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Virus variant transmissibility: empirical data or spike protein shape?
Well first I am not in the field of Biology or Medical Sciences. Since these days we are waiting scientists to tell us if the Indian variant of SARS Cov 2 is more transmissible than the original virus, so these two questions come to my mind:

Do scientists confirm increase in transmissibility based on empirical data?
Can scientists know how transmissible the variant is based on how well the spike protein binds to receptors of the cell, for example by doing a simulation of the 3D folded shape of the spike based on its code in the genome?

Generally based on (1). To quote the website:

The variants that are cause for most concern may:

spread more quickly,
evade natural or vaccine-related immunity,
cause more severe disease,
evade detection by available tests, or
are less responsive to treatment.

If you look at the features they say they look for in variants, the one they mention first is "spread more quickly" - the only way to tell that is to look at the genetics and how often the variant is turning up in a relative to a previous time-point. Bullet point 2 can only be seen by testing - do people who have had an infection, get infected again? Bullet point 3 - look at the severity of disease in patients.
I am sure by now that you get the drift; surveillance is the answer - all empirical data.
(2) may or may not help, all that does is tell you the potential affinity for a receptor, which is only one part of the transmissibility. In fact, we generally need to wait for the emergence of a variant (no point chasing each one; every infection will have some form of variant in some of the virions produced - they exist as a quasispecies), before modelling will take place. In addition, modelling is actually a pretty poor method of working out whether the virus will bind to a receptor. Check out this paper and see that many of the Mustelid family have poor binding by modelling, but in reality they actually get infected easily by SARS-CoV-2.
Many other factors come into viral transmission, such as viral titre in the patient, number of viral particles being shed in each droplet/aerosol, survivability of the particles in the air/droplet/aerosol etc.
Here's a nice paper from PNAS that covers the biological features of transmissibility based on a statistical/modeling analysis. While this doesn't specifically address SARS-CoV-2, it does cover a bunch of the features that are looked at for increased transmission in viruses overall.


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By what means do "nerve agent" compounds like VX spread through the human body so quickly?
It has been demonstrated that so-called nerve agents are able to incapacitate and kill a human in only minutes after exposure. One such substance, VX, only requires a few milligrams to contact the skin of a human in order to trigger such a reaction.
I understand that many nerve agents are acetylcholinesterase inhibitors, so they promote a surplus of acetylcholine which causes loss of muscle control due to the inability to "rest."
What I totally fail to understand (or find in my searches), is how such a small drop of e.g. VX is able to cause full-body symptoms. In other words, if a drop of this substance gets on a person's hand, how is it that in 1-2 minutes, they begin to have full-body convulsions, defecate uncontrollably, etc...? I would understand this if the substance was sprayed all over someone, but having a drop in the order of milligrams on an extremity, it's not clear how this triggers such a severe reaction all over the body. I am aware that it gets absorbed through the skin, but otherwise I have no idea what happens after that.
My lack of understanding is partially due to:

(A) Neurotransmitter (acetylcholine, ACh) released from nerve endings binds to receptors (AChRs) on the muscle surface.

Kuo IY, Ehrlich BE. Signaling in muscle contraction. Cold Spring Harb Perspect Biol. 2015;7(2):a006023. Published 2015 Feb 2. doi:10.1101/cshperspect.a006023
How does VX so quickly get to the surfaces and inhibit acetylcholinesterase all over the body? Is it a matter of simple blood circulation after the agent makes its way through the skin and into the blood stream?

Welcome to Biology.SE. Please [edit] your post to clarify your question. As written you seem to be asking about the circulatory system, but since that is common knowledge I'm guessing you have a more sophisticated question that isn't clear.

hi @tyersome thanks for explaining that. So I made a minor edit to clarify the final question. After doing a bit of research on that subject, I found that the circulatory system can indeed push blood in full circulation from heart and back in a matter of sub minute. But I'm still only speculating here.

You're welcome! It seems to me that all of the symptoms you describe could occur due to effects on the CNS, so a nerve agent doesn't even need to get to the whole body. I'm doubtful that anyone on this stack has the kind of background to answer this with any specificity — you could try [MedicalSciences.SE], but be aware they may expect more prior research. Also, please do not crosspost, instead request migration after consulting that sites help.

source: Stack Exchange

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What does does a blood sample tell you about an individual's identity?
I am working on a fiction/mystery scene where blood is found in a particular character's residence but the character themselves is missing. I would like to understand whether the law enforcement/forensics team are realistically able to identify if the blood belongs to the missing character, without having a prior blood sample or DNA of this missing character? If not what details are they able to learn from the sample eg. gender, age, physical characteristics etc?

I'll add a full answer later, but sex, some physical characteristics (e.g. hair, eye, skin colour) and ethnicity can be determined (not perfectly) using DNA from blood.

@user438383 thank you very much, looking forward to your full answer.

The forensics team would also use other sources of DNA found around the house, like hairs and skin cells, to confirm (or not!) that the blood is from the person who lives there. You can also take samples from this character's relatives to establish if the blood came from someone related to them.

@MattDMo very helpful, but if there's other people (or even one person) who visit(s) on a regular basis then their hair, skin cells etc would also be there?

@FrontEnd there would, but the majority would likely be from the resident. They would do things like look at the hair under the microscope to see if it matches the occupant, take DNA samples from the bed, a toothbrush, comb, or towel, that kind of thing. It wouldn't be just one sample, they'd take dozens. If one of the visitors is a romantic partner who sleeps over regularly, then it might be a little more difficult, but forensics people have lots of tricks up their sleeves. There are lots of accessible sites out there that explain aspects of criminal forensics, you might want to look them up.

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Where is coagulation factor v produced?
My textbook (Physiology 9th edition, People's Medical Publishing House, Beijing) tells me that coagulation factor V is produced in endothelial cells and platelets, which is different from some other books (saying in liver). So where exactly is it produced? Or is it just not quite clear?

Why the downvote? Surely a very good question?

Not the downvoter, but I somewhat suspect the reason is that the question fails to demonstrate the basic biological knowledge to know that the liver is made of cells, including endothelial cells.

Factor V is indeed mainly produced in liver (Wikipedia), which releases it into blood plasma.
But platelets are also an important source of factor V and "may account for "~25% of the circulating factor V" (ref 1)
Platelet factor V is synthesized in megakaryocytes (ref 1), which of course are the bone marrow cells responsible for the production of platelets.
The following reference gives a much more detailed explanation of factor V
Reference (available as free pdf)
Factor V Is Complexed with Multimerin in Resting Platelet Lysates and Colocalizes
with Multimerin in Platelet α-Granules *

Catherine P.M. Hayward
Emilia Furmaniak-Kazmierczak
Anne-Marie Cieutat
Michael E. Nesheim
John G. Kelton **
Graham Côté

Journal of Biological Chemistry (1995) Vol 270, pp19217-19224

The coagulation factor 5 is primarly produced in the liver, meaning that the liver isn't the only place where this factor can be produced (F5 can possibly be produced by megakaryocytes too). After it is manufactured it circulates in an inactive form and is then converted into the active form with the help of thrombin and is able to attach to platelets. However, as far as I know, factor v is not produced in endothelial cells, there are a few factors that are, like Von Willebrand factor (VWF), factor VIII, thrombomodulin, endothelin etc., but F5 is  not one of them.


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Effects of direct and indirect cholinergics on cardiovascular system
If I give intravenous acetylcholine then the effects produced on cardiovascular system are hypotension , tachycardia and increased cardiac output.
Now if I give intravenous acetylcholine esterase inhibitors on another patient then the effects produced on cardiovascular system are hypertension, bradycardia and decreased cardiac output.
Ach esterase inhibitors also increase acetylcholine concentration then why does the effects are exactly opposite in the above mentioned two cases.
Reference for abovementioned data : Cholinergic drugs ,
katzung pharmacology, 14th edition.
Is it because Ach esterase inhibitors increase Ach concentration locally in the tissues which produce more direct effects while the Ach is rapidly metabolised in the plasma so can't reach into the tissues so it produces more indirect effects via baroreflex ?

Welcome to Biology.SE. Please take the [tour] and then go through the [help] pages starting with [Ask] questions effectively on this site. In particular, this question seems like it might be a better fit for [MedicalSciences.SE], but please do not crosspost, instead request migration. However, please note that that site (and this one) expect you to document your prior research (ideally with references to reliable sources). Thanks!

Okk ,, I added my reference now

Now can you please explain something about that ? Does the way I m thinking correct ?

That's a start, but I encourage you to do more than read from a single textbook. In addition, given the poor fit between your question and this site you may wait a long time before someone posts an answer — that is why I recommended you check out what seems to me a more appropriate site ...


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Are dragons real animals?
I'm having this debate with someone on whether or not dragons are real. They say they are real because there's been thousands of historical sightings of them, and there was at least one historical case where a teenager said that a dragon had appeared and killed his friends, one by one, but he got away. For the rest of his life, this person was obsessed with dragons to the point of wanting to obtain the personal information and telephone numbers of anyone who claimed to have seen dragons.
So, now I am kind of conflicted, I responded to that person saying those people saying they've seen them are lying, and Wikipedia says that dragons are fictional, but they directed me to a paranormal forum saying that Wikipedia is unreliable, and also it's up to me to prove that the people who've seen dragons are just lying.

Welcome to Biology.SE! Please take the [tour] and then go through the [help] pages starting with [Ask] questions effectively on this site. In particular, this question seems like it might be appropriate on [Skeptics.SE], but please do not crosspost, instead request migration after consulting that sites help. ———— In addition, we expect you to demonstrate prior research with references to reliable sources — e.g. peer-reviewed research (definitely not paranormal forums) . Thanks!

