effect on the colonies present in the lung?

@Lefty I didn't know that such a thing was practised. It seems like an ineffective therapy though.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1079536/ This is the reference I found today but I've read it 2 or 3 times before.

@Lefty You're seriously underestimating how dangerous oxygen is - even at atmospheric concentrations it's very dangerous; twice that means spontaneous combustion for a lot of materials, and of course increased oxidative stress in general - a great way to change your TB into cancer, I'd guess (well, at a very low probability still, but...).

As we know, this is not effective, but if it were, we could simply treat one lung at a time. The general understanding of this principal is important.

The accepted answer is correct but I would like to point out a misunderstanding that has to underly the idea that this would work:
In reality lungs aren't a magical one-way gate for the various gases.  Lungs do not extract oxygen from the air, they permit oxygen exchange between the air and the blood.  Since the air normally has a higher oxygen level than the blood the result is a net transfer of oxygen to the blood--something we see as extracting the oxygen from the air.
However, in an anoxic environment you'll get the reverse--oxygen will move from the blood (admittedly, not too much of it as this is blood that has already passed through the body) into the air.
This is also why too much carbon dioxide is lethal--the lungs likewise have no magic ability to expel it.  It just normally moves from the higher concentration (the blood) to the lower concentration (the air in the lungs.)  Too much and you can't get rid of it, or in extreme cases even get more of it.

Thanks! I see what you mean now. But it was mentioned in @WYSIWYG's answer earlier, so I thought yours would add something.

@Christiaan I'm explaining why WYSIWYG's answer happens.

What a great question! I'm sorry to add another blow to your theory... but... Even if oxygen depletion would be effective, treating the lungs alone is not good enough. True enough, Mycobacterium tuberculosis bacteria usually attack the lungs, but TB bacteria can also attack other parts of the body such as the kidney, spine, and brain (Fig. 1). If not treated properly, TB disease can be fatal. Because the TB bacterium has the potential to infect various parts of the body, a systemic treatment is necessary and long-term antibiotics are therefore the first-line treatment (Source: Center for Disease Control and Prevention).
In fact, there is tuberculosis of the lungs, tuberculous meningitis (infection of the brain membranes), skeletal TB, genitourinary TB and gastrointestinal TB (Source: Medscape).

Fig. 1. Signs and symptoms of TB infection in various parts of the body. Source: Infection Landscapes.

The most evident problem I can see for this TB treatment is that mycobacteria can survive without oxygen. Latent TB (a person is infected but doesn't have active TB) is one of the major problem in the treatment of TB and one-third of the world's population has latent TB! (http://www.who.int/mediacentre/factsheets/fs104/en/). A new TB treatment can only be effective if it targets both active and dormant bacteria.
Of course other aspects need to take into account. Over 95% of TB deaths occur in low- and middle-income countries with reduced access to medications and hospitals. A new treatment needs to have an affordable price, be simple and fast (http://aac.asm.org/content/53/3/849.full). These are other limitations of to the suggested treatment.

Well, the fact is, it would never work, I understand that now, but irritating reality aside, I would have thought it would be massively cheaper than antibiotics and hospitalisation wouldn't it? I can see that imaging would add a lot to the cost but I'm not certain it would be necessary.

@Lefty good heavens, of course not! Even if your theory worked, it also implies extensive hospitalization. More, in fact, than antibiotic treatment. The patients would need to be hospitalized for the full duration of the treatment, the concentrations of the various gases constantly monitored, doctors and nurses hovering for days etc.

@terdon Surely that all depends on exactly how long it would take to kill the bacteria? If they could survive 3 minutes - like humans - then the entire procedure could be over in 10 minutes. Given they can survive indefinitely then yes, I accept that an infinite amount of time in hospital might be quite expensive.


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View ABI chromatogram plots with python
I would like to view the chromatogram traces from a few ABI (.ab1) files. I would prefer to use python for this, or a function with python bindings, or at least some open source package such as EMBOSS.
If after viewing a plot I could also extract the plotted data in the form of an array, that would be a huge plus.
I have tried the relevant Biopython parser, but it only returns a list of nucleotides (not th actual chromatogram). I have also tried abiview, but I get plots such as this, which makes absolutely no sense:

Could you help me out?

Is there anything that you want to do with the file that you can't already do with a free package such as Finch TV?

I think this might explain the .AB1 file format, which would be the first step toward writing your own file parser. If you can get the X and Y data out for each channel, you could plot it however you wanted. Good luck.

Have you looked at abiview from the package EMBOSS? See this Biostars post: https://www.biostars.org/p/4097/

The Biopython 1.65 ABI parser should expose the chromatogram data, as of Biopython 1.66 it should expose everything.
UPDATE: Example using this for plotting here: http://biopython.org/wiki/ABI_traces

I would strongly recommend using Matlab, it does not read ab1 files directly, but it can if Staden tools are installed. The reason being is that the data is presented a lot better and there is documentation. I recently wrote a QQC calculation script in Matlab as proof of principle and I will rewrite it in python (due to server building) using it as a benchmark.
In Biopython the manual says:
from Bio import SeqIO
handle = open("myfile.ab1", "rb")
record=SeqIO.read(handle, "abi")

And that is where it stops. It does have the data. The trace data is stored as annotations:
import json
print(json.dumps(record.annotations, sort_keys=True, indent=4))

Specifically, the actual raw data is:
print(record.annotations['abif_raw']['DATA9'])
print(record.annotations['abif_raw']['DATA10'])
print(record.annotations['abif_raw']['DATA11'])
print(record.annotations['abif_raw']['DATA12'])

but wait! The bases are not ATGC. but GATC as found here:
print(record.annotations['abif_raw']['FWO_1'])

Having played with ab1 files in Matlab using the command:
[Sample, Probability] = scfread('temp_via_matlab.scf');

I know that there is not only raw data (Sample), but also probability data. The latter tells me where the peaks are (e.g. Probability.peak_index(100)) and how good they are. This will be very helpful. That data is saved here:
print('PLOC1 is peak_index in Matlab', record.annotations['abif_raw']['PLOC1']) # tuple of int
print('PBAS1 is what base it is', record.annotations['abif_raw']['PBAS1']) # str

Note that it is a wee bit different from MatLab and there are keys called PLOC2 and PBAS2 which have the same data as their 1 counterparts from what I can tell.
For a more detailed explanation of the many keys present see the vignette for the R package sangerseqR, which does the same and is annotated slightly better than biopython —although scfread in Matlab is a lot more neater (hence my recommendation).


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Is there a short-term, very strong anesthetic, which can be blocked/delayed by other substances?
Question migrated from World Building SE.
(For fictional use only, of course :) )
I am looking for an anasthetic which fulfills the following criteria:

very strong effect, induces a comatose state in a very short period of time
quickly wears off (30 minutes~) with only minor impediments to the recipent upon awakening.
Can be 'partially blocked' or delayed by the intake of some medicine or other substance (which needs to be not too uncommon). Maybe some receptors for the anasthetic are blocked by the substance or something similar. Ideally this would result in the recipent falling into a dream-like state for some minutes before the real effect kicks in.

