text:
What is an attenuated virus? What is done to it?
UPDATE: So it looks like I've asked a complex and technical question. I'm going to take @tyersome's advice and study Khan Academy on immunology, and from there, formulate a better question if I still need to.

The question is based on the idea that often a vaccine is based on an attenuated virus.
My understanding is that a virus isn't really alive in the same way a bacterium is, which means they can't be killed. (Correct?)
My (layman) understanding is that a virus is simply DNA or RNA wrapped in a thin layer of protein. (So is it a lifeform, or merely an extremely complex set of molecules?)
I can imagine it's possible, chemically, to obliterate the molecular structure of a virus, and thus "kill" it, but would such a "debris soup" be useful in a vaccine?
I'm not asking how a virus is attenuated, ie, by growing it in certain cultures. I'm asking what it actually means for a virus to be "attenuated"? What's different about it, yet still makes it usable as a method to teach the immune system?

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@tyersome  Well, everything that came up when I googled described various techniques as to HOW a virus is attenuated. But nothing in layman's terms as to what it actually means for a virus to be "attenuated", except "less virulent."  So why is it attenuated and not, say, just a different strain or variant by that time?

What is the distinction you are trying to make between "attenuated" and "different strain or variant"? Have you read the Wikipedia page? ——— You seem to have multiple questions some of which (especially the last) are much too broad to be answered here. (Both of these are also criteria for closing questions.) ——— To really understand this take/read an introduction to immunology and then post any questions you still have. I have found Khan Academy a good introduction.

Presumably, attenuated means it can't replicate.

@DKNguyen - not necessarily, it may be able to enter and replicate part of the virion, but not the whole genome or perhaps has a packaging defect. Polio and MMR vaccines are both attenuated. The polio one in particular is known to occasionally cause infection and be passed on to others.

@Stewart - you can edit your post with the things that you have found out about how attenuation is done and how this helps make it less pathogenic. Note that the exact mechanisms are specific to the type of virus. Killed (inactivated) vaccines have the structures preserved by fixation or by chemically damaging the genome so that it no longer functions, but proteins remain intact.

@tyersome Thanks for that top tip on Khan Academy

@DKNguyen  That would be a clear well-defined distinction. If factual, that would be the answer I'm looking for.

@bob1 What I found about HOW attenuation is done were long, varied descriptions. None of them went on to explain what actually happens to the virus itself to make it less pathogenic, yet still workable for immune system response.

@Stewart - that's because what happens to the virus is specific to the group of viruses - so changes in an influenza would be different to a coronavirus for example.

@bob1  OK. That sounds like the beginnings of an answer. Does that imply that the functional definition of "attenuated" is different for each group?  And that "attenuated" is therefore a colloquialism for the layman?

Lower entry/binding to receptor, lower replication, lower pathogenicity, sometimes missing proteins or parts of it, and with genetic engineering more curious changes such as lesser replication fidelity, sub-optimal TRS, some proteins replaced by other ones (influenza reassortant). Historically it was just about finding a closely related virus circulating in a different species, giving fewer symptoms and cross-protection (vaccinia virus). Then it was about selecting passaged viruses giving fewer plaques in lab, adapting to eggs so that after enough passages it replicates poorly in human cells.

@Stewart As reuns notes, "attenuated" doesn't really have a technical definition, it just means giving fewer or less severe symptoms while still provoking an immune reaction that will provide protection against the strain(s) of interest.


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Can anyone identify this locust / grasshopper in Florida, USA?
Found in Pinellas County, Florida

Can you add some more details about it, including some more images?

Two more images added. What other details would you like?

@Stewart, for future reference, see the guide for writing species ID questions -- it'll tell you what info is typically needed. Thanks.

It appears to be Romalea microptera, or the eastern lubber grasshoper, which is commonly found in the southeastern United States, including Florida.
https://en.wikipedia.org/wiki/Romalea_microptera
http://eol.org/pages/965190/overview

Photo by Steve Smith.

