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Bilirubin metabolism and UGT1A1 inhibition in human vs. monkey?
In human UGT1A1 seems to be the only relevant enzyme to glucuronidate unconjugated bilirubin into excreted forms. Is the pathway the same for e.g. the Cynomolgus monkey (Macaca fascicularis) in vivo? In case UGT1A1 is potently inhibited are there any other potential enzyme to metabolise/glucuronidate unconjugated bilirubin in monkey? Furthermore, bilirubin is analytically challenging in UGT1A1 inhibition studies in vitro. Estradiol (3-glucuronidation) has been used as a specific substrate for human UGT1A1. Do you know any substrates known to be specific for monkey UGT1A1?  Any reference would be highly appreciated.

UGT1A1 is common among most mammals that produce bilirubin (rats too). I haven't seen any evidence to suggest an alternate pathway in mammals of bilirubin metabolism. Look into gilbert's syndrome patients (they have a SNP which is a mutuation that causes UGT1A1 to be less efficient and thus a rise in plasma unconjugated bilirubin).
Also, bilirubin analysis from plasma/tissues is fairly straight forward with HPLC or LCMS assays. UGT1A1 inhibition in vitro however, is still a challenge. I think the trick will be to use CRISPR to knock it out.

It would be great if you could add some references.


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Infer gene frequency within a species over time
I was reading Karlsson et al. (2014) and I came into this:

A selected variant that increases rapidly in frequency in the past ~250,000 years can be detected as an unusual reduction in genetic diversity.

I realised that I do not know how to infer a specific allele frequency over time within a given species.
I tried to googled some keyword but was flooded by other concepts. Could you please direct me to some appropriate documentation/kewords?

Welcome to Biology.SE. By tracing over time, I suppose you mean observing the signature of an allele sweeping to high frequency, are you?

I edited your post to include the link and fix the grammatical mistake (among vs within). You might want to add where the sentence comes from exactly (which page, which paragraph).

There's two parts to your post that I want to address, the first is the quote (because I want to make sure you understand it well), and the second is about general inference methods for estimating the genetic composition of ancestral populations.
The Quote: Selective Sweeps

A selected variant that increases rapidly in frequency in the past
  ~250,000 years can be detected as an unusual reduction in genetic
  diversity.

When an allele is selected for it will spread relatively quickly in the population, relative to the spread of neutral alleles for example. If an allele becomes fixed in the population the diversity in that gene is zero; there is no standing genetic variation in the gene. Selection will often reduce genetic variation but see this post too.
However, selection doesn't just reduce genetic diversity at the selected locus, but at loci near to the gene, those that are linked. The loss of genetic diversity at linked loci occurs by a process called a selective sweep. This is defined (somewhat poorly) in the web version of your linked paper:

Selective sweeps; Reductions in genetic variation caused by positive selection at particular loci.

Basically, a selective sweep occurs when strong selection causes one allele, and the loci it is highly linked to, to spread through a population. Genetic diversity will be lost from the linked loci at a rate determined by the strength of selection and the degree of linkage (where tighter linkage and stronger selection increase the rate of loss). This paper (see section 7 for selective sweeps) provides a good discussion of the factors affecting genetic variation in natural populations, and draws on the example of the Y chromosome:

One or more selective sweeps will have left the Y chromosome
  with little or no variability

Inference of ancestral populations
You could sample DNA of the population at the time you are interested in. However, doing so is very tricky. DNA degrades over time therefore it is important to have an understanding of how DNA degrades over time if you want to infer about the population. (I saw a talk a while back by a researcher, can't remember the name, taking samples from graves. I don't know how correctly I remember this, but much of the DNA they collect is bacteria. Just a tiny fraction of the DNA they got from sampling human bones found in graves that were just a few hundred years old was actually human. Another issue of studying old DNA). It's often difficult to find sources of DNA samples which a) of high enough quality and b) with enough individuals to allow good inference of the ancestral population; a small sample will be prone to sampling bias.*
There is another approach, and its commonly used now. If you are interested in finding more you should be searching for coalescent theory and methods. This infers back, based on current (or relatively more recent) genetic composition of populations and using population genetic theory. It's not really an used as an attempt to estimate the specific allele frequency, rather as an attempt to infer population size, migration rates, and recombination rates. It infers when was the most recent common ancestor (MRCA). This paper reviews coalescent methods for phylogenetic trees and this is an introduction lecture on coalescence. Coalescent theory has many considerations that need to be made; Are some mutations more common than others (see Molecular Clock)? How does selection affect genetic variation (e.g. selective sweeps)? Does linkage vary across the genome? Do rates of mutation, drift, and selection vary across the genome? Are different parts of the genome differently affected by migration?
Both approaches raise serious considerations which are at the forefront of evolutionary genetics right now. Research groups all over the world are developing lab methods, statistical methods, and mathematical models in an attempt to make inference more accurate. Right now the only way, in my opinion, to infer the frequency of specific alleles in ancestral populations is to use ancient DNA methods; coalescent can be used to infer population genetic parameters, but regarding specific alleles it just has so many factors to consider which (right now, but certainly less so in the future) we just don't have a thorough understanding of. In other words, the assumptions that need to be made for coalescence to be able to estimate specific ancestral allele frequencies are rarely going to be satisfied. However, as long as this is properly discussed there is no problem with the method being used, and I am certain that the future is bright for such methods.

*On sampling bias: Imagine you want to work out the frequency of the number two on a six-sided die (we know the true frequency is 0.167). You roll the die four times and the die shows the side with two dots once. You population frequency estimate is 0.25. Your friend rolls the same die 4000 times. They see the two dots 652 times, which is 0.163, much more representative of the true population frequency. The moral of the story, small samples can give misleading estimates of the truth.

Your answer has been very useful as I did not understand the selective sweeps at first, where the linkage explanation makes it clear. The first aim was to understand how to read the history of a gene frequency and @Resonating explained it well.

I was just updating my answer to cover more of the approaches to inference @zakrapovic

There are really two ways to infer past genetics.

Sample the past.

Only really works if you have well-preserved uncontaminated archaeological samples, but works surprisingly well, considering. The accuracy and completeness goes down fairly quickly as you go back in time but thousands of years to hundreds of thousands of years is roughly possible. You won't have many samples so it's hard to say anything about population genetics. Don't do this. It doesn't work very well and it's very hard.

Molecular clocks.

Same-sense mutations that affect nothing accumulate in genes over time as they're passed down. Genes that have a distant common ancestor are more different than genes that are just a generation or two apart. This is essentially how phylogenetics works as a whole, but you can use age differences between nearby genes to detect selection in this way. If gene A is nearly identical in every individual in a population, but gene B has thousands of pretty distinct variations, it's clear gene A does something important and has been powerfully selected for. Don't just count the number of kinds of alleles for gene A, calculate the average relatedness between all the alleles you find. If the relatedness for gene A is much lower than for other genes, gene A is being selected on. This is what the text means by 'reduction in genetic diversity', bringing down the average relatedness.
You can't work out allele frequencies at arbitrary times in the past without a time machine(even ancient sampling, you can't really be sure you're getting an accurate sample of the historical population), but phylogenetics can hint at what happened by detecting the signatures of evolution.

I did not know that we could sample the past and this is awesome. Then same-sens mutation explanation is clear. Many thanks

I would guess, the article the OP is referring talks about genetic signature of selection (such as a selective sweep). Shouldn't this be a third big category? It is also unclear to me what inference on change in allele frequency through time you would like to do by comparing number of fixed difference in a single population.

Your guess is right

@Remi.b Sorry, I think this needs an edit to be a little clearer. Measuring the number of fixed differences in a population isn't particularly robust, especially if the selection happened in the past. Something like 'mean relatedness' between many sampled alleles is much better and also lets you detect selection in subpopulations by looking at the distribution of scores.

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Analysing the ERP (Event-Related Potential from EEG recordings) in terms of the P-300 wave
I am trying to understand how to analyze ERP (Event-Related Potentials) from EEG recordings in focus on P-300 waves.
I have come up with a few questions which I hope you might be able to help with:

I assume that the number of repetitions af the stimulus should give better results, but I am not sure why. On one hand, it should help because avaraged results are usually better in neurosciences. On the other hand, maybe there is an unwanted adaptation effect of the subject?
So, are repetitions wanted in this kind of experiment?
There are time points in which I didn't expect P300 but the brain activity was not 0. I understand that there is always a background activity in the brain, but maybe there is more to it? And if so, are there certain frequenciess that are more likely to happen under these conditions?
When I analyze the data using an avarage response, should I assume anything on the P300 response?
What is the recommended Sample Rate I should use. I guess there should be an advantage for high sample rate for more resolution bur I am not sure about that. Am I right, or maybe a lower sample rate has advantages thst I am not aware of?

So, are repetitions wanted in this kind of experiment?

Yes: repetitions reduce random noise and increase the chance to tell artifact from response. Adaptation is a serious issue, typically becoming apparent by decreasing amplitudes, or even skipped responses. Reading the existing literature and/or performing experiments with various stimulus intervals may help to identify adaptation effects, if any.

...there is allways a background activity in the brain...

Correct - if not, you are either not recording properly, or subject is brain dead.

are there certain frequencys that are more likey to happen under these conditions

If you repeat your recordings and average them out, this is not an issue. Otherwise, yes, FFT analysis shows several frequency bands, dependent on the state of the subject. For example, awake subjects show primarily fast beta activity (13-30 Hz), drowsy or sleeping subjects much slow-wave alpha (1-4 Hz).

should I assume anything on the P300 response?

In short: The P300 response is most commonly elicited in an "oddball" paradigm when a subject detects an occasional "target" stimulus in a regular train of standard stimuli. The P300 wave only occurs if the subject is actively engaged in the task of detecting targets (Picton, 1992).

What is the recommended sample Rate I should use. I guess there should be an advantage for high sample rate for more resolution bu[t] I am not sure about that. Am I right, or maybe a lower samp[le] rate has advantages th[a]t I am not aware of?