Does the anasthetic have to be made up, or does something like this actually exist?

better off on health SE perhaps

Maybe this is obvious, but please note that a “comatose state” is going to require endotracheal intubation + ventilation in order to be anything approaching safe.

Xenon. It is like an ideal anaesthetic. See here.

Xenon is an interesting anesthetic as it appears to lack negative
  inotropicy and vasodilatation, giving great advantages to both
  patients with limited cardiovascular reserve or those who require
  hemodynamic stability. It has low toxicity and is not teratogenic.
  Xenon gives rapid induction and recovery, due to its low blood/gas
  partition coefficient (0.15), and has a MAC of 63%. Several vitro
  studies showed that Xenon may protect neural cells against ischaemic
  injury. Its low blood solubility can take to diffusion hypoxia if
  Xenon is not substituted by 100% oxygen at the end of anesthesia. It
  has been shown that, compared to other anesthetic regimens, Xenon
  anesthesia produces the highest regional blood flow in the brain,
  liver, kidney and intestine. In conclusion, the most important
  positive effects of Xenon are cardiovascular stability, cerebral
  protection and favourable pharmacokinetics. Negative points are high
  cost and the limited number of ventilators supplying Xenon.

Wow, Thanks! This seem to cover #1 / #2 really good. But what about #3? Could there be a substance to partially block / delay the effect of xenon?

@openend I guess if oxygen/air content is increased, it should delay xenon mediated anaestheia. But this is just like reducing its effective concentration.

Would something like using an asthma-inhaler be able to work this way?

@openend Perhaps yes. But the pharmacokinetic model is required to design such inhalers.

Well, I want to use is as an accidental effect, a (fictional) patient who should get unconscious, is only partially / delayingly affected due to some common substance which interferes with the anasthetic.

if its fiction then why not just make it up?

That's a good question, I'll just go for it. Just striving for some 'realism' :) And I hope the reader will notice, if certain facts can be verified.

I can't tell how much I agree with you, thanks a lot!

Well I might have to add there are good story without reseach, but the problem is when you have something, that is easy to research, wrong, which will break the immersion for the reader.

@openend you can discuss these things in chat because these comments are not really about the answer.

Propofol: a widely used i.v. anesthetic with GABA agonistic properties. It has to be given continuously i.v. When stopped, effects wear off in minutes, and recovery is without serious side effects and little nausea. Induction is quick (minutes or less) since it is administered i.v. Aminophylline (theophylline ethylenediamine), an adenosine receptor antagonist, antagonizes its effects (Sakurai et al., 2008).
Sufentanil: an opioid. Like propofol, when administered i.v. it is fast acting (onset after 6 minutes) and its effects wear off in ~30 min. Because of its short half life, recovery is fast with few side effects (Mendel, 2014). Full opiate antagonists like naloxone, or partial antagonists like buprenorphine can be used to counteract opioid anesthesia. Note that more common opioids, including heroin, morphine and fentanyl are longer-acting (say hours after injection) and probably not what you are after.
Ketamine: an NMDA receptor antagonist. It is only used in animal surgeries nowadays, and it is not used for human anesthesia anymore. I mention ketamine only because its effects last for about 30 min after oral ingestion, and it's onset is very rapid (within a minute i.v.). Its side effects include delirium and hallucinations up to 24 hours after use. These side effects are pretty serious and reason for its ban in human anesthesia. I am not aware of antagonists of ketamine action. Theoretically spoken, NMDA receptor agonists would do the job, but that is a pretty dangerous class of drugs and mostly applied experimentally as far as I know.
References
- Mendel J Clin Anesth (2014); 26: S1–S7
- Sakurai et al., J Anesth (2008); 22:86–8


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How does Celiac's Disease cause people to stop growing?
In all of the sites I've looked on, one of the symptoms of Celiac's Disease is the failure to grow in children. Why would an immune attack against gluten cause stunted growth?
Celiac.org states:

When people with celiac disease eat gluten (a protein found in wheat,
  rye and barley), their body mounts an immune response that attacks the
  small intestine. These attacks lead to damage on the villi, small
  fingerlike projections that line the small intestine, that promote
  nutrient absorption. When the villi get damaged, nutrients cannot be
  absorbed properly into the body

Journal of Pediatric Gastroenterology & Nutrition published a study on catch-up growth in children diagnosed with Celiac's before the age of nine.
Is the lack of nutrients what causes the impaired growth, or is there more to it than that?

The "proper" term to use in a case of growth retardation in children is "failure to thrive", abbreviated as FTT.
Celiac disease is one of the most common Organic endogenous (genetically based) FTT syndromes.
The main cause of FTT in pediatric celiac patients is malabsorption. In a case of insufficient vitamins and nutrients absorption the child will (have) FTT.


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Extract protein sequences from a GBK(GenBank) file without scripting
How to extract protein sequences from a GBK(GenBank) file without scripting?
I know that writting a script using BioPerl or Biopython can do this, but it is not elegant and daunting to beginners and biology persons. Any thing like
$ SOME_PKG_MANAGER install PKG
$ CMD ab.1234.gbk > ab.1234.faa

Coding is definitely elegant but not un-daunting perhaps ;)

I'm not sure what you mean by "it is not elegant". If scrolling through the file and copying and pasting sequences out is your version of elegant I'm not sure what the question is... Scripting languages like perl and python were made exactly for this situation. But I can't see the biological question here so I'm going to flag as off topic.

Yeah I agree with Good Gravy. This is like asking "I want to do cell culture but I don't want to go through the process of changing media. Is there a robot that can do this for me?"

@WYSIWYG If there is, you cannot afford not knowing that. This is the value of my question.

@FrozenFlame Extracting sequences is not very difficult. Usually most people write their own scripts to do that. Packages are generally made to deal with more complex data extractions and computations. What you want to do can be done using a one liner code.

@WYSIWYG I know that extracting sequences is not a difficult task. But why waste time writing the same script again. If you can do it in one line, show me your code; that's what I want.

@FrozenFlame That is off-topic in biology. See the meta-post that Good Gravy mentioned.

@FrozenFlame I posted my answer previously (in Perl) and it would be trivial to convert it into a one-liner, but everyone jumped all over it in the comments because they thought you did NOT want some code, and because I provided no instructions. Then Chris, a moderator deleted my answer. I guess you didn't see it in time. Oh right, as WYSIWYG mentions you cannot post answers that your so-called "peers" here have determined to be off-topic. Because being on-topic is so much more important than actually helping someone.