Eastern lubber , although they get to TX. Kill it , if you have any bulb type flowers . They will eat all amaryllis leaves and the whole bulb. The immature are gloss black with red pinstripes ; so pretty I made the mistake of not killing them one year.

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sort: votes


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Can we modify plasmids to eliminate antibiotic resistance?
This is a very general question regarding the potential use of artificially modified plasmids to block or modify bacterial evolution in order to prevent development of anti-biotic resistance.
For example the most crude example might be to modify a suitable plasmid with some gift for target bacteria but also to add a mechanism to block their pili after delivering the gift.
If this general principle was practical, there would be huge scope for ingenious biologists to create a plethora of ideas to modify bacteria is many ways to our advantage, even to the extent of creating completely artificial plasmids for such purposes.
My question is twofold: Is this likely to be a valid approach for modulating bacterial evolution to our purposes, and if so, is there already research in progress in this field?
Of course, in the example above, the gift from the plasmid must significantly benefit the plasmid and the poison mechanism must be delayed long enough for a large % of the biofilm-bound bacteria to be infected by the poison before it takes effect. Also the plasmids must include their own slow-acting poison/fast acting antidote to deal with reproducing cells that do not contain the plasmid. A more subtle approach might be a gift that did not kill the bacteria but modified them to be significantly less virulent.

You can't artificially modify bacteria, decreasing their fitness in an environment containing antibiotics, and then expect them to outcompete bacteria which resist antibiotics.

This will not work since having these bacteria means a strong advantage over bacteria which do not have them, so they will be overgrown.

@Chris Unless the antibiotic in question is currently present, there's no competitive (dis)advantage. Evolution happens without foresight, so it's entirely plausible that a deleterious mutation might sweep a population under neutral conditions. This mechanism is plausible enough that similar mechanisms were (are being?) researched to outcompete wild strains of viruses.

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short-day facultative plant, abrupt change to photo period
What happens biologically to a short-day facultative plant that is a few weeks into its short day (flowering) photo period and is suddenly given a 24 hour period of light? Does anything change biologically after one day of long day (vegetative light) or  will it take several days?
If there is any biological change that is of concern what can one do to try and alleviate any negative affects?
https://en.wikipedia.org/wiki/Photoperiodism -

Please clarify - which plant are you speaking of, and are you saying it is already flowering - decided to answer anyway.

Yes in this example the plant is already a few weeks into flowering photo period. I am curios in regards to the entire class of short-day facultative plants.

There is nothing you can do to alleviate the effects of (presumably) gro lights being left on for a 24 hour period. It's not likely to have a particularly dramatic effect on a short day facultative plant, other than to possibly delay flowering - it may mean the plant has to go through an extra period of darkness at nights for a longer time, in other words, the process of flower induction may need to start all over again. Or, as it was just one night, you might get away with it and they'll flower when you expected them to. The biggest interruption in the flowering process of a short day plant is a few minutes light in the middle of a period of darkness over successive nights, but either way, it's not possible to offset that 24 hour period of light in any way. I'm afraid you'll just have to wait and see, but in the meantime, maintain the growing routine with regard to lighting.


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Distribution of Exon and Intron Sizes
Goal
I am trying to get a distribution of Exons and intron sizes in Three-Spined Stickleback (Gasterosteus aculeatus).
Data downloaded
I downloaded some data from Ensembl. More precisely, I went there, selected "structure" in the "attributes" and selected
- Ensembl Gene ID
- Exon Chr Start (bp)
- Exon Chr End (bp)

There are two issues in these data:

Some exons end before they start
Some exons overlap

The first point is I assumed due to reversion, so I just reversed the start and end position when there was such inversion. The second point is more problematic as I don't know what such overlap could mean realistically.
Can you help finding the distribution of exon and intron sizes in three-spined stickleback?