The higher the sample rate, the better. Downsampling can always be done later. Typically 1000 Hz is sufficient for fast responses. For a P300 this should already be kind of an overkill.
Reference
- Picton, J Clin Neurophysiol (1992); 9(4): 456-79

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How can I calculate Shannon-Wiener diversity index in caves with different kind of habitats and different sampling methods?
I would like to start a monitoring in different caves, compare caves of different age and length, and find out if there are any correlations between these factors and their diversity.
I was thinking to use the Shannon-Wiener diversity index and to use both pitfall traps and timed area search sampling methods. Can I get a single index value for each cave even if the methods are different?
And what about that caves which have stream or pools where the fauna can be very abundant? Even if I collect a sample of water, the abundance of certain species can be very high (e.g. copepoda).
Sorry for the banal questions but I have not experience with indices and statistics.

Clarification after comments:
I have about 20 caves to sample, I want to compare the fauna of these caves (richness and abundance). As I told they have differents lenghts, ages and others environmental features. Some of the caves have also streams or little pools (where I will take water samples to identify the fauna).
I have read some articles where the diversity indices were calculated using just one single methodology (traps or timed area search), and also for only one kind of fauna (acquatic or terrestrial).
The question is: Is there any methodology to get one single value of diversity index for each cave? (which considers both terrestrial and aquatic fauna). Or maybe in these conditions I can just get the number of species for each cave, without being able to compare their abundance?

So if I understand you have cave with only pitfall and cave with only timed area search sampling methods ?

Not sure if you can read the answer that I wrongly deleted. No, I was thinking to use both method in each cave. The question is, can I get a single index value for both aquatic and terrestrial fauna?

Why do you wanna do that ?

If I have two index values for caves which have both aquatic and terrestrial fauna, and just one value for others which have only terrestrial fauna, how can I compare them (in terms of abundance and richness)?

You could account for this in your statistical analysis. I'm assuming that  you will collect terrestrial fauna the same way in every cave, and if there is a stream in a cave aquatic fauna will be collected in some standardized way as well. I'll also assume you'll have some other explanatory variables that you think may explain diversity, richness, or abundance. These could include cave temperature, length, depth, etc.
Calculate the Shannon-Weiner index for each cave using all species and abundances, whether they came from an aquatic or terrestrial sample. Then for you statistical model include whether a stream was present in a cave or not using a binary variable along with your other explanatory variables.
index ~ f(temperature, length, depth, stream)
In this example temperature, length, and depth are all continuous variables. Stream will be a binary variable (either 0 or 1, 1 meaning a stream was present). This will give you a measure of how much a stream contributes to diversity.


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Why is Fast Fourier Transform applied to raw EEG data?
I am trying to understand why Fast Fourier Transform (FFT) is used in the analysis of raw EEG channel data.
My understanding (at the 30,000 ft view) is that FFT decomposes linear differential equations with non-sinusoidal source terms (which are fairly difficult to solve) and breaks them down into component equations (with sinusoidal source terms) that are easy to solve. It then combines each of these component/partial solutions to solve the original equation. In laymen's terms, it's like taking a smoothie, and breaking it down into its recipe of ingredients. Doing so would allow us to study and analyze each ingredient as it relates to the final product (the smoothie).
But how does this relate to raw (non-FFT-decomposed) EEG data? My understanding of a single channel of EEG data is that it is essentially a measurement of voltage over time.  What is to be gained by using FFT to break this voltage/time signal down into its constituent frequencies (alpha/beta/gamma/delta/mu waves), etc.? What additional information can each frequency tell us about the raw data?

Update:
Here is my updated understanding of how FFT relates to raw EEG data per @Mark's description below:

Somewhat simplistic answer so I'll post it as a comment, but Electrical Engineers Fourier Transform EVERYTHING. "Huh that's an interesting signal, what happens when I Fourier Transform it?" My guess is in the case of EEGs case some one did that and it showed patterns so they kept doing it.
FYI, "it is essentially a measurement of voltage over time." Can describe literally everything a computer can do. Camera? voltage over time, Force sensor? voltage over time, Current? voltage over time (through a very precice inductor or resistor), Rotation? voltage over time

Thanks @Sam I think I get what you're saying! However, when you say "My guess is in the case of EEGs case some one did that and it showed patterns so they kept doing it", can you elaborate a bit more? What patterns might "they" have found? Perhaps reading a concrete example of what patterns/information emerge when EEG data is ran through FFT might help clue me in to its underlying value proposition. Thanks again!

You may have success approaching this differently.  Rather than trying to learn the specifics about how a FFT might be used in a given scenario analyzing EEG data, you might benefit from learning what a FFT does (to any signal), what the results look like, and why we do them, and only then worry about how it lines up with EEGs.  FFT is actually only one of many transforms we use, each has its own flavor.  FFT happens to be an easy one that shows a lot of data that is hidden in the time-domain solution, but wavelts are another approach with different tradeoffs.

Thanks @CortAmmon (+1) - I've already (sorta) done that. But the fact that no one here (even Christiaan) has been able to yield a clear-as-day explanation as to what the benefits of FFT are (that is, specifically what new information is exposed via FFT) tells me that Sam is on to something above. It tells me that people just use FFT because it presents some interesting new ways of looking at the same data.

For injstance, I would have expected a possible answer to this question to be something like "Raw waveforms just give us electrical activity that occurs at the surface of the brain. But we use FFT to decompose that general waveform into signals that represent things happening deeper in the brain." Or something like that. But so far, no one has been able to give me anything like that. Christiaan came close with his Theta wave example in sedated vs. awake subjects, but then was unable to explain how a researcher might actually be able to put that information to use.

@smeeb, first of all, the "Fast" part of FFT is just a technique to make the computation easier, you are not interested in the computational aspects of the Fourier transform so you really should just be asking about the Fourier transform ignoring the "fast" part.
Christiaans answer kind of shows you in right side of figure two, it looks to me like low and high frequency signals are effected by propofol more than the middle bands. That would lead me to ask the question "what do the high and low bands do? how are they different from the middle"  you could than start playing around with that.

So, as @sam points out, Fourier transforms don't create any new data, they merely present the data in a way which exposes the information people cared about better.  In the case of EEG, many signals that people are interested in are repeating oscillating signals.  Why?  Because it has been found that the repeating oscillating signals carry useful information for us.  Science can be circular that way.  On the other hand, if you're trying to observe the P300 waves, which are in the time domain 300ms after a novel event, a Fourier transform would obscure them rather than accentuate them.

Fast-Fourier Transform (FFT) transforms a signal from the time domain into the frequency domain. Basically, any time-dependent signal can be broken down in a collection of sinusoids. In this way, lengthy and noisy EEG recordings can be conveniently plotted in a frequency power-spectrum. By doing so, hidden features can become apparent. By adding all the sinusoids up after FFT, the original signal can be restored, so no information is lost.
A notable application of FFT in EEG is shown in Fig. 1, which shows an EEG in an awake person (top blue trace) and an EEG in a propofol-sedated person (bottom red trace). The traces are different, but exactly how different? Scientists like to quantify stuff.
Now look in Fig. 2, which shows the same data but filtered in the delta band (low-pass filtered EEG with a cut-off frequency of 1.5 Hz, left panel). Here it already becomes more apparent what's going on, but what exactly is the difference between the two traces? That difference becomes readily apparent in the frequency domain by using FFT (Fig. 2, right panel); The frequency spectrum has a peak at 0.2 Hz in both traces, but that peak is about twice as big in the anesthetized state than in the normal state. In other words, the anesthetized brain reveals more low-frequency activity.

Fig. 1. Raw EEG of an awake person (blue) and propofol-anesthetized person (red). source: Wang et al (2014).

Fig. 2.  Filtered EEGs (<1.5 Hz) of an awake person (blue) and propofol-anesthetized person (red) (left panel) and corresponding FFT spectra (right). source: Wang et al (2014).
This is reminiscent of the drowsiness encountered in slow-wave sleep; which is yet another example of why FFT is useful; various stages of sleep are markedly different in their EEG. For example, early stages of sleep are characterized by slow-wave EEG, while REM sleep is characterized by high-frequency EEG activity. By using FFT, these differences in frequency content can be captured in simple, quantifiable data.
Another widely applied FFT-based application is filtering in the frequency domain. Look at the sleep EEG in Fig. 3. By splitting the raw signal up in frequency bands, the noise can be removed (high-frequency components), but even better, the k-complex (a characteristic hallmark of normal sleep) can be beautifully isolated from a messy EEG signal (bottom trace, 12-15 Hz).

Fig. 3. EEG FFT-based filtering. source: Neurology
Reference
- Wang et al., Front Syst Neurosci (2014); 00215

A few followup questions if you don't mind: (1) my understanding of the processing flow was: Capture Raw EEG Data -> Perform FFT -> Analyze Component Frequencies (Alpha, Delta, etc.). But it sounds like you are saying that the typical "flow" is: Capture Raw EEG Data -> Filter by a Particular Frequency -> Perform FFT on that Frequency -> Analyze the Frequency. Can your confirm this or clarify it for me?

And then, (2) OK great so now we see that in the sedated patient, the Theta Frequency wave is - on average - twice as big as the awake/conscious patient. So it sounds to me like the whole point of using FFT on raw EEG data is to just give a researcher more information than what is contained in the original waveform. Can you confirm this for me as well? Thanks again for such an excellent answer!

comment 1: It can be either way - option 2 is basically FFT-based filtering followed by frequency analysis. useful when there is noise in your data and you wish to clean it up first. There is no typical flow, whatever milks your Jersey.

comment 2: I agree with that statement. Often, raw data needs signal processing (tag added) to be able to deduce useful information from it.