Dragons are real animals, but that almost certainly doesn't mean what you think it means
'Dragons' are a diverse group of reptiles mostly within the Family Agamidae, comprising over 300 species throughout Africa, Asia, Australia and southern Europe. As an example, here's an Eastern Bearded Dragon, which is relatively common throughout Southern and Eastern Australia. The largest male Eastern Bearded Dragons grow to around 60cm total length, and a personal friend of mine once kept one as a pet.

The largest real 'dragon' is the Komodo Dragon in the family Varanidae. These grow to a maximum length of around three meters and a maximum weight around 90 kilograms. They are top predators in the ecosystems where they live, and adults will take large mammals as prey. Here's a Komodo Dragon stalking a deer. You'll notice that it's wearing a GPS collar - it was being tracked by scientists trying to record their foraging behaviour. You can read their full paper in PLoS ONE.

However, it seems like you are mostly asking about mythological dragons. Mythological dragons aren't real.


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Genomic library preparation: Why does the restriction enzyme not cut into the gene?
I am currently trying to understand creating a genomic library more profoundly. In most textbooks I read (as well as wikipedia), they mentioned that the genomic library is created by isolating the DNA and fragment it with a specific restriction enzyme that cuts approximately as many times as there are genes. However, that cannot really work, can it?
Let's say E. coli has 4000 genes with a 4,600,000 bps genome. That means I must generate fragments of more than 1150 bps in theory (if each gene is the same length and no other sequences are present). That would mean I need a restriction enzyme that cuts about 4000 times creating over 1150bps fragments. So I would either use a restriction enzyme with a recognition site of 5bps (cuts every 1024bps) or with 6bps (cuts every 4096), of course just if the base-pairs are random.
Now you already see, with the first restriction enzyme I will (even in theory) cut through many genes, while with the second I might get genes of the appropriate size but I will also fragment others.
Furthermore the genes, especially in more complex organisms are not spaced out equally, but may be concentrated in some areas, while in others just repetitive sequences are located.
So why does every textbook mention that I can create a full genomic library with one restriction enzyme? Wouldn't it make more sense to shear many copies of DNA randomly, just as it is done for shotgun sequencing, to get a higher coverage?
So my question is, how is a genomic library REALLY prepared without knowing anything about the sequence? Do you just take into account that much genes will be cut in the middle and hope for the best? It seems like a very weird strategy to amplify complete genes.
Thank you! :)

A genomic library is generated for the purpose of encapsulating the full genetic component of an organism.
You do this by fragmenting the genome with  restriction enzyme that cuts at its recognition sequence. These fragments are then taken and cloned into a plasmid, so that they can then be sequenced inside the plasmid using common sequences that are found on the plasmid but not (generally) in the organism itself. The sequencing would traditionally be performed by Sanger sequencing, which has a limit to how long a sequence you can do at once - really good sequencing will get you ~1000 bp, with sequence quality tailing off after about 600 bp.
The genomic library are not intended for expression - once genes are identified, they can be sub-cloned into an expression plasmid to see what they do. So for this purpose, cutting a gene into fragments isn't a problem, as you will find the rest on another plasmid and can re-construct the full-length gene by taking the two fragments and assembling them.
The reason you use the restriction enzyme is that the sequence that it cuts at is also used for inserting it into the plasmid.
So, at this point you might be asking yourself how do you match the ends of genes (or any sequence for that matter), when they are all cut by an identical sequence?
Well, the answer that is that you use multiple restriction enzymes, either by themselves or in combinations to generate a variety of fragments cut at different sites. This will mean that once you create the libraries from these different digestions, you can sequence through the different libraries and find where the fragments overlap, and then assemble all the sequences together into the original sequence.
For instance if you look at the picture below. If you imagine the the black boxes  are the same gene, and the in the top one is cut with restriction enzyme A and the bottom with B, you can see that the two restriction enzymes generate overlapping fragments, so if you sequence the fragments from both restriction enzymes, you can find where the ends in A match others in A, by looking at the fragments in B.

Thank you!!
So I guess genomic libraries are mostly used for sequencing, whereas cDNA libraries are used for expression (since you always get a full gene), which makes sense.
It really confused me, since in many "general" textbooks, they explain, that creating a genomic library is used to e.g. find a gene - which I associated with "they want to express that gene later". I didn't quite understand why you would want a fragmented gene. But I guess it makes more sense now. Thanks!

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Unknown pentatomid from India
I found this bug last year in August in Bharatpur, Rajasthan, India. The scale at the bottom of the picture is in mm and the size of the bug seem to be about 1.5 cm.
It looks a bit like Halyomorpha halys, but all the pictures of H. halys I've seen show white markings and sometimes more colour and not the dark markings as shown here.

Oh, and it was feeding on the bird dropping shown in the upper left corner.

For me it does not look like Halyomorpha halys because as you said the white marking but also there are some white sections in the antennas (see here). We have also this specie in Europe and considered as invasive.

Also the head is more squared on the Halyomorpha halys as shown in this picture from the same site, yours has a triangle/sharp head.


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Are there animals that usually sneeze with their eyes open?
As per title. I believe that most animals tend to sneeze with their eyes closed, but wonder if any normally keep their eyes open.
Is it the case?

Maybe they can!

These marine iguanas seem to:
https://www.youtube.com/watch?v=6G6ZL0fbLHQ
This duck does too:
https://www.youtube.com/watch?v=ZaqeRn6B7sg
Your "most animals" might just be mammals.
This turtle, however, does seem to sneeze with its eyes closed:
https://www.youtube.com/watch?v=rQTJjmfPHdE


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Does gene isoform have different genetic location?
I would like to understand how a gene is assigned to a specific coordinate (e.g. NOC2L Chr1 879583(Start)   894679(End)). Next, let's say NOC2L gene has a few different isoforms, do those isoforms have different start and end site? What is the definition of start and end site of a gene?

You are asking several questions here, and on this site we can prefer to deal with one at a time for indexing purposes. One of your questions is the definition of the start and end of a gene. This question is easily answered in terms of the transcript, but more difficult if one considers enhancer sequences. I suggest you read about this. Your other question is about isoforms, which arise in two ways, either by gene duplication and divergence or differential splicing. Again these topics are well covered in the literature. As for assigning co-ordinates, that is just mapping.

The genes are defined by a reference genome - one that is considered the standard for comparison to. This reference is defined by which build of the genome you are comparing to, so there are different builds based off different assemblies of information. These are updated as new, more complete sequencing of the genomes (e.g. low complexity areas etc) are completed. At the moment for humans we are up to GRCh38 (Genome Reference Consortium, human build 38).
In general most gene positions won't change significantly between builds as these are fairly well defined - we can recognize promoter sequences, and the start condon (ATG) and stop codons (TAG, TGA or TAA) too. The sequence of the genes is largely conserved, so by comparing several sequences of the same area, we can see which bits are conserved within that sequence and thereby identify if it is a gene. We can also compare to sequenced cDNA (from mRNA) which is the transcribed form of the DNA and identify the sequence from that.
With respect to isoforms; it depends on the isoform, sometimes this form has a shorter or longer sequence, but often it is an altered (but same length) sequence that a proportion of the population carries. However, generally most of the sequence will be the same or very similar to the reference gene.

With all due respect for your attempt I must point out the weaknesses in this answer as someone upvoted it. I don't see that reference genomes have anything to do with this. People were looking at genes for different isoforms long before reference genomes. Also start and stop codons for translation are not relevant to the ends of the gene. And some isoforms result from gene duplication and divergence and others from differential splicing.

@david - The OP asked how the start and  end positions were defined on the chromosome, I simply gave the most parsimonious answer based on the likelihood that the OP has a bit of a limited understanding of the topic. AFAIK this is how it is done these days; no longer assigned relative to banding in a karyotyping exercise.

This answer does not mention the word "co-ordinate". Nor does it explain how the co-ordinates were defined in the chromosome, given that the ends are not sequenced. The title of the question suggests that the main concern of the poster is isoforms and asks about starts and ends. This answer does not address this.

Thanks a lot for all of these responses, I apologize for responding so late, and yes it's true that I have very limited understanding about this. I raise this question because, I have a data from NCBI that tells the position of each gene (like the example I've given in the post). However, I heard from my colleague that gene isoform can actually have different start and end position. Thus, I am wondering why NCBI just give us 1 fix position instead of giving us all the known isoform start and end position (if it's really different).


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Beta-diversity for data with zeros
Background
I am looking into enterotyping the gut microbiome data, obtained by shotgun sequencing. This essentially means performing the Principal Coordinates Analysis (PCoA/NMDS) of the beta-diversity matrix (distance matrix), based on the abundance table of species/gene-counts for all possible species/genes in all samples: for sample $i$ the counts of species/genes are $\mathbf{x}_i=(x_{i1}, x_{i2},...,x_{im})$. Such a table necessarily contains many zeros, whenever a species/gene is not present in a sample. Some of the possible beta-diversity measures are listed in table 1 of this article.
Problem
Statistically speaking, such data are considered to be an example of compositional data, i.e., the meaningful information is contained only in the ratios of the counts, which makes log-based distances the most suitable ones. These are used in many statistical methods (see, e.g., here). However, the log-transofmation is meaningful only when all the entries in the count table are strictly positive $x_{ik}>0$. One thus faces a problem of dealing with zero entries.
Possible solutions

Using metrics not sensitive to zero entries (excluding alr, ilr and other similar log-transformations, highly recommended by statisticians - this limits the choice of statistical methods)
Imputation of zeros: this requires a principled/biologically-sound way of imputing the zeros, since the results may be potentially very sensitive to this choice.