What I did with the above data
Preparation
# read data

d = read.table(file.choose(), header=TRUE)
names(d)= c("ID", "start", "end")

# Reverse exons when needed

toReverse = which(d$start > d$end)
s = d$start[toReverse]
d$start[toReverse] = d$end[toReverse]
d$end[toReverse] = s

Exon Sizes -> Looks good
# Exon sizes

ExonSizes = d$end - d$start
hist(ExonSizes) # Cool, looks good!

Introns Sizes -> Looks bad
# Intron sizes
# This is a little slow. One might have better to switch to C++.
# The idea is to look at the distance between the end of an exon and the start of the next exon within each gene.

d$ID = paste(d$ID)
IntronSizes = c()
uniquegenes = unique(d$ID)
nbgenes = length(uniquegenes)
i=0
exon = 0
for (gene in uniquegenes)
{
    i=i+1
    cat(paste0(i," / ",nbgenes,"\n"))

    while (TRUE)
    {
        exon=exon+1
        if (d$ID[exon] == gene) {
   IntronSizes = append(IntronSizes, d[exon+1,]$start - d[exon,]$end)
        } else
        {
            break
        }
    }
}
hist(IntronSizes) # There are negative values. I don't know how to interpret them.

hist(log(abs(IntronSizes))) # That looks good but I doubt it makes much sense!

When it looks like an exon ends before it begins, thats because its on the minus strand. Strand information should also be in your gtf file, so you can check for this just in case. As for overlapping, sometimes an exon has multiple splice sites. Also, genes can overlap. Both cases would cause exons to overlap. Nothing weird going on there, you can include them without worry

At the command line:
wget ftp://ftp.ensembl.org/pub/release-84/gtf/gasterosteus_aculeatus/Gasterosteus_aculeatus.BROADS1.84.gtf.gz
gunzip Gasterosteus_aculeatus.BROADS1.84.gtf.gz

Then in R:
library(GenomicFeatures)
txdb = makeTxDbFromGFF("Gasterosteus_aculeatus.BROADS1.84.gtf")
hist(log10(width(unique(exons(txdb))))) # exons
hist(log10(width(unique(unlist(intronsByTranscript(txdb)))))) # introns

Note that some of the annotated exons (and the introns between them) are incredibly unlikely to be correct. For example, there are 1 base long exons and introns. I don't think anyone actually believes those are correct, but the distribution is probably otherwise approximately correct.
Edit: For what it's worth, there are no negative sized exons in the GTF file. My guess is that the overlapping exons are from different transcripts (or genes on opposite strands).
Edit2: If you want to get your introns from a "union gene model", then use something like reduce(exonsBy(txdb, by='gene')) and then lapply gaps() to that. The results will be correct and the process will probably take less time than what you've been trying.

Perfect +1 Thanks a lot! For future users, please note that GenomicFeatures is a bioconductor package and that with MAC OSX wget must be replaced by curl

May I ask you to further explain your second edit? What is a "union gene model"? Do you mean that I might want to get a distribution of per-gene average exon length?

The explanation of a union gene model ends up being a bit long for a comment. Have a look at the right part of subfigure A here: http://www.nature.com/nbt/journal/v31/n1/images/nbt.2450-F1.jpg The "exon-union model" is the same thing as a "union gene model". In this case, you're still getting the distribution of all introns in the genome, you're just redefining "intron" to not be something biologically correct (though this redefinition can be useful for some purposes).

Oh sure I got it. I did not thing of that. I will need to go with a union gene model for my purpose. I'll try to get that as soon as I'll understand what kind of objects I am dealing with!


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DNA modifications other than 5-mC/5-hmC/5-fC/5-caC in vertebrate genomes?
Other than 5-Methylcytosine and the more recently discovered 5-Hydroxymethyl, 5-formil and 5-carboxylcytosine DNA modifications found in DNA sequences, what are other DNA modifications present in vertebrate genomes? There is apparently a whole bunch of modifications that can be found in prokaryotic and eukaryotic genomes, but I would like to know which ones have been found relevant in vertebrate/mammalian genomes of the eukaryotic ones.