Any periodic waveform can be produced by adding up a series of sin waves of the appropriate frequency and amplitude.  The FFT looks at a complex waveform and calculates those frequencies and amplitudes.  The result is a new curve which plots amplitude vs frequency.  Thus, it transforms the signal from the time domain into the frequency domain.
I don't have any knowledge of EEG signals, but have worked with FFTs.  If you wanted to know what frequencies make the largest contribution to the original waveform, the FFT will provide that information.
Here is a paper I found which uses FFT analysis of EEG waveforms to look at the relative contributions of different frequencies.  It includes the following table which indicates that "Delta" waves are in the 0.5 to 4 Hz range, Theta waves are in the 4 - 8 Hz range, etc.  The FFT analysis quickly breaks the overall waveform down into its constituent frequencies to identify which of these ranges contribute the most or least.

Thanks for the insightful answer @Mark (+1) - however a few followup questions for you, if you don't mind: (1) once the FFT is performed on the original waveform, is each component "rhythm" (alpha, beta, gamma, etc.) now represented by it's own graph of amplitude over frequency? Meaning, the original waveform was a single graph of voltage/time: do we now have five graphs of amplitude/frequency, 1 for each rhythm? And (2) this still seems unnecessary to me! Why would I care if a beta rhythm contributed more or less to a subject thinking about birds? In other words: why do this?!?

Obviously, that second question above is quasi-rhetorical, as I suspect that there is a very good reason for decomposing brain waves with FFT, I'm still just not seeing it! Thanks again!

@smeeb - (1) The FFT creates a single graph of amplitude vs frequency.  To actually generate the original time series from the FFT result, you would have to create a sin wave of the right amplitude for each frequency in the answer, and then add them all together.  But the FFT result is just a single graph.

@smeeb -  (2) Sorry, I can't help there as far as the biology goes.  FFTs are used in a lot of applications where the particular frequencies are important, including mass spectrometry where the specific frequencies allow gas components to be identified; machine analysis where the individual frequencies can be used to identify defects in couplings or bearings; and audio processing where it can be used to remove noise, or analyze the harmonics of a violin.  Often, there is a great deal of information within the frequency spectrum

Thanks again @Mark, please see my updated question. I have provided an illustration of my current understanding, given your excellent answers here (thanks!). First off: please let me know if something about my illustration seems wrong to you! But if my understanding/illustration seems to be correct, then my last question for you is: can we then assume (given your data table) that the x-axis (frequency) will always be the same for any given FFT-transformed EEG data - with ticks at the 4Hz, 8Hz, 13Hz, 30Hz units...

...and that the only variable/difference when comparing different original waveforms would be the amplitude (y-axis) values?

The x-axis is definitely not always the same, but is dependent on the window (determines longest wavelength detectable) and the sampling rate (determines maximum frequency and frequency resolution)

EEG is simply measuring voltage fluctuations over the scalp caused by electric dipole(s) within the brain tissue. Communication between neurons can happen at different spatial and temporal scales. At each scale, this communication results in a distinct pattern of current flow. Because EEG measures voltage fluctuations over a relatively large patch of the brain, it reflects current flow created by communication at various scales between millions of neurons (each scale presumably associated with a different frequency of oscillations).
Current research is attempting to understand what neural processes in the brain tissue result in the EEG. There is still not a complete understanding of what each frequency band represents, but studying similar types of oscillations within brain tissue (called local field potentials) have provided invaluable insights (for instance, small range communications are associated with higher frequency bands). How the oscillations recorded inside the brain tissue are related to the EEG is an active area of research.


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Function of the alpha subunit in mitochondrial ATP-synthase?
Within the catalytic core of mitochondrial ATP-synthase there are two different types of subunits; $\alpha$ and $\beta$. From what I have read, the catalytic sites occur only in the $\beta$ subunit so clearly it is in these where ATP is synthesised.
However, I can't find what the function of the $\alpha$ subunit is. A source would be appreciated.

Wikipedia tends to answer your question by this (emphasis mine):

In F-ATPases, there are three copies each of the alpha and beta subunits that form the catalytic core of the F1 complex, while the remaining F1 subunits (gamma, delta, epsilon) form part of the stalks. There is a substrate-binding site on each of the alpha and beta subunits, those on the beta subunits being catalytic, while those on the alpha subunits are regulatory. The alpha and beta subunits form a cylinder that is attached to the central stalk. The alpha/beta subunits undergo a sequence of conformational changes leading to the formation of ATP from ADP, which are induced by the rotation of the gamma subunit, itself is driven by the movement of protons through the F0 complex C subunit.

For above information, wikipedia cites this. I have put some info from it below.
From Introduction:

There are three regions to which ATP is hydrogen bound. First, the so-called Walker or P-loop (residues β-Gly-159-β-Val-164) at the beginning of helix B. Second, the beginning region of helix C, namely β-Arg-189. And third, the residue α-Arg-373 from the α-subunit (see Fig. 1, c–e) (Abrahams et al., 1994). Sequential formation of these 15–20 hydrogen bonds ensures nearly constant force generation throughout the whole duration of the binding transition. This “binding zipper” sequence would lead to the smooth closing motion of the pocket and continuous conformational changes throughout the β-subunit (Oster and Wang, 2000a; Elston et al., 1998; Oster and Wang, 2000b).

In Results:

Fig. 4, a–d provides four representative snapshots of the equilibrated closed and open binding pockets according to our simulations. These show that, in the closed pocket, the ATP molecule is surrounded by all three hydrogen binding regions. By contrast, in the open pocket a space has formed between the P-loop on one side and the helix C and the pocket's α-subunit region on the other side, with a gap between the ATP molecule and the P-loop so that ATP stays close to the α-subunit and helix C regions. An equilibrated intermediate structure where the pocket is half open is shown in Fig. 4 b. In that state the α-phosphate oxygen of ATP is still close to the P-loop, but the distance is already increasing between the β/γ-phosphate oxygens and the P-loop. The phosphate axis has rotated ∼30° and ATP is bridging the pocket. At the end of our simulations (Fig. 4 d), the ATP molecule is located between the two subunits as expected after its primary movement into the binding pocket. In that weak binding state, a newly docked ATP is expected to have contacted the ATPase, but not yet have induced conformational changes. Our simulations are consistent with this expectation. Further, we see that contacts are formed mainly between ATP and Mg2+, and between Mg2+ and the binding pocket.

In Discussion:

The importance of β-Lys-162, β-Arg-189, and α-Arg-373, for example, was connected to their formation of hydrogen bonds with ATP. In our simulations, these residues form the strongest and the last to break hydrogen bonds with the γ-phosphate oxygens. In addition, α-Arg-373 forms a hydrogen bond with one of the α-phosphate oxygens, acting as a restraining force for ATP during the movement of the P-loop and therefore facilitating the migration of the α-phosphate oxygens along the P-loop.

You can find further information here or here.

It's interesting that this source seemingly conflicts with the uniprot and interpro records from @JordiZambrino. I must say I can't find anywhere in that article that explicitly says it acts as a regulator specifically - the α-subunit still sounds to be intimately involved in catalysis.

@James I too couldn't find explanation for alpha subunit's regulatory role. The article justs points out that it helps in stabilising ATP but doesn't say a word about regulation. But since wikipedia clearly states this, so I quoted it to them. Maybe the author of wikipedia article cited wrong article for that line or misinterpreted the conclusion.

Why the downvote??? Please tell me so that I could improve any mistake in the post.

Would you agree that the article you have linked to is basically saying that the $\alpha$ subunit's role is to increase the efficiency of ATP-ase by allowing more locations for hydrogen bonds to form with? (btw the downvote wasn't me :) )

@Quantumspaghettification It seems so from what it (article) says, but it seems to me more that α subunit is for stabilizing ATP inside ATPase. I am continuously searching more details on this.

@Quantumspaghettification http://www.uniprot.org/uniprot/P0ABB0 alongwith many other links, clearly states "The alpha chain is a regulatory subunit." but gives no detail/citation for it. It seems like we don't exactly know its function yet, but someone just said its regulatory and everyone agreed on it. ;)

From: Uniprot

Mitochondrial membrane ATP synthase (F1F0 ATP synthase or Complex V) produces ATP from ADP in the presence of a proton gradient across the membrane which is generated by electron transport complexes of the respiratory chain. F-type ATPases consist of two structural domains, F1 - containing the extramembranous catalytic core, and F0 - containing the membrane proton channel, linked together by a central stalk and a peripheral stalk. During catalysis, ATP synthesis in the catalytic domain of F1 is coupled via a rotary mechanism of the central stalk subunits to proton translocation. Subunits alpha and beta form the catalytic core in F1. Rotation of the central stalk against the surrounding alpha3beta3 subunits leads to hydrolysis of ATP in three separate catalytic sites on the beta subunits. Subunit alpha does not bear the catalytic high-affinity ATP-binding sites.

From: InterPro

There is a substrate-binding site on each of the alpha and beta subunits, those on the beta subunits being catalytic, while those on the alpha subunits are regulatory. The alpha-subunit contains a highly conserved adenine-specific non-catalytic nucleotide-binding domain, with a conserved amino acid sequence of Gly-X-X-X-X-Gly-Lys. The alpha and beta subunits form a cylinder that is attached to the central stalk. The alpha/beta subunits undergo a sequence of conformational changes leading to the formation of ATP from ADP, which are induced by the rotation of the gamma subunit, itself is driven by the movement of protons through the F0 complex C subunit [PMID: 12745923].

Sources:

http://www.ebi.ac.uk/interpro/entry/IPR005294
http://www.uniprot.org/uniprot/P25705
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3278611/
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1679692/

The α- and β-subunits have a similar fold, as would be expected from
  their sequence similarity. All of the α- subunits are bound to the ATP
  analogue AMP–PNP, and the three subunits adopt very similar
  conformations. The three β-subunits, however, are in three
  nucleotide-bound states: the first, termed βTP, has AMP–PNP in the
  catalytic site (FIG. 3c); the second (βDP) has ADP; and the third (βE)
  has no bound nucleotide (FIG. 3c–e). So, the native structure of F1
  looks like a snapshot of the working rotary engine, with three
  reaction chambers representing the moment just after exhaust and
  intake (βΕ), ignition (βDP) and compression (βTP) (BOX 2). The lower
  part of the slightly bowing, asymmetric coiled-coil structure of the
  γ-subunit is displaced towards the βE , forcing the carboxy-terminal
  domain of this β-subunit to swing ~30° downwards. Thus, the βE adopts
  the ‘open’ (O) form, whereas βTP and βDP have the ‘closed’ (C) form.