My colleagues and I have recently published a toolbox of command-line utilities that are modeled after GNU Textutils (e.g. grep, cut, tr, uniq) called FAST: Fast Analysis of Sequences  (cite).
To install FAST you can use:
perl -MCPAN -e 'install FAST'

Or install the current development version from github. The FAST install comes with a cookbook, sample dataset and all utilities have man pages. The cookbook is also available on the github wiki.
To convert the features annotated in a genbank file to fastA sequences you can use gbfcut. Below are examples of using gbfcut:
To convert all annotated features of a genbank file to fastA format:
gbfcut genbank-file

Output only tRNA features:
gbfcut -k tRNA genbank-file

Output all feature sequences with a "note" qualifier containing "telomere":
gbfcut -q note=telomere genebank-file


text:
Hodgin-Huxley model for a single neuron - continuation
As  continuation to This question I have posted. Another set of questions is given for the same model :

This time it is also given that for $t<0$ the membrane potential is $u_0$, and at $t=0$ it is instantaneusly jumps to $u'$, and that $u_0,u',u_2,u_{ion}$ are maintained for $t \geq 0$.
So, with this additional data I could actually compute $r(t)$ and $s(t)$ for $t \geq 0$ in the following way:
For $t \geq 0$, $u(t)=u'$
so $r_0(0)=r_0(u') \approx 1$ and $s_0(0)=s_0(u') \approx 0$.
Also, $\tau_r(0)=\tau_r(u') = 1$ and $\tau_s(0)=\tau_2(u') = 15$.
So, we get:
$r' = -\frac{r-1}{1}$ and $s'=-\frac{s-0}{15}$ $\Rightarrow$
$r' + r = 1$ and $s' + \frac{s}{15} = 0$.
These are a first order linear diff. equation of the form:

$y'(x)+p(x)y=q(x)$

So their solution is of the form:

$e^{-\int{p(x)dx}}(\int{q(x)^{\int{p(x)d(x)}}}+C)$ ($C$ is a constant)

So I get:
$r(t)=C_1e^{-t}+1$ and $s(t)=C_2e^{-t/15}$.
I think that we have no way of knowing our initial conditions $r(t=0)$ and $s(t=0)$ (am I right?)
So for simplisity let's assume that $C_1=C_2=1$ and we get:

$r(t)=e^{-t}+1$ and $s(t)=e^{-t/15}$.

So for example: $r(t=100) = e^{-100}+1 \approx 1$ and $s(t=100) = e^{-100/15} = 0.0013$ and $r(t=3) = e^{-3}+1 = 1.0498$ and $s(t=3) = e^{-3/15} = 0.8187$
So my question is:
How come answers 4 and 7 below denoted as true?

Actually there is a way to determine the initial conditions for this problem.
We assume that the gating variables $r$ and $s$ are at a steady state before the membrane potential jump from$u_0$ to $u'$.
If we look at the first graph(the steady state relation versus membrane potential) we can see that at the membrane potential $u_0$ that $r(t=0)\approx 0$ and $s(t=0)\approx 1$.
Then we make our jump in voltage from$u_0$ to $u'$ and now our $r(t)$ and $s(t)$ will change towards their new steady values $r'=1$ $s'=0$ with time constants given by the second graph.
Your equations $r(t) =C_1 e^{-t}+1$ and $s(t)=0+C_2 e^{-t/15}$ are correct, but using the information from the graphs we can se that $C_1= -1$ and $C_2= 1$.
From these equations we can see that $r(3)=1-e^{-3}\approx .9502$ and $s(3)= e^{\frac{-1}{5}}\approx .81$ (note that .81 is close enough to 1 for this particular problem). This means that the channel is mostly open as statement 4 asserts.
Now for statement 7 we have $t= 100$ and while $r(100)= 1-e^{-100}\approx1$, and $s(100)=e^{\frac{-100}{15}}\approx 0$. This means the product $r(100)^2* s(100)\approx 1^2 * 0 \approx 0$ thus the channel is mostly closed.
Hope this helps.

All true. Just remember that a particular channel is pretty much either open or closed (as verified by single-channel electrical recordings), so 0.81 open means that 81% of channels are open at $t=3$.


text:
Blood urea nitrogen and usage of proteins?
I have been reading some books and am getting slightly confused. Here is roughly what I have read.

The greater the blood urea nitrogen the greater the usage of proteins (since blood urea nitrogen is created via the metabolism of proteins).

and

A positive nitrogen balance (where nitrogen balance $=$ N intake $-$ N loss) indicates a greater usage of proteins.

To me these two seem to be contradictory, if you have greater blood urea nitrogen then surly you will have more nitrogen lost meaning the nitrogen balance would be more negative. So do these two statements contradict and either way please can you explain.

Well, nobody said N intake is a constant. Allow that to vary and you'll see that the balance can remain positive even with increasing loss

The quotes in the question provide incomplete information at best.
Let's focus on the nitrogen (N) that comes from protein, ignoring for now how metabolism differs among amino acids. We'll assume for simplicity that liver function is OK so that the urea N production rate is a reasonable measure of protein N degradation rate (say in units like moles of N per hour).
First, consider simple conservation of mass:

Intake = Loss + Net Storage

If you think about this in terms of protein N from the perspective of the whole body, you start to see the limitations in those quotes.
First, loss from the whole body of metabolized protein N in the form of urea N mostly occurs via the urine. Impaired kidney function may lead to less urea being excreted than is being formed. You will then have "Net Storage" of urea N in the blood and body fluids, so BUN can rise even if metabolism of protein is unchanged. In fact, BUN tests can be used with other tests to provide information about kidney function. Thus BUN levels don't necessarily tell you much about the urea N production rate or protein degradation rate.
Second, you have to consider that one "use" of proteins can be "Net Storage" to build muscle mass. That type of "use" won't show up in increased urea N production. Alternatively, body protein in muscle can be broken down to be "used" for metabolism/catabolism during an overnight fast, possibly leading to increased urea N production rate depending on how the mix of protein, fat, and carbohydrate catabolism has changed. Furthermore, the effects on BUN levels will depend on kidney function.
Both storage and degradation/catabolism are important "uses" of protein N, but they have different implications for production of urea N, and BUN levels don't necessarily represent the urea production rate.

Urea is the major nitrogenous end product of protein metabolism, and its production reflects both the dietary intake of protein as well as the protein catabolic rate. The magnitude of these processes have a similar influence on urea concentration in the blood stream. Please note, that BUN, aka blood urea nitrogen is a misnomer, here plasma or serum urea nitrogen is meant and not the urea concentration in the red blood cells.
Urea biosynthesis from ammonia is carried out exclusively by liver via urea cycle. As explained above, dietary protein intake as well as the protein catabolism end up with a certain amount of ammonia which converted to urea in the liver. This is why, the higher the protein intake, the higher the urea concentration; the more protein disintegrated via catabolism, the higher the urea concentration. The excretion of urea is carried out mostly by kidneys in a certain (almost constant) rate.
Now, the positive nitrogen balance means that in the system there is an increase of “net storage” of nitrogen (and we deal with azotemia). The important point is - the urea metabolism is a continuous event where two processes are going in almost (but not absolute) parallel: the urea production (liver) and urea excretion (kidneys). There is a constant “delay” between the urea production and urea excretion and if the organism is in high protein intake and/or high protein catabolism rate this delay will bigger. On the other hand, the bigger the excessive amount of urea, the more urea is excreted until its concentration is normalized. If the urea production will decrease (normal protein intake, normal metabolism), the system will eventually excrete the excessive amount of urea and nitrogen balance will be around zero value (never will be absolute zero though).  So,  if the blood sample from an otherwise healthy individual is taken in the middle of the explained cycle (higher protein intake/catabolism), there will be a positive nitrogen balance.
As can be seen from the explanation (I hope that it can), there is no contradictions between the clauses in the question.