Question
What are the commonly used imputation methods? What is their advantages/disadvantages and justification for their use?
Some references

Here is a statisticians' view on imputation in compositional data: the methodology may get rather involved, but does not have any specific biological basis.

An appropriate way to handle this is with a so-called zero-inflated model: https://paul-buerkner.github.io/brms/articles/brms_families.html#zero-inflated-and-hurdle-models-1.

I'll often use the pseudocount method for exploratory analyses. Basically just apply a pseudocount of 1 to every count in the table (since Log(1) = 0), so the action you actually end up performing is Log(x+1).

@MikeyC Pseudocount is what I meant by "imputation of zeros": it is certainly a good method when one is interested only in the most abundant species (those that have counts), but I see how it may create problems when detecting low abandance species is important.

@vkehayas if I understand correctly, zero inflation corrects for undersampled species - one will still have actual zeros in the count table?

Correct, you still keep them in the sample. The model estimates parameter values for a separate process leading to ratios at zero and parameter values for a separate process leading to values $\in (0, 1)$. A great introduction to similar models can be found in McElreath's Statistical Rethinking book (slides, lecture 13).

To be honest I feel like this question would be better on CrossValidated ...

I'll also point out that most of the suggestions here focus on what to do about zeros, but don't consider compositionality. You'd ideally want a solution that considered (1) presence of zeros (via inflation or just low means), (2) discrete nature of data (i.e., potentially higher sampling variance at low densities) and (3) compositionality. This example uses Dirichlet-multinomial mixtures, but then reverts to Bray-Curtis-based PCoA for ordination.

I would recommend using a Bray-Curtis dissimilarity matrix since it accounts for abundances and can handle the so called double zero problem. I'm on my commute now and so must return to this for a complete answer later

I am looking forward to more details!

@Roger I'm not trying to ignore you. Started teaching summer classes + summer research students, so I've gotten a bit tied up. I'll get back to this soon.


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What is the probability of having a son with blood type O?
So I was solving some biology questions, and this question left me confused. The response is supposed to be 22% according to the answers, but all the methods that I used did not lead me to it. I really hope that somebody can help me with it, the question sounds as follows:
Human ABO blood type is determined by two genes (H and I). First, the H gene codes
for the antigen precursor. The dominant allele (H) leads to expression of the precursor; the recessive
allele (h) does not. Second, the I gene has three allele forms, IA, IB and i, and determines blood type
(A, B, O or AB).
A male with blood type A and a female with blood type B marry. Each of them is heterozygous for
both the H gene and the I gene. What is the probability of having a son with blood type O? Give your
answer as a percentage (%) rounded to an integer (without any decimals).

7/16 for any child to be blood group O, half of that (21.875%) for a son with blood type O.
P  x  M
HA    HB
HO    HO
hA    hB
hO    hO
HA = 0/4 blood type O
HO = 2/4 blood type O (HO-HO, HO-hO)
hA = 2/4 blood type O (hA-hB, hA-hO)
hO = 3/4 blood type O  (hO-HO, hO-hB,hO-ho)
= 7/16 chance of type O
son = 1/2
Chance of blood group O = 7/16 * 1/2 = 7/32 = ~0.22
Edit: as it's not clear from the question, anyone who has genotype hh is not able to express either A or B antigens, so is always blood group O.

Welcome to the site, Greg. I wanted to make sure you're aware of the homework policy on the site: https://biology.stackexchange.com/help/homework For homework questions, we want to make sure people are providing their own reasoning and work and that answers to these questions are about explaining the approach someone might take to a problem rather than answering it for them. Otherwise there's a high risk that people will ask questions here just to get the answer and won't really learn from it.


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What are the essential criteria for sleep in living organisms?
I was reading this fascinating article about how sleep evolved before brains, and it got me thinking "can plants sleep" and "what is sleep anyway".
A line in the article states:

Studies by a team in South Korea and Japan showed that the hydra
periodically drops into a rest state that meets the essential criteria
for sleep.

But after looking around I am unable to find the "essential criteria for sleep" - Before I am able to answer questions like "can plants sleep" I think I first need to understand what these essential criteria for sleep actually are.
Can anyone here help me out?  Many thanks in advance.

I have edited your question because criteria is a Greek plural (the singular is criterion). This is relevant to your question because the answer to your question is not a single thing. (btw Have you researched this at all? I imagine there is a wide literature on this topic.)

I take it you did not even read the article. The article quite nicely expounds on this question. The whole idea is that sleep used to be considered as a human or psychological phenomenon that could only be studied by EEG, and clearly sleep was a phenomenon that goes deeper and wider in biology than just EEG waves. From the same article, just a little below:

She distilled a set of behavioral criteria to identify sleep without the EEG. A sleeping animal does not move around. It is harder to rouse than one that’s simply resting. It may take on a different pose than when awake, or it may seek out a specific location for sleep. Once awakened it behaves normally rather than sluggishly. And Tobler added a criterion of her own, drawn from her work with rats: A sleeping animal that has been disturbed will later sleep longer or more deeply than usual, a phenomenon called sleep homeostasis.

The keyword is behavioral criteria.
This is cited, I recommend a read-through.
I also recommend reading the rest of the article, it guides you through the status quo in sleep science which may further answer more of your questions. At the end, there is an exposition on animals without brains that seem to be sleeping. The hydra is one of them.


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Do COVID-19 vaccines produce more spike protein than natural infection?
The SARS-CoV-2 spike protein has been shown to be harmful on its own.
However, a news article quoted an "expert" as saying

The spike protein components of the vaccine are not produced in the same amounts as a normal viral infection (probably a million times less)

On the other hand, I've also read that

Antibody levels induced by mRNA COVID-19 vaccines are much higher than those induced by natural infection

These statements seem contradictory: I'd expect higher antibody levels in response to larger amounts of the spike protein.
So do COVID-19 vaccines produce more spike protein than natural infection?

There is also an argument that the spike proteins produced by the vaccines are localized in the muscle. I think this issue is orthogonal to the question of how much protein is produced.
It should also be noted that the Pfizer, Moderna and J&J (unlike AstraZeneca and Sputnik V) vaccines encode a defective spike protein, which may help with their safety.

Edit: The variation from person to person is a red herring. Here is why:
Let's take a person who has not had Covid or vaccine. Give him the vaccine and count the number of spike protein molecules he produces and call it x. Now, in a different scenario, take the same unvaccinated person and give him Covid. Count the number of spike protein molecules he produces and call it y.
The above claims are, respectively:
y = 1000000 x

and
y < x

We don't even compare x from one person to y from another. We could also understand them as statements about average values. There would still be a contradiction.

Whose natural infection?

Well, for example, someone with an asymptomatic case is probably going to have less overall spike protein produced (might as well just think of it as proportional to active viral particles shed) than someone with a severe case. There also seems to be good evidence that like previous SARS/MERS outbreaks a lot of spread comes from "superspreaders" who have higher viral loads and are producing more active virus than the typical case.

@BryanKrause So what. The variation from one person to another is a red herring.

"... spike proteins produced by the vaccines are localized in the muscle.I think this issue is orthogonal to the question of how much protein is produced." - Possible to elaborate? As I just read related question stating that muscle cells seem to not express MHC1 at all, thus not being involved in immune reaction at all.  ... and  is that what you mean?


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What is the map unit between the genes?
While doing some biology I came across this question:
The following tables present results of plant crosses involving two linked genes: S is a seed-color gene, and L is a plant-height gene. Each gene has two alleles with one allele exhibiting complete dominance over the other allele. Dominant phenotypes are yellow seeds, and tall plants; recessive phenotypes are green, and short, respectively. Assume that crossing-over between two genes occurs once.

Calculate the map unit between gene L and gene S. (One map unit = distance of 1%
recombination)
This question confuses me because what I understand from the table is that from crossing over we get short flowers with green seeds(0.01) and tall ones with yellow seeds(that 0.01 from 0.51). So as a result from the fertilization we get a 0.02 frequency, that as I understand also equals the recombination rate. And so the map unit would need to be 0.02*100= 2%. But the response I have to get is 20%, which I do no understand how to get to.

Here's a hint: you're right about 0.01 being the recombinant (crossed-over) double homozygous recessive offspring. I think you're forgetting that each offspring contains 2 gamete haplotypes, and so predicting the percent of offspring with a given genotype involves multiplying the frequencies of the individual gametes that produce that offspring. To calculate the gamete frequencies (and thus the recombination frequency) you need to reverse that multiplication process.
Full answer:
I find it easiest to deal with uniform genotypes first. Of the 4 offspring phenotype categories, only the double homozygous recessive category (short/green) has uniform genotype - llss. If l and s were unlinked, 1/16 of the total offspring would be short/green=llss.
However, many fewer than 1/16 are short/green=llss, so linkage is confirmed and (as you note), ls must represent one of the crossed-over haplotypes. The other crossed-over haplotype is then LS. Thus, the parental (non-crossed-over) haplotypes must be lS and Ls.
Let's call r the recombination distance (in map units) between l and s. Each recombination between l and s produces 2 recombinant gametes: 1 ls and 1 LS.  Thus the fraction of ls gametes - f(ls) - will be (r/2)/100 and the fraction of LS gametes - f(LS) - will also be (r/2)/100.  Since each F1 offspring consists of 2 gametes, the fraction of offspring with an ls/ls genotype will be f(ls) x f(ls) = (r/2)/100 x (r/2)/100 = r^2/40000 = 0.01 (from F1 data).
Solving for r, r^2 = 0.01 x 40000 = 400, so sqrt(r^2) = r = sqrt(400) = 20.
r = 20 map units

From How do I write a good answer? in the [help], Answer well-asked questions. Not all questions can or should be answered here. In particular, the OP has asked many homework questions with little or no effort towards meeting the criteria for such questions on this site. Answering such questions encourages undesirable behavior and is actively harmful to students. In addition, please don't post multiple answers to the same question — you can and should edit your posts instead. Thanks.