Yoshida, M., Muneyuki, E., & Hisabori, T. (2001). ATP synthase--a
marvellous rotary engine of the cell. Nature Reviews. Molecular Cell
Biology, 2(9), 669–77. http://doi.org/10.1038/35089509


text:
Relationship of the DNA of a eukaryotic gene to the 5'-UTR of its mRNA
In eukaryote pre-mRNA I am having a little trouble grasping exactly what the 5 prime untranslated region is defined as.
It seems that it could be defined as the difference in pre-mRNA between the transcription and translation, is that right?
If so, does the transcription begin immediately after the end of the promoter sequence? Immediately after the promoter region on the DNA ends, does the next nucleotide become part of the 5'UTR?

Possible duplicate of http://biology.stackexchange.com/q/38783/3340

Concise Answer
The 5′-UTR region of a eukaryotic mRNA is derived from the RNA transcript of the region of a gene between the transcription start site and the DNA corresponding to the translational initiation codon. It differs from that region of the initial transcript in most cases by having a modified guanosine nucleotide added at the 5′-end in a ‘cap’ structure, and in some cases by lacking an intron present in the former.
The Transcriptional Start Site (TSS) and the Promoter
This answer is in terms of the TSS rather than ‘promoter region’ or ‘promoter sequence’, favoured by the questioner because in eukaryotes, at least, there is no simple and easily definable promoter sequence. A 2012 review of metazoan promoters begins with the definition: “Gene promoters are the loci overlapping transcription start sites (TSSs), at which the total regulatory input of a gene is integrated into the rate of transcriptional initiation. The immediate role of the promoter is to bind and correctly position the transcription initiation complex…”.
A simplified diagram of some components of human promoters (adapted from an earlier review by Hahn) is shown below. The position of ‘Inr’ — the Initiator box which contains the TSS — varies with respect to the TATA box (usually from –25 to –30 in humans),  as does its sequence. Hence, even ignoring the alternative TSSs mentioned by @Luke it is not possible to identify the TSS unequivocally by inspection of the DNA sequence of a gene in the way the questioner would apparently wish.

The Translational Initiation Codon
It should be mentioned that it also not possible to identify the mRNA translational initiation codon unequivocally by inspection of the DNA sequence of a gene. Although 90% or more cases follow Kozak’s rule, so that it corresponds to the first ATG, in some cases it may correspond to the second ATG, and in a small minority — where internal initiation occurs — to some distal ATG. There are also a very few examples of initiation of translation at codons other than AUG.
Introns in 5′-UTR regions of pre-mRNA
A common rationalization of the intron/exon nature of eukaryotic pre-mRNA is in terms of alternative splicing to produce different protein sequences. Perhaps because of this, the presence of introns in the primary transcript containing the 5′- and 3′-UTRs is often ignored (see Bicknell et al. for a review). In fact, approximately 35% of 5′-UTR precursors contain introns, not present in the mature mRNA.
5′-capping of eukaryotic mRNA
The vast majority of eukaryotic mRNAs have a cap at their 5′-end, produced by the addition of a guanosine nucleotide in a 5′-5′ pyrophosphate linkage to the 5′-residue (usually A) of the pre-mRNA. This is subsequently methylated at carbon-7; and 2′-O-methylation of the ribose of the subsequent one or two nucleotides may also occur.

Footnote
Although “DNA makes RNA makes Protein”, I do think it important to describe features of information molecules in terms of their actual composition. Thus, the 5′-UTR has to be defined in terms of mRNA, not promoters or exons. If one makes reference to the information content of DNA or RNA than I think a form of words should be used to make that clear. Muddled forms of words can easily lead to muddled thinking.

Short answer
The 5' UTR on the mRNA includes sequence from the Transcriptional Start Site (TSS) to the first exon. Promoters are usually associated with a corresponding TSS.
Longer answer
In defining a UTR we must consider where transcription begins. Strictly speaking transcription begins at the Transcriptional Start Site (TSS). A useful website for exact definitions for terms such as this is the Sequence Ontology - see here for the TSS definition (link) - although admittedly in this case it does not provide more information than that (i.e. the TSS is where transcription begins... which we might have gathered from the name). TSS will have an associated promoter region, which is where the RNA polymerase (and various regulators and transcription factors bind).
To expand: a gene may have multiple TSS' which may be promoted under different circumstances (e.g. with different factors present). We therefore have the concept of a TSS region "The region of a gene from the 5' most TSS to the 3' TSS" (link). TSS have to be empirically determined, and seem to vary by tissue (a database of all known TSS has been accumulating evidence since 2002: DBTSS). I have just performed a search for gene APOE (chr 19) and this is a small excerpt of the data presented, just to give an idea:

From DBTSS

So the TSS for a gene is context dependent on the when/where the transcript is being produced.
In searching for further information I found a paper that describes the 5' UTR on the mRNA, which is not necessarily an exact copy of the genomic DNA (e.g. by addition of the cap). Here is the corresponding paragraph and diagram, which explains it better than I can paraphrase (highlighting by me):

Transcriptional control is mediated by transcription factors, RNA polymerase and a series of cis-acting elements located in the DNA, such as promoters, enhancers, silencers and locus-control elements, organized in a modular structure and regulates the production of pre-mRNA molecules, which undergo several steps of processing before they become functional mRNAs. Introns are removed, a 7-methyl-guanylate (m7G) cap structure is added at the 5' end of the first exon, and a stretch of 100-250 adenine residues (the poly(A) tail) is added at the 3' end of the last exon, which is itself generated by endonucleolytic cleavage of the primary transcript. Sometimes the sequence of the mRNA is also altered in a process called mRNA editing, and the resulting coding sequence of the mature RNA differs from the corresponding sequence in the genome. The resultant mature mRNA, in eukaryotes, has a tripartite structure consisting of a 5' untranslated region (5' UTR), a coding region made up of triplet codons that each encode an amino acid and a 3' untranslated region (3' UTR). Figure 1 shows these and other features of mRNAs.

Figure 1: The generic structure of a eukaryotic mRNA, illustrating some post-transcriptional regulatory elements that affect gene expression. Abbreviations (from 5' to 3'): UTR, untranslated region; m7G, 7-methyl-guanosine cap; hairpin, hairpin-like secondary structures; uORF, upstream open reading frame; IRES, internal ribosome entry site; CPE, cytoplasmic polyadenylation element; AAUAAA, polyadenylation signal.
Reference: Mignone, F., Gissi, C., Liuni, S., & Pesole, G. (2002). Untranslated regions of mRNAs. Genome Biology, 3(3). http://www.ncbi.nlm.nih.gov/pubmed/11897027

Promoters
To conclude my answer (apologies for its lack of brevity) there are different types of promoter, not all of which directly include a TSS, but the majority of promoters are called "Major promoters" and do include the TSS (in addition to the RNA polymerase binding site, and other transcription factor binding sites).

Eukaryotic Promoter Database
Dreos R, et al. EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era Nucleic Acids Res. 2013 Jan;41. http://www.ncbi.nlm.nih.gov/pubmed/23193273

Well I guess it's just not as simple as I had hoped, but I feel like now I have more questions than I started with. Thank you for such a really thorough answer.

Thanks for accepting it so quickly - I've updated it some more as I realise it was quite long and hadn't quite answered you question. Included more info on the promoter aspect. :)

You state in your summary that "The 5' UTR on the mRNA includes sequence from the Transcriptional Start Site (TSS) to the first exon". But this is surely wrong as exons are those parts of the transcript that appear in the mRNA, so the 5'-UTR will include the first exon (at least). Indeed, that is what appears in bold in the piece you have quoted.

Since there are genes with different transcriptional start sites regulated by different promoters, it would be interesting to see if the resulting differences in 5' UTR are used to regulate expression of the mRNAs. This could allow an extra layer of regulation, such that gene X not only needs promoter Y, but micro RNA Z to function, but when promoter W is used, the 5' UTR is different and RNA Z is no longer needed.

Hi @David - maybe I've also misunderstood something but the exons by definition are the translated regions, therefore would not be part of the UTR (un-translated region). On the diagram you can see there is a long stretch of sequence between the start of the mRNA (the cap) and the "coding sequence" i.e. the exons (bear in mind introns have been removed by this stage so all included exons appear together). -- user137 that would be interesting, and I expect it would affect pre-mRNA processing to some degree.

No. The definition of exon given by Wikepedia (which is consistent with every other you will find in a text) is "An exon is any part of a gene that will become a part of the final mature RNA produced by that gene after introns have been removed by RNA splicing." I shall address the question of introns in the 5'UTR in a replacement for my original answer which should appear later today.

Actually, not every text. I found a definition in terms of coding regions at http://www.nature.com/scitable/definition/exon-exons-270. But that is nonsense. What else would you call the part of a transcript that is not an intron and appears in a final mRNA but an exon? It can include the 3'UTR as well. Exons and introns are to do with splicing of pre-mRNA. One function may be to allow for different coding regions in differential splicing, but they can also allow for different 5'UTRs.

The UTR is the region of the transcript upstream of the starting methionine.  The promotor is not itself transcribed.

Sorry to push the issue, but I don't feel like you answered my question. This is exactly how the book phrases it as well. Is it everything after the promoter but before the methionine?

In other words, does transcription start immediately where the promoter ends?