There can be other modifications resulting from DNA damage (oxidising agents etc). Those may include Xanthine and Hypoxanthine (caused by chemical deamination of guanine and adenine).
There are no other known modifications resulting because of a physiological process.


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Why doesn't enzyme reaction rate rise linearly with substrate concentration?
This is the graph of the Michaelis-Menten equation which describes the relationship between reaction rate and substrate concentration:

I don't understand why it is hyperbolic. Intuitively, I would think that it would be linear.
Here is my logic for a specific scenario:
Let's say enzyme $E$ converts substrate $S$ at a rate of $1 S$ per second. Now let's say we have a container with 1000 enzyme E's. And there is a tube connected to the container that pumps in $10 S$ per second.
Logically, in the one second, all 10 substrates should be catalyzed. The rate of reaction would be $10/s$. Now if we increase the pump to 500 substrates per second, you would think that all 500 substrates would be catalyzed in one second, making the rate $500/s$. Thus, a linear relationship.
The formula for rate of enzyme-substrate complex formation is also linear:
$$\frac{d[ES]}{dt} = k[E][S]$$
What am I missing here? I am looking for a mechanistic explanation (diffusion, dissociation, etc)
Edit: I've done some more thinking and I have a potential explanation. Could it be that, as the concentration of substrate goes up, the probability of a substrate interacting with an unbound enzyme goes down.
In my scenario, the first substrate has a 1000/1000 chance to find a free enzyme, the second substrate has a 999/1000 chance of finding a free enzyme, etc.

Removed the comments here. If you want to answer the question, use the answer box please.

I also use the analogy of a drive thru for my students. if you have X drive thru windows increasing the number of cars puts more car through the drive thru up to the point until all of them are full at which point adding more cars does not move cars through the drive thru any faster. The closer you get to all the drive thru's being full the less adding more cars matters.

Disclaimer
This answer is a conceptual explanation, as appears to be the request, rather than a repetition of the derivation of the Michaelis–Menten equation, which I feel inappropriate, partly because it can be found elsewhere, e.g. in Wikipedia and Berg et al.. I appreciate that, like the derivations on which it is based, some of its assumptions do not always hold, or do not hold for all enzymes, and that some with an expert knowledge of enzyme kinetics will feel that it would be better to say nothing than present a conceptual simplification. Although I welcome corrections — I am not an enzymologist — but disagree with this point of view.
Summary
The reason that increasing the substrate concentration of an enzymic reaction does not increase the reaction rate beyond a certain maximum (Vmax) is that the reaction rate is dependent on the concentration of enzyme-substrate complex, not its rate of formation. Increasing substrate concentration only affects the reaction rate in so far is it affects the proportion of enzyme molecules involved in enzyme–substrate complex, which reaches a maximum when all of the enzyme is in such complexes.
More detailed explanation
The question presents a rate constant for only one of the reactions involved in an enzyme-catalysed reaction. In fact there are four that must be considered: the forward and backward reactions for the formation of a complex, ES, between enzyme (E) and substrate (S); and the forward and backward reactions for the formation of product (P).

The Michaelis–Menten treatment considers the initial velocity of reactions, at which time little product has formed so that it is assumed that the back reaction from product can be ignored, simplifying the situation to a consideration of the rates of three reactions:

A key assumption — the steady state assumption — in Michaelis and Menten’s original derivation is that there is that there is a considerable excess of substrate over enzyme so that the substrate is in instantaneous equilibrium with the ES complex. So, in effect, at a particular concentration of substrate the concentration of ES is constant. The implication in the question that the rate of formation of this complex would determine the reaction rate is incorrect.
The rate of the reaction depends only on the ES concentration and k2, the rate constant for the formation of products, also known as kcat, which is a characteristic of any enzyme and can be thought of as representing its catalytic capability:

d[P]/dt = kcat[ES]