The thing to remember is that no living system is 100% efficient. Firstly lets deal with your statement here:

A positive nitrogen balance (where nitrogen balance = N intake − N loss) indicates a greater usage of proteins.

Basically means what it says on the tin. If your body is utilising more nitrogen then it is giving out:

Positive nitrogen balance is associated with periods of growth, hypothyroidism, tissue repair, and pregnancy. This means that the intake of nitrogen into the body is greater than the loss of nitrogen from the body, so there is an increase in the total body pool of protein.

Now we really cant measure notrogen on a weighing scale. So the blood urea level is used as an estimator for nitrogen metabolism.
Now, as mentioned below, no system is 100% efficient, and the purpose of urea is infact to be as a vehicle to excrete toxic ammonia. Sure the body looses a few nitrogens in the form of urea, but as a whole, it gains a lot more from the metabolism.
An analogy:
In other words, think of it like this: When you make something out of wood (proteins), you get wood shavings (ammonia). Now you cant reconstitute the wood shavings back to a full wood (proteins), nor can you use wood shavings (ammonia) as they are toxic for you. So you group up the wood shavings (urea) and get rid of it.
The formula is essentially:
The number of furniture made = Function of (Weight of wood delivered - weight of saw dust taken out)
If you use a lot of wood (proteins), and are a good craftsman (healthy human)  you'll ultimately have a bit more shavings (blood urea nitrogen), but a lot more furniture to move around. Now since you cant see the furniture (proteins in tissues), but you can see the shavings coming out (blood urea nitrogen) there will be a rise in the BUN.
Now if you are a poor carpenter (disease or something), you mess up and you end up wasting a lot more wood as shavings than getting the furniture right. So for say 10 kilos of wood, you can only make one couch and 9 kilos of saw dust..  you are really not doing it right, and this is where the BUN gets in the negative territory (I know 10-9 is not negative, but remember the body has a baseline need of say 6 couches and you are only giving it 1, the body will get that from somewhere to the detriment if your health)
Apologies for the long winded analogy. I hope you understood what I was trying to imply. I am pretty sure someone out there will give a more scientifically accurate answer, so you might want to wait on that also.
Further reading from the wiki:

Amino acids from ingested food that are not used for the synthesis of proteins and other biological substances — or produced from catabolism of muscle protein — are oxidized by the body, yielding urea and carbon dioxide, as an alternative source of energy.
The oxidation pathway starts with the removal of the amino group by a transaminase; the amino group is then fed into the urea cycle. The first step in the conversion of amino acids from protein into metabolic waste in the liver is removal of the alpha-amino nitrogen, which results in ammonia. Because ammonia is toxic, it is excreted immediately by fish, converted into uric acid by birds, and converted into urea by mammals.
Ammonia (NH3) is a common byproduct of the metabolism of nitrogenous compounds. Ammonia is smaller, more volatile and more mobile than urea. If allowed to accumulate, ammonia would raise the pH in cells to toxic levels. Therefore many organisms convert ammonia to urea, even though this synthesis has a net energy cost. Being practically neutral and highly soluble in water, urea is a safe vehicle for the body to transport and excrete excess nitrogen.


text:
Differences in structural target for LPS detection by LAL and MD-2/TLR4
I remember there being a significant difference in the pathogen-associated molecular patterns (PAMPs) used for detection of lipopolysaccharide (LPS) by mammalian cells and the Limulus amoebocyte lysate (LAL) assay. However, I cannot seem to find a paper that indicated what structure on the LPS lipid A moiety is used for detection.
In humans, structure-and-function relationships exist between the phosphate groups of bisphosphorylated glucosamine disaccharide backbone as well as the six fatty acyl tails and the MD-2/TLR-4 co-receptor system.
Does anyone have a paper, or a general lead, as to where I can find what area LAL uses for detection? I haven't had much luck with Google Scholar.

I guess the LRR of the TLR is responsible for LPS-PAMP identification (Lipid A). Not too sure.


text:
What are these tiny dark red insects?
I found these creatures stuck to a leaf on my tomato plant in the greenhouse/glasshouse, in early June, in south Oxfordshire in the United Kingdom.
They are each about 1-2mm in size. They didn't move much, even when gently nudged, although some did crawl tentatively.
I would be very interested to know what these are, especially if they are a beneficial species or a pest!

These little creatures look a lot like White-margined Burrowing Bug or Sehirus cinctus nymphs.

Very interesting thing about the Sehirus cinctus is their brooding behavior. The female watches the eggs and even feeds the young for a few days. Have a look around. You might see it.

AFAIK they don't harm your plants.
More info:
http://polyploid.net/bugs/bugs.html
https://en.wikipedia.org/wiki/Sehirus_cinctus


text:
What are the programming languages important to learn for a geneticist or bioinformatician?
I am interested in learning more about both genomics and bioinformatics, with emphasis on genomics. I was told after taking an introductory course of genomics that the programming language "R" and "python" are widely used. However having asked about learning genomics before on this fourm, I was suggested alot of books for learning genomics with "Perl". I was also told anecdotally that "perl" is falling "out of favor" and "Python" and "R" are desired.
To give background on my coding skills, I flunked out of computer science due to poor coding skills and poor math skills. I still had an interest in the field and therefore started studying genomics and biology instead.
I would categorize my self as a beginner in java and python in terms of skills.
I was also told anecdotally that the best way to learn coding is by having a project. But I feel incompetent in my coding skills to even attempt anything.
I tried to learn Python via "Codecademy" and their 13 hour course for python. Having completed it I felt lost enough to make this post, looking for further guidance. I tried to read a bioinformatics book based in Python, but I missed the handholding and directed exercises that Codecademy gave me.
Should one interested in genomics learn the coding language, R or Perl? Also what would be the best ways to learn these for one who does "not excel" in coding?
If this post seems opinion based mods feel free to delete it or move it, I just was not sure how to phrase my concerns adequately.

Which language do you prefer? Our lab uses fortran, bash/shell, python, perl, a bit of ruby, R, VBA (excel) etc. I've found python to be the most re-applicable language. These days it has 90% of the functionality of R too. I like discussions on this sort of thing, but it is off topic for the site. Perhaps browse around on the bioinformatics or biology subreddit.

Python bioinformatics learning opportunities that might be up your alley: http://rosalind.info/problems/locations/

Matlab! Nothing else ;-)

@InactionPotential    This sounds very cool !! Thanks.

@GoodGravy      I figured it was offtopic, but I cant resist the vast amounts of knowledgeable people on this site. To answer your question, I guess I am use to Java, but Python seems very strong and simple.