DPEs can be in the transcribed region. http://biology.stackexchange.com/q/38783/3340

@WYSIWYG It seems like there are no hard and fast rules. Thanks for finding that, it pretty much answers my original question.


text:
Are there proteins that stabilize mRNA:RNA polymerase or mRNA:ribosome complex?
Actinomycetes are known for their ability to produce rich variety of natural products, and particularly, polyketides. Many of the genes that encode the biosynthetic pathways are pretty big, as they could reach 20kb. These bacteria most probably have some kind of mechanism that would ensure efficient transcription and translation of these long ORFs. This could be done in two ways: either by stablizing the mRNA:RNA polymerase complex, or by stabilizing the mRNA:ribosome complex. Have any of you heard of such mechanisms in bacteria?

What makes you think this is so? Where did you read it? There is a specific mechanism for termination of translation and release of ribosome subunits. I may be wrong but I see no reason that ribosomes would fall off long mRNAs.

A professor I talked to at a conference mentioned it, so I know they exist, I just don't know how they are called. And it definitely makes sense for them to exist, since these ORFs are so long.

Don't believe everything you hear from professors. And don't be so definite about your logical rectitude. Does the RNA polymerase need extra proteins so it doesn't fall off the DNA when transcribing longer genes?

I am not saying they exist 100%. I am just saying that it makes sense.

To me it raises as many questions as it answers. For example how do these proteins distinguish short from long mRNAs?

That's a very good (and interesting) question. Will let you know if I find the answer.

One obvious candidate is the PABP (poly-A binding protein) that interacts with the cap to circularize and stabilize the RNA (in Eukaryotes). Ribosome recycling factors also play some role but I am not sure about an Actinomycetes specific mechanism. And nothing at all about proteins stabilizing RNA-ribosome complex. Moreover, I believe that these long genes are polycistronic.

I have searched for information on this topic as I used to work in the area of protein biosynthesis, but without success. However one argument against there being a tendency for ribosomes to fall off mRNA is the very specific mechanism for ribosome release after the stop codon has been reached (described here).
There are other possible problems with very long mRNAs:

Possible degradation of (prokaryotic) mRNA during a long translation time. I would imagine that the mRNA would have to contain structural features that prolonged its life.
Possible misfolding and precipitation of the protein product before the whole protein is completed. I think there are probably chaperone proteins that protect hydrophobic regions or encourage folding of domains to prevent this.

As a footnote, the longest mRNA is thought to be that for the muscle protein, titin. The assembly of that on the sarcomere would not be typical of polyketides, say, but other problems encountered might be similar.

There is indeed a gene, called nusG which is believed to be antiterminator of RNA transcription. NusG binds to RNA polymerase which leads to increased RNA elongation rate, decreased time in which the polymerase is into short-lived paused states, and suppressed long-lived stabilized pause states 1.
NusG has been shown to be important for natural product discovery and biosynthesis:

Overexpression of nusG in C. cellulolyticum enabled the isolation of seven novel analogues of closthioamide, named closthioamides B–H86. These new compounds are truncated and partially O-substituted derivatives of the antibacterial parent hexathioamide compound, but have substantially lower antibacterial activity 2

Furthermore, nusG-like gene is found in the Myxococcus xanthus gene cluster eccoding myxovirescin (TA) antibiotic. One of the polyketide-encoding genes in this cluster is ~27kb long [3]:

Herbert, K. M. et al. E. coli NusG inhibits backtracking and accelerates pause-free transcription by promoting forward translocation of RNA polymerase. J. Mol. Biol. 399, 17–30 (2010)
Peter J. Rutledge    & Gregory L. Challis. Discovery of microbial natural products by activation of silent biosynthetic gene clusters. Nature Reviews Microbiology 13, 509–523 (2015)
http://mibig.secondarymetabolites.org/repository/BGC0001025/index.html#cluster-1


text:
Is curling the fingers considered digit flexion?
Is curling the fingers considered finger flexion and straightening the fingers considered finger extension? I feel like this is correct in everyday language, but I am not sure if it is correct in anatomical terms.

In the vertical plane, bending of the fingers is indeed referred to as flexion, while straightening is called extension. In the horizontal plane, spreading the fingers is called abduction, and approximation is called adduction (Fig. 1).

Fig. 1. Finger movements and terminology. source: US Dept. of Veteran Affairs


text:
What signal processors comprise an Event-Related Potential system for EEG?
So my 30,000 ft. understanding of the EEG signal processing data flow is:

Capture raw EEG data ("raw waveforms")
Run these raw waveforms through a Signal Processing Framework that consists of 1+ "nodes"/processors, where each processor is doing some kind of transform on the raw waveform. In doing so, new information is unlocked from the raw waveform that was previously hidden
Perform individual analyses on this unlocked data based on what suits your research/application

So first off, if the above understanding is misled in any way, please begin by correcting me!
Assuming I'm more or less correct, my specific application at hand is that I want to correlate certain raw waveforms with events (such as "thinking of a turtle", "moving head up and down", etc.). I believe Event-Related Potentials are what I'm looking for, but...
What I'm struggling with is: what does my "Signal Processing Network" need to look like in order to implement ERP? From an architectural perspective, I'm looking for this "network" to take raw waveforms in as input, and to output events, like the few I mentioned above.
Is FFT a player here? Some kinds of filters? What processors comprise an ERP system, and what does their respective "network" (data flow pipeline) look like?

Thanks @daniel - when you say "of course the data will be transformed from the time to frequency domain", why do you say "of course"? What is there to be gained by viewing the data in the frequency domain over viewing it in the time domain?

In particular the goal is often to get an idea of the distinct frequencies that comprise a complex signal, and if the signal is really complex it may be impossible get that information by observing the time signal.

Thanks @daniel - let's stick with your heart rate example. You stated "You are typically looking for the average behavior of a signal over time." Can you provide 1 concrete example of one such "behavior" about the human heart that can be observed by running heart rate data (observed in the time domain) through FFT and transforming it into the frequency domain?

Sure, so can you provide any concrete example where decomposing an original waveform into component frequency-based waveforms yields more actionable information than on the original waveform? Any example at all, but a concrete example?

Thanks again @daniel, but I'm just not seeing the value of this (still). Let's use your violin example. If we already know what the violin sounds like, why break it down into its major frequency components? To reconstruct the sound of the violin?!? We had the sound in the first place! To me, this is like saying: let's tear a car engine apart so we can have all its individual parts...so we can then put the car engine back together...and have ourselves a car engine! No, you already had the car engine in the first place!

And: https://en.wikipedia.org/wiki/Quantitative_electroencephalography

Note that the terminology is unconventional; filtering is typically a serial process, especially in EEG. Secondly, a processor is mainly associated with hardware, e.g. a cpu.

@smeeb Can you please expand your step (2)? It is too unspecific. Please, give concrete examples what you are trying to do.

Event-related potentials are an issue in many aspects of physiology, not just in EEG analysis, so this answer is more general.
The main problem is that electrical or other signals that are associated with some "event" are typically much lower in magnitude than the background noise in the system being examined. Electrocardiograms are the exception in this regard. The trick then is to get the event-related signal out of the noise.
This is done by multiple repetitions of the "event" combined with signal averaging. You mark the electrical record with the time of each event, line up portions of the record by the event markers, and average the lined-up signals around the multiple events. This can be the full waveform for sensory evoked potentials, or if you are examining action potentials in a nerve, the histogram of post-event occurrence times of action potentials.
The particular signal-processing mechanisms used have evolved with technology. I'm old enough to have known people who 60 years ago recorded electric neural activity on magnetic tape and processed the data, after analog-to-digital conversion, on what were then state-of-the art computers with 65,000 words of memory. Even then, there was discussion about the best technologies to use for specific purposes, as this 1959 MIT monograph indicates.
The main problem, the low level of event-related signals to the noise from other electrical activity, still remains 60 years later. The noise is biologic rather than technical, so more recent improvements have more to do with ease and speed of processing rather than the fundamental signal-to-noise problem. For event-related potentials, use whatever technology allows you to collect the electrical data along with linked notations of the event times, then to average the signals synchronized on the events to build up the signal out of the noise. In a standard clinical application of brainstem auditory evoked potentials to evaluate hearing, you may need to average over 500 or more stimulus presentations.
This signal-in-noise problem is also seen in functional magnetic resonance imaging (fMRI) of brain function, which must consider not only the signal over time but also differences among brain regions within individuals and differences among individuals. The Wikipedia page on fMRI goes into some detail on methods for getting specific signals out of the background noise. You should pay particular attention to the issue of "Block versus event-related design" on that page. My understanding is that for "events" of the type that you consider, like "thinking about a turtle," the block design works best in fMRI. The paper by Thierry et al, provided in the helpful answer by @Christiaan, shows how to apply this type of blocking approach to EEG measurements during presentation of visual stimuli.
The methods in that paper by Thierry et al also indicate the type of practical care that is used in professional analyses of this sort. The authors go into detail about the nature and placement of the electrodes, choice of which electrodes to use for analysis, signal-sampling rates, and so on. For example:

Scalp activity was digitized at a 1-kHz sampling rate from 64 Ag/AgCl electrodes distributed throughout the scalp according to the 10–20 convention using Cz as a reference. Impedances were kept below 7 kOhms. The electroencephalogram was filtered on-line between 0.01 and 200 Hz and off-line low pass at 35 Hz using a zero phase-shift digital filter. Eye blink artifacts were mathematically corrected, and signals exceeding $\pm$ 75 $\mu$V in any given epoch were automatically discarded.

Reading papers like this are probably the best way to learn about what's involved and to find people who can teach you how to do it.

This seems like a reasonable answer to me. Does filtering play a role in this?

@daniel, filtering is inherent in all this signal processing as any real recording system has finite bandwidth, and that bandwidth must include the signal of interest. In this type of application the electrical recording is typically AC-coupled so that low-frequency signals (e.g., drift) are omitted. If the particular waveform of the response is known then filtering to emphasize its frequency components could help, but the main technique is multiple repetitions of the event and averaging the multiple signals after they have been lined up by event times.