What the substrate concentration does affect is the position of equilibrium for the formation of ES from E and S, i.e. how much enzyme is converted into complex. However there is obviously a maximum, approached asymptotically, equivalent to the total enzyme present. This explains† the hyperbolic nature of the curve for reaction rate.
The concentration of substrate needed to reach this saturation (more easily measurable as that to achieve half maximum initial velocity) is another characteristic of the enzyme (and substrate) and can be thought of as reflecting the affinity of the enzyme for substrate (the dissociation constant). The hyperbolic nature of the rate curve is similar to that for binding of small molecules to non-catalytic proteins such as the oxygen-carrier, myoglobin, and hormone receptor proteins.
Just as not all proteins in this latter class (e.g. haemoglobin) show simple hyperbolic binding of their ligand, so not all enzymes (e.g. allosteric enzymes) show hyperbolic kinetics. But that’s another story, which has been told many times on this site.

† I have given a more extensive explanation of the hyperbolic nature of the curve in an answer to a previous question, which I must admit I had forgotten about.

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Human Digestion of Cellulose?
Most animals can digest the cellulose in grass because of the anaerobic bacteria called Fibrobacter succinogenes living in their rumen (gut). The bacteria produces the enzyme cellulase and is therefore able to break down the β-Glucans(1,4) glycosidic bond in the cellulose. Animals like cows and giraffes grow up to be very large simply on a diet of grass!
Is there a way for humans to ingest the Fibrobacter succinogenes bacteria and be able to digest cellulose?

Animals can digest the cellulose. Most animals (incl. humans) cannot. The one who can are only thanks to this symbiotic relationship with a anaerobic bacteria that you describe.

@Remi.b I think the question was centered around whether or not humans could ingest the cellulolytic bacteria and attain that symbiotic relationship/ability.

@HarrisWeinstein Sure. I just wanted to highlight this point which might have been of confusion for the OP.

The few animals that can digest cellulose have to have HUGE guts to provide enough space for enough bacteria to be worth it, or they have to do the rabbit thing and eat their feces to run it through multiple times. Cellulase is slooow.

This is the type of question that could rather go on WorldBuilding.SE rather than on a science website.

This isn't a complete answer, but searching Google Scholar for "Fibrobacter succinogenes human" I found this paper:
The cellulose-degrading microbial community of the human gut
varies according to the presence or absence of methanogens
The abstract of which begins this way:

Cellulose-degrading microorganisms involved in the breakdown of plant cell wall material in the human gut remain rather unexplored despite their role in intestinal fermentation. Microcrystalline cellulose-degrading bacteria were previously identified in faeces of methane-excreting individuals, whereas these microorganisms were undetectable in faecal samples from non-methane excretors. This suggested that the
  structure and activity of the cellulose-degrading community differ in methane- and non-methane-excreting individuals.

Note also in Table 1, Fibrobacter succinogenes is one of the strains that seem to have been detected, so that answers the part of your question that seems to ask if humans can incorporate those bacteria into their gut fauna: yes, some already do.
This suggests to me that:

some humans do have a limited ability to digest cellulose through their gut bacteria, though this ability is probably limited enough that "humans can't digest cellulose" can still be considered true in the large scheme of things,
the challenge in digesting cellulose isn't so much getting the gut bacteria in there, but providing them with an environment where they will thrive and digest a lot of cellulose. This is probably where the specialized gut structures of ruminants come in; it becomes not so much a matter of digesting cellulose, but of digesting it well enough that you can live off of it. I'm guessing this is also related to the differences the paper found between people who excrete methane and those who don't but I wasn't able to figure out what that was supposed to be related to (is it a cause or an effect of the presence of certain gut bacteria? is it related to diet, genes, something else? I couldn't find that information in the paper though I didn't read every word)
according to the paper there isn't much research done into this.
the paper gives no info as to how significant those cellulose-digesting bacteria are to the host's energy balance, which I imagine is what you're really interested in. Is it significant, is it too small to be noticeable, does the host even benefit at all calorie-wise? They don't address that question in the paper.