Also, not to be rude, but doing meaningful work in bioinformatics requires an excellent understanding of the underlying biology, the underlying computer science and mathematics, or, ideally, both. Sometimes life issues can make keeping up with difficult, but if you don't feel like you could make it through the fundamentals of CS and upper level statistics (and or linear algebra, differential equations). You are not likely to succeed as a bioinformatician. (Not to discourage your exploration, but I wouldn't quit my day job).

flagged for closure as opinion-based

@RoSiv Java is overkill IMO, but ultimately it's more about using a tool to understand the biology. If you find that by using Java you spend your time getting data, not debugging, then go for it using Java!

Bash and Python are the most used those day. If you want to do more data analysis/statistics, then R is a good choice as well. Perl is also quite common in the field.

this question is like asking what language to learn. There are bunch of people in china, so is in france or germany. As well as jobs and opportunities.

I have found that this chapter by Lincoln Stein is a very easy, accessible, and useful introduction to writing a Perl script: Using Perl to facilitate biological analysis. It is Chapter 18 in "Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins" 3rd edition, by Baxevanis and Ouellette.  I use Perl and bash for almost everything, but most of my co-workers prefer Python.  I only use R when I need to.  One software engineer I know swears by Ruby.  As long as your scripting language has lots of useful libraries I don't know if it makes a big difference which one you select.

Comparing some commonly used languages in bioinformatics
I think that indeed Perl is losing users but there are still quite a lot of people using it. Bash (or other shell) is essential. While one can replace Python with C or Java and eventually replace R by Python or Julia, I find Bash to be really necessary. To compare Python to R, Python is much faster. If you are a purist, you'll like Python much more than R. R is a weird language, I use it a lot but you always have to deal with very special kind of objects. The big advantage of R is its community; There are tons and tons of functions. Typically, for any task you you want to do and related to statistics or to phylogenetics, you'll find an already existing package that will do the job for you. C is nice and very popular. It is super fast but it is not super handy for most tasks. Moreover, C doesn't have an interpreter which is always kinda annoying when you're use to high level languages such as R typically. I know nobody who uses Ruby and I don't code in Ruby but it is an important language. Some may use it for bioinformatics and it is very much used in many other disciplines (unlike R, which only statisticians know).
What I personally use
Personally, I use mostly R, Python, C/C++ and Bash. Bash to organize stuff, modify files and manage processes on my machine and on remote machines. R for data analysis. Python when R is too slow (to analyze more data such as sequence data for example) and C/C++ to run individual based simulation. I happen to use some other languages for my work but not as often.
How to learn
Nobody excels at coding to start with. Actually, after my first class of programming I had in mind that programming was not meant for me. Now, I don't excel, I am just a biologist who codes a bit and not a computer scientist but I spend the majority of time coding and I love that. I learn a lot everyday but ever since I started to feel a bit confident, I found that I could do much with this knowledge. You'll probably realize after some time that few people are really good at coding and you can already do much with only a few days of training. Don't be afraid. I think that indeed one of the best way to learn a language is to use it for a project. Here are a bunch of things you could do:

Go on Dryad.com, get some data and analyze them. You can redo the analysis they did on the original paper for example. Or, better you could try to discover something by yourself by combining some existing data.
individual-based (OOP) simulations in population genetics

simulate genetic drift in populations. Try to estimate the probability of a new mutation to fix and the time it takes to get fixed.
Simulate the process of selective sweep with defining chromosomes.
You'd basically have to have a function that creates a list of objects of type individuals. Each individual has two objects called chromosomes. You have a function that simulate reproduction (without selection, recombination nor mutations to start with). And you need a function to print the statistics you need on a file. I think you could do a simple individual-based simulation in a few days only and then you'd feel more comfortable with the language and you would also have a better grasp on how one can numerically simulate a population through time.

I particularly like the idea of practicing on real datasets from Dryad.


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Where do most mutations come from, mitosis or meiosis?
According to this (old) paper there are 10 times more mutations during meiosis than during mitosis. One reason for that is that recombination often causes replication error and therefore mutations. However, in many multicellular organisms, germ cells undergo many cell divisions (mitosis during development) before finally undergoing meiosis.
I think that intuitively many people (me at least) think of new mutations* that are transmitted to the offspring as mutations occurring during meiosis. I would like to have a notion of how important are the two processes (mitosis and meiosis) in generating heritable new germ-line mutations in multicellular animals.
Let's consider humans for example. Out of 100 new germ-line mutations transmitted to the babies, how many of those mutations occurred during mitosis (during the development)?

*I call "new mutations", any mutation that is transmitted to a baby but that was not present in any of the two eggs of the parents (even before the parents started the gastrulation).

"Many more mutations occur during meiosis than during mitosis.":  well recombinations, yes but mutations? Are you sure about it. Can you cite a reference for that? This is new to me.

@WYSIWYG I added a reference. The estimate is 10 times as large (I would have expected something more extreme though). Note that it's an old paper.

That is an interesting info. I'm trying to check the articles citing this for any clues on mechanism. One factor in the mutation contribution would be the number of events. A gamete undergoes 1 round of meiosis but the gametocyte would go through many rounds of division. So even though mitotic mutation rate is lower than that of meiosis, the total number of mutations due to mitosis may be higher.

@Remi.b I think we can do better than 1962. Let's fast-forward here to 2014, in the post-genomic era.  I think this paper provides some data relevant to your query: Possible mechanisms for Chromothripsis .

Yes, indeed we can probably find much better results. Note that whether or not the mutation rate is different between a given mitotic and a given meiotic event, the question remains of whether most segregating sites are polymorphic due to mitotic divisions or meiotic divisions.


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What are the most important factors influencing a person’s gut microbes?

You are your bacteria! The probiotics and the antibiotics...

There has been on going discussions about how our gut bacteria is important for a healthy lifestyle.

Figure 1: Schematic diagram illustrating potential or known mechanisms
  whereby probiotic bacteria might impact on the microbiota. These
  mechanisms include (1) competition for dietary ingredients as growth
  substrates, (2) bioconversion of, for example, sugars into
  fermentation products with inhibitory properties, (3) production of
  growth substrates, for example, EPS or vitamins, for other bacteria,
  (4) direct antagonism by bacteriocins, (5) competitive exclusion for
  binding sites, (6) improved barrier function, (7) reduction of
  inflammation, thus altering intestinal properties for colonization and
  persistence within, and (8) stimulation of innate immune response (by
  unknown mechanisms). IEC: epithelial cells, DC: dendritic cells,
  T:T-cells. For further details, see main text.

Also, an online search provides numerous articles about diet aspect that alters our gut bacteria.
e.g.
1.A New Diet Quickly Alters Gut Bacteria-(not sure about the credibility and relevance to humans as the study is on mice)
2.Artificial Sweeteners Change How Our Gut Bacteria Work, Paving The Way To Diabetes And Obesity
3.Changing gut bacteria through diet affects brain function
As far as the GIT is concerned, health, lifestyle, eating habits and the environment a person is living may be at large influence and affect one's gut microbes.
Are there day to day encounters, in-depth examples and references that can prove the factors which influence our gut microbes the most?

There was an interesting paper that showed fecal transplants actual can switch a skinny mouse to an obese mouse and visa versa!