Thanks for your suggestions!


text:
Why there is Cp/Ct (crossing point) value for negative sample in COVID-19 RT-PCR?
I am from IT/ Engineering background and have some confusion in RT-PCR. So far my understanding we extract RNA from test specimens and transcribed it into complementary DNA (cDNA), and inside PCR DNA double helix is separated. Then primer a short nucleic acid sequence that provides a starting point for DNA synthesis with the target sequence and multiples the target DNA.
Since a higher cp value indicates a negative result but why negative results have cp value? Why it cross the threshold when it does have any target sequence (viral DNA)? Fluorescence is the measure of target DNA right??
In extraction, RNA is extracted does only COVID viral RNA is deducted? What happens to other viral and human RNA?
I have seen result were we have low cp value and test result is negative, the reason for this was because curve do not have Sigmoid function. I need more explanation on it.

I do research on these tests for a living, and I've never found a good explanation for why some PCR reactions produce these linear amplification artifacts, even amongst my clinical diagnostics colleagues. You could try running the product of them out on an agarose gel to see if you see the expected band (or bands). But I agree with the Chris's answer that, if you're unsure about a result because of a strange shape to the amplification curve, I would re-test the sample, probably doing a fresh RNA extraction, rather than simply making a negative determination.

PCR is highly specific (meaning it amplifies only the DNA the primers bind to), so the results in general are good. But: Due to the exponential nature of amplification, even very small mispairings can lead to the amplification of a very small amount of DNA and subsequently to a fluorescent signal. There are basically two ways of getting this amplification (except from the desired one): Contaminations and primer self-amplification. The first one should be clear, the second one happens when primers are able to bind to themself (or the complimentary one). This usually shows up late in the PCR, which is the reason why you assign a Ct-value to it after which you do not expect any meaningful amplification result anymore.

Depending on the method, you extract all RNA from your sample and translate it into cDNA. However, for the amplification in the PCR you need primers that fit to this cDNA. And since these primers have been proven to be highly specific (meaning: they bind absolutely no other sequence, not even from closely related viral species) we can be sure that any signal which is about the criteria mentioned in 1. comes from viral RNA and shows an infection. See the reference for one of the often used PCR tests.

Without seeing the curve, I cannot say anything specific about it. In general a real-time PCR curve follows a sigmoid shape. This gives some information about the way the reaction progressed over time. If it looks differently, something may have either happened to the reaction or to the sample. It may contain a PCR inhibitor, the sample can be contaminated or something else. I wouldn't trust such a result and repeat it with a new sample.

Reference
Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR

Just a side comment, but for diagnostic purposes it's often not the case that All RNA is reverse transcribed into cDNA. Most nucleic acid tests for SARS-COV2 (and other RNA pathogens) use a target specific RT step, so that only regions containing the PCR targets are reverse transcribed, helping improve specificity and reducing RT bias. Since the reverse primer for the PCR will generally perform this function well, these tests often use a 1-pot (or 1-step) RT-PCR, which is faster and also slightly more sensitive since you don't dilute the cDNA when adding it to the PCR mix.

While @Chris's answer is correct, there is a complication that comes into it:
The principle behind quantitative PCR (qPCR) is that you have a reaction which, if your reaction is 100% efficient, doubles the number of DNA strands produced at each cycle. In theory, you can start from one strand and get 2, then 4, then 8 in a 2n style equation, where n is the cycle number.
However, in reality this rarely happens, most reactions are not 100% efficient; generally in the 90% range is considered acceptable, so if you do the maths, it doesn't quite work out at a doubling each cycle.
The qPCR tests run on COVID-19 samples are generally run out to 45 cycles, which if you work out the maths, means that if a sample is showing some amplification at this point, then there is less than 1 copy of the RNA (actually cDNA) in each reaction, so the signal is likely to be non-specific amplification or spurious signals from other products in the reaction, such as primer-dimers (shouldn't be the case in these tests though, they use a system that all-but eliminates this problem).
Non-specific amplification can be detected by looking at the reaction and comparing the melt-curves of the products of the PCR. Specific products will have melt-curves that meet certain criteria (all with a peak at the same point), while non-specific products will have different melting points/curves.


text:
Longevity and extent of transfection after SARS-COV-2 vaccination with Janssen
The Johnson vaccine, unlike the RNA vaccines from Pfizer & Moderna, uses a vector containing DNA encoding for the SARS-COV-2 spike protein. This vector DNA needs to enter the cell, allowing for transcription and translation into viral spike proteins to occur (Fig. 1). These spike proteins are then exposed on the cell surface by the host cells, thereby exposing the antigenic epitopes, creating the immune response.
Basically, this turns the immune cells against their own host, because they are expressing foreign proteins on the cell surface. In effect, I presume, those host cells are subsequently destroyed by the immune system.
Now I was wondering if there has been any research done revealing the consequences of this auto-immune response. In particular how long does it take to get rid of all the transfected cells (or 'vaccinated cells' as they tend to be called) and what is the extent (volume/area affected) of transfected cells? Basically you are genetically altering the host's cells  that are subsequently attacked by the immune system. So I am basically interested in the pharmacokinetics of the vaccine I guess.
This safety report from EMA says that in 1 animal (from how many?) there was still genetic material after 180 days to be detected. However, the report says nothing about the extent of transfected cells nor anything about the auto-immune damage after the host attacks its own cells. No references are to be found either; it all seems to be about internal safety studies.
In retrospect I'd rather not have had the Janssen vaccine; I bet mRNA vaccines are a lot more instable, guaranteeing safe removal of the foreign genetic material.
Anyway, I'm drifting... In short, (1) how long does it take to clear all signs of foreign DNA from the host after Janssen, and (2) what is the extent of the cells that are transfected and subsequently attacked and damaged/killed by the host?

Fig. 1. Schematic depiction of the DNA vaccine from Janssen. source: Livingstone et al. (2021)
References
- European medicines agency, Assessment report COVID-19 Vaccine Janssen
- Livingstone et al., JAMA (2021); 325(15): 1575

In your last sentence, I'm not sure that I'd use the word transformed to talk about the cells, as that implies integration of the vaccine DNA into the host cell's chromosome(s), something that is definitely not supposed to happen.

@MattDMo good point, I meant to say transfected; edited!

Both the AstraZeneca (AZ) and Janssen (J) vaccines against SARS-CoV-2 work by using what is known as a viral vector to generate an immune response. In both cases these vaccines use an Adenovirus as the vector (vector in this case means "carrier").
Adenoviruses are a family of double-stranded DNA viruses, most commonly known for being one of the causes of the illness known as the "common cold". There are about 88 human serotypes (how people's bodies react to the virus, different serotypes may not cross-protect by raising an immune response)  of the virus divided over the seven species: Adenoviruses A-G. Overall there are about 50 species from a wide range of animals. Because of the diversity and the abundance of the "common cold" most people will have some immune response to at least some of the human adenoviruses.
Adenoviruses are good vectors for vaccines and for other therapeutic uses such as oncolytic viral therapy because the genomes are easily modified and can be made replication incompetent very easily. To do this essential replication genes are removed and the viruses are replicated in cells that provide the essential proteins for the virus to complete its life cycle, so that you get a viral particle that no longer contains the full genome. These viruses are also tolerant of additions to the genome so they can be used to produce proteins that are not normally found in an adenovirus.
On the other hand the adenovirus component of these vectors can cause its own immune response, lowering effectiveness of the immune response to subsequent vaccines developed using the same or a similar sero-type virus.
The AZ vaccine is based on a chimpanzee adenovirus. The J vaccine on the other hand is based off serotype 26 (Ad26) of the human adenoviruses. In both cases the adenovirus is rendered replication incompetent by removal of essential replication genes.
For adenoviruses to work as vaccines they need to invade a cell and then insert their DNA into the nucleus (as seen in the diagram from OP), where cellular mechanisms produce lots of mRNA copies of the gene(s) and concomitantly produce proteins from these mRNA. These proteins are then translocated to the surface of the cell (this is a natural process).
However, in the process the viral DNAs and proteins are recognized by the cell as non self and trigger the innate immune system causing antigen presentation and activation of the adaptive immune system. This whole process is a natural response of the body to infection and does not result in "self recognition", so there is no auto-immune response to the cellular proteins.
Both the AZ vaccine and the J vaccine are administered intra-muscularly, which is very typical for vaccines. Intra-muscular injection is a good site for injection because it can tolerate a big volume and has a relatively high blood flow (both compared to sub-cutaneous (sub-skin)). However, once injected the virus doesn't necessarily stay in the muscle, it can travel in the blood and a portion will end up in other locations, particularly the liver and alveoli of the lung (places where there is a big blood supply and lots of small blood vessels). The adenovirus can infect any cell that expresses the cellular receptor called Coxsackievirus and Adenovirus Receptor (CAR) on its surface, which includes immune cells. There's a fairly comprehensive review of all the mechanisms and cell types here, but it is in written for the scientist. Here's a suitable quote about cell types:

The cell types present when vaccine is administered i.m. include myocytes, skeletal muscle cells, fibroblasts and endothelial cells, with APCs such as DCs or macrophages representing a minority when compared with the abundance of murine skeletal muscle cells

How long the viral genome persists depends on the paper, but not many people have looked. The data I could find seem to indicate that most protein expression is lost after about 3 weeks, but it may persist longer. e.g.:

Although Ag expression could no longer be detected 3 wk after immunization, examination of Ag presentation within the draining lymph nodes demonstrated that APCs were loaded with Ag peptide for at least 40 days postimmunization, suggesting that Ag remains available to the system for a prolonged period, although the exact source of this Ag remains to be determined.