Thanks for the great answer @EdM (I'd upvote it if I had the rep to do so)! So it sounds like the two main problems with "raw" EEG data are: (1) signal-to-noise ratio (which is a long-standing problem seeing that the majority of the noise is coming from the subject's own body) and (2) using tricks like FFT (as well as others) to unlock more information than what was present in the original waveform. Can you please confirm these statements and correct me if I am misunderstanding or overlooking anything? Thanks again!

FFTs don't "unlock more information than what was present in the original waveform," they just help to bring a signal out of the noise in some circumstances. That's really an important distinction; the information is there but you have to find it. Yes, signal-to-noise is the main issue. For your application, signal averaging in the time domain may be more important than frequency-domain analysis with FFT, unless you happen to know the frequency characteristics of the signal you are looking for.

Thanks for the clarification @EdM - so then just to confirm, for once and for all, that ERPs are typically done by averaging the "raw" (unprocessed) EEG waveforms? And that there are no other "tricks of the trade" (filters, perhaps other signal processing transforms, etc.) exacted upon this raw data? Thanks again!

There really is no such thing as an "unprocessed" electric waveform in practice, as any real-world recording device always selects a certain range of frequencies. There are certainly some "tricks of the trade" in general for how to collect high-quality data, and there may be some specific choices of extra filtering for specific applications, but this isn't my professional area of expertise. If you want to do something like this, work with someone who has practical experience.

@EdM Can you give any code-project/site which does event detection by averaging some shape or studying histograms? I would like to understand better how they manage the signal detection. I want include whole signal in time (not just intervals). Gerstein 1960 + Arieli 2006 + Urner 2013 show that the noise includes also physiologically significant details. I would really like to understand how to avoid filtering out physiological data i.e. some elements of noise. Then, understand how the situation is with Cardiac signals. How much of noise is physiological in ECG signals?

@Masi : ECG signals are typically very large with minimal noise when electrodes are properly applied and high-quality electronics are used; don't know that there's much "physiological" noise to find within them. The more general case is beyond my current professional expertise, as I haven't had to think about signal processing in many years. I recall that wavelet analysis can be useful for finding signals of a specified shape in a noisy signal, in a way that's superior to Fourier analysis, but I have no direct experience with that.

@EdM Assume you see a signal in your Wavelet analysis/Fourier analysis. How do you confirm that it is really a signal than artifacts? Results of wavelet analysis are very noisy itself.

@Masi :sorry, this is beyond my expertise. Try the Stack Exchange Signal Processing site for better-informed answers.

ERPs are typically analyzed in terms of amplitude and latency. FFT is not really an option; it doesn't make sense. There may be some applications where it may be useful, but these are quite specific. For example, if repeated stimuli are presented and you are not averaging them, but you are collection an EEG, then FFT may help you to deduce whether stimulus frequency (e.g. 1/s) matches the response frequency and what the phase difference is between them. Note that FFT yields amplitude and phase data. But generally, again, ERPs are analyzed simply by taking amplitude and latency (Fig. 1)

Fig. 1. ERPs are characterized by taking the peaks, here the positive peak (P) at 100 ms (P1) and the negative peak (N) at 170 ms (N170). The amplitude of the N170 peak was analyzed and plotted. source: Thierry et al., (2007)
Reference
- Thierry et al., Nature Neuroscience (2007); 10: 505-11

As always, thanks again @Christiaan! Please see my last comment/followup question underneath EdM's answer...I have the same question for you! Thank you immensely!

@smeeb - Filtering is only necessary when it's necessary. If it's not, it's not :)

...Fast Fourier transform (FFT) of EEG data, is ...a reformatting of data into component frequencies. This transformation is useful because many pathological EEG patterns produce spectral patterns that can be easily recognized without the extensive training required to interpret routine EEG tracings... From the abstract of this paper. Clearly FFT does make sense and is done in some cases.

QEEG [FFT/wavelet analysis] has been accepted for clinical application in some areas, such as cerebro-vascular disorders and epilepsy, though it remains yet to be accepted in other clinical areas, such as diagnosing mild traumatic brain injury or psychiatric disorders. https://en.wikipedia.org/wiki/Quantitative_electroencephalography.

@daniel - that's EEG,  not ERP; question & answer are on ERP

Yes I see that now. I was focused on EEG and I see that FFT doesn't have much bearing in the ERP context--thanks.


text:
Why doesn't the Tsetse fly wipe out all animal life in its range?
The Tsetse fly, which is native to interior West Africa, carries the protozoan that causes sleeping sickness, a disease which was apparently invariably fatal before the advent of modern medications. Since there is no way to prevent being bitten by this insect, why did it not kill off all animal life in its range?

Firstly, as Remi.b points out, although Trypanosoma brucei infection is invariably fatal in humans, the same is not true for all host species. Domestic animals, particularly cattle, are an important reservoir of T. brucei rhodesiense and can also be infected with T. brucei gambiense (although it's not clear how important a reservoir they are for gambiense).
Secondly, just because all hosts in an area get bitten by tsetse does not mean they all become infected with trypanosomes. Even in endemic areas most vectors never become infectious. The transmission cycle for trypanosomes and other insect-borne pathogens requires an incubation period in the insect vector which is often longer than the median lifespan of the insect, so even if all insects get infected early in life, most will never survive the incubation period to become infectious. The later the average time of first infection gets, the higher the fraction of hosts that will have the opportunity to reproduce before dying.
Finally, it isn't entirely accurate to say "there is no way to prevent being bitten"; it is possible to reduce exposure to insects behaviourally. For example, in most endemic areas local residents will know which areas are most heavily infested and should be avoided.

Good answer +1. I think I've upvoted all of your answers so far! It is good to have you on Biology.SE.

Thank you very much! I only discovered the site recently but it seems to be a great community.

Trypanosoma, the parasitic protozoans that uses the TseTse fly as vector to reach its definitive host, a mammal. In wild mammals, Trypanosoma brucei has no noticeable impact (Chatterjee, 2009). I don't know about Trypanosoma cruzi.
Parasite who recently transferred from one host to another are often the most virulent. In the particular case of a parasite that transfer from an animal species to humans is called zoonosis. Ebola virus disease, AIDS, salmonellosis and influenza are all examples of zoonosis. I know little about Trypanosoma evolution but this lab probably have a number of publication that will interest you


text:
Have there been any studies on 'surf balls'? (not related to surfing)
I know the title sounds like something else, but I'm actually wondering about these things, which are also apparently called 'whale burps':

This one was found on a beach on Lake Michigan.
Does anyone know if any scientific studies have been done on these? If so, can you refer me to them?

Are they supposed to come out of the lake? To me it looks like it is made up of land plant material, so possibly a birds nest?

Okay, I have to retract my suspicion. I found this, which states that there seems to be a publication, but I can't find the relevant publication mentioned. Maybe someone else has more luck?  http://blogs.oregonstate.edu/breakingwaves/2011/01/12/osg-beach-publication-solves-a-great-lakes-mystery/

The publication @skymningen mentions is "Flotsam, Jetsam, and Wrack", available here. The explanation for "whale burps" in the publication is the same as given in Bob Specter's answer.

These surf or beach balls are made of plant material throughout the structure. They form by wave action along beaches. As the naturalist at Wilderness State Park, the campers and I discovered over 30 of these balls along the beach the summer of 2017.  In previous summers we had found 0. Lake Michigan and Lake Huron were at record levels during the summer of 2017, the highest since 1998. This may explain the formation of these grass balls in abundance. We found some of the balls still in the water, in a depression, just offshore. Others were rolling in and out with the waves. Some had been deposited on the shore and were filled with large quantities of sand. I shook out nearly a cup of sand from one ball. I dissected one surf ball and found predominantly roots and grasses. I will continue to investigate the species of plants that form these curious creations and try to discover the secret to their formation


text:
How to calculate relative fitness from absolute fitness with recapture data?
In Grant 1986, it says:

For this analysis the absolute fitness of an individual id scored as 0 if it disappeared and 1 if it survived, and these scores are then converted to relative fitness values by dividing them by the mean absolute fitness.

Grant, P. R. 1986. Ecology and Evolution of Darwin's Finches

If we have this dataset (for example):
   id x1 x2 x3 x4 x5
1   1  0  1  0  0  0
2   2  0  0  1  1  0
3   3  1  0  1  1  0
4   4  1  0  1  1  1
5   5  1  0  1  0  0
6   6  0  0  0  0  1

Imagine that id is a column of individuals and the rest (x#) are the absolute fitness. Then, how would you calculate the relative fitness? For an example, what would be the relative fitness between x3 and x4?
X1 is the first event of capture. I.e. I went on the field and capture birds for examples and noted who was there (0). I went on the field on X2, X3, etc. and noted which bird was there.

I don't fully understand the data set. Can you please explain what each column $x_n$ mean?

I edited the question

An individual cannot have more than one value of absolute fitness. I still don't fully understand the data. I don't understand how can an individual disapear at reappear. I suppose the data just say present/absent (or found/not found). I don't understand why knowing an individual is present at time $t_2$ give us as information about its fitness. Do you know or is it part of what is unclear to you too?

Let's say you're calculating the fitness of an individual from one year to another (say X3 to X4). ID 2 to 4 are surviving, but ID 5 is not. Than the absolute fitness for year x4 would be 1,1,1,0 (for ID 2 to 5). I wonder how to calculate a relative fitness. Like the citation basically.

Well for one thing each individual lived at least as long as the interval between first and last capture - e.g. although individual #5 was not captured at time point X2 you know it was alive then because it was captured both at X1 and X3. I don't see where fitness comes in to this - you can score relative lifespan but longevity is a poor proxy for fitness

OK, but if you refer to the citation, it is possible, right? How then it's possible to calculate relative fitness with absolute fitness in this fashion?

My guess would be that the capture-recapture analysis will give you an estimation of the survival rate (1/0) for each individual for each time point (taking non-observations into account), along with an average survival rate at each time step (often the purpose of a capture-recapture study). With this information, you can divide the absolute fitness at each time (survival, 1 or 0) by the average survival at that time point to obtain a measure of relative fitness. So for a timestep where few survived, rel fitness will be >1 (e.g. 1/0.2), and when all survive rel fitness will be 1 (1/1).