In this article it later says:
"For example, in the work of Van Soest's group10 11 in which healthy
volunteers were fed controlled diets with the addition of cellulose from
either cabbage, bran, or
a purified cellulose (Solka Floc), average cellulose
digestibility was 74% on the control diet, 75% in the cabbage, about 53%
in the bran but only 25% from the Solka Floc."
This is to illustrate how naturaly sourced cellulose is way more digestable than a chemicaly isolated one. So the answer is that cellulose found in vegetables is 53%-75% broken down. The article also mentions that it seem to vary between people.
The Human digestion system is very much similar to this of primates. In fact, a noticable difference is that the human gut is actually relatively longer. Primates feed on huge amounts of cellulose. It seems obvious that it is energetically beneficial for them. An adult male gorilla will eat up to 30kg of plant matter a day!
So yes, without independetly breaking this stuff down, we seem to do fine with a little help from our friends. Their big contribution is not surprising considering the fact that more than 80% of the cells in the human body does not carry his genes (most commonly because it is bacteria, but sometimes because it is fungi). Methane btw, is just one of three common byproducts of the bacteria breaking cellulose down. That is why cattle growing has a severe adverse effect on atmospheric ch4 (extremely potent green house gass).

This doesn't seem to answer the question. Can you please [edit] your answer so that it addresses the exact question?

Please don't copy and paste swaths of text. Instead, extract the relevant information to answer the question and combine that with other sources to generate your own synthesis of the solution to the question at hand.

Thank you for your answer, you gave some very interesting information! :) @Krugo

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text:
What exactly are genes, DNA and chromosomes. How do they relate to each other and what is their function?
So I am new to biology and I have been reading, and searching the web, but I couldn't understand the whole framework of a cell. So as I understood:
A DNA, is a double chain of Nucleotides (A,T,G,C and a sugar) and a phosphate residue. Some parts of a DNA code a gene and some do not, exons and introns accordingly. As I understand it these the sequences of exons hold genes and the whole sequence is called genome. Now I read in the book Introduction to Computational Molecular Biology by J. C. Setubal and J. Meidanis. that I cite Each cell of an organism has a few very long DNA molecules. Each such molecule is called a chromosome. So each X shaped thing called a chromosome holds one DNA molecule?
Or is each "hand" of the chromosome one complete double helix of DNA so we have 23 pairs of such chromosomes? or is it that each we 23 pairs Xs?
Shouldn't we have just one uniquely identifiable DNA sequence? I am asking this, because I have also read that each chromosome encodes a different part of one's genome. Does this mean we have several DNA sequences?
This is really bothering me, since I can't figure out the correct structure of a cell.
EDIT:
Now if each chromosome contains a pair of DNA helices. We have 46 chromosomes so overall within the cell there are 92 DNA sequences? All of which are the same? Or are they different? If they are different how do they code for different aspects of one's genome?