@GoodGravy mice are cute, but I am keen on human. Nonetheless please share the paper if you have. ;)

I'll look it up. It is basically showing that gut bacteria are far more influential on us that previously thought, to the point where maybe (big asterisk/disclaimer) they can be applied in medicine.

Our Unique Microbial Identity (Gilbert 2015) suggests that the gut microbiome is shaped and stablized during infancy and tends to restore equilibrium if it is disturbed later in life. Antibiotics temporarily change the composition of the gut microbiome (by suppressing the growth of certain groups of bacteria more than others), but it tends to drift back to resemble its original (stable) community after treatment is stopped, likely due to the interdependency of individual strains as producers/consumers of particular compounds (i.e., some bacteria will eventually arrive to serve a particular role in the microbial community). A recent study in antibiotic treatment provides some empirical evidence of the long term effects of antibiotic treatment on the human gut microbiome.
The American Gut Project is currently open to the public for participation, and it has the goal of identifying exactly the factors you describe. They are accepting samples from individuals across the country, along with lifestyle information, including dietary habits. A related publication by the group provides a summary of the common gut bacteria implicated in a variety of diseases.
H. pylori infections are a common gut microbiome health issue. This bacteria is present in about half of gut microbiomes and are not generally harmful unless the stomach lining is already compromised (e.g., due to medication or diet). In those cases, they can become opportunistic pathogens that can persist for decades and cause peptic ulcers which can lead to a variety of difficult to diagnose ailments such as arthritis.

I am taking a look at the second link. But I am not sure about the first and third points of your answer. Diseases like Chron's disease changes the gut microbiome and that's quite obvious. 1, Do you mean that during pylori infection, the basic medication (to control acidity of the intestine) cause microbe colonies in gut to be altered mainly due to the drug more/rather than the infection itself? 2. Can you explain point one in detail?

@bonCodigo I've made some edits to address your questions.

It helped. Appreciate the update.


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How to make a fake auditory signal?
My question is about making fake auditory signals. The ear collects sounds from the environment, which are transformed into a neural signal by the hair cells in the inner ear. This signal is sent through the auditory nerve to the to brain, where it is decoded into a hearing sensation.
Now can we make fake signals and feed them into the auditory nerve to bring it to the brain? E.g., instead of the ear collecting sound, could we use an electrical instrument for collecting sound and convert this signal into a neural signal in the auditory nerve?

It is possible to electrically trigger the auditory nerve but I am not sure how well can (with the current technology) it reproduce the normal hearing.

Devices that bypass the hair cells in the inner ear and directly stimulate the auditory nerve are called cochlear implants. Cochlear implants are used to treat deafness caused by the loss of hair cells in the cochlea. The hair cells are the sensory cells that convert sound vibrations into electric neural signals (Purves et al., 2001). With state-of-the-art devices, the better performing implant wearers are able to talk over the phone(!) An exclamation mark is in place, because it means they can understand the spoken word without the need for lip reading or sign language. Hence, cochlear implants are the answer to your question, but they definitely are not designed to generate fake signals. Instead, they are used as an effective treatment for deafness and are capable of transmitting meaningful speech information.
Cochlear prosthetics consist of an array of typically 20-24 electrodes that are inserted in the scala tympani of the cochlea. The inner ear is tonotopically organized, which means that high frequencies are coded in the base of the cochlea, while low frequencies are encoded in the tip. Hence, each electrode stimulates a separate frequency band (Fig. 1). By using a microphone and sending the acoustic signal through a filter bank, a number of auditory frequency bands can be obtained equal to the number of electrodes in the implant. After this speech processing step, the acoustic frequency bands are converted into trains of biphasic pulses. These pulse trains are sent to the electrodes in the cochlea, which then activate the auditory nerve directly, effectively replacing the degenerated hair cells in the inner ear (Stronks, 2010).

Fig. 1. Cochlear implant. Source: Mayo Clinic.
References
- Purves et al., Neuroscience (2001) 2nd ed.
- Stronks (2010). PhD thesis, Utrecht University

AliceD I got it . when i say fake signal  i mean the signals that are not generated by the real haircells. the exactly things that you said was in my mind.Can you show me a refrence that introduce this Cochlear implant completely with details ?

@Nimda - I linked a PhD thesis which has a nice introductory text on cochlear implants. Please refer to section 1.5. The other sections in chapter one may also be of interest as they deal with normal functioning of the ear as well.

:) @AliceD I had a small doubt. I thought there is a continuum of frequency sensing- something like firing rate proportional to frequency. If it is so then how stimulating just six physical zones can produce all types of tonal perceptions? In other words- is it easy to differentiate the neurons that sense frequency vs those that sense amplitude? Even if you suggest me a review, it would be fine.

@WYSIWYG -  :) here 2 :)  I'm not exactly sure where you obtain the '6' from, but you are pretty much spot on that 7 channels are enough for speech understanding. The gross reduction of the nr. of inner hair cells (16k)  to the nr. of effective channels (~7) still leaves enough info for speech understanding. The trick is the efficiency at which the auditory system retrieves temporal information. Electrical info is temporally very fast, but spatially very crude. You are spot on.

@WYSIWYG - cont. There is just one neural cell type that is stimulated here: the spiral ganglion cells. They sense mainly amplitude. Frequency of stimulation is extremely fast in present stim. strategies, in the order of thousand to tens-of-thousands Hz. Frequency (i.e., pulse rates) in itself are irrelevant. It is place-coding (akin to perceptual frequency/tones) and intensity coding (akin to perceptual loudness) out of which the temporal changes contain the most relevant info.

@AliceD I don't know why I said 6. Anyways, thanks for the info. You can add this to the answer as well. :)

@WYSIWYG - with your permission I'd rather leave this in-depth analysis of electrical stimulation to the comments :)

@AliceD Your choice. It is your answer.

As @AliceD mentioned, cochlear implant is one of the earliest achievements of neural engineering. However, there are orders of magnitude more inner hair cells (IHC) and even more auditory nerve fibers (AN) in human cochlear than the current cochlear implants offer electrodes. If you are interested in a more detailed model of IHC to AN signal transmission, there are tons of research, for example:

Meddis, R. (1986). Simulation of mechanical to neural transduction in the auditory receptor. The Journal of the Acoustical Society of America, 79(3):702-711.
Walsh, E. J. and McGee, J. (1987). Postnatal development of auditory nerve and cochlear nucleus neuronal responses in kittens. Hearing Research, 28(1):97-116.
Sumner, C. J., Lopez Poveda, E. A., O'Mard, L. P., and Meddis, R. (2002). A revised model of the inner-hair cell and auditory-nerve complex. The Journal of the Acoustical Society of America, 111(5):2178-2188.
Heinz, M. G., Colburn, H. S., and Carney, L. H. (2001). Evaluating auditory performance limits: I. One-Parameter discrimination using a computational model for the auditory nerve. Neural Computation, 13(10):2273-2316.

There are only 2 auditory nerves. You probably meant AN fibers?

@AliceD correct! thanks for pointing it out. Fixed.