So the dataset is this:
  id   x3 x4
   2    1  1
   3    1  1
   4    1  1
   5    1  0

Lets assume that we caught the entire population so that we know that a 0 is really a dead organism (the estimate is proportional to lifetime fitness). Furthermore, there is no density-dependent selection and the generations are non overlapping.
The survival for next year would be the fitness value of all individual. So in year x4, the absolute fitness would be W = c(1,1,1,0). Then the mean absolute  fitness would be: W_bar = (1+1+1+0)/4 = 3/4. Then to get w, the relative fitness, you divide W by W_bar. w = c(4/3, 4/3, 4/3, 0). Therefor, if you calculate mean(W/W_bar), it should give you 1.
This is as in Endler, J. A. 1986. Natural Selection in the Wild. Princeton University Press, p. 168.

Not sure I get your question here, with your capture history you coulde estimate a survival for the populaiton. Therefore you could add covariable to your model like body mass and make a realtion between higher survival and bigger weight by example. A proxy of absolute fitness coulde be surviving until sexual maturity or something like that ... So what your question (objective )

To illustrate my comment:
   id x1 x2 x3 x4 x5 group
1   1  0  1  0  0  0   1
2   2  0  0  1  1  0   0
3   3  1  0  1  1  0   1
4   4  1  0  1  1  1   1
5   5  1  0  1  0  0   0
6   6  0  0  0  0  1   1

Lets say the 1 are the bird with dark wing and the zero the bird with white wing. So You can easily run a model in MARK or Rcapture (or others...) to estimate survival for each group.
Let's say you obtain a survival between X2 and X3  of 0.70 for the dark wing and of 0.85 for the white wings. Assuming that they have the same number of offspring, you can calculate the relative fitness for the white wing :
w(white)= 0.7/0.85 = 0.83

Is that what you want to do ?

Not exactly, I wanted to calculate relative fitness values, like the Grants. I mean, look at the citation in the yellow box. That's what I mean.

What's your fitness proxy ?

Survival, like what the Grants used.


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Can ELISA be used to detect a plant enzyme? Creating assay for a new enzyme
If the goal is to generate a rapid assay for an enzyme of plant source what are the typical options?
i.e. Could one do something like: Generate an antibody to the enzyme and then use it to create an ELISA? Would animal-injection be the way to generate the specific antibody needed?
If not, what is typically done in such cases. How does one go about creating an assay for a new enzyme of plant source. Are there alternative approaches that avoid the antibody creation?

I want to add something on top of Chris answer.
The production of an antibody it is usually a quite slow (and expensive) process, an alternative that worth to consider is phage display (https://en.wikipedia.org/wiki/Phage_display). Once you find the phage that effectively bind your protein of interest, you can use it instead of an antibody in what is called a Phage-ELISA assay.
However, mind that the ELISA (or Phage-ELISA) will give you information about the presence and the concentration of the enzyme, it will not give you any hint about its activity.

Thank you. I did not know about this. Sounds like this could be what I need.

Feel free to contact me for more info. I got quite a lot of experience with phage display.

you can send me an email at alec_djinn-at-yahoo.com

I think the most promising routes use antibodies. You could either develop an ELISA or do western blot analysis of plant material - both need a good and specific antibody. To generate these, the protein of interest (or at least parts of it) are injected into animals (typically mice or rabbit) and then antibodies are pourified from the blood of these animals. These antibodies are polyclonal, but this approach is rather fast and can be done in a few weeks.
If you want to use a more sustainable source of antibodies, the antibody producing cells from these animals are isolated, immortalized and characterized as single clones to get monoclonal antibodies.
Since you are using an enzyme, you could also think about activity assays. So either a chromogenic or fluorogenic substrate is metabolized, or you could use coupled reactions where your enzyme uses a substrate which is refilled by another reaction - classical examples here are coupled reactions which use NADH or ATP.
It is also possible to measure the metabolic rate if the substrate or the product of your enzyme shows fluorescence. Then you can either measure the decrease of your substrate or the increase of your product.

Thanks Chris! Can you elaborate more about the chromogenic / flurogenic substrate part? The enzyme I'm interested in converts FPP (Farnesyl Diphosphate) to a specific terpenoid.

Since I don't really know your enzyme, I can't tell you about this. But reactions like this are widely used.

In paragraph 2 you talk about the production of monoclonal antibodies, but isn't the process more like that mAb antibodies are antibodies which are derived from a B cell clone, then an animal is injected with the antigen which thus produces plasma cells, those are hybridised with tumour cells (because of endless divisions), and the resulting hybridoma is then synthesising large quantities of specific antibodies, or am I misunderstanding something?

@JordiZambrino I am not sure if I get your question completely. Monoclonal antibodies are made from animals where the animal is vaccinated with the antigen of choice, then the spleen is removed and from it lymphocytes are isolated. These are then grown into single clones and the secreted antibodies are characterized. These are then fused to make hybridoma cells which produce large amounts of antibodies.

Thank you for the explanation. The characterisation of antibodies could be seen as a structural specialisation, right? Where the composition and sequence of AA are determined?

@JordiZambrino if you have specific questions about antibodies, please ask, as the topic is quite broad. Sequencing is not necessarily a part of monoclonal antibody characterization, unless you are planning on producing it recombinantly. Plenty of mouse monoclonal antibodies out there for research use are grown in ascites where all you need is a healthy hybridoma culture. I used to work for an antibody company producing research reagents, so I have some experience :)

@JordiZambrino or, you can always ping me in chat.

@MattDMo what you said is really interesting and helps my understanding a lot. Also, thank you very much for your offer :)


text:
What cause the urge to inhale in humans?
The urge to inhale in humans result from
(a) rising pCO2
(b) rising pO2
(c) falling pCO2
(d) falling pO2
p stands for partial pressures. I am guessing the answer is falling pCO2.... but I wanted what is the answer and the reason for it?

It sounds like a homework question, so I added the tag. Feel free to correct me if I am wrong.

As a homework question, guessing doesn't suffice @MukilanSC

@skymningen This was not an homework question. It was a question given by my biology faculty and he said the answer was fallin pO2.. I wanted to know the reason behind this to counter my faculty and to make the class aware of the real reason.... So you are wrong and I did not violate the community guidelines?

In the help section on how to ask, the first sentence is "Have you thoroughly searched for an answer before asking your question? Sharing your research helps everyone. Tell us what you found and why it didn’t meet your needs. This demonstrates that you’ve taken the time to try to help yourself, it saves us from reiterating obvious answers, and above all, it helps you get a more specific and relevant answer!" You simply guessed. @rg255 is correct.

@MukilanSC I don't think we have a general difference here to distinguish between (written) homework questions and "classwork" or "in class" questions/ exam questions and so on.

The correct answer would be a) rising pCO2. The rising CO2 level in the blood causes an urge to inhale and take in more O2.
For how it works, this is controlled by respiratory center:

The respiratory centers (RC) are located in the medulla oblongata and pons, which are part of the brain stem. The RCs receive controlling signals of neural, chemical and hormonal nature and control the rate and depth of respiratory movements of the diaphragm and other respiratory muscles ... In healthy individuals the presence of elevated carbon dioxide levels in the blood is the stimulant that the RC responds to in order to signal the respiratory muscles to breathe. Chemoreceptors found in carotid bodies and aortic bodies are responsible for detecting decrease in blood pH by this carbon dioxide.


text:
Understanding the configuration of channels in the 10-20 EEG montage
In a typical 10-20 system there are 21 electrodes placed on the scalp. However, this does not mean there are 21 distinct "channels" or voltage sources.
I've heard that some of these electrodes are "reference" electrodes, while others are "active" electrodes. Sometimes electrodes can setup in a "bipolar" or "differential" fashion.

How many actual channels (that is, distinct input sources) will there be?
What's the difference between reference and active channels?
How does this bipolar/differential setup work differently than a "normal" setup?

Update
Here is my updated understanding based on @Christiaan's answer below:
time (t)    F2v    F3v        F2-F3         F3-F2
=================================================
1           2      1            1             -1
2           3      6           -3              3
3           5      3            2             -2

So, if my understanding is correct, then at time t=1, the voltage of the F2 electrode might be, say, 2 (units; volts, microvolts, whatever), and the voltage of the F3 electrode might be 1. If F2 is the arbitrary active electrode, then the potential difference between F2 and F3 is 2 - 1 = 1. But, if F3 was the active electrode (and F2 the reference), then the potential difference would be the inverse (that is, 1 - 2 = -1).

EEG 10-20 system. source: Wikipedia

How many actual channels are there?

21 in the figure, i.e., the number of active electrodes.

What's the difference between reference and active channels?

The active electrode is the electrode under investigation. Basically this is arbitrary. Consider electrode F2 and F3. When you measure the potential difference between them and F2 is the active electrode and F3 the reference, the signal will be exactly identical as in the reverse situation, only the polarity (the sign of the voltage) is reversed. That's all there is to it. The active electrode is typically the electrode linked to the location you wish to record. The reference is elsewhere.

What is the difference between a bipolar and a normal
  (unipolar/monopolar) setup?