However, the genomes seem to persist for a very long time, perhaps as much as a year or more:

By days 30 and 90, vector sequences remained detectable in most animals in these tissues but gradually declined 102 to 103-fold. After 1 year, relative copy numbers in liver and the injected muscles were comparable with those seen on day 90, whereas a further decline was seen in spleens.

Note that both of the quotes above are not about either the Janssen or AZ vaccines, but about adenovirus vaccines in general, and several Ad vectors were tested in both. It is highly likely that the AZ and J vaccines will have similar data.
Edited to add: I don't know why the DNA persists for so long, but it does not integrate into our genome. It seems that most replication deficient adenoviruses lack the E1A gene, which allows induction of the cell cycle and frees up resources for viral replication. However, the genes involved in viral DNA replication are E2A and E2B, so are not included in the deletion.
TLDR: Viral genome can persist for a very long time, but protein expression does not - possibly because the immune system deals with it quickly after a few weeks (effective vaccination...). The vectors can reach and infect many tissues in the body, particularly ones with high blood flow and lots of small blood vessels such as lungs and liver.

Wow +1 I'll have a thorough read through all this material! Sweet.

There's also a concern regarding Sputnik (which also uses adenovirus vector) where in some combination, the gene which was removed to stop replication can get regenerated in host & cause adenovirus to replicate (something vaccines are usually designed to not do).

@RedBaron, yes. This is actually a feature of all adenovirus vaccines, not just Sputnik, and why "good" ones are generated in adenoviruses from other species, which are very unlikely to have a human infection. The function is called complementation - cells expressing the missing protein(s), and actually has to happen in the cells that they use to grow up the vaccine. However occasionally the virus reacquires the missing genetic component and becomes replication competent again, but this is tested for in manufacture.


text:
Visualizing selection's effect on a population with a fitness landscape in R
I'm trying to write a script to demonstrate the effect of selection in a population. The problem that I have is that it is not realistic in the sense that not only the mean would change for directional selection, but the Skewness of the distribution would change as well. How would that be possible to code that and show the movement of the population going toward the maximum fitness peak?
# Define the normal distribution
slect <- function(x,mu,sigma,constant) {
  temp = exp(constant*((x-mu)/sigma)^2)
  return(temp)
}

# Multiply 2 functions
Multiply=function(a,b){
  force(a)
  force(b)
  function(x){a(x)*b(x)}
}
# Set some parameters
ylim = c(0,2)
# X limits
from = -4
to = 7
# Sequence of X
x = seq(from,to,length.out=100)
# Means
muinit = 1
mu1=muinit
mu2=3
# Number of generations
n.gen = 10
# Variance
sigma.0 = 1.1
sigma = sigma.0

# Initialize population
init0 = function(x)slect(x,mu = muinit,sigma = sigma.0,constant = -.5)
# How the population will move because of selection
popmov = function(x)slect(x,mu = mu1,sigma = sigma,constant = -.5)
# Final fitness function
finl = function(x)slect(x,mu = mu2,sigma = sigma,constant = -.5)

# Empty data
data.all = NULL
gen = 1
for(gen in 1:n.gen){
  # system("sleep .2")
  # png(paste0("Desktop/fittest/test",gen,".png"))
  int1 = curve(expr = init0,from = from, to = to, col = "red",
               ylab = "Fitness", xlab = "Phenotype", ylim = ylim, main = paste("Fitness result gen:",gen))

  if (gen ==1) {
    # Result of the first generation
    h0 = Multiply(init0,finl)
    h1 = curve(expr = h0,from = from, to = to, col = "black", lty = 3, add = TRUE)
    finl1 = curve(expr = finl,from = from, to = to, col = "black", lty = 2,lwd = 2, add = TRUE)
    mu1=mean(c(mu1,mu2))
    popmov1 = curve(expr = popmov,from = from, to = to, col = "red",lwd = 2,add = TRUE)
    h = h0
  }
  if (gen>1) {
    mu1 = mean(c(mu1,mu2))
    popmov1 = curve(expr = popmov,from = from, to = to, col = "red",lwd = 2,add = TRUE)
    h = Multiply(h,finl)
    h1 = curve(expr = h,from = from, to = to, col = "black", lty = 3, add = TRUE)
    finl1 = curve(expr = finl,from = from, to = to, col = "black", lty = 2,lwd = 2, add = TRUE)
  }

  popmov1$type = "popmovement"
  finl1$type = "final"
  h1$type = "movement"
  int1$type = "initial"
  popmov1$it = gen
  int1$it = gen
  finl1$it = gen
  h1$it = gen
  df1 = as.data.frame.list(int1)
  df1.1 = as.data.frame.list(popmov1)
  df2 = as.data.frame.list(finl1)
  df3 = as.data.frame.list(h1)
  df4 = rbind(df1,df1.1,df2,df3)
  data.all =c(data.all,list(df4))
  # dev.off()
}

Someone here may be able to answer your question, but if not I would try SE Bioinformatics, which might have been a better bet in the first place.

Modeling for a single genotype parameter or a single phenotype parameter is (IMHO) very unrealistic. I know that doesn't address your question, but it may be relevant to your goal.

@S.McGrew You are right! But my goal here is to have a "simple" case to be visualized (adding traits would start making the thing in 3d and further make it even harder to visualize). But as a first approximation, I wonder how to "show" the effect of a fitness landscape on the population. Therefore, I'm pretty sure that not only the mean is changing, but also the skew of the distribution. This is the core of my question.

If your selective "force" works equally on all parts of the population, you will keep the shape of the original distribution.  In real life, though, the "leading edge" of the population will procreate faster than the "trailing edge".  Account for that and I'm pretty sure you will get the skew you're looking for.

@S.McGrew, This is it, but how to 'account for that' is my question! I don't know how to simulate this part. Now I'm only modifying the mean of the distribution (the     mu1=mean(c(mu1,mu2)) in the script). But there should be something modifying the shape and skew of the distribution (and Kurtosis if the actual density of the population changes) as well.

There are at least several different approaches that can work.  Without examining your code, it seems that the population is described as a distribution of phenotypes.  You need a way to allow that distribution to change in a fairly complicated way.  So if you define a function to describe the change something like this, you should get results closer to what you're expecting:

define distribution as N(p), where (p) is phenotypic distance from the original peak of the distribution, and N is the number of population members at that distance p.
Define D(pf-p) as the distance from the new fitness landscape peak to a point in the distribution that change.
Define a forcing function that depends on D. This function will either decrease or increase the number of population members at each point along the phenotype distribution, depending on D.  If you want to get fancy, it can also depend on the number of population members in the previous iteration at that point in the distribution.
If you want the population size to stay constant, you should make sure the forcing function adds and subtracts the same number of members from the population.
I would suggest using a forcing function that gets stronger as the phenotype gets closer to the new fitness landscape peak, and make sure the phenotype distribution is nowhere zero when you begin.

The above function should in effect move population members toward the new fitness peak, though may tend to sharpen the population peak.  To fix that you can introduce another function that "blurs" the distribution, like randomly moving members to nearby phenotype values.


text:
Is there a closed path through the circulatory system that never passes through the liver?
I know the liver is responsible for doing lots of stuff to the blood, including both adding glucose and removing "weird stuff" (stuff destroyed by hepatocytes).  And I know that statistically every volume element of blood will eventually pass through the liver.
However, if you were magically minaturized a la Fantastic Voyage, and traveled with the blood flow, making ideal turning decisions at every arterial branching but never going upstream or out of the blood vessels themselves, could you completely avoid the liver?

The liver gets blood from two sources: the hepatic artery and the portal system of venous drainage from the gastrointestinal tract.
As long as you don't follow one of these paths, you'd avoid the liver. Some examples would be circulating through any of the limbs or head, but any path that avoids those two paths will do. As you point out this is statistically unlikely without your magical navigation abilities.


text:
What is the best method to check for differential gene expression in large dataset? I want to identify aging differentially expressed genes
My data is RNAseq data and I want to know which all genes (which I am looking for) are differentially expressed with age. I have tried the Kruskal-Wallis test and fold change methods. Do you have any better suggestions?

Someone here may be able to answer your question, but if not I would try SE Bioinformatics, which might have been a better bet in the first place.

A standard pipeline for DGE would be Salmon for (pseudo)mapping + DESeq2 for statistical analysis.
Salmon is one of a set of modern, fast and accurate mapping software. Requires a transcriptome to be defined.
DESeq2 is a mature R (Bioconductor) package which can handle reasonably complex designs, including time series, but not mixed models. The vignettes are pretty thorough. It can handle data from pseudomapping software as well as data generated using more traditional methods, e.g. Star + FeatureCounts.
Using a pre-made RNAseq statistical package is a much better idea than trying to do, e.g. a Poisson/Neg.bin GLM manually for each gene (even though ultimately this is what DESeq2 does).
(agree this question is a better fit for SE Bioinf)


text:
What is the difference between using an open or a closed configuration for a microfluidic chip?
I am doing a college BioMed project on Lungs-on-a-chip to make disease models and I have seen some authors use open configurations, while others use closed configurations. What advantages and disadvantages do they offer on the making of this device?


text:
What can we expect when we culture a mould in a liquid?
I am currently running a short project in my school, where I am trying to look into the biodegradation of dyes from textile effluents using Fursarium sp. Most of the articles that I have read uses a spore suspension as the seed, because it is easy to quantify, and also perhaps reproducible. Well, I was thinking about how vegetative cells, may have different metabolic traits, so what can I expect are the results if I use the vegetative cells as the seed? Also, if I were to use the vegetative cells as the seed, is it considered semi-quantitative if the seed (vegetative cell) is retrieved on an agar plug of equal diameter?