Looks good now :) +1;   20+ auditory nerves would become interesting anatomy :)


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What is the name of this tree?
I noticed this plant growing on the Azores islands. It has a surprisingly perfect distribution of branches, which gives it the appearance of a fake decorative plant. Does anybody know what type of tree this is?

I apologise for the image, I could not find any better ones.
Edit: here is another image I found:

Looks like a Norfolk Island pine to me.

I agree - https://en.wikipedia.org/wiki/Araucaria_heterophylla#/media/File:AraucariaHfoliage.jpg looks very similar to the first image

Answered probably here http://biology.stackexchange.com/questions/35653/what-is-the-scientific-name-of-this-evergreen-plant/35676#35676

I would guess that the symmetrical distribution of branches is because it was trimmed to look like that, it's probably doesn't look like that in nature.

@CactusWoman I think that you are correct, although I did see some very symmetric trees growing in wild habitats.

It's worth taking a look at the tree over Autum and Winter. You will see how dramatically its branches adapt to Winter. The conical shape of these trees has a direct relationship towards their native climate.

"Coniferous trees are very well adapted to the climatic conditions. Their slender, conical shape minimizes snow accumulation on the branches, allowing the trees to generally withstand the weight of the snow for the entire winter without breaking (Radford University 2008). The narrowness and small surface area of the needles, together with a waxy coating, allows the tree to resist the drying effect of strong, cold winds.

Keeping the needles throughout the winter allows the tree to photosynthesize on the occasional sunny, warm winter day, and to begin photosynthesizing in spring as soon as temperatures permit (Lakehead University 2007)." Extremely interesting :)


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C Form DNA Base Pairs Per Turn
How does C-DNA have 9.33 base pairs per turn? The number of base pairs should be quantised. How can it be a decimal?

There is no requirement that DNA should have a whole number of base pairs per turn. As a thought experiment, say you have a helix of dsDNA with 10 base pairs that completes one helical turn (ie it has 10 bp/turn). Now, twist that DNA along its axis so that it completes 1.5 turns. There is still only 10 bp and thus 6.67 bp/turn (which you should notice is a decimal).


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why not classify organisms based on genetic distance?
When learning about "families of animals" in mid-primary school, my son asked me if there is not a better way to classify animals than by looking at them (they were taught that animals are grouped into families based on what covers the skin (hair, feathers, nothing), number of legs, the way they breathe, etc.).
He was wondering if there would be a more quantifiable way of grouping them (this is my wording). I told him that since all animals share the same genetic coding principles, we could try to calculate how different their genetic material is, and derive a "distance". This would allow grouping animals in possibly novel ways, and maybe have some of them cluster in "families" (possibly different from the existing ones).
After having played my role of super-hero-dad, I now wonder if this makes sense? (and if so - is it used? and if so - why still using some classifications from centuries ago?)

Welcome to Biology.SE. Did the other post answer your question? If not, don't hesitate to open a new post and address why the other post did not answer your question.

Do not open another post. Edit this one if you think your question is about something else.


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What are some (bioinformatic) methods to characterize potentially novel gene transcripts?
I am working with a few novel transcripts of genes- before I confirm their existence experimentally, I would like to perform some bioinformatic analysis. I have already considered coding potential, protein domain prediction, transcription factor binding sites, sequence homology, and RNA secondary structure (still a little unsure how to use this one). These transcripts were discovered using RNA-Seq. Are there any other elements of genes/confirmed transcripts that I should look for in the sequence of my transcripts and corresponding softwares? (I can find the software myself if necessary, but I have run out of characteristics to search for). I would like to characterize these transcripts structurally and functionally as completely as possible, including potential protein function, mRNA degradation, etc - some new features for what to look into would be appreciated.

The current version of your question is unclear. The RNA-Seq reads you are analyzing clearly exist (otherwise there would be nothing to analyze) yet your question makes it sound like the transcript does not exist. If the transcript does not exist then what is your alternative hypothesis? Where could the reads come from?

Thanks for your response! Let me clarify: these transcripts are created by alternative splicing, and are unique in that they contain retained introns. Their expression levels are quite low, and I have been told that there is significant likelihood that they are merely artifacts ( I am not too familiar with rna-seq to understand where this may arise) -"novel" transcripts with intron retention apparently do not often hold up to experimental validation. I want to gain evidence that they exist in silico before I move on to qpcr.

Furthermore, I would like to characterize these transcripts as much as possible - gain insight into whether they would just be noncoding transcripts or be of some interest- I attempted to look into what types of protein domains may arise, whether the transcript retained introns have coding potential, and new tfbs. Essentially I want to gain as much insight into the structure and potential function of this transcript as possible, using only the sequence. I am merely a student, so please ask for further clarification! *I edited to question to match my responses here.

@mdperry- As an example, I am currently looking into potential miRNA binding site introduced to the transcript through intron retention - I am open to investigation of any interesting features that these transcripts may bring!

You might want to check the genomic sequences and how the transcript is spliced so that RNA-seq data could be confirmed. You may also find alternative splicing isoforms. You could check poly A signals in the genomic sequence.

How was the library prepared? Poly-A or Ribo-minus? First of all you should validate the novel transcripts. Sometimes you may end up with false positives.

@WYSIWYG The library was prepared Poly-A. I am currently awaiting the necessary primers for validation (about a week), and in the mean time I wanted to characterize the transcripts as fully as possible (predict function, structure, etc) and was looking for more ideas on how to do so. I have read a number of papers performing in silico analysis and have done many of their methods and was looking for a few more.

It sounds like you have considered most of the obvious alternatives (and thank you for clarifying the question).  I suppose the first question in an alternatively spliced transcript with a retained intron is whether the open reading frame of the protein is maintained.  If there is a termination codon that now becomes in-frame due to the intron then the protein would ordinarily be truncated.  The mRNA could also get targeted for degradation due to the nonsense-mediated decay pathway (i.e., from introducing an early stop-codon).
If the alternatively-spliced transcript lacks any significant ORFs, or if there is an ORF but no suitable translation initiation codon then you have entered the realm of non-coding RNAs, of which there are two loose categories, microRNAs (there are several classes) or long non-coding RNAs.  most miRNAs have recognizable sequence motifs, may be contained in mir-Base, and may be complementary (in part) to a regulatory target elsewhere in the genome. lncRNAs are less well-defined. Perhaps the best criteria are transcripts that are stable enough to be detected, but lack noticeable protein coding features.  I don't even think there is a consensus on the minimum length of a transcript to be considered an lncRNA.
What sort of databases have you searched? For example the ancient dbEST contains all kinds of short cDNA reads from all over the planet, dating back to 1992 (or so).  Evolutionary conservation of exons and splice-sites, or reading frames can all be used to support the hypothesis that a transcribed region has a biological function.
For a really deep fishing expedition you can use tblastn, etc. to take a potential translated ORF and search all 6 conceptually translated reading frames of all the sequences in a database--it takes longer, and there can be many spurious matches that you have to search through, but if you are studying something that has never been annotated it might be worth considering.
RE: folding RNA (secondary structure) every sequence can be folded into some kind of structure (try it and see), but structurally conserved compact loops typically fall into one of a small class of selected families (like GNRY). Stems are easy to find, but if the single-stranded loop is huge, how likely is that structure to fold up in real time?
RE: TFBS not clear to me at all what you mean by this. Transcription factors bind dsDNA not ssRNA. There are families of protein domains that bind RNA but not typically with such sequence-specificity.  So unless this alternatively spliced transcript is being transcribed from some type of alternative promoter, I am not sure where you would even be looking for TFBS.  It's true that some large introns can indeed contain regulatory regions for the gene as a whole (cf. immunoglobulin heavy chain enhancer is conveniently stored in an intron), but it is not clear to me if that would have an affect when transcribed into RNA.
I do like your idea that a retained intron could now contain a binding site for a regulatory miRNA, but if I understand the theory this would lead to the transcript being degraded--so it would be more difficult to detect.