Your understanding of DNA is correct. DNA is a heteropolymer of monomers called nucleotides and each nucleotide is made up of a sugar (deoxyribose), phosphate and a "base" (A, T, G, C).
As you already know, DNA is organized as a double helix with the Watson-Crick pairing rules.
A chromosome contains a single DNA double helix. Many organisms have multiple chromosomes which are very different in their DNA sequence. In diploid cells (such as the somatic cells in humans), each chromosome has two copies. The two copies are almost identical and are called "homologous chromosomes". As you know each parent contributes to one of the homologous chromosomes.
During eukaryotic cell division, each chromosome is duplicated. The duplicates (or sister chromatids), are joined at a region which is called the centromere. Centromere is an attachment point for spindle fibres that pull the sister chromatids apart.
Each chromosome has multiple genes. A gene is a region of DNA that is transcribed i.e. it can give rise to an RNA (which can either be a mRNA that in turn codes for proteins or a non-coding RNA). There are other regions of DNA that are not transcribed and these are generally referred to as intergenic regions. In many eukaryotic genes some parts of the transcribed RNAs are removed as a result of splicing. The retained parts are called exons and the removed parts are called introns.
Some organelles like mitochondria and chloroplasts also have a unique genome within themselves. A genome is the entire set of chromosomes including the homologous chromosomes and the organellar chromosomes.
However, when talking about a reference genome, which is just a bioinformatic information and not an actual genome, we just have one sequence for each unique chromosome (for example, in humans, chromosomes 1-22, X-chromosome, Y-chromosome and the mitochondrial DNA). A reference genome is used as a basal set and in a population genomics study, differences from the references are highlighted instead of presenting the entire sequences.

Picture source: https://cnx.org/contents/9TxHOD3O@4/The-Nucleus-and-DNA-Replicatio  Note that paired chromosomes (X shape) are found only during replication. Usually the chromosome is like a single thread. Chromatin is the name of the assembly (for e.g., we don't say chromatin-21).

Bottomline: We have 46 chromosomes then it means we technically have 92 DNA sequences. However you should consider the double helix as one entity (or a metamolecule) because the strands always stay together. For more details you should read a book. Genes by Benjamin Lewin and Molecular Biology of the Gene by James Watson et al. are good books on this topic.

By saying "Many organisms have multiple chromosomes which are very different in their DNA sequence." You mean that each of my 22 (3) chromosomes has a different DNA helix? Or did you mean that different species have different helices? I'm assuming the former (well obviously the latter is also true) given what you wrote about reference genome, but I am still under the impression that all organism should have just one DNA sequence.
I am sorry, my English isn't very good.

@VahagnTumanyan Yes each chromosome has a different DNA helix. Different species have different number and composition of chromosomes and within each species the chromosomes will have different sequence.

I'd suggest adding some images to illustrate this answer - it's the sort of problem some people will understand better with pictures.

@arboviral Done

@WYSIWYG and now upvoted! :)


text:
Why do some bacteria have an asymmetric replication?
Our teacher said that Bacillus subtilis has an asymmetrical replication fork.
I know that this happens only in some strain of the bacteria . She asked us to find an explication for this mechanism , but all that I was able to find is that the chromosome is more compacted in some areas, therefore it slows the replication .
Are there other reasons?

Are you sure that your teacher was not referring to asymmetric replication as in asymmetric cell division? B. subtilis undergoes asymmetric cell division, whereby the two daughter cells have different fates, in this case during sporulation. Are you at a college level? If so, then there is this article. It might be a little too advanced if you are not at the college level. If it was in fact replication, then it is likely that she wants you to explain leading and lagging strand synthesis.

The replication fork itself is always asymmetric. This is true for all organisms and is due to the fact that DNA polymerases can only add nucleic acids to the 3'-OH end of DNA. This leads to different mechanisms for the replication of the so called leading and lagging strands. You can find more details on wikipedia https://en.wikipedia.org/wiki/DNA_replication
Most Bacteria have circular chromosomes that they usually replicate using two replication forks. Both forks start from the origin of replication and progress in opposite directions to meet at the replication terminus which is opposite to the replication origin on the circular chromosome. Each replication fork replicates about half of the chromosome. We talk about the left replicore and the right replicore. I assume that your teacher was referring not asymmetry in the replication fork itself, but to asymmetry in the size of the replicores. In B. subtilis the left replicore is 196 kb longer than the right replicore (J Bacteriol. 1993 Feb; 175(3): 741–749.). I do not know whether there is any evolutionary explanation for this, but mechanistically it is caused by the fact that the terminus of replication is not placed opposite to the origin of replication on the circular chromosome.


text:
Why do we breath oxygen?
I like this contradiction. But seems like my Biology teacher doesn't.
If oxygen oxidates us, why do we breath it? Why can't we evolve into a new kind of human being that breaths other component of air? Following the need of all species to survive longer.