In a regular unipolar setup, each of the electrodes is either measured against a distant reference (e.g. the earlobe) or against the aggregate of all the electrodes. Either way, the reference is distant. This means that the signal will be high, but artifacts will be large too, because artifacts from both the electrodes will be merged into the signal. Consider a reference in the neck; neck musculature activity will add artifacts (EMG) to the signal. Likewise, a reference in the chest area will add ECG to your signal.
In a bipolar setup adjacent electrodes are recorded. This means that large artifacts occurring through eye blinks or whatsoever are recorded by both electrodes and as their polarity is opposite, the large artifact responses will be cancelled and the signal is much cleaner. However, the downside of the coin is that commonalities in the signal, which will be larger when the electrodes are closer will also be subtracted and hence your signal will decrease accordingly.
I have used bipolar setups when measuring eERGs and eCAPs, i.e., electrically evoked activity. The electrical stimuli generated large artifacts and actually so large that their amplitudes were approximately similar between active and reference electrodes. The reduction in artifact by far outweighed the loss in signal. It depends on the situation you are at.
An authoritative work in this field is listed below.
Reference
- Pivik et al., Psychophysiol (1993); 30: 547-58

As always, thanks @Christiaan (+1)! And, as always, a few followup questions for you (if you don't mind)! (1) Please see the update to my question. Regarding your explanation of active vs. reference electrodes, can you please confirm that my understanding (based off the table I provided) is correct?

(2) Whereas I now understand the cons of unipolar setups, I am failing to understand the cons of the bipolar setup. Using the table I provided above, are you saying that - under a bipolar setup - the electrodes are close together and hence will have similar voltages? Hence F2 and F3 might both measure very close to one another (say, F2=2.6 and F3=2.5), hence the potential difference between them (+/- 0.1) will be very small? Is this what you're saying?!

ad(1): My pleasure! The table is kind of OK as a simplification scheme, but you should understand a voltage is always a potential difference. On 1 electrode there is no such thing as a voltage of -3. Voltages exist across points. An electrode placed on the positive pole of a battery and the reference in the air yields 0V.

ad(2): The signal may be smaller in bipolar. The noise as well, simply because the electrodes (may) have overlapping areas of interest.

Sorry I forgot the +1 @Christiaan, I added it! Also +1 for both comments above (thanks!). So it sounds like in my #2 followup above that my understanding is correct (that is, that because the electrodes are close, their voltages will be nearly equal and opposite, which makes using their data more difficult). One last followup question here, I promise!

(3) In a 10-20 system where there are 21 active electrodes, it sounds like the technician has the ability to arbitrarily choose the reference electrodes for each active one. It sounds like - for EEG reading #1 - I might choose F2 to be the active and F3 to be its reference. But, perhaps for EEG reading #2, I might choose F2 to be the active and, say, F8 to be its reference. How does the practictioner decide each reference electrode, and then subsequently, configure the EEG device to measure difference between each arbitrary active/reference combo?

The reference above describes exactly these considerations. As you say, they are arbitrary. But there are guidelines that may help: https://www.acns.org/practice/guidelines I suggest to take a look.


text:
Does the body prioritise the use of available sugar before fat?
Let's say you eat a very unhealthy snack that contains 90% sugar and 10% fat. Would somatic cells not start using the fat for energy until all the sugar is used up?
Does the body not use fat at all if there is more bio-available sugar to be used? Or is there always a proportion of fat being used for energy?

I think this is an interesting question; adding your own research would make it a better question. :)

In a very simplified explanation  - the body usually picks the process which is easiest/ fastest which would be to absorb the glucose  then the fat would be used for energy as this takes longer to digest and absorb because it is a relatively complex molecule which also requires bile to emulsify it.
Starchy/ high glucose products will already be broken down in the mouth by amylase and absorbed in the intestine + pancreatic amylase etc. which takes around 20 mins-1hr whereas soluble fats are digested primarily in the small intestine (by enzyme lipase) for (very approx.) 1-4 hours depending on the amount ingested. Insoluble fats are broken down into soluble fatty acids and glycerol which can be absorbed into the blood.
The amount of either you use for energy just depends on how much energy you expend.


text:
Are non-lytic viruses reproducing themselves in the host cell?
Non lytic viruses can cause cancers like the HPV. But aren't they reproducing themselves as they don't ruptures the cell. Or are they just not reproduced in enough numbers so that the cell keeps working. But if they stays in the cell and/or not reproducing how are other cells infected to cause a tumor? Just by normal division or within a stem cell?

You are conflating production of infective viral particles with lysis of the host cell. While true for some well-characterized bacterial viruses, like lambda, or T4, there are other bacterial viruses that can produce infective particles without lysing their host cell (e.g., M13, and related viruses).
Likewise, in mammalian cells there are plenty of examples where either an RNA virus (like the RNA-tumour viruses, also called retroviruses) where the viral genome integrates into the host genome, but can also produce infective viral particles. These viruses have external coats that include both proteins and lipid bilayer membranes (the viruses pinch-off, or bud, from the host cells' surface).
Many DNA viruses also integrate a copy of their genome into the host cells' genome following infection (e.g., Epstein-Barr Virus (it can cause mononucleosis), and members of the Herpes Simplex virus family that infect neurons and can cause chicken pox, or genital herpes).  Once you are infected with these DNA viruses your body is never rid of them, rather your immune system and cellular defence mechanisms "fight" the infection such that the titre of infectious viruses drops to nil, but you still have a reservoir of circulating antibodies that can be detected--and used if you are exposed again.

Do you think a tumor is growing due to viruses are replicated and leaves the host cell and infects another cell, or is it more likely that that hostcell creates a tumor by dividing himself again and again?

@Marijn it depends on the tumor, but frequently tumors grow clonally (although there may be multiple "starting" clones), wherein the initially-transformed cell multiplies and makes copies of itself. Remember, cancer is almost never caused by a single event - multiple things need to happen inside a cell for it to become cancerous. See the Knudson hypothesis.


text:
Where do class 2 preservatives end up after digestion?
Do anybody know what happens to class 2 preservatives in general after they are digested - i.e details like weather they are absorbed into blood as simplest elements or excreted out or assimilated to a part of the body. If the question is oversimplified, please tell where sodium propionate or any other convenient ones eventually end up at.


text:
Are all the atoms in your body replaced with others over a 30 year period?
I was reading Creation - Life and How to Make It by Steve Grand. (This is the book that inspired Jeff Bezos to start the AWS initiative. ) In it he makes the following statement:

Anyhow, by now I hope you have thought of an experience from yoru childhood. Something you remember clearly, something you can see, feel, maybe even smell, as if you were really there. After all, you really were there at the time, weren't you? How else would you remember it? But here is the bombshell: you weren't there. Not a single atom that is in your body today was there when that event took place. Every bit of you has been replaced many times over (which is why you eat , of course)....

That didn't sound quite right to me. How could the atoms in your bones be replaced? Aren't your nerves pretty much fixed in place?
My question is: Are all the atoms in your body replaced with others over a 30 year period?

Bones, although they are hard, are very dynamic. They are literally in continual resorption and reconstitution, even in adult life when there is no growth. So you shouldn't be too surprised. And, in principle, yes, after a given period of time, all the atoms in your body would be "replaced". If you like that kind of thing, look up the Theseus paradox.

The lens of your eye is not replaced, IIRC.

There is no solid scientific evidence that this is true. It's sensational, and is likely true for much of the body (though I don't know the timeframe.) While many proteins and even some entire tissues turn over, some do not. Tooth enamel, for example, does not regenerate or get replaced. That makes carbon dating - to the year of birth +/- months - possible.


text:
Why all trees have cylindrical stems?
Why a huge number of  trees/plants have cylindrical stems? I have been told that this question's answer lies in Physics but I don't know where to find. Can any one help?

Short answer: not all do.  Many tree trunks are fluted, especially at the base.  Then there's the Blue Ash (raxinus quadrangulata) which gets its botanical name from its square stems.

@ jamesqf Thanks for reply. I have edited my question taking your answer in account. But your answer raises an interesting question , why * Blue Ash (raxinus quadrangulata)* is an exception?

1)
You know most bridges have vertical cylindrical pillars.
This is probably because cylinder is among the strongest 3D structures to support anything.
So, cylindrical trunk provide best support for tree crown.
2) Most accurate
It is natural for any cell to grow equally in all directions. This, leads cells to expand in all directions equally in a circular girth.
3)
Resistance
Cylindrical girth provides least resistance to air. So, tree naturally takes this shape to prevent damage by air strikes.
4) Read
 https://m.reddit.com/r/askscience/comments/1973mh/
for more info.
5) http://www.stevespanglerscience.com/lab/experiments/balancing-act-cylinder-strength/
for strength of Cylinders.

No. Most bridges do not have cylindrical beams. Angles, channels, built up girders seem most common for bridge beams.

http://www.stevespanglerscience.com/lab/experiments/balancing-act-cylinder-strength/

@curious_cat I meant pillar. Thiss was pillar I first saw on search. http://t1.gstatic.com/images?q=tbn:ANd9GcTQKaTDU7vmR-Q64HxUcE6MYmcPMj0abgeXiIzCvYpAQZGoOM97uYofoM8

I think you have the air resistance thing backwards.  Look at pictures of trees growing where there are strong prevailing winds (coasts, mountain tops, &c), and you'll see that they are markedly non-cylindrical.  The growth part seems intuitive: trees grow (in diameter) by adding to a thin layer (the cambium, IIRC) under their bark.  In the absence of any outside force, every part of that layer is likely to grow at the same rate, so you get a cylinder.

@jamesqf Trees in costal and mountains are rather more cylindrical and straight (less branched) than at plains. I am writing from my experience from Himachal, Jammu, Goa, and Mumbai

@Anubhav Goel: Do these places have strong winds coming from a consistent direction?  I see this along the California/Oregon coast, and in the Sierra Nevada and inland mountains.

@Jamesqf For Sierra Nevada , on googling, Trees were all cylindrical. For coast of California/Oregon , I found , yes they were much branched, bent and stooping, but all had cylindrical trunk.
Well, for me Goa/Mumbai have some gusty winds.

@Anubhav Goel: Well, all I can say is go and look.  Here's an example: https://en.wikipedia.org/wiki/Pinus_longaeva Or this: http://www.gettyimages.com/detail/photo/gnarly-foxtail-pine-in-the-high-sierra-royalty-free-image/575358267

@Jamesqf Look at these trunks you link more carefully. They are more or less cylindrical in geometry. Or what you suggest, is it cuboidal or triangular prism like or pyramidal?