Thanks! Again, I believe I have not shared all necessary information; in some of the transcripts, the alternative spicing generates an alternative 5' UTR, in which I am searching for TFBS (these alternate 5' UTR's appear to be retained introns). The miRNA idea comes from that these transcripts are actually differentially expressed in the disease phenotype I am analyzing, so I was looking into  introduced miRNA binding sites as a potential reason. The ORF analysis is interesting-I had not considered the meaning behind a lack of orfs within the retained intron. Thanks for your time!

Some suggestions.
For identifying function do a homology search. There is little functional annotation of lncRNAs. So homology based information can be obtained only for protein sequences. So you can try these:

Check the coding potential. Find ORFs (perhaps set a minimum length cutoff). To be stringent you can also check for Kozak consensus sequences (for eukaryotes) in these novel transcripts. This program apparently includes the Kozak rule in start codon prediction.
Translate the RNA and run psi-BLAST. Psi-BLAST is better than normal BLASTp in identifying distant homologs.
You can obtain the GO (functional annotation) data for these homologs and relate them to your novel transcript.
For lncRNAs, you can find if they overlap with any other known transcript. Many lncRNAs seem to have overlapping loci with their target genes (both sense/antisense).
Search for RNA motifs. This article reports an extensive study towards identification of different RNA motifs (RBP binding sites) in humans and Drosophila. Other interesting motifs include quadruplex forming motifs and ARE.
If you have the genome sequence then you can map your transcripts to the genome and identify the introns. Actually your RNAseq assembly, if reference guided, would produce a GTF file. You can extract introns using the GTF. You may check for potential miRNAs in these introns, based on stem loop predictions. However it is better to have a small-RNAseq data for predicting miRNAs reliably.
As you had already considered, you can check for miRNA binding sites. For this purpose miRanda is the best for novel transcripts (TargetScan is a pain). You can use RNAhybrid too and take a consensus of miRanda and RNAhybrid results.
You can do other kinds of analysis based on just the sequence of the transcripts. These include checking for codon usage (for potential protein coding RNAs), GC richness (comparison with the known transcripts) etc.


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Can I explain the kinetics of a ligand looking at the ligand-protein co-structure?
Can I explain the kinetics of a ligand binding to a target protein (association and dissociation rates) by looking at the protein-ligand co-structure?
Editing my question after a few comments:
I have a set of ligands targeting the same protein. These ligands show different kinetics properties, i.e. different association and dissociation rates. Would it be possible to explain why they have a different kinetics if I had the x-ray structure of the different protein-ligand complexes? Would I be able to say that ligand A has a slower dissociation time compared to ligand B because it makes for example 3 H-bonds instead of 2? How accurate would this conclusion be? Would I need in any case MD simulations?

Theoretically, structure defines function. However, it is very complicated. When people design enzyme on paper and then synthesize it, predictions are rarely accurate.

I am not sure what you want to know exactly.

It is not generally possible to have an idea about dynamic properties from static data. I guess you can use molecular dynamics simulation based on the structural data to have some idea about kinetics (I am no expert in this area).

yes you probably need some molecular dynamics simulations. And what about if I have several co-structures with the same protein but with different ligands with different kinetic properties? Can I explain their different kinetics based on their binding mode?

Specifically what data do you have? Do you have a docked crystal structure?


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DNA dependent RNA polymerase
How does a RNAP locate a specific gene? For instance, growth hormone has to be produced and the RNAP has to locate the gene. But the promoter (TATA box) will also be present infront of all cistrons. So how does the RNAP choose the specific gene? Are the promoters specific for specific gene?

Yes, promoters are specific to genes and there is much more to them than the TATA box. There is also a requirement for specific transcription factors. And there are regulatory regions independent of the promoter.

How exactly RNAP find a gene quickly that it is like finding a needle in the haystack

Diffusion. There are thousands of RNAPs in a single cell. Furthermore, it is thought that these polymerases are localized to discrete sites of high transcriptional activity within the nucleus. Also keep in mind that transcribing genes quickly isn't necessarily the objective of the cell. Rather, a cell seeks to transcribe genes at a specific rate (which could be slow or fast).

This question is actually quite relevant. There are many mechanisms that increase the rate of TF-DNA binding- molecular crowding and nuclear organization are some factors. I'll address this in a while. Have a presentation to make.

It could be difficult for RNApol to access genes inactivated because inactivated genes are often tightly packed by chromatin. Many genes have enhancer elements(specific DNA sequences) as well as TATA, and transcriptional enhancers, which are proteins, would recognize enhancer elements in a sequence dependent manner and loosen chromatin structure to make RNApol access TATA sequences easily. Transcriptional enhancers also bind to the General Transcriptional Factor complex (GTF), which contains RNApol, therefore enhancers could bring RNApol to TATA sequences.
Many tissue specific and/or developmentally-regulated transcriptional enhancers are identified. You could say transcriptional enhancers regulate RNApol which to bind.
You can check this link and this.


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What happens to IP3 molecules after release from IP3 receptors?
IP3 molecules bind to IP3 receptors and open up the calcium channels on the endoplasmic reticulum. I am wondering what happens to IP3 molecules after they have been released from the IP3 receptor? Do they remain as IP3 molecules, or do they break down? Do they have any purpose after they have been released, and perhaps after been broken down?

All biomolecules are eventually broken down. This process is called turnover. Since inositol is a signalling molecule it is necessary to remove in order to terminate the signal, despite adaptive mechanism in the ER calcium channels.
IP3 is generally dephosphorylated by a family of phosphatase enzymes called inositol polyphosphate phosphatases. After dephosphorylation, the inositol can be recycled into the membrane by phosphatidyl-inositol synthase.
Reference:
The IUPHAR/BPS Guide to PHARMACOLOGY 1

1 I'm citing this website instead of a research article because apart from citing the original research articles, it also has other informative links about the enzymes and the substrate molecules. It is also from a very reliable source.

thank you so much @wysiwyg By the way, how do we know the lifetime of such signalling molecules.

@nashynash There are different assays by which it can be found out. One way is to label the metabolite using an isotope (like ¹³C) and track the loss of radioisotope in the sample using mass spectrometry or NMR. In this case the "lifetime" depends on the phosphorylated state of inositol - so the phosphorous has to be labelled instead of carbon.