There is no other air.

@AliceD I think he means another constituent of the atmosphere, IE nitrogen or carbon dioxide.

@jzx - and walk with a fractional distillation column on your back? Now that's evolution

I can answer this question so if anybody wants to close, please first let me answer

Oxygen is the only reactive species in a large enough quantity in the atmosphere with which to catalyze biochemical reactions. Life is a compromise shaped by natural selection and evolution in a particular environment. Without reactive species everything is inert and Life cannot happen.

When life evolved, first came heterotrophs that obtained their organic molecules from their environment. Then came autrotrophs who made their own organic lecyles. The most important autotrophs are photosynthetic organisms. These photosynthetic organisms lived in the water and made glucose from CO2 and H2O. A byproduct of this reaction was O2.
Now, O2 killed off many of the species since it is very reactive. But the remaining species used the O2 for cellular respiration. Before, the cellular respiration was done anaerobically, without oxygen. The way this wroked was that the glucose was oxidated and the electrons from the glucose was used to make ATP, the main source of cellular energy. Once those electrons were used, they were deposited in a highly electronegative element compound, such as Nitrogen containing compounds. When plants produced O2, instead organisms doing anaerobic respiration, since oxygen is an extremely electronegative element, organisms deposited the electrons in the oxygen molecule. This is aerobic respiration. This from of respiration was more efficient and advantageous for land organisms. That is why we use oxygen.
Lungs and breathing is one of the many ways organisms obtain oxygen for delivering to cells to do cellular respiration. Fishes use gills. Most mammals use lungs. Many single celled organisms just obtain their oxygen through diffusion.

Good thing you answered before they bullied me, lol.

lol always forget that part, thanks for reminding


text:
How to compare models of evolution to make assumptions about selection?
I'm having trouble understanding the concept of comparing models of evolution with LRT. I have following models: M0 (neutrality), M1A (negative selection) and M2a (positive selection) with following results from Maximum Likelihood:
M0: w=0.81; lnL=-5702.55
M1a: wo=0.50, po=0.65; w1=1.0, p1=0.35; lnL=-5650.21
M2a: wo=0.46, po=0.64; w1=1.0, p1=0.25; w2=2.38, p2=0.11; lnL=-5632.10.
I want to do the Likelihood Ratio Test to compare the models and make assumptions about the selective pressures on the analyzed sequences. So, first I compare M0 and M1a and calculate LRT=2(lnL1-lnL0)=104.68.
Now I want to take a look at a Chi table, but what am I using there? Am I supposed to used the po value for the M1a model? And if that value exceeds the value of the p-value in the table then the simpler model is chosen?

The answer to this question depends strongly on how you are formulating the different models. Are you for example computing this from dN/dS from a sequence alignment? Or based on explicit phylogenetic models? Those can have quite different formulations. The model names you are using out sound like they might come from PAML, but it's not clear. There are some exercises for this specific case elsewhere.
I am assuming that what you are really asking is how many degrees of freedom to use when e.g. computing a p value.
For this question, you have to think about the LRT a little. What it is really asking is "do I explain the data better by adding parameters to a model (while accounting for the fact that likelihood will by definition increase with more parameters)?"
So what you need to first establish is what the parameters of your models are. Assuming that the models are "nested", you can just use the difference in the number of parameters as the degrees of freedom. "nested" in this context means that the base model (e.g. H0, probably M0 for you) doesn't have parameters that are missing for the alternative model (which is assumed to have more parameters).
More flexible than the LRT are things like the AIC, which gets rid of Chi square and just computes a value from likelihood and model