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What is the limit of time resolution in fMRI?
I was reading about a few studies on estimating functional connectivity between brain areas using fMRI signals. However, as far as I know that fMRI has a very poor time resolution, roughly in the order of 1 second. Individual neurons communicate with delay less than 100 ms, and many brain functions happen within a second, in which case fMRI wouldn't have enough time resolution to determine if things happened simultaneously, or in a sequence at all. This problem would be even greater for causality analysis such as Granger causality framework.
So my question is, what is the theoretical, and practical limit of time resolution in fMRI on human?

The quick and dirty answer is: fMRI doesn't perform well in the temporal domain, but excels in the spatial domain among non-invasive imaging methods. If you need an answer as to where the action is, use fMRI. If you need answers to temporal questions, I recommend using electrophysiology such as EEG, which excels in the temporal domain, or MEG for a combination of good temporal and spatial resolution.
As to what the limit exactly is, I would say a few seconds. fMRI measures blood oxygentaion responses, (See the answer on the question: "What does fMRI measure exactly"), which are inherently slow. Neural processes in local neural networks occur in milliseconds, in larger networks in the order of hundreds of milliseconds. Blood flow differences, in contrast, occur in the order of seconds.

I don't think it excels in spatial domain either. Though there has been a recent report about correlations at a single neuron level. See Schulz, K., Sydekum, E., Krueppel, R., Engelbrecht, C. J., Schlegel, F., Schroter, A., Rudin, M., and Helmchen, F. (2012). Simultaneous BOLD fMRI and fiber-optic calcium recording in rat neocortex. Nat Meth, 9(6):597-602. http://dx.doi.org/10.1038/nmeth.2013

I say excel because there is no non-invasive imaging method that matches that accuracy as far as I am aware


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How does strength of a pulse related to EKG(ECG)?
There are variabilities in the heart rate as well as strength of heartbeats. I want to know if one can predict which heartbeat will be strong or weak based on the corresponding EKG/ECG signal. I am interested in the displacement of brain due to the heartbeat, which probably correlates with the peak pressure, and total volume caused by the beat.
If there is such a correlation, which part of EKG would be related? The interval between P and R? The amplitude of QRS?
Human or primate information is preferred.

image from wikipedia

Predict? I have no idea. I know V-tach and A-tach can be reasonably predicted by increasingly chaotic signals on an EKG, but I don't think the equipment would have the granularity to predict "strong" and "weak" beats.

Are you referring to stroke volume, pulse pressure, or something else?

@louism Thanks for the interest. I suspect volume would be the best, but pressure or delay could also be useful. Any reference is welcome!

@MCM to me it's not a matter of granularity, but fundamental plausibility. I can always improve signal-to-noise ratio, and do signal processing to improve the readings. But is there any signal related to beat strength at all?

Is it plausible to estimate stroke volume from EKG (with significant measurement error)? Perhaps. Is it likely that you will achieve reasonable results without several months of research and access to large databases of patients who had both an EKG and a cardiac echo (gold standard for SV) in order to validate your results? Probably not. That said, if you did find a way, you would probably become incredibly rich.

Higher QRS complexes particularly in the lateral leads would suggest hypertrophy of the left ventricle. This in turn would suggest a stronger pressure is in place, although it is not known whether this is because it is required (incompetent valves) or a physiological response to exercise (where the volume is likely to actually be bigger).
Alternatively, if it really is just displacement of the brain you're interested in, why not use an EEG taken with an ECG and measure if there is a variation with each heartbeat. Then you can tease apart whether this is due to pressure or oxygenation or many other things.
Otherwise you could place something overlying the carotid which will loosely, but perhaps more accurately, reflect the pressure and one could potentially calculate the volume this way once enough other factors are known.

Thanks for the response. Hypertrophy would be a long term effect, right? I am interested in beat-to-beat variability. Do you have a reference for this by any chance? We are sure that it is the displacement of brain due to the heartbeat, and the displacement varies from beat-to-beat. We wish to control it as much as possible, but just wondering if there are better features in ECG that can be used to do so.

It is unclear from your question what exactly "beat-to-beat variability" means from a physiological perspective. It would help if you could provide more information on the application you are working on and what kind of sensors you have access to.
If you wanted to measure the force of the arterial pulse, you would need to measure pulse pressure (which is the systolic blood pressure minus the diastolic blood pressure). However, these pressures do not only depend on the heart's action, but also on blood vessel compliance - which is influenced by ageaing, disease processes, and changes in the concentration of vasoactive substances in the blood. The EKG signal does not provide any of these latter parameters, and therefore it is highly unlikely that you would be able to estimate pulse pressure from an EKG. That being said, you could very easily measure pulse pressure with an arterial line, which would be the gold standard for such a task.
If you wanted to measure the force of the heartbeat, you could look at ejection fraction, which is a measure of how much blood is ejected from the ventricle in one heartbeat. Again, this is not related purely to the electrical activity of the heart (contractility and heart rate), but also to extra-cardiac parameters (preload and afterload). The EKG, which only looks at the intrinsic electrical activity of the heart, does not provide information on these extra-cardiac factors.
You could settle and look purely at cardiac contractility, which measures how much force the heart generates during a heartbeat. Keep in mind that this does not reflect how "well" the heart is working - you could have very good contractility, but a poor ejection fraction for other reasons. This is also not necessarily related to how "strongly" a person will feel his/her heart beating. I'm aware of
 one paper on how to measure cardiac contractility from EKG tracings.

Thank you for the detailed response! We have a setup where surface of cortex is imaged at high speed, and the image is contaminated by the displacement of the brain. This displacement significantly varies per beat, and we want to remove the effects. EKG and the video are pretty much all we are measuring.

The paper you shared could be very useful. Thank you again.

The displacement of the brain would be related to pulse pressure. I doubt you can measure this from an EKG signal. Again, the paper I shared shows a correlation between EKG signal and contractility, but moving from contractility to pulse pressure requires more information.

If you want a likely predictor candidate for the pulse wave amplitude (which seems to be what you are looking for), you will need to take into account some factors:

is the heart normal or pathologic?
are the vessels normal or pathologic?

Now from your question, I assume that you want your model to represent a sane vascular system. In this case, the best predictor of a strong heart beat would most likely be the area under the curve (AUC) of the QRS complex. However, you seem interested exclusively  in the arterial pulse wave of the great circulation. If that is the case, Maybe it would be better to reduce your sampling to the QR waves AUC in an attempt to restrict your signal to the left ventricle contraction.
In short, you are trying to predict the beat-to-beat cardiac output from ECG. Some have already investigated that sort of thing. You could try looking at this device for a start, and then look up the list of supporting reference for additional information.
good luck!

Great suggestions. Yes, we are interested in the normal heart. I'll test with your suggestions. Thank you.

@Memming BTW it seems that your question is just geared towards correction of other measurments. Can you give us a sample image or video of what your cortex camera is recording? Because it could be far better to try to adjust these images using fixed image features and compensating for the movements in code than trying to derive that from ECG...

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Is there a biophysical causation from local field potential (LFP) to spikes?
Many experiments showed that neurons tend to fire at some phase (usually trough) of local field potential (LFP) oscillations, such as theta or gamma rhythm. LFP is supposedly generated by a population of neurons with coherent currents induced by spikes. So there is a causal link from spiking activity to LFPs. Is there also a significant influence on the spikes directly from LFP (not via the hidden spikes that generated the LFP)?
Is it just an epiphenomenon, and or is there a possibility that it is partly a serious mechanism for neural computation?

I am not sure that kind of action would be a "significant influence", but the general understanding is that LFP and spike frequency are inter-correlated (1, 2). An interesting recent publication on the topic (3), however, doubts this correlation as the nature of LFP recording and signal processing might introduce some artifacts to the recordings.
It would be interesting to see a correlation between the spike shapes and LFP, but I failed to find a relevant paper here.
References:

 Belitski A, Gretton A, Magri C, Murayama Y, Montemurro MA, Logothetis NK, Panzeri S. 2008. Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information. The Journal of neuroscience : the official journal of the Society for Neuroscience 28: 5696–709.
 Rasch MJ, Gretton A, Murayama Y, Maass W, Logothetis NK. 2008. Inferring spike trains from local field potentials. Journal of neurophysiology 99: 1461–76.
 Zanos TP, Mineault PJ, Pack CC. 2011. Removal of spurious correlations between spikes and local field potentials. Journal of neurophysiology 105: 474–86.

Some evidence from external field modulations:

F. Fröhlich and D. A. McCormick, “Endogenous Electric Fields May Guide Neocortical Network Activity,” Neuron, vol. 67, no. 1, pp. 129–143, Jul. 2010.
S. Ozen et al., “Transcranial Electric Stimulation Entrains Cortical Neuronal Populations in Rats,” J. Neurosci., vol. 30, no. 34, pp. 11476–11485, Aug. 2010.

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Single hormone opposite effects
Often, a smooth response to a hormone means that some processes must be sped up and others must be slowed down.
How can a single hormone have opposite effects like this?

An hormone is not different from most other molecules. To have an effect on a cell it binds to a (more or less specific) receptor, located either on the plasma membrane or inside the cell, and it initiates an intracellular cascade of events1.
There are several ways an hormone can have different effects:

there can be multiple receptors for the same hormone. For instance, prolactin can bind to two receptors, called prolactin receptor (PRL-R) short and long form. The short form of the receptor has been shown to lack the ability to promote milk protein genes transcription (See Lesueur et al., PNAS - 1991).
the same receptor can be coupled to different intracellular pathways in different cell types / physiological conditions, thus resulting in different effects.
each cell type/tissue expresses a set of protein that will interact in a different manner with the intracellular cascade promoted by the hormone.
an hormone can interact with receptors for other molecules. For instance allopregnanolone, a metabolite of progesterone, is a potent agonist of the GABA-A receptor, giving it anxiolitic properties.