I expect the vegetative cells (mycelia) to grow much faster


text:
Can you find the mass of drug X remaining in the body after an oral dose with only bioavailability and half-life?
I'm reviewing this question and think you can't find the answer with the information given.

A patient receives 1g of drug X as a single oral dose. Drug X has a
half-life of 12 hours and an oral bioavailability of 40%. What mass
(in milligrams) of drug X will still be present in the body 24 hours
after a 1g dose?

I think the fact that it is an oral dose means the question is more complicated, involving absorption and distribution of the drug, meaning more factors are needed which are provided in the question.
Is it as easy as:
$1000mg \times 0.4 \times 0.5 \times 0.5 = 100mg$
Or there is is more it than that as I suspect? Many thanks!

I'm no pharmacist at all, but I think you have given yourself a red-herring. The bioavailability surely means that this is the proportion absorbed?

@bob1 It does but it doesn't tell you over what time-scale the drug is absorbed. You could answer what mass of drug in total will make it into and out of the body (400mg) but not how much at a specific time point (24 hours).

Unfortunately, it is not so easy, the easy calculation you suggested might be a good approximation if the drug was administered intravenously via bolus, but for oral administration, as you say in the comment, we need to know something about the dynamics of absorption from the gastrointestinal tract, such as Time to Peak Concentration (how long it takes for Drug X to be fully absorbed and reach peak concentration in the bloodstream.), and Absorption Rate (the rate at which drug is absorbed, as this influences the concentration profile in the initial hours after administration.

Well $40\% \times 1 g = 400 \space mg$ That's how much drug is in your body's fluid compartments.
If the drug's half-life is $12$ hrs, the amount of drug in your body (after $24$ hrs) = $\frac{1}{2} \times \frac{1}{2} \times 400 = 100 \space mg$.
If dose = $d$ and bioavailability = $b\%$ and half-life = $h$ hours ...
The amount of drug left in your body after $t$ hrs = $\left(\frac{1}{2}\right)^{\frac{t}{h}} \times b\% \times d$

EDIT 1 START
This fancy geometric/mixed (?) series might be of interest ...
$1 - \left(1 \times \left(\frac{1}{2}\right)^1 + \frac{3}{2} \times \left(\frac{1}{2}\right)^2 + ... \right) = 0$ where $1$ = bioavailability, the exponent is $\frac{t}{h}$ and the rate of drug absorption = $1$/hr.
EDIT 1 END

No, that's not how it works. Bioavailability is how much of the drug ends up in the body ever; your answer is making an unstated assumption that it all enters immediately at time 0. Robert's answer explains already why this is a problem. You're just restating the attempt OP mentioned ("is it as easy as") without acknowledging this is what you did. Do you understand the question?

Ah, a simple error. You mean to say that at no point in time (after drug ingestion) is there around $40%$ of the drug in the body? We must then discuss concentration of the drug in the body as a time function $f(t)$ so that by integration we get the average concentration which we can then use to find bioavailability which is exactly what cogito is $40%$. What's bioavailability then if it isn't that simple. Why calculate it if it serves no purpose? Also, the $40%$ may be the maximum amount of the drug in the body which will be important for toxicity assessment I suppose.

No, that is not what bioavailability is. Bioavailability is the amount that ever gets into the body. If it takes time to be absorbed and is eliminated at any rate, it will never all be in the body at the same time. That doesn't mean it's not a useful concept, it just means it's not sufficient information to answer the question.

Indeed, but $40%$ is the grand total amount of the drug that can exert a therapeutic effect. What do you make of, $1 \times \left(\frac{1}{2}\right)^1 + \frac{3}{2} \cdot \left(\frac{1}{2}\right)^2..$

@BryanKrause, much obliged for the feedback. I edited my answer.


text:
What volume of elution buffer for plasmid-containing filter paper?
I am attempting to elute a 9kb plasmid from a square of filter paper sent to me to be transformed into DH5a E. coli (photo below). Each filter paper square has been spotted with 1ug of plasmid DNA.

I have two questions:

AddGene's protocol states to elute the DNA using 30ul of TE (I'm using H20 as that's all I have atm). Using a similarly sized piece of filter paper to test, I added it + 30ul of h20 to a 1.5ml tube with a quick 4k spin down and have observed that the 30ul is completely absorbed by the paper as shown in the photo. How should I resolve this? Add an additional 30ul? I also tried to use a pipette tip to shove the paper down and squeeze out the water but you can see it didn't work.

What volume should I use for transformation knowing that the filter paper contains 1ug and I need to use additional elution volume? Most protocols I've read call for 1-5ul of DNA and AddGene calls for 2ul assuming 30ul elution volume.

Thank you.

Are you using the same kind of filter paper (thickness matters)? Did you follow their directions, which say to use 1/2 the piece of filter paper (because they send you a two spots) and to use 30 µL (not the 3 µL you wrote)?

My sample isn't from AddGene. And I can only estimate it's the same thickness. It seems to be the same based on close inspection. The spot on the paper isn't marked and so I wouldn't know exactly the boundaries to cut out of the paper. The 3ul is an error - I'll fix that now. I used 30ul.

1 ug is a LOT of plasmid, so your elution doesn't have to be very efficient. I would figure out how much water your test piece of filter paper can absorb by adding more and spinning down until you start to see free liquid. I would then use double that volume to elute your actual sample, take 30ul and proceed with your transformation.  If your centrifuge is a benchtop eppendorf-style, I'd use the max speed rather than just 4k.

The answer to this is... some.
You only need a tiny amount of plasmid DNA to transform bacteria like DH5$\alpha$, typically in the 1-10 ng range, but much less, as low as about 10 pg (10 picograms) may still work. If you can get even 0.2 ul of the liquid out of that tube, try using that in the transformation.
Things you can try;

remove the paper, spin the tube down and see if you can recover even a tiny amount of liquid.
Add a bit more liquid - another 30 ul will be fine
Snap the tube lid closed on the paper to hold it in place, then spin the liquid out of the paper - this is quite difficult to do.

Note - even with 10% DNA recovery (very low for this method), with 30 ul you will still have 100 ng of DNA, or about 3.3 ng/ul.

These are all excellent suggestions, especially using the lid to spin out the paper. Thank you!

FYI, I was able to use a total of 60ul of TBE to elute the plasmid on filter paper, using 1ul for my transformation with success.

Good, so you probably transformed a few ng. In my experience smaller amounts <5 ng work better than higher amounts anyway.

Indeed. I did 16 experiments with varying volumes ranging from 1ul to 10ul and the lowest volumes performed best. It was a ~10kb plasmid. I actually posted most of my diy transformation protocol here.


text:
How can we know which alleles are together on a chromosome?
This illustration says that if the two homozygotes pr+ pr+ vg+ vg+ and pr pr vg vg are crossed to produce a heterozygous offspring (pr+ pr vg+ vg), then:

this cross gives us exactly what we need to observe recombination: a
fly that's heterozygous for the purple and vestigial genes, in which
we know clearly which alleles are together on a single chromosome.

(My emphasis.)
Source: Khan Academy - Genetic Linkage & Mapping

I can't see the logic of this: surely if there were four chromosomes involved instead of two, wouldn't we get the same result via independent assortment?
In this scenario, the red-eyed, long-winged fly would have pr+ on chromosome 1, pr+ on chromosome 2, vg+ on chromosome 3 and vg+ on chromosome 4.
Conversely, the purple-eyed, vestigial-winged fly would have pr on chromosome 1, pr on chromosome 2, vg on chromosome 3 and vg on chromosome 4.
As the mother and father are both homozygotes (the mother for the dominant alleles and the father for the recessive alleles) the offspring couldn't fail to inherit pr+ and vg+ from the mother and pr and vg from the father, and the offspring would be the same as in the illustration.
I can't see how we can get any information as to whether genes are on the same chromosome or not from this crossing (unless we know they are on the same chromosome before we do the cross!).
I'm sure I've got my own logic wrong - can anyone enlighten me?

I think they assume that the two genes are on the same chromosome. The question is, given a situation where the individual is known to be pr+,pr and vg+,vg which of the following two configurations is the case: pr+ vg on chr1 and pr vg+ on chr2 vs pr+ vg+ on chr1 and pr vg on chr2

I'll read the full text again, and see if that assumption is implied anywhere, thanks.

With reassortment you would only get proportions of the F1 with that genotype. If the genes are on the same chromosome, then all the offspring will have the given phenotype...though it has been 20+ years since I last did basic genetics so I could easily be wrong.

(moved from comments)
We don't know from the pictured cross that the genes are linked. However, IF they are linked, we know from that cross which alleles are together on a single chromosome.
Thus, the description should have something like
", if indeed they are linked."
added to the end of
"this cross gives us exactly what we need to observe recombination: a
fly that's heterozygous for the purple and vestigial genes, in which
we know clearly which alleles are together on a single chromosome."
It's the further crosses of the F1 offspring that tell us if the genes are linked and if so, how closely. The original cross is necessary but not sufficient to determine this.

Your headline question:

How can we know which alleles are together on a chromosome?

is not quite the same as the question at the bottom of the body of your post:

how we can get any information as to whether genes are on the same chromosome or not from this crossing

These questions are different in two ways that matter: (1) the second question is asking about a single crossing, the one shown in the diagram, and (2) the second question asks about knowing if the genes are on the same chromosome or not, whereas the first asks if the genes (I presume, it says "alleles") are "together on a chromosome" (depending on how you take that phrase).
On difference (1): the single crossing in the diagram does not give information about distinguishing gene locations, but the Khan Academy page you got this from goes on to describe further crossings using the flies created in the first crossing, and those crossings can show information about gene locations.
On difference (2): I would take the genes being "together on a chromosome" to mean "linked to each other", that is close enough to each other on a single type of chromosome to tend to stay together during recombination events.  Maybe you meant "on the same chromosome", but that is, importantly, a different thing.  Recombination testing cannot tell you if genes are on the same or different chromosomes; it can only tell you if they are on the same chromosomes and are close enough together that recombination will not sort them randomly, but rather tend to sort them together.  If they are far apart, the genes will stay together or be separated by recombination with equal probability, which is the result that also happens when genes are on different chromosomes.
So, the full procedure that is described on the Khan Academy page will show how much recombination you get in the fly grand-offspring.  If you get few recombinant forms compared to the number of parental forms, then the genes are close together on the same chromosome.  If you get equal numbers of recombinant and parental forms, it indicates that is not the case, and therefore the genes are either on separate chromosomes or so far away on one chromosome that recombination has full effect and randomly separates them.

Hello. I'm aware of what the rest of the article goes on to say. However, my problem is with the statement accompanying (and printed on) the illustration:  "this cross [a double heterozygote] gives us exactly what we need to observe recombination: a fly that's heterozygous for the purple and vestigial genes, in which we know clearly which alleles are together on a single chromosome." HOW could we "clearly" know from "this cross" "which alleles are together on a single chromosome"?

@Armand Thanks for the confirmation - the penny has finally dropped - not helped to do so by the ambiguous language used in the article plus the way the genes are shown as linked for both the parents and the offspring in the illustration.

@Armand If the parents are homozygous dominant and homozygous recessive for two alleles then the F1 offspring will all be double heterozygous. Crossing this offspring with a "Tester" which is homozygous recessive will give us a mix of parental and recombinant offspring (The F2 generation). By looking at the actual numbers of parental vs recombinant offspring in the F2 generation we can determine  whether the genes are a) linked (recombinant % is low) b) *on same chromosome but far apart * (yes, if the recombinant % is high) or c) on different chromosomes altogether (if recombinant = 50%).

@Armand - would you agree with that last comment?

@Naj (b) and (c) are one category -- independent assortment means either on different chromosomes or far apart on the same chromosome. In practice, one uses a "scaffold" of linked genes/markers on every chromosome to map an unknown gene. If neighboring genes in the scaffold are all linked to their neighbors, any unknown gene will show linkage to at least one of the scaffold markers.

@Armand Got it, thanks :)


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colocalization by IFA?
I have done IFA (immunofluorescence assay) previously but i targeted only one protein or i tagged my endogenous protein with GFP/RFP then i performed IFA. I want to do the IFA in parental cell line (HEK cells). My aim is to show the two proteins are co-localized or not?

Protein-1 (secondary antibody Mouse)
Protein-2 (secondary antibody Rabbit)

How can i co-localize two different proteins in the same sample? Which primary antibody do i have to add first?  can any one please suggest me something?

If you search the web or Google Scholar, you should be able to find plenty of examples of colocalization protocols.
In general, though, for each target you're going to want to have an unstained control sample (you can use one for all targets), a secondary-only sample to look for non-specific background staining, and a single-color sample (primary plus secondary). Then, you'll have a sample with both primaries and both secondaries. You always add the primaries at the same time, and the secondaries at the same time.
As you start getting into higher numbers of colors, you'll also need an FMO (fluorescence minus one) control for each color so you can test for signal bleed-over from one channel to another. This sample will combine all the colors except for one.

Thanks for your answer. It is really helpful. one more important question i have. Is there any way to do the RNAase treatment in IFA? I suspect if my protein of interest co-localization RNA based?

@Rengaraj yes, it is possible, although I haven't done it myself. Here is a ResearchGate discussion (with references) on how to do it.

@Rengaraj, also, if this answer addressed your problem, please consider accepting it by clicking on the check mark/tick to the left of the answer, turning it green. This marks the question as resolved to your satisfaction, and awards reputation both to you and the person who answered. Since you have >= 15 reputation points, you may also upvote the answer if you wish. There is absolutely no obligation to do either.


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Why are microtubules absent in and around the nucleus?

I was going through fluorescence microscopy and found these four images. The top left corner shows the actin cytoskeleton (rhodamin phalloidin) and the bottom left image shows the microtubule cytoskeleton (Alexa 488). One thing I noticed is that the actin cytoskeleton is continuous throughout the cell as seen in the top right image (rhodamin phalloidin + DAPI composite), but in case of the microtubules, the nuclear position and microtubule localization seem to be mutually exclusive. I can't seem to figure out why this might be the case, and it definitely does not look like a coincidence.

Welcome to Biology.SE! Please provide a citation/source for the images you are using — images should be credited to their creators just like quotes are to their authors. This is generally considered good practice in scientific communication and will help other users understand by providing context. ——— Please see the [tour] and the [help] pages starting with [Ask] questions for information about this site . Thanks!

tl;dr Cells are 3D and the cytoskeleton does not exist within the nucleus.
There are several things you have to be aware of when looking at such pictures. Remember, you are looking at a 3D object which is not homogeneous and uniform in depth.
Shape of a plated HeLa cells
You are looking at HeLa cells. When plated like in your case, they adhere to the plate and make a generally thin, 2D-ish shape with lamellipodia (the extruded parts that look like they are grasping the plate on the sides of each cell). The nucleus is the most prominent vertically bulging part of the cell - the nucleus takes up most of the depth of the cell and there is little space around it. You have to appreciate the vertical dimension of the cell around the nucleus especially. The shape is a little like an egg cooked sunny side up; the yolk makes a bulge and the egg white is thinner around the yolk and there is little space for large filaments like microtubules. Remember, cytoskeletal filaments are so in size: small (actin) filaments < intermediate filaments < microtubules. This is exactly why you see more actin, a little less intermediate filaments, and even fewer microtubules exactly on top or below the nucleus in these 2D plated cells. Also, please remember that these cells are not representative of most cells.
The nucleus is a separate compartment
The nucleus is also a separate compartment, within which the actin and microtubule cytoskeleton system do not exist. The nucleus performs its own structure-keeping in the form of the nuclear lamina, which is composed of protein meshes that sit on the inner membrane of the nucleus.
You are not seeing the small actin filaments inside the nucleus, only around it. The microtubules are too large to see above and below the nucleus in most cases, since they are much larger filaments. In some cases though, you would see microtubules in HeLa cells above and below the nucleus.
See picture below: microtubule stain shown in green.

Other images for a more wholesome appreciation of the cytoskeleton.
Here follow a few micrographs of HeLa cells.
In green, actin.
In red, intermediate filaments stained with vimentin:

And now here you can see a microtubule stain in HeLa cells, in pink. Notice that some microtubules do actually make it around and across the nucleus.

Just to make this point a bit clearer: There are neither actin nor MTs inside the nucleus, but both of them may be on top of the nucleus (and thus appear in the same location in an image). The explanation for the difference between actin and MTs in OP's image is the same as in the ones posted here: it looks like actin is quite uniformly distiributed also on top of the nucleus, while MTs do seem to prefer not to sit right on top of it. Judging just from these images, it seems as though the nucleus creates what looks like an "exclusion zone" for MTs but not for actin in the imaging plane.

@S Pr that sunny side up eggs analogy was spot on. Great answer, Everything's crystal clear for me now.

@MasterOfGalaxy one of the roles of the microtubule cytoskeleton is the mechanical support of the cell. The nucleus has its own specific skeleton. The nuclear lamina is a proteinaceous meshwork located on the nucleoplasmic side of the inner nuclear membrane. On the other hand, microtubule filaments allow for water and minerals transport to organelles including the nucleus from exoplasmic surface while the nucleus has not independent accessibility to exoplasmic leaflet because of its topological position. There is no function for microtubules in the nucleus.


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In plasmid-based transient transfection of mesenchymal stem cells, do I have to select for transfected cells and verify GOI expression via a reporter?
I am transiently transfecting mesenchymal stem cells with a mammalian plasmid-based expression vector that does not contain any mammalian selection marker and also the gene of interest will be cloned into the plasmid without co-expression of any reporter fusion gene.
I would like to know if selection or expression testing is good practice and mainstream in plasmid-based transient transfection of mammalian cells.
As you know, in bacterial transformation selection against non-transformed bacterial cells is mandatory so that transformed bacterial cells could be enriched for. I wonder whether this is also mandatory in plasmid-based transient transfection of mammalian cells. If not, then  would not non-transfected cells outgrow transfected ones and affect downstream cell-based functional assays? Also, do I have to measure transfection efficiency beforehand? Also, how long can a mammalian cell retain a plasmid without a mammalian selection marker? Thank you.

Welcome to the site. This is not a tutorial site to explain every aspect of a given paper, concept, or idea. I strongly suggest you take the [tour] and carefully read through the [help] to learn more about the site, including what is on-topic and what is not, and how to ask a good question. Please try to narrow down your question to one specific question instead of trying to get a tutorial on transfection as a whole.

You will need to verify the quality and extent of your transfection reaction after 24-48 hours, depending on the cell line and the promotor. The most common methods in situations where you don't have any reporters are Western blot, immunofluorescence staining, and flow cytometry. The best way to do this is to have a good antibody to the target. If you don't, make sure your construct includes an N- or C-terminal epitope tag like Myc, FLAG, HA, or something similar so that you can detect your protein of interest.