An interesting example is that of estrogenic compound. Several receptors exist for estradiol (E2). The "classical" receptors are called ER-alpha and ER-beta, and they are located in the cytoplasm. The binding of E2 to the ER promotes their dimerization and entrance into the nucleus where they can promote the transcription of various genes. ERs can also bind to other transcription factors and modulate their activity. So, depending on which transcription factors are present different genes will be transcribed in response to E2. Moreover, ER-beta can have opposite effects then ER-alpha (see for instance Weihua et al., PNAS - 2000). In addition, receptors like ERs can be activated also in absence of the endogenous hormone: for instance, dietary amino acids activate ER-alpha in liver by a mTOR-dependent phosphorylation (Della Torre et al., Cell Metabolism 2011)
To make things more complicated, membrane receptor for estrogen have been described, such as GPR30 (Revankar et al., Science - 2005), membrane "versions" of the classical ERs, plus various splicing variants of the ERs (mostly expressed in tumoral tissue).
GPR30 in breasts has been shown to activate certain molecular pathways (notably Erk1/2) that contribute to cellular growth (Filardo et al., Mol Endocrinol. - 2000), possibly linking it to proliferation of ER-negative breast tumours. Other tissues that do not express GPR30 will lack this E2-induced proliferative stimulus.
So, tissue- or time- dependent modulation of the receptors and of the intracellular pathways associated with it can allow the body to respond to the same hormone (or, more in general, to the same molecule) in opposite ways.

1 This is a simplification, not every substance (and not every hormone) acts by binding to receptors, but let's keep things simple.

Also, I would add that often hormonal system have all kinds of negative feedback loops that allow for "smooth" responses, although these include at least two hormones.


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Are homozygous carriers of the CCR5-Δ32 allele fully immune to all known strains of HIV?
Or is there documented evidence of even one homozygous carrier contracting HIV and staying infected?

I would say no.
Some HIV strains are X4-tropic and not R5 tropic. That means that the virus uses the CXCR4 coreceptor and not the CCR5 coreceptor for entry into the CD4-positive cell.


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How, on a physical level, does ATP confer energy?
When ATP is used as the energy currency to make, say, reaction X + Y → Z happen, is what happens on a physical level down at the molecular scale that during the reaction

ATP + H2O → ADP + Pi       ΔG˚ = −30.5 kJ/mol (−7.3 kcal/mol)

that 30.5 kJ/ mol is conferred by ATP molecules physically bumping around the reactants X and Y, the kinetic energy of the above reaction being what does it?
I mean, is the energy coin of ATP conferred to reactions by molecular collisions, or is it an electric field effect in the spatial geometry the way the ATP molecule tends to break apart?

Usually in biology (and being ATP, it most probably is biology), it's one of two things.
The gamma-phosphate (the third one, the one farthest from the adenosine) is very unstable, meaning the phosphoanhydride bond is easy to break.  The cell "allows" it to break, but only at the cost of moving the phosphate to some other molecule, such as a serine or glycerol or fructose or whatever.  This phosphorylation creates a bond with lower energy than the phosphoanhydride, and so is overall favored.  Imagine the personification: the gamma phosphate hates being attached to anything, but hates being attached to an ADP the most.
Alternatively, if ATP hydrolysis is coupled via an enzyme, it is usually done through transient storage of the energy is protein conformation.  An enzyme binds ATP, which makes the protein structure "bend" or conform around the ATP.  This puts loads of strain (energy = A) on the protein which is offset by the stabilization of binding the ATP (energy = B).  This strain can make an enzymatic surface open up on the protein which itself takes a lot of energy to make (energy = C).  The surface can catalyze some reaction (X+Y->Z in your example) that costs some energy (energy = D).  The completion of that reaction alters the enzyme's catalytic site to something new and higher energy (energy = E), which can be alleviated by cleavage of the ATP (-7.3 kcal/mol).  Alas, ADP and P do not fit well into that site of the enzyme, so they float out, restoring that original ATP-binding surface to it's original state.  Provided A>B>C>D>E>-7.3, the cycle will continue until the ATP is exhausted or you have no more Z to make.
Typing "enzyme catalysis cycle ATP" gives a few examples. Here's a few:
DNA gyrase
Actin-myosin cycle

Another interesting scheme of the myosin functioning

@KAM You're arguing from the basis of a thermodynamic ensemble, which really makes your argument more "on a biochemical level" than "on a physical level". I want to know, electron by electron, particle by particle, exactly mechanistically how the potential energy inherent in ATP flows from point A to point B. Where do I go for that answer?

Researching quantum constituents in ADP energy transfer gives this page which describes the ATP reaction with some geometry, aside from electrical/proton transfer of the many energy-expensive reactions like of cells like F-actin filament molecule treadmill lengthening process:
https://pubs.rsc.org/-/content/articlelanding/2016/cp/c6cp01364c/unauth#!divAbstract
This page describes F-actin length and reconstitution using ADP: https://m.phys.org/news/2014-09-scientists-uncover-clues-atp-mystery.html
Video actin treadmilling simulation:
https://m.youtube.com/watch?v=VVgXDW_8O4U
Two parallel F-actin strands must rotate 166 degrees join correctly together. This creates the double helix structure of the microfilaments found in the cytoskeleton.  ATP  is well described on wiki in at that level.
It seems to imply that there is a "static/magnetic" electrical charge differential surrounding the actin molecules and transferred in the minuscule H2O medium which attracts rotates and bonds them.
The electrical differential of Actin is described as heterogenous with surrounding salt bridges and protonationdeprotonation, by this general research:
https://www.google.com/amp/s/www.researchgate.net/figure/The-charge-distribution-on-the-surface-of-F-actin-is-highly-heterogeneous-leading-to_fig5_7090136/amp
I'll try to synthesise some quantim articles of that kind for a coherant sequence of events, ill have to pause because im writing on a phone.
Some info on the H2O alignment involving a different view of ATP is here :
https://www.google.com/amp/s/phys.org/news/2017-03-atp-hydrolysis-energy-large-scale-hybrid.amp

I’d wait to post until you have an answer. A list of references is not acceptable as an answer, as you will see if you read the help. I also wonder whether it might be better to re-ask this six-year old question on SE Chemistry, where it might come under more rigorous scrutiny than here.

If i delete it temporarily will it dissapear after a day? A week?

If you delete it it will disappear immediately. Just be sure to go into edit it first and then copy it and paste it into a word processor document so you can read use the relevant material when you have your full answer ready.

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What is the base cancer rate for an arbitrary carcinogen?
Are all carcinogens equally potent? Is the relationship between dose and probability of cancer roughly equal, or are there some carcinogens that provoke cancer significantly more than their cousins?

To answer this question in its entirety we have to split it into two questions:

What are the underlying mechanisms of carcinogenity?

One of the main mechanism behind carcinogenity is the mutagenity of the cancerogens, i.e. the ability to cause mutations, that are abberations of the cell DNA leading to uncontrolled proliferation. This classical paper investigates the relation between cancerogenity and mutagenity.
One should mention here that there are many types of mutations possible, mutations are not equally dangerous for cells and some mutations can be successfully repaired using the intact strain.
Therefore the following parameters of the source substances need to be measured to estimate the cancerogenity:

Substance concentrations or absolute amounts (both being indirect measures of their chemical activities, the lower concentration is needed for causing cancer the higher is the chemical activity and vise versa).
Substance radioactivity (for the mutagenesis due to radiation).

How can we measure the carcinogenity of different substances?

The most general approach here is to introduce certain amount of cancerogen into the animal body or to the cultured cell and to see the effect. The effect is calculated as the percentage of cells that undergo the transformation from normal into cancer cells. Two metrics are available here:

DT = Tumorigenic Dose (the amount of substance causing certain
  percentage of cancer in treated animals, all treated animals are taken
  for 100%)
CT = Tumorigenic Concentration (same, but adjusted for
  concentration and used in cell cultures).

(They are written CT and DT because in science people tend to used Latin abbreviations where the adjective actually follows the noune).
The common metrics are DT5/CT5 (5% cells/animals get cancer) and DT50/CT50 (50% of the animals). Those are similar to other common metrics, the most common is LC50/LD50 -- lethal dose for 50% of the animals/cells.

Unfortunately I couldn't not find any pre-compiled list with most known cancerogens and their TD/TC values. These seem to be interesting primarily for scientists. But going back to your question: you are absolutely right: some cancerogens are much more potent in causing cancer than the others!


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Does testosterone increase female sexual behavior?
According to my lecture notes testosterone generally increases sexual behavior.
Given that it's generally thought to be the male hormone I'm not quite sure whether they mean that it also increases sexual behavior in females.
What is the effect of testosterone on female sexual behavior?

I'll point you to this article by A. Tuiten et al.
To quickly answer your question, yes,  testosterone does have an enhancing effect on the sexual behaviour of human healthy adult females.
The (admittedly small) study indicated that after an dose of testosterone was given to the women there was found:

a statistically significantly [sic] increase in genital responsiveness (P = .04)
a statistically significant association between the increase in genital arousal and subjective reports of "genital sensations" (P = .02) and "sexual lust" (P = .01)

A time lag was reported from the administering of the dose to effects being seen of approximately 4.5 hours.

References

 Tuiten A. 2000. Time Course of Effects of Testosterone Administration on Sexual Arousal in Women. Archives of General Psychiatry 57: 149–153.

What's harder to answer is whether (or to what degree) that's due to testosterone to estrogen conversion or due to testosterone receptors.

Although male testes are responsible for huge testosterone secretion, testosterone can be produced by other organs both in males and females. So, women do have testosterone. Similarly,  estrogen is also produced in men and not only in women. In addition, both sexes produce the androgen and the estrogen receptors, so endogeneous or external testosterone will be active in women. Among other things, testosterone supplementation have been shown to be useful in clinical studies conducted on women suffering from hypoactive sexual desire.
Woodis et al., Pharmacotherapy. 2012 Jan;32(1):38-53. doi: 10.1002/PHAR.1004.

And let's not forget that testosterone can be converted into estrogen by the aromatase, so it can actually work through the estrogen receptor.

Right, and aromatase KO male mice develop steatosis, clearly indicating that estrogen is important for male liver.


text:
Questions to ask to a panel of people that will be sequenced
Some genes have been shown to be associated with left-handedness.
Working with some clinicians, I've recommend them to ask their patients (whose genome will be sequenced) if they are "right or left-handed".
It's a simple phenotype (without any ethical issue) that will be later fun/interesting to analyze through the sequencing data outside of the main study.
Do you know any other such kind of simple phenotype that we could ask to our patients ? :-)

I've heard wet/dry earwax is a very simple and genetically identifiable phenotype.

23andMe is a company that provides sequencing. Before they sequence your genome, they ask you to fill in a couple of surveys, one of which is about your physical features. Here is a list of some of the questions they ask:

How much hair do you have on your upper/lower back?
When you walk, in what direction do your feet naturally face?
Do you have a line or indentation (dimple) in the middle of your chin?
Excluding any hair loss, does your natural hairline have a widow's peak (a V-shaped peak of hair at the front of the hairline)?
Can you flip your tongue over so that the underside is on top?
Can you lift one eyebrow while keeping the other eyebrow in place?
Which is longer, your big toe or the toe next to your big toe?
On your left hand, which finger is longer, your index finger or your ring finger?
What is your shoe size in U.S. shoe sizes?
How would you describe the arches of your feet?
Can you bend down and place your entire palms on the floor while keeping your legs straight (see above image)?
Which image best matches the natural shape of your face (with your hair pulled back)?
  ...

That's a small list of phenotype questions you can ask ;-)

The full version of this list is long and that can bring about interview or questionnaire fatigue. Do not forget to factor in the length of time for a subject to answer all questions nor the cost of entering and analyzing those responses. Frankly, if 23andMe is asking these types of questions, you shouldn't because they will beat you to a report on the association. Deep phenotyping is critical, but direct that effort to collecting lifestyle data - like diet, sleep, exercise - which all influence the genotype-phenotype association

I think that if 23andMe ask these questions, there is a strong reason for their selection. And if you pay the money for your genome to be sequenced, you would want to answer as many questions as they ask you.

Just a small nitpick, 23andMe does not offer what is generally considered genome sequencing (which at current market prices would be northward of $8k). Their product is SNP genotyping via microarray analysis (at $10s-$100 / chip depending on the company). The array currently repported in use by the company is the Illumina OmniExpress.

Yeah, the strong reason for the selection of these traits/questions is 23andMe's desire to engage a customer base with curiosity questions - like smelling asparagus metabolites in the urine. Their (business) model is quite different from a researcher trying to convince a review panel to fund their proposal, hence my call to focus on more medically oriented phenotypes. At the same time, 23andMe has an obstacle in obtaining reliable data on one's lifestyle. I have nothing against these questions when asked via the secure 23andMe site, but better questions can be asked by Pierre's colleagues.

Another list of simple human traits can be found on the Western Kentucky University site.
I will ask also AB0 and Rh Blood Groups, you can use this also to check associations with other genotypes/phenotypes, see this stackexchange question
If you are going to sequence also the mitochondrial DNA, you may want to reflect on questions to ask also about their mother phenotypes.
Finally you may want to find also the windows, mac or linux preference gene.

I trust that the linux gene is recessive? ;-)

I hope that the Linux gene is dominant! More likely that it is rare, and in combination with certain deletions ;)

Please people, don't talk about dominant/recessive traits! Best case scenario, there are some associations ;-) Besides, the 'linux gene' would have more environmental effect.

+1 Gergana! It is definitely a gene-by-environment interaction!. As GxEs are our forte, we'd take keen interest in the study.

One that I wish we asked the subjects in our studies is: When do you normally go to sleep each evening and when do you normally wake up? This can get more detailed, but these 2 basic questions can allow segregation into morning or evening types (or both).
Deep phenotyping is critical to a better understanding of the genetic influence on disease risk phenotypes, and so one should collect lifestyle data - like diet, sleep, exercise - which all influence the genotype-phenotype association (these are called gene-environment interactions). If you can come up with phenotypes that distinguish different paths to the "same" disease outcome, that would be relevant to ask. In other words, there are often many paths to a very similar disease outcome, but as those paths are different, the disease may be slightly different. This requires sub-classification of disease - think of the different types of breast cancer. So, collecting these phenotypes, if you know them, in conjunction with diet, exercise, etc, will give you some very interesting possibilities.

Some other ones:

Are you a Singer? :-)  AVPR1A and SLC6A4 Polymorphisms in Choral Singers and Non-Musicians: A Gene Association Study http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0031763
Do you have sunbed sessions?
Do you dance?  (via C.Dina. ) http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1239939


text:
Does digit 1 have an extensor sheath?
Extensor digitorum and extensor digiti minimi both insert into the extensor hoods of digits 2-5 (or just 5 in the case of e. digiti minimi).  In every source that I can find, extensor pollicis longus & brevis insert directly into the base of the distal and proximal phalanxes respectively.
Does this indicate that the thumb does not have an extensor sheath as the remainder of the digits do?

From $n=2$ textbooks, the results are equivocal.
Moore et al. (2013)

There is no mention of an extensor expansion associated with the pollex.
EPL and EPB are listed as inserting on the distal and proximal phalanges, respectively.

Rohen et al. (2010) (p. 394)

"Extensor expansion of extensor of thumb" The label points to the middle of the diaphysis of the proximal phalanx. It's odd that extensor is singular here, given that there are two extensors.

However, Abdel-Hamid et al. (2013) present data from dissection of 95 limbs.
Extensor pollicis longus:

"EPL tendon was attached to both proximal (PP) and distal (DP) phalanx through the extensor expansion in 94.7% of specimens (Figs. 1A, B; 2A). In 5.3% of upper limbs, the EPL tendon was attached to the DP through its extensor expansion (Fig. 2B; Table 2)."

Extensor pollicis brevis:

In 55.8% of upper limbs, [EPB] was attached distally to the base of the [proximal phalanx] of the thumb (Fig. 1B). While in 41%, EPB tendon was attached to the extensor expansion of the thumb at the level of the base of [proximal phalanx] (Fig. 2A). However, in 3.2% of specimens its tendon was inserted into the extensor expansion of the thumb at the level of the base of [distal phalanx]."

And the answer appears to still be equivocal.
Abdel-Hamid GA, El-Beshbishy RA, Abdel Aal IH. 2013. Anatomical variations of the hand extensors. Folia Morphol 72:249–257.


text:
Does electricity cause damage on a cellular level?
As I understand it the mechanism of death when a mammal is electrocuted is that the current disrupts the SAN/AVN in the heart causing it to fibrilate or arrest.  That's on a macro scale, however.  What damage, if any, does electricity cause on a cellular level?  I've noticed some sort of moss or lichen growing on the third rail of the train lines so I'm aware that they must be able to cope with the current, do they have adaptations ot allow this or is it just that they are not connected to the earth?

Regarding the moss or lichen on the third rail on British train lines: there is no current passing through the moss/lichen as they are not completing a circuit. They are just sitting on one connector. If another connector passed over them, completing the circuit across them, then a current might pass across them for a very short time. But I doubt they grow on the top of the rail (which is the contact part in the UK system) as that is constantly being polished smooth by the contacts from trains sliding across it.
Regarding cell damage: Apart from fibrillation in animals, and burns caused by Joule heating, electricity does cause cellular damage.
At low frequencies (<10kHz), electricity disrupts cell membranes and makes them much more permeable (we actually harness this when we use electroporation to transform bacteria). All organisms rely on electrochemical potential differences across membranes for their metabolism (Berry, 2002). The exact effect is dependent on the previous electrical state of the cells, for example the electrical potential difference across the membrane, and on the surface area to volume ratio of the cell (greater volume relative to surface area leads to more disruption). In any case, extreme electroporation can cause solutes to flow in or out of a cell, and generally disrupt the balance of solute concentrations and cause organelles and other bodies to move out of a cell. When the electrical stimulation stops, the contents are then fixed in a disrupted state. The cell then has to expend enormous amounts of ATP using ion channels and transport proteins in an attempt to reinstate the necessary chemiosmotic potentials, and in doing so exhausts the entire ATP supply and goes into biochemical arrest (no metabolism occurs), which is when a cell is dead. Dead cells break apart because there are is no maintenance occuring.
At higher frequencies (10-100kHz) proteins become permanently denatured. Many proteins carry charges which give them an overall polarity. When placed in an electric field, the proteins reorient themselves and will undergo conformational change to achieve the optimum dipole moment in the direction of the field. Ion channels and pumps are particularly sensitive to these disruptions (since their charges are crucial to their function).
Rather than provide lots of references, there is one excellent review from which I drew all this information, and which you should read for more of the physical detail (Lee et al., 2000).
References:

Berry, S. (2002) The Chemical Basis of Membrane Bioenergetics. Journal of Molecular Evolution. [Online] 54 (5), 595–613. Available from: doi:10.1007/s00239-001-0056-3 [Accessed: 9 February 2012].
Lee, R.C., Zhang, D. & Hannig, J. (2000) Biophysical injury mechanisms in electrical shock trauma. Annual Review of Biomedical Engineering. [Online] 2 (1), 477–509. Available from: doi:10.1146/annurev.bioeng.2.1.477 [Accessed: 9 February 2012].

I did think it was more likely to be physics rather than biology for the third rail.  Really interesting answer though!

Well, to add a bit of biology to the third rail part: lichens often have a very low moisture content, being poikilohydric they can withstand extremely low moisture for a long time. And since water is the medium of conduction during electric shock of an organism, there's a good chance that lichens would be less sensitive than most organisms to electrocution.

Great response; I learned something today! I also have a vague sense that, over time, flowing current through tissue can deplete the buffering capacity of the system and lead to pH changes, with all sorts of biochemical consequences; don't quote me on this but I think cardiac pacemakers switched from unipolar pulses to biphasic pulses to help avoid that kind of problem.


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What are known methods of non-surgically disabling neural tracts/pathways (esp. Corpus Callosum)?
I think I've read something about usage of TTX and optogenetics, but I cannot find the papers for either anymore. Any reference would be much appreciated, especially those concerning mice and the Corpus Callosum.
Edit: By surgical, I mean to say methods that are based on actually cutting through the tract using a surgical knife (or lasers etcetera), as is commonly found in literature.

Welcome to Biology. How exactly do you define surgery? Sticking a needle in a mouse's skull and penetrate the corpus callosum to inject TTX may as well be considered a surgery?

Sorry, yes this was a poor choice. By surgery I mean methods using a surgical knife to cut through the CC which is the standard method. And thanks for welcoming me :)

Can I conclude you are after non-invasive methods of ablating the corpus callosum?

Probably yes. I say probably as I have to admit that I have very limited knowledge about these things. I'm basically interested in any non-standard approach to temporarily or permanently disabling the CC (or other tracts if methods are transferable).

I was able to find one non-invasive method to sever the corpus callosum (callosotomy). This method is referred to as gamma knife radiosurgery (Mathew et al., 2007). Basically, 201 beams of gamma beams are focused on the corpus callosum and where the beams intersect, the energy is high enough to permanently damage the tissue. I doubt this technique is available for routine use in animals, though.
Another interesting technique is photothrombosis. This method is based on the systemic injection of a photosensitive dye named Rose bengal. Subsequent focal illumination of the brain with a cold light source through the intact skull leads to focal cortical infarcts of reproducible size, location and geometry (Schroeter et al., 2002) . Brain damage is induced due to photo-activation of the dye and subsequent endothelial damage with platelet activation and thrombosis, resulting in local blood flow interruption. The light source can be applied on the intact skull with no need of craniotomy, which allows targeting of any cortical area of interest in a reproducible and non-invasive way (Labat-Gest & Tomasi, 2013). However, although the light source penetrates the skull, I don't think the method is useful for the induction of deep-brain lesions, as I was able to locate only studies using it to model cortical lesions.
Having not answered the question, I'd like to say that either surgical sectioning, or chemical ablation using micro-injection of neurotoxins are the most commonly encountered methods to perform a callosotomy.
References
- Labat-Gest & Tomasi, J Vis Exp (2013); 76: 50370
- Schroeter et al., J Neurosci Methods (2002); 117(1): 43–9
- Mathew et al., J Neurosurgery: Pediatrics (2007); 106(4): 312-5

Thanks you very much for your well researched answer. Could you maybe elaborate a on the chemical methods you mentioned at the end?

@JohnSmith - maybe that would better be done in a new question. The comments preferably shouldnt be used to ask new questions. Surgical methods were not the question. If you have questions about those you might consider posting a new one.


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Which part of the brain needs to be shut down to lose conciousness?
Whether it is mechanical (trauma), chemical (anaesthesia) or electrical - which part of the brain is shut down to cause loss of consciousness?

I am not sure but I think it is reticular formation.

This is a very tricky question, since it is difficult to measure consciousness.

Related: http://biology.stackexchange.com/questions/10351/mechanical-cause-of-loss-of-consciousness

I agree with biochick, it's reticular formation The reticular formation has projections to the thalamus and cerebral cortex that allow it to exert some control over which sensory signals reach the cerebrum and come to our conscious attention. It plays a central role in states of consciousness like alertness and sleep. Injury to the reticular formation can result in irreversible coma. http://en.wikipedia.org/wiki/Reticular_formation#Functions BUt again I guess there are a lot of other control mechanisms in the brain on the cellular and bigger levels.

Do u regard speep as non-conscious state?

It depends on what you call consciousness. Is consciousness without emotion still consciousness? Emotion is described as a Mammalian "apomorphy" located in the cerebral cortex (see e.g. Carl Sagan, The Dragons of Eden). That may be part of the answer.

There is no widely-accepted neurological structure that mediates 'consciousness.' Even if some structures have been shown to be necessary for consciousness, they have not been shown to be sufficient. This is true with anesthetic mechanisms as well -- their ability to paralyze and block pain signals is fairly well-understood, but the mechanism of loss-of-consciousness is still unknown.
Still, 'consciousness' has to be there, somewhere between being awake and being dead, states which anesthetics can readily bridge (review):

Nevertheless, at some level of anesthesia between behavioral unresponsiveness and the induction of a flat EEG [indicating the cessation of the brain’s electrical activity, one of the criteria for brain death (22)], consciousness must vanish.

Later in the same review:

The evidence from anesthesia and sleep states (Fig. 2–3) converges to suggest that loss of consciousness is associated with a breakdown of cortical connectivity and thus of integration, or with a collapse of the repertoire of cortical activity patterns and thus of information (Fig. 2). Why should this be the case? A recent theory suggests a principled reason: information and integration may be the very essence of consciousness (52).

This is consistent with my own take. Consciousness itself is the subjective experience of 'brain,' so it can't be lost, just poorly integrated.

@Rayan, very useful reference (Consciousness and Anesthesia). Thanks

"somewhere between being awake and being dead". I think that the gap between the 'dream' and the 'sleep without dreams' is narrower, so the study of sleep and dreams may be closer to the answer than the use of anesthetics.

Consciousness is a difficult term to grasp in a short answer, and the question is as interesting as it is challenging. The reticular formation as mentioned by others before me is crucial, as the thalamus is the gateway of information to and from the brain. Therefore, subcortical structures such as the thalamus, when 'shut down', induce unconsciousness. Mircea Steriade (2005) has an excellent review on this and he is a leader in the field the field of sleep and absence epilepsy, thalamo-cortical interactions and consciousness. He describes there the cortical responses seen in the EEG during sleep: the sleep spindles, or related spike-and-wave discharges (SWDs) seen in the EEG during absence epileptic seizures, where patients temporarily loose consciousness. Sleep spindles and SWDs are highly synchronized responses that basically block out sensory input and cortical output. The blocking of sensory input and blocking of output to the body may be regarded as a loss of consciousness, as one is not able to sense the environment or undertake action in response to environmental stimuli (e.g., Kostopoulus (2000)).
However, since the question is what part of the brain needs to shut down, it is technically the neocortex, as the neocortex holds the key to being aware of one's surroundings, i.e., processing of sensory input as well as driving motor neurons to move, talk and respond to stimuli (see wiki page on neocortex). So the end organ mainly associated with consciousness is the neocortex - as described in a review paper by Nobel laureate sir Francis Crick (1990). The authors also indicate the thalamus and other structures as possible sites involved, but principally it is the neocortex. I hope this helps you a bit further.

It would be great if yo added some references to support your response! As it stands this is more similar to a comment!

This does not provide an answer to the question. To critique or request clarification from an author, leave a comment below their post - you can always comment on your own posts, and once you have sufficient reputation you will be able to comment on any post.

Hi Bez, I am a newbie in Stack Exchange. I will try to improve my answers!

Hello new Chris and welcome to the Biology StackExchange. Yes, that would be fantastic! It simply requires some references added, which are supportive of the response :)

I hope my edits help and please excuse me for my inexperience.

Two structures are required for consciousness: the brain stem reticular activating system (RAS) and one cerebral hemisphere, as stated here.  e.g. a lesion in the RAS or bicerebral injury will induce loss of consciousness.

Some references would be nice.

As mentioned by @Chros (The old Chris that is :) ), this response requires references! As it stands, this is more similar to a comment!


text:
Where are the gastralia (stomach ribs) attached?
Crocodiles and some dinosaurs have a second set of ribs between the sternum and pelvis called gastralia. Where are the gastralia attached to the rest of the skeleton?


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Ventral stream pathway and architecture proposed by Poggio's group
Please can you give me a very brief explanation about all functions in the ventral stream architecture summarized in this figure:
This figure is from Serre et al.'s A quantitative theory of immediate visual recognition. Prog Brain Res. 2007.
I read multiple articles about this model, but I still don't understand the basic aim, especially behind the two operations (Gaussian-like and max-like operations).. So please can someone explain to me in details the ventral stream pathway (from V1-V2-V4-IT-PFC) including the two operations in this model.
For example : I don't understand how the cells in S1 are constructed...

The title is misleading. This is just ONE model of the ventral stream processing among many possibilities. Can you be more as to which references you read? Looks like a convolutional deep learning architecture, but you need to tell us more.

thank you for your response. Can you tell me in details what's happen in this picture ? (in S1, C1, etc. ) . Thank you in advance.

The picture doesn't tell much. It seems like it's alternating finding local linear features then aggregating same features across space. You need to point us to where you got the picture, and if you could also write down the equations that would help. Not enough information from the picture!

i edited my picture.. i need only the concept

where did you get the picture?

in my question above

I think @Memming is asking you to give credit to the source of the image, from where did you download this?

http://serre-lab.clps.brown.edu/wp-content/uploads/2012/08/Serre_etal_PBR07_wfig.pdf

I made edits to your question to include proper references. I hope you agree with my changes.

This is a typical architecture of computation proposed as a model for ventral stream of visual processing in primates. It has a long history (e.g., Neocognitoron by Fukushima was 1980) and still widely accepted in machine learning (e.g., deep learning) and neuroscience.

It is motivated by the organization of V1 simple cells and complex cells. Simple cells in V1 can be approximately thought of as edge detectors at a specific retinal location. This is why on the figure you cite, they are represented as a circle with a bar (a cartoon receptive field). The simple cells can only detect things very locally, meaning if the edge appears at a different location in your field of view, it will not respond.
Mathematically, you can think of a spatial filter that detects an edge (e.g., oriented Gabor patch) multiplied to your retinal image, and summed. For example the filter below will detect match a 45 degree bar aligned on the hot-colored area, but will have less activity if the bar is shifted out of the specific position.

The complex cells in V1, on the other hand is still an edge detector, but has some location invariance. In other words, when the edge is slightly displaced, the response of complex cells does not seem to change. It is believed that this is because complex cells pull from multiple simple cells with the same orientation. This is what you see in your figure where a single complex cell pulls information from the same orientation simple cells but at different locations.
Mathematically, a soft-max operation or a max operation over the simple cell outputs can lead to a good complex cell model. But, it is not limited to such operations. In fact, quadratic or other nonlinear models are also widely used in computational neuroscience.
The full hierarchy for ventral stream is then simply obtained by extending repeatedly using the simple-cell-complex-cell analogy. For each stack simple cell layer extracts some local feature (by computing on the previous layer's complex cell's output), and complex cell layer makes it invariant over space. From edges in V1, one can get corners on the next layer, then complex contours, and all the way up to objects. At least that's how the story goes.

I don't know how to thank you about this response. I really appreciate it very much... But i still don't understand some details, i will tell you my opinion in the second comment.

I know that in area v1, we have simple and complex cells. each simple cell receive some inputs from the Lateral Geniculate Nucleus (LGN).These inputs are combined with a bell-shaped tuning (gaussian-Like tuning) with the preferred orientation. And all we know that each simple cell can response to a specific oriented bar (in the case of gaussian-like tuning, the response of the cell must be optimal?? because we are making a tuning with the preferred orientation of the receptive field of the cell ?).

However, if we consider such an image, please can you explain to me in details what happen in order to get the tuned simple cells in v1 ? In other words, and in case of the considered image, what we mean about the inputs of such a simple cell? is there a segmentation of the image before the tuning operation? i didn't understand the phenomenon...is there a convolution of the image with a specific filter in order to obtain the image as a form of bars ?? please i need your appreciated help :) and thank you very very much Dear.

@Liszt You're welcome. The orientation tuning curve can be thought of as a consequence of LN model. If you ask this in a separate question, I could write down the equations for you.

@Liszt In fact, the tuning curve would have a cosine shape, not exactly Gaussian or von Meises...but they look very similar. This comes from the relation between dot product and cosine.

for the first step (tuning the simple cells in v1), can you give me an example which can include in details and step by step the operation of this tuning in order to obtain the simple cells (the S1 layer). So please can you give me this example in a specific image that you choose ... I will appreciate very much your response !! :) if yes, please put your example in a second answer :) you don't need to make calculation, you can only give me a brief example (cartoon scheme).

@Liszt I suggest you ask a completely separate question. Each question should have only one question in it. That's how SE works. :)

ahh sorry, so i can ask another question in this site, and can you response me (to give me an example ) ? :):)

@Liszt Yes, I'll answer your question. (unless someone else does) :P

Dear Memming, can you explain to me how the euclidean distance in S2 layer is computed between a patch X of size nxn (but contains nxnx4 C1 units) and a stored prototype? how the result must be an S2 image? please i need your help :) thank youu


text:
How precise is EEG?
Imagine we want to read impulses within single neurons in the brain. Can we do that now for a single region within the brain? If not, what is the smallest region that can be monitored for an activity in real time? Is there a more advanced method than electroencephalography for scaning brain activity in real time?

EEG is pretty much useless for seeing a single neuorn's activity. With current technology, only invasive technology can see single unit activities.

Okay. But then how "precise" is the EEG right now? Can you monitor 1cm^2 or you only get a "faded" image of an activity for a whole brain due to interference?

Electroencephalography has a good time resolution (milliseconds) but poor spatial resolution (several centimers). The usual estimated figure is that at least 50000 neurons need to fire simultaneously so that the activity can picked up by EEG.
The answer provided by @Jeremy Kemball is not very accurate. The reason why the spatial resolution of EEG is poor is that the signal from even a point-like source on the cortical surface spreads about to every sensor along the scalp due to the conductivity properties of the brain, CSF, skull, and scalp.
This means that in order to get that few centimeter accuracy I mentioned above, one must solve an inverse problem from sensors -> cortex. Inverse problems are mathematically ill-posed, and have infinite number of solutions unless some constraints to the solution are added.
I'll give a practical example. Say that a subject is presented some sounds. You record the EEG. Now, if you look at the sensor-level data, you can find brain activity related to the sounds from almost any sensor, from both sides of the brain (each ear projects to both hemispheres). If you want to know where the signal actually came from, you need to do the source localization using some inverse solver. Some commonly used are for example dSPM, sLORETA, or MNE.
Finally, I want to emphasize that

Brain regions deeper in the brain produce a 'blurred' signal that shows up on several electrodes at once.

that this happens to all activity recorded by EEG. Actually, EEG does not even pick subcortical activity too well.

Is there a more advanced method than electroencephalography for scaning brain activity in real time?

Yes, that is called magnetoencephalography (MEG). MEG records the magnetic fields related to the electric activity. It reaches sub-centimeter localization accuracy, while still having millisecond time resolution. However, this is now strictly only for the cortical activity; MEG does not see subcortical activity almost at all. Google for Elekta Neuromag or CTF to see some MEG systems.
If you would be interested in studying some deep structure, like amygdala, your best choice is then fMRI, which has about ~2 second time-resolution and 1mm^3 spatial resolution.

The smallest area that the EEG can measure is related to the electrode density. Even with research setups with 100s of electrodes, the smallest measurable region is on the order of a square centimeter or so on the skull surface. Brain regions deeper in the brain produce a 'blurred' signal that shows up on several electrodes at once.
fMRI, on the other hand, can measure smaller areas in three dimensions, but not as often.


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Which direction are wild beehives combs oriented?
Is it on the North-South axis, the East-West axis or some other orientation?

I've usually observed wild Indian Bees (Apis cerana) orient their hives in an East-West direction. The entrances are usually oriented towards the Sun.
This is purely from my own observation (in South India) and not from any research.

So are the combs parallel or perpendicular to the E-W axis ?


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Why do plasmids replicate on their own?
I know how plasmids can replicate independently of the main genome and know that they confer various properties to the bacteria and are useful in conjugation.
My question is - what is the advantage of plasmid replicating on their own? (Would there be any disadvantage if they were controlled by the main genome instead?)

In plasmids with high number of copies, the plasmid needs a certain density to be able to supply its function. If a given cell contains a few dozens of plasmid mollecules, and this is its optimal density, the easiest way to achieve this is to downregulate the replication by its own presence or the presence of one of its components. This also allows a random segregation during cell division, due basically by simple diffusion. It's also easier for the plasmid to achieve its optimal density once it has enter in a new cell by conjugation or transformation.
Plasmids are also subject to selection, so it is reasonable to assume that the more independent and more contagious a plasmid is, the bigger its presence in the population. In fact, it is a reasonable hypothesis to assume that many viruses are parasitic plasmids (many viruses in fact have a similar genomic structure and share many traits with them).
However, in plasmids with low number of copies the replication events can be dependent of the genome replication as you describe (despite the fact that many plasmids of this kind still regulate its own replication by itself). In this case, though, the line between plasmid and chromosome is less clear. In some organisms some big plasmids seems to form part of the genomic organization in the whole strain, and they usually contain important genes. This plasmids behave just like regular chromosomes, and they segregate by the same mechanisms the rest of the genome use. For those plasmids the terms minichromosome and megaplasmid are used, and the choice usually depends of the kind of genes it contains.

It is also an interesting thought that plasmids may be pieces of DNA that light be "parasites" on the bacterial cell which they use to provide resources for replicating themselves.

They are parasites. As long as they provide an advantage to the cell (antibiotic resistance for example) they stay in the cell, when this is not needed anymore, they can get lost over time.


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Mathematical relationship between membrane conductance and conduction velocity?
By what factor would the myelin need to decrease membrane conductance (gm) if you wanted conduction velocity in a 10 μm myelinated mouse axon to be 100 times faster than in a 10 μm unmyelinated mouse axon?
This is a question from a homework set. I understand WHY myelin decreases membrane conductance, and WHY decreasing membrane conductance would increase conduction velocity. I just do not understand how to calculate what factor specifically the myelin would need to decrease membrane conductance by.

See also https://biology.stackexchange.com/questions/53003/what-is-the-mechanism-by-which-myelination-reduces-the-capacitance-of-the-axon-m/53007#53007 tl;dr: capacitance matters too

The conduction velocity is inversely proportional to the membrane time constant $\tau_m$ and proportional to the length constant $\lambda$. Membrane time constant is proportional to membrane resistance (inverse of membrane conductance), and the length constant is proportional to the square root of membrane resistance.


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What would EEG recordings reveal if their resolution was better?
1. Temporal resolution
Of course, we would see higher frequencies. But would these be informative? Aren't the frequencies measured today near the border to noise?
2. Spatial resolution
I guess LFP measurement goes in this direction (closer to the sources of the signals being measured). But what would we learn by that? Finding small-scale regions (below Brodman area size) which behave phase-locked/synchronously?
3. fMRI
The same questions could be asked with respect to fMRI. But I guess, as long as temporal resolution is as poor as it is today (and which cannot be much better), we don't have to ask for spatial resolution, or do we?

Short answer: No. The limiting factor is not the resolution (it cannot get much better), is the fact that is a signal coming from billions of cells doing different things so by definition noisy and messy. Only when there is very high synchronicity we can get something out of it (e.g. sleep phases).

I assume you answer to question 3.
Billions of cells?

No, for fMRT the problem at the moment is "poor" temporal and spatial resolution. If we had faster imaging we could have better spatial resolution. However remember that fMRT it's only a proxy for neurons activity...hence it measure slightly different things than electrophysiology and will never have its resolution.

I've read that MRT measures process that take up to a few seconds to occur (neurovascular). What benefits could a higher temporal resolution have?

The temporal resolution of EEG is already considered to be very good. The problem is spatial resolution. Even the loss of very high frequency activity, like individual spikes, is really a problem of spatial rather than temporal resolution: the spiking cells are too far away, so it is only possible to detect them if they are very synchronized (in which case they still show up as lower frequency signals).
Because EEG recordings are so far from the sources they are recording from, and because even structures deep in the brain can contribute to surface potentials,the signals end up as complex averages over a large space. There is an entire field called EEG source modeling that tries to use signal processing methods to improve this spatial resolution.
However, you don't really have to ask this as a hypothetical question, since we already have methods that have better resolution than EEG. Electrocorticograms (ECoG) are the most similar, and most applicable to human studies. ECoG arrays are very similar to EEG, but they are placed directly on the dura, under the skull. They increase spatial resolution because they cut down distances dramatically by not having to record through the skull.
As you mention in your question, local field potentials (LFP) get even closer to where the measured currents are occurring, by recording from within the brain itself. The spatial resolution of LFPs themselves can also be improved by recording from multiple electrodes and computing a current source density. This also helps mitigate the effects of volume conduction.
fMRI is a completely separate issue. Often, EEG and fMRI results can act in conjunction, using EEG to determine time and fMRI to determine space (note that although there are limits to the spatial resolution of fMRI, for a human subject it is dramatically more spatially precise than EEG, especially for any structures not at the very surface of the brain, including any of the sulci of neocortex). fMRI spatial resolution can be improved by using stronger magnets, but eventually there are technological (and expense) limitations, but as you say the temporal resolution is fixed, and even the spatial resolution has some practical limitations.
To generally answer your title question: if the spatial resolution of EEG is improved (practically, this means either source modeling or using ECoG or LFP), one is better able to identify the brain structures that are producing the signals observed in the EEG, and able to be more confident that the signals observed are "real" rather than an artifact of filtering over space. For example, a higher-frequency traveling wave could appear as a lower-frequency signal on an EEG, or a localized, high-intensity event could appear like a generalized, low-intensity event; higher spatial resolution would allow you to distinguish between the two in both cases.