No and Yes, respectively.
No, you don't need to select for and generally you can't unless you use something like FACS to sort and capture cells expressing your marker/GOI.
Transient transfections are generally not selected for, if they are it is to make a more stable line, and either requires integration of the marker site into the genome (most common) or in some cases use of a plasmid and cell system that supports propagation of the plasmid.
With transient transfection it is normally a good idea, at least for the first few times, if not every time, to confirm that the plasmid is expressing as you expect. You can do this by immunological methods, most commonly western blotting (WB) or immunocytochemistry (ICC). ICC will give you a percentage transfection, which can be useful in assessing how well you would expect an overall population to respond to a stimulus and how well you can see that effect - what if only 10% of your cells transfected compared to 50% or 90%, but it can't tell you how much protein is being expressed. ICC can also tell you how cytotoxic your transfection is - commonly used methods like the Lipofectamine family of reagents are often in the 20-50% cell-death range. WB on the other hand gives you how strong your expression is - how much protein is there, but it can't tell you how many cells were transfected or how much cell-death there is.
In theory at least FACS can do both number and strength, but it would require calibration of your antibody by both western and ICC.
You can also look at RNA expressed off the plasmid, but this may not bear a direct relationship to how much protein is produced.


text:
Why do more complex DNA strands take longer to anneal?
My textbook and several websites told me that more complex DNA strands take longer to anneal because it is harder to find the correct sequence. A simpler, repeating sequence like ATATATATA would be faster to anneal since it would be really easy to find a spot to reanneal.
I kind of get the logic here. Yet, this wouldn't always produce the exact complement strand. Consider the following:

The point is to illustrate that there could be matching of strands without correctly pairing the whole thing (i.e. there is a shift of the strands).
This means that if you want to fully anneal the strands, you need to find the correct spot in the second strand to begin annealing - which is no better than if these were completely random strands.
So then, why is annealing more complex strands slower? I've tried searching it up on the internet but all I get are people telling me it is true, and without much of an explanation.

What is meant by complexity?
This is the crux of the matter. As is often the case, confusion arises from not defining one’s terms. In this case the three possibilities we have to deal with are:

The extent to which a sequence is not homogenous. e.g. ATATATAT is more complex than AAAAAAAA; ATGCATGC is more complex than ATATATAT; ATGCTCAG is more complex than ATGCATGC. This would seem to be the definition assumed by the questioner (and also in another answer).
The length of the sequence. This is the definition given in the article quoted in the question: “Complexity - the total length of different sequences”. However it is not one that would have occurred to me on encountering the expression.
The extent to which a length of DNA (especially a chromosome) has regions present in multiple copies. This seems not to have been considered in the question, but is historically what was meant be genome complexity deduced from annealing experiments. This is shown in these extracts from a venerable text on nucleic acids, long out of print:

In human cells unique DNA has a Cot1/2 value of 1000 (moles nucleotide seconds litre–1)…Repetitive DNA includes all of the rest of the DNA. Among the repetitive DNA is a fraction which reanneals with a Cot1/2 value of between 100 and 1000…thought to be those [genes] coding for proteins which form major structural components of the cell… [some examples given of multi-copy genes like tRNA]…At the other extreme DNA with a Cot1/2 value as low as 10–3 consists largely of satellite DNA.

Explanation for Case 3 (complex because of multiple copies)
As the article quoted in the question correctly states, “Renaturation, or duplex formation requires random collisions between two single-stranded molecules”. However such experiments are performed with fragmented DNA in solution, not whole chromosomes, so the two molecules that must collide are gene-size or smaller in length. The more copies of geneX or sequenceX there are in the DNA, the greater the number of targets there will be containing geneX or sequenceX, hence the faster fragments containing such genes will anneal.
Explanation for Case 1 (complex because less homogeneous)
Assuming that one is comparing single-copy genes of different degrees of homogeneity, one can imagine that for something like ATATATAT there will be more ‘hits’ that give partial overlaps that may be detected as double-stranded:
A T A T A T A T A T A T A T A T
        T A T A T A T A T A T A T A T A

Alternatively, once bound imperfectly, there may be a greater chance of perfect binding after dissociation because of proximity.
However, it is also possible that writers are confusing or amalgamating this with Case 3, as simpler DNA may actually be present in more copies in a genome.
Explanation for Case 2 (complex because longer)
The explanation here is trivial. If you perform reannealing experiments with two geneomes — E.coli (4.6 Mbp, c. 4000 protein-coding genes) and Carsonella ruddii (0.16 Mbp, 182 protein-coding genes) — employing the same amounts of DNA as 300bp fragments, the fragments from the smaller genome will be approximately 20x more likely to make a random collision with a complementary fragment.

You are correct that this won't produce the correctly complemented strands 100% of the time, indeed that's what the experiments outlined in the link you provided are aiming to measure... how much DNA is duplexed over time after a single denaturation. Simple sequences will be so alike that an approximation of 100% can anneal fairly quickly, as measured by the sensitivity of the experiment (A260/A280 is not sensitive; these things are done much differently these days).
The information that you are missing is that if those bits in the middle don't match then the DNA there won't anneal - it will still exist as single-stranded or partially duplexed strands and so will show up as not 100% annealed in the assay. To fully anneal you need full complementarity of the strands, and given that you have a single denaturation, the chance that 100% will anneal in any given aliquot is low, so in a rate equation it will take a "long" time.
I'm no chemist or physicist, so if someone has a better answer, please feel free to chime in.

From physics point of view, annealing is about finding the lowest energy configuration of the two DNA strands. The configuration space of two strands has many minima, and after every annealing cycle the strands settle in one of them, which is not necessarily the lowest. One then reheats the DNA to allow it to explore teh surrounding minima and hopefully settling in the one of even lower energy. In a sequence containg many identical elements, such lower energy minima a readily available - in the OP language it would be really easy to find a spot to reanneal. On the other hand, if the sequence elements are very different, a large deviation from the current configuration is required to find a more favorable configuration.


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Structure coordinates in PDB file
How the atoms-residues-amino acids structure information prioritized? Specifically, I am trying to understand how the first atom is identified since it does not seem to correspond to the first amino acid in the sequence. I assume it's based on the structure but is it bottom to top, left to right and such. Thank you.

Welcome to SE Biology. I have provided an answer to what I think is your question, but it would have been better if you could have referenced a PDB file so it was clear what you were referring to. And what do you mean by bottom, top, left and right? These terms have no meaning in relation to protein structure.

The atom numbered 1 in a PDB file is the most N-terminal atom visualized by the structure determination — the most N-terminal amino acid, the co-ordinates of which are presented.
If the X-ray diffraction (or whatever technique used) does resolve the first amino acid, then it will, in fact, be the first amino acid. The following is an arbitrary example from 4LI3 (chain X):
ATOM      1  N   MET X   1     100.329 111.822  35.431  1.00 66.81           N
ATOM      2  CA  MET X   1     100.845 110.803  34.526  1.00 67.85           C
ATOM      3  C   MET X   1     101.961 111.361  33.646  1.00 71.14           C
ATOM      4  O   MET X   1     102.979 111.840  34.153  1.00 77.28           O
ATOM      5  CB  MET X   1      99.722 110.256  33.648  1.00 61.70           C
ATOM      6  CG  MET X   1      98.581 109.621  34.412  1.00 66.53           C
ATOM      7  SD  MET X   1      97.327 108.947  33.300  1.00 82.23           S
ATOM      8  CE  MET X   1      96.071 108.440  34.463  1.00 69.80           C
ATOM      9  N   ALA X   2     101.754 111.288  32.331  1.00 67.01           N

If the X-ray diffraction does not resolve the first amino acid, or if the structure is of a fragment of the protein that does not contain the first amino acid, then the co-ordinates of the first amino acid are not listed in the file and ATOM 1 is from the most N-terminal amino acid. The following is an arbitrary example from 1BTE (chain A), starting at amino acid 7:
ATOM      1  N   GLU A   7      15.888  47.505  13.150  1.00 34.63           N
ATOM      2  CA  GLU A   7      14.455  47.789  12.947  1.00 33.53           C
ATOM      3  C   GLU A   7      13.538  46.900  13.794  1.00 29.91           C
ATOM      4  O   GLU A   7      12.342  46.832  13.476  1.00 30.54           O
ATOM      5  CB  GLU A   7      14.084  47.715  11.461  1.00 37.16           C
ATOM      6  CG  GLU A   7      14.284  46.356  10.821  1.00 41.38           C
ATOM      7  CD  GLU A   7      14.124  46.366   9.313  1.00 43.82           C
ATOM      8  OE1 GLU A   7      12.959  46.323   8.849  1.00 46.10           O
ATOM      9  OE2 GLU A   7      15.151  46.420   8.600  1.00 45.51           O

(Knowledge of the first amino acid is from nucleic acid sequencing.)


text:
How do brains find the peak/valley point on a curve?
It costs O(n) time if we find the max/min value in a number sequence with the length n. However, if we convert this sequence into a curve and find the peak/valley point with our eyes, it will use much less time.
How do brains complete this computation? I guess the visual nerve process it in divide-and-merge, like breaking down the curve into small segments and comparing them in parallel. If this is correct, what is the size of the smallest segments?

You might read about orientation selectivity in V1. Receptive field size is a function of distance from the fovea.

The question is not very precise: e.g., if we know that there is only one minimum, and the sequence is sorted as it is for plotting a curve, then finding its minimum is trivial and rather quick. If there are multiple minima, then indeed we need to go element by element and we will find a minimum in $O(n)$... but we can do it in $O(n)$ for sequence of any length, whereas the eyes can do it only for finite resolution and finite picture size.