May I contact you directly by email? If so, please confirm, when you grabbed my mail address: stricker@syspedia.de - so I can delete this comment as soon as possible.

@HansStricker You can invite me to chat here if you'd like.


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Does caffeine actually enhance cognition?
I have heard, respectively:

Caffeine measurably enhances cognitive function.
Caffeine does not measurably enhance cognitive function in any significant way.
Caffeine enhances cognitive function, but only indirectly, in that it counteracts the effects of fatigue.
Caffeine enhances cognitive function, but only indirectly, in that it counteracts the effects of caffeine withdrawal.
Caffeine has no physical effect on cognitive function, but has a psychological effect.

What is the actual behavior of caffeine in relation to cognition?  (I'm particularly interested to know if any research addresses the claim that it only counteracts the effects of caffeine withdrawal)

Welcome to Biology.SE. If you can link to your sources, then that question would be a great fit on SkepticsSE.

Hi!  To be honest, I posted here rather than on Skeptics because a) I don't really have sources per se; these are just things that I have been informally told about caffeine over the years - I tried to do some research but I don't know enough about biology to be able to read papers in the field and b) I'm more concerned about learning the actual behavior of caffeine re:cognition than on fact-checking and figured that it would be better to just ask people who know (i.e, here).  But if you think I'd have better luck on Skeptics I can ask there instead.

Edited to make my actual query more clear.

Caffeine response is dependant on the individual. Caffeine has a very similar chemical make-up to adenosine which your body uses to transfer the energy you eat into energy your body can actually use (chemical energy). When your body has enough adenosine, it begins what is called a negative feedback cycle that stops adenosine production and sustains homeostasis. The feedback loops works by delivering a signal to put the body in a regenerative phase upon further adenosine binding. However, caffeine, being the wonderful individual that it is, disrupts that negative feedback loop and continues the conversion of excess (eaten) energy to chemical energy. But where in your body its dispersed is dependant on the individual.
The conversion to chemical energy is indirect though. Since adenosine is a principal neuromodulator, it actually tricks your body into behaving as it would at your most natural wakeful state. For example: Glutamate, GABA, norephinephrine, serotonin and acetylcholine are all inhibited by adenosine build-up at A1 receptors (a type of adenosine receptor) but caffeine stops adenosine from inhibiting these neurotransmitters. These five neurotransmitters all assist in metabolism (Glutamate is an essential compound in cellular metabolism).
Caffeine will never give you energy, it just uses your energy faster, in fact it probably causes energy debt from the work your body does to flush out the toxin.
In short: Yes it can enhance cognition, if the extra ATP (energy molecule made from adenosine) is carried to the brain. As it is not a homeostatic process it's difficult predict.
When the body is constantly put out of it's homeostasis, it adapts the toxin into its homeostatic balance (kind of like a beat em' or join em'). In this way, an individual's body begins to need the caffeine to function normally. Caffeine dependance is very low however, since it has been around for centuries, we've evolved to know it as a toxin but to not depend on it.
References:
J Alzheimers Dis. 2010;20 Suppl 1:S3-15. doi: 10.3233/JAD-2010-1379.
http://thebrain.mcgill.ca/flash/a/a_11/a_11_m/a_11_m_cyc/a_11_m_cyc.html
http://pharmrev.aspetjournals.org/content/51/1/83.short

Are you sure this is how caffeine works? It actually acts on the adenosine receptors as far as I know and does not affect cellular metabolism of adenosine. Can you add some references to support this?

The adenosine receptors caffeine acts on directly is different from those involved in ATP production. Looking at my answer now, I can see how it could be confusing. It would have been best had I specified the ATP (and other chemical energy) synthesis is indirect. Thanks for pointing that out, I'll adjust my answer.

In a study by Wight et al. (2013) [1] it was shown that bees feeding on nectar containing caffeine seem to enhance the bees' memory of those flowers. They hypothesized this based on the fact that:

Two caffeine-producing plant genera, Citrus and Coffea, have large
  floral displays with strong scents and produce more fruits and seeds
  when pollinated by bees (8, 9)

They tested this hypothesis by feeding bees sucrose solutions with different concentrations of caffeine and measured their behavioural response.

We hypothesized that caffeine could affect the learning and memory of
  foraging pollinators. To test this, we trained individual honeybees to
  associate floral scent with 0.7 M sucrose and 7 different
  concentrations of caffeine and tested their olfactory memory. Using a
  method for classical conditioning of feeding responses (proboscis
  extension reflex, 11), bees were trained for 6 trials with 30 s
  between each pairing of odour with reward. This inter-trial interval
  approximated the rate of floral visitation exhibited by honeybees
  foraging from multiple flowers on a single Citrus tree (see methods).
  The presence of low doses of caffeine in reward had a weak effect on
  the rate of learning (Fig. 2A), but it had a profound effect on
  long-term memory. When rewarded with solutions containing
  nectar-levels of caffeine, three times as many bees remembered the
  conditioned scent 24 h later and to responded as if it predicted
  reward (Fig. 2B, logistic regression, χ72 = 41.9, P < 0.001). Twice as
  many bees remembered it 72 h later (Fig. 2C). This improvement in
  memory performance was not due to a general increase in olfactory
  sensitivity resulting from caffeine consumption (Fig. S2A). In fact,
  the effect of caffeine on long-term olfactory memory in bees was
  greater than that produced by high concentrations of sucrose when the
  same experimental methods were used (e.g. 2.0 M, Fig. S2B).

However, bees would not prefer nectar (or sugar solutions, as  at higher concentrations (>1mM) as they would find it distasteful.
I am not sure if this hypothesis is extensible to higher organisms. It may have some effects on alertness and attention but there are no stringent studies to assess its effects on learning.
This review [2] on the possible cognitive effects of caffeine concludes that:

Thus,  caffeine  apparently  cannot  be  considered  a 'pure'
  cognitive enhancer. Its indirect action on arousal, mood and
  concentration contributes in large part to its cognitive enhancing
  properties.

[1] Wright, G. A., et al. "Caffeine in floral nectar enhances a pollinator's memory of reward." Science 339.6124 (2013): 1202-1204.
[2] Nehlig, Astrid. "Is caffeine a cognitive enhancer?." Journal of Alzheimer's Disease 20.S1 (2010): 85-94.


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

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

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

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

My school is Nador in Morocco north of africa :)

Its medditeraneen climate


text:
Absorption ratios 260/280 and 260/230 for RNA
I extracted RNA from different cell lines, an I want to perform reverse transcription and then PCR. To get good results, in which range should the absorption ratios 260/280 nm and 260/230 nm be? And what is the idea behind measuring those ratios?
I got values for the 260/280 ratio from 2.1 to 2.13 and values for the 260/230 ratio from 0.98 to 2.01. Are these values acceptable, and what information can I gain from them?

DNA and RNA absorb at 260nm. Proteins absorb at 280nm. The 260/280 ratio is a good estimate of how pure your sample is. For RNA, the 260/280 should be around 2. If it is lower, this might be an indication from contamination or proteins, phenol, or other contaminants in your sample. The 260/230 ratio is a second measure for purity of the sample, as the contaminants absorb at 230nm (like EDTA). The 260/230 ratio should be higher than the 260/280 ratio, as it is usually between 2 and 2.2. Lower ratio might be an indication of contamination.
In your case, I would be worried about the purity of the sample which gave you 260/230 ratio of 0.98. If you are using nanodrop, looking at the plot can also be a good indication of the quality of your sample. For a pure sample, a well defined peak (no shoulders or wiggles) at 260nm is expected.
Take a look at the NanoDrop manual for more information.

Indeed I remember than the sample with the ratio 260/230 had two peaks. In my case the values for the 260/230 ratio are never higher than the 260/280 which indicate I have contamination. What can be the consequences not to have the correct values for these ratio for a PCR? I will extract RNA soon. How can I prevent a contamination next time? What is the good practice?

If you have two peaks is a clear indication of contamination. Don't look only at the ratios because this gives you some idea, but doesn't make a strong statement. The PCR might fail due to contamination but you might try it anyway. You might also try isolating your RNA again.

For the sake of completeness and relevant detail: The aromatic amino acids (Phe, Trp and Tyr) are the primary contributors to the ability of proteins to absorb at 280nm. Cysteine double bonds can also absorb but its both rare and the extinction coefficient is smaller in comparison to the aromatic residues.


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How to find ion/water channel related genes
We now have a collection of transcripts at hand. We would like to investigate some particular ones, which are ion/water channel related. How to perform this? Could anybody point out how to find the annotated genes those are ion/water channel related? If there are some places particularly for fish, that would be perfect. Thanks.

In case you don't know it, this should be of help: http://www.geneontology.org/

i'm afraid a literature crawl is the only reliable way to get domain knowledge like this.

I think your best bet is to blast your transcripts against the TCDB which has all ion channel templates you can get in the literature. Second best would be to blast against a general protein database like UniProt.


text:
Is there a way for a 19th century scientist to prove that the octopus doesn't revive?
Say a sophiscated scientist in the 19th century noted that applying soy sauce on a dead octopus leads to movement of the legs, as a result of the voltage differences resulting from the salt in the soy sauce. (For example, see this video)
Is there a way for that scientist to prove, using only the methodologies available at that time, that the decapitated octopus doesn't regain the functions that a octopus brain normally has, for even a few seconds, when the soy sauce is applied?

I'm not sure what you mean by delude by medicine.  If you can clarify that, don't hesitate to add it back in, but I don't think it was adding anything to the meaning.  Also, this has nothing to do with bioinformatics.

Unsure if this is suited for this site. It's really a history question.

@LanceLafontaine - Nope, this question is really about how could a scientist use less advanced techlogy to conduct a experiment to develop advanced result

Although a proper theory of action potentials was not developed until the beginning of the 20th century, Galvani's experiments date back to the 18th century...

@nico - But that might lead some realigous group think the brain function is come from the heart...

@Victor: Galvani's experiments were done on frog legs, I don't think the heart was present.

Yes. The scientist could cut a small piece of the octopus's tentacle and perform the experiment again. If the same result is observed, no other organs are involved.


text:
How to prevent protein precipitation?
I'm trying to label a protein with fluorescent dye (TMR succinimidyl ester), but having trouble getting the protocol to work.
The protein seems to be stable in distilled water at a the recommended concentration of about 2mg/ml (determined by OD280 with an absorption coefficient calculated from its sequence).
However, the protocol calls for the addition of 1:10 1M sodium bicarbonate to raise the pH to ~8.5. When I perform this step the solution immediately turns cloudy. I have also encountered this precipitation problem when dialyzing against PBS.
What steps can I take to get around this? I assume a lower concentration of protein might prevent precipitation, but I have read that the labeling reaction becomes very inefficient below 1mg/ml.

What is the pI of the protein? If you get near that one you'll usually precipitate the protein.

Thanks - literature says pI can range between 7.1 and 7.4, so this is probably the issue.

Is there a standard technique to get around this? I guess I could dilute a bunch, adjust pH then reconcentrate? Or would I be better off just looking for another labeling chemistry?

There are a lot of possibilities here - i) Depends on your protein, what is it? ii) You should be using molar concentrations for everything, pls make the conversions.

that sounds like a good thing to try.  also changing salt concentration , mono and di cations can help.

Alas, the great problems with protein formulation. I assume that the labeling chemistry forces you to use the 8.5 pH. (I don't think this is necessarily true since succinimidyl chemistry does work at pH=7.2 and the NHS ester is fairly unstable above pH=8.6)
We usually tackle this problem entirely by brute force ie. testing multiple buffer condition with different bases and different salt concentrations. First attempt would be to try out all of the Good's Buffers notablely HEPES, TRIS, MOPS, Tricine, Maybe you should look at both zwitterionic buffers and non-zwitterionic buffers. In your case, the succinimidyl is vunerable to primary amines so avoid things like Tris/Glycine.
The second thing to test would be various salt concentrations. Since you're already dumping in Sodium Bicarb, NaCl should be great. Unfortunately, this is where things get tricky since it is hard to know if your protein precipitates at low salt or high. A test from 20 mM, 50 mM, 100 mM, 150 mM, 200 mM, 500 mM should cover most of the interesting regimes.
Alternatively the sudden change in the ionic environment around your protein may be causing it to precipitate. You probably should dialyze or purified your protein in PBS to allow it to refold appropriately.


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Is it possible to isolate cells harboring a specific DNA sequence?
Suppose on cell subpopulation harbours within its genome a specific DNA sequence that do not exist in other cell subpopulations. Is it technically possible to isolate such subpopulation ? and if yes, how ?
Some additional information about the cells. All different subpopulations are B-cells. But only one of these subpopulation harbors a specific DNA sequence. We do not think that this sequence is transcribed, and if so in a very lowly expressed manner. We also know that this sequence is only present in one of the two alleles. The main idea of this experiment is to follow this subpopulation at different time points. As the relative abundance of this subpopulation compared to the whole B-cell population is low, we wanted to isolate them in order to study them (DNA mutation, RNA expresion, etc...)
Thanks

If you can distinguish these cells from other cells somehow, yes, there are possibilities.

The sequence you're trying to find, is it something you're inserting in the organism, and you want to find which have accepted the sequence, or is this an arbitrary sequence?

All the cell subpopulations (say subpop A, B and C) are part of the same cell type (in my case T-cells). And subpop A harbos the specific DNA sequence (~50-100nt long).And the sequence is not something that I insert into the cell.

@NicoBxl Is the sequence in population A transcribed? In my mind, staining for a specific mRNA sequence would be a lot easier than looking for a DNA sequence, especially because of signal amplification. For DNA, there are at most 2 sequences per cell (assuming the cell is homozygous), while there could theoretically be tens to hundreds to thousands of mRNA copies per cell. Please [edit] your question and include as much detail as possible about your cells and the sequence you're trying to detect (obviously, you can anonymize specific genes or whatever if necessary).

I can't think of any such technique off hand. Would it be more convenient in your case to genetically engineer the cells to include that specific DNA sequence instead?

@MarchHo No it's not possible as we have mutliple in-vivo samples (differents time points). We do not want to have an artifical scenario (even if might be a interesting idea to dig into)

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0113549 here's an interesting technique

The only technique I can think of off the top of my head is FISH.


text:
Complementarity Determining Regions (CDRs)
Complementarity determining regions (CDRs) are part of the variable domains in immunoglobulins (antibodies) and T cell receptors, generated by B-cells and T-cells respectively, where these molecules bind to their specific antigen. (source: WIKI, with a minor change)
Now does this mean that this CDR is the paratope? As it has the same functionality as paratope, to bind to epitope of the antigen. So is CDR a type of paratope?
(I have not studied biology since last 8 years and now I am going through it because I need it for my research. So if someone can describe it in simple language it would be very helpful)

The paratope is the part of an antibody that binds the epitope on the antigen. The CDRs (heavy chain CDRs shown below) are part of the structure of the variable domain, and contain the hypervariable regions that bind to the epitope.

From Wikimedia
The actual paratope is within the hypervariable regions, which are within the CDRs - the paratope is not necessarily made up of the entire CDR region, and in fact may only be composed of amino acids contained within one or two of the three CDRs on each antibody chain (heavy and light).
If you're in the market for a good introductory immunology text, I highly recommend Janeway's Immunobiology. An earlier version is also available on the NCBI Bookshelf.

You should attribute the diagram.

@AMR sorry I missed that originally. Done.


text:
How long do your eyes need to adapt to darkness and reach full contrast?
I heard you should wait some time until your eyes adapt to darkness and are able to see smallest luminosity differences, otherwise you might overlook faint objects in the ocular. Are there any rules of thumb here?
What about looking for 5 minutes on your bright laptop, do you have to wait again the same time or wear some sunglasses? ;)

Thirty two minutes on an average

Actually, the US Army and Air Force (and I assume the Navy) teach their life support troops about this (as well as the aviators), since so many missions are conducted at night.  It is actually some very interesting physiology involved in this.  This Army Instructional manual should be of help (PDF File).

On an average it takes 30 to 45 minutes for your rods to be fully dark adapted to night vision.

It also covers things such as Mesopic vision and Scotopic vision, eye health, and how to actually dark adapt.  I suggest it as a good read.  It's less than 14 pages.

@Joe, Uh, any citations or simply old wives' tale?


text:
What is the smallest number of amino acids required for life?
Is there any hypothesis on the minimum number of amino acids required for life?

One could argue that the answer is zero as long as you define a virus as a living thing, even if it uses (living) amino acids to reproduce it's not needed for survival. If you even talk about survival in viruses... This is however more of a philosophical sidenote...

You can divide the 22 (including selenocysteine and pyrrolysine)  proteinogenic amino acids into broad groups of similar amino acids. There are the hydrophobic amino acids like trypthophane, valine and leucine, the charged amino acids like glutamate and arginine and the polar amino acids like serine and threonine. There are some amino acids with unique features like cysteine which can form disulfide bonds.
Some amino acids are very similar, for example isoleucine and leucine, it is plausible that one of those would suffice to create most protein folds.
There are several examples of proteins designed with a smaller alphabet of amino acids, one example is the E. coli orotate phosphoribosyltransferase (Akanuma et al., 2002). The simplified enzyme consists of only 13 different amino acids and 88% of it are composed of only nine different amino acids. Even after those drastic changes the enzyme still folds correctly and has enzymatic activity.
There is one study (Fan and Wang, 2003) that tried to answer exactly the question you asked. They came to the conclusion that around 10 amino acids are necessary to create properly folding proteins:

First, we study the minimum sequence complexity that can reserve the
  necessary structural information for detection of distantly related
  homologues. Second, we compare the ability of designing foldable model
  sequences over a wide range of reduced amino acid alphabets, which ﬁnd
  the minimum number of letters that have the similar design ability as
  20. Finally, we survey the lower bound of alphabet size of globular proteins in a non-redundant protein database. These different
  approaches give a remarkably consistent view, that the minimum number
  of letters required to fold a protein is around ten.

Akanuma, S., Kigawa, T. & Yokoyama, S. Combinatorial mutagenesis to restrict amino acid usage in an enzyme to a reduced set. Proceedings of the National Academy of Sciences 99, 13549 -13553 (2002).
Fan, K. & Wang, W. What is the minimum number of letters required to fold a protein? J. Mol. Biol. 328, 921-926 (2003).

Thanks for pointing this study out, though don't you think it would be suprising if every codon for isoleucine in a bacterium were substituted for leucine that the bacteria lived?  Not sure it would make it as there are often single cases where an otherwise innocuous particular amino acid was important.

If you allow for the "RNA world" hypothesis, then the minimum number of amino acids needed for life would be zero, because RNA in that context would be self-replicating and would not need proteins (or their amino acids).
Since this question was asked in 2012, with respect to modern life on Earth circa 2019, Craig Venter's lab put out a 2016 paper in Science about their engineered organism called JCVI-syn3.0.
This organism is engineered to contain the simplest, smallest set of genes required to sustain life, i.e., metabolism, reproduction, etc.
The latest version of its genome is 531 kb long and is made up of 432 protein-coding genes. In the Supplementary Materials of that paper, there is a file called Database S1, an Excel spreadsheet. The authors state that this spreadsheet contains the amino acid sequence of proteins coded for by genes in JCVI-syn3.0.
When we take this column and filter out non-protein-coding or untranslated genes, we get a listing of sequences, like so:
MSFNKLNQTYLDWINHPNLDQELKELLNKADDNELNAAFNLELKFGTAGIRGILGAGPGRFNVYTIKKVTIAYAKLLQTKYSNDLNKGVVIGHDNRHNSKKFAKLVADILTSFNIKAYLFKNNDLQPTPVVSFATKALNCIGGIVITASHNPAEYNGYKIYDPYGCQLMPHDTDVIANYMNEITNILDWTFISNNNLLEIVDQTVIDKYFEMIKNLEFYKDQDKSNLKIIYSAVNGTGSLYTPIVLKQSGYEVIEVKEHAFEDETFKNVINPNPEFDPAWKIPLEYAKKYDADIIILNDPDADRFGMAIKHNNEFIRLNGNQTGAILIDWKLSNLKRLNKLPKNPALYSSFVTSDLGDRIASETYNANVVKTLTGFKWMGQEMLKEPLNGLNFVFAYEESYGYVIDDSTRDKDGIQASIIAAEACWYYKNQNMTLVDYLNQLYEKYGYYYTTTYNLNFKPEEKDSKIAPIMKLLRTTGIKQINNLKVVKIEDYINGLYNMPSEDLLKIYLEDKSWIAIRPSGTEPKLKIYFVIVDSSLQKAENKAEKIYTELKTILNI
MFKVKFADIGEGLTEGTVAEVLVKVGDVVKEGQSLYFVETDKVNSEIPAPVAGKIAVINIKAGQEIKVGDVVMEIDEGSGASVASEPKAEAKQEAKVEVVEENASVVGATPVSNDLIVRKQASTVTKSSTIKATPLARKVAADLNIDLSLVTPTGPNQRILVADIKNYHSSSAQPASQPAPTPTLVASQPAPAPTPAITPAIKVVEPSAPLSWDEVPMNGVRKATVKAMTKSHTEIAAFTGMKNTDITETHKMRTELKDHAAASGIKLTYLAFIIKAVAKSLRDMPNINVRGDFANNKIQFMHNINIGIAVDTPNGLMVPVIKGADHLSVFEIAIKISELANKAKDGKLTRAEMTEATFTVSNFGSVGLDYATPIINSPESAILGVGTMSQTPLYINGELQKRFIMPLSMTCDHRIIDGADAGRFLIKVQDYLSKPVLLFM
MQIPIIKPKKAPPLTIEEINEIKQHSSYEKSYLKTFNKYKKKVEHRIYFKTSFWWDIFIIALAALANTITTDYFILATGDTGLFPGGTATIARFLSIVLNKHITSISTSSSFFIFLFIVNLPFFVFGFIKVGIKFTLTSLLYILLSIGWNQIITRLPIINPNEWSLIINYKLISSLPTEWSSKLWLFVFSIFGGFFLGITYSLTYRVGSSTAGTDFISAYVSKKYNKQIGSINMKINFTLLLIFVVLNTVIMPIYKIDSTAKLSVLNTLTDEQFTEIYNKAKDSGKFILDFNSHHHFYLPSNWSVSDQQIWTRQQIAQIIASNTNFTNYDNLTTIIKLKFVFGPSLFASFICFVIQGVVIDRIYPKNKLFTVLISTTKPREVKNYLFESGYRNNIHFLENQTAKKENGYIAQSVIMIHIGLMNWKPLQAGANNIDPDMMISFIRTKQVKGPWSYSLDTQKRELSLYKKVITDRRLMARIEKESILLTKQKITNDKKLKSKSKTF
...

This text can be processed with a simple Python script to count the number of unique residues, e.g.:
#!/usr/bin/env python

import sys
import re

with open(sys.argv[1], 'r') as f: a = f.read()
b = re.sub('[\n-]', '', a) # strip out newlines and indels
len(list(set(b))) # count the number of unique codes

This answer suggests a minimally-viable organism codes for proteins that, altogether, use 20 amino acids.
I did not create a frequency table to see what is most commonly used, or what is least used, and what might be hypothetical targets for substitution with other amino acids. But that might be an avenue for further exploration.
In the future, Venter or other labs may find that there are ways to engineer an organism to use fewer genes, or protein-coding genes that use fewer amino acids or which use synthetic amino acids that have redundant chemical features that could allow it to substitute multiple residues, while preserving the protein's function.

The RNA world hypothesis does not preclude a peptide world preceeding it — only that DNA came later and that RNA had catalytic properties. Ventner's experiments do not relate to evolution, where it is difficult to envisage all amino acids emerging at once. It is more likely that certain simple amino acids were generated in the primaeval soup and formed peptides before the more sophisticated RNA bases emerged. The peptide backbone has the ability to bind certain metal ions, especially the Fe-S complexes involved in ancient redox reactions.


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Which organism has the sharpest vision?
Which organism has the sharpest vision in terms of distance and spectrum. Please propose any other useful measure.

You might also want to consider field of vision and acuity.

you may want to pick a metric, humans have decent acuity but see a much smaller portion of the spectrum than non-mammals. Almost any bird see's more of the spectrum than almost any mammal.

A good measure for "sharpest vision" is known as acuity of vision. Visual acuity denotes the ability to perceive detail and resolve temporal motion. This metric informs how sharp or blurred an organism perceives its surroundings. There is software which helps quantitate this metric with visual examples.
The following example shows how a comparison of different organism's perception may look like, visually:

When we broadly compare the various vision systems of organisms based on acuity, a plot below may give one a better perspective:

From the above, it is noted that eagles have specially evolved eyes with high visual acuity. Of note, the Australian wedge-tailed eagle can see 8 times more detail than humans can. Interestingly, their eyeball have the ability to squeeze and elongate similar to a zoom lens on a camera.
Reference:

Wedge-tailed Eagle Factsheet

Visual Acuity and the Evolution of Signals

AcuityView Software

So I guess without glasses, my vision is as good as that of a butterlfy.

This paper suggests that bats have a comparatively high depth of focus.
It's believed that butterflies (depending on species) have the widest visual spectrum.

Don't hawks have exceptional range?

Birds of prey - hawks, falcons, eagles, vultures - along with some owls all have exceptional vision.

I thought bats were blind.

How about the Mantis Shrimp?

T-rex
As an archosaur it sees a wider portion of the spectrum than mammals and it has the largest eyes known in terrestrial vertebrates, and eye size correlates well visual acuity, so the largest eye should be close the the highest acuity.
So yes Jurassic park could not be more wrong about T-rex vision, T-rex would see better than the humans trying to hide.
https://www.karger.com/Article/Fulltext/357830#:~:text=Thus%2C%20as%20eye%20size%20increases,eye%20length%20(table%201).
https://www.academia.edu/13386081/Evolution_Beyond_the_rainbow


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Robotic surgery for treating cancer?
We can localise cancer cells in the body. We can manufacture materials thin and hard enough to penetrate the body without harming it. So what stops us from creating an automated surgery where the patient lies down for some time whilst a machine locates cancerous cells and, using micro tubes, kills them one by one?

Actually we aren't that good at localizing cancer cells.
There need to be around 100,000 cancer cells at a single location for the cancer to be visible on fMRI.
If you want to treat a cancer via chemotherapy it's good to be able to see the cancer. It allows you to see whether the drug you are giving works.
When dealing with a metastasized cancer where you don't think you know every location in which cancer is hiding it therefore often makes sense to avoid to take out everything you can see via surgery.

I'm assuming you are not talking about a single solid tumor, but rather one where the tumor is loose and is distributed throughout the tissue, or has metastasized
I guess the answer is you could, but it would be one amazing machine.  This robot would have to examine each individual cell and destroy it based on what you could sense about the surface properties.  Identifying cancer cells on the basis of the proteins and glycosyl (carbohydrate) molecules on the cell surface is not embodied in a reliable way in a touch sensor.  Cancer can manifest lots of such patterns and it could quite easily be that if you looked at healthy cells, they look very similar.  Cancer cells don't have an obvious sign they are always holding out saying 'im cancer'...
Then there is the structure of tumors. For instance glioma is one of the most difficult brain cancers you can have.  The glioma cells push out axons - they can be quite long.  Its hard to imagine a robot that could find that in a brain without tearing up all the neural connections.  In prostate cancers - one of the most common cancers in men, the cells are embedded in the tissue surrounded by necessary and healthy cells.
This is why chemicals and nano  structures are the most commonly pursued means of combating cancer; they can enter into cells that are embedded in solid organs.  Differentiating the cancer cells from other sorts of cells in vivo is a pretty hard problem - some antibodies are given as a possible help there, but they are not reliable.
This blog post is a pretty up to date summary of where we are
http://www.sciencebasedmedicine.org/index.php/personalized-medicine-vs-evolution/

May I ask you if there's some way to make cell cancers identifialy? For example, a virus inserting radioactive DNA on cell cancers only? Or anything like that?

@Dokkat : If you would have a virus that could insert DNA in all cancer cells and not in non-cancer cells you could simply let the DNA produce proteins that kill the cells. We don't have such a virus.

@Christian: there are, however, (far from perfect) approaches to that, such as receptor-mediated or pH-mediated targeting of nanoparticles for drug delivery.

@Dokkat radioactively tagged antibodies specific to cancer cells have been used in the past

@nico: It still doesn't make sense to tag cells with such methods and then locate the cells and punch mirco/nano tubes with a robot into them.

@Christian: no, but you can release a drug specifically on those cells.

This is a multibillion dollar question.  jury is still out. I doubt there is a specific 'i'm cancerous' label on the surface of all these cells.

We can manufacture materials thin and hard enough to penetrate the body without harming it.

It’s not a question of materials. After all, we have the materials to build flying cars – so why are there no flying cars? The problem is reliably locating, recognising and selectively killing tumour cells.

using micro tubes, kills them one by one?

“one by one” … for billions of cells? Even ignoring all the other technical problems already alluded to in other answers, this is simply a problem of insurmountable scale. In order to do this efficiently we’d need in the order of millions tiny robots.
Either way, creating appropriate robots isn’t something we can do today, and even if we can someday do it, it will be easier (and much, much cheaper) to just bio-engineer a bacterium which detects and kills tumour cells. While neither nanobots nor bio-engineered cancer killers are reality today, the bio-engineering approach is much easier, and much closer to realisation than the nanobot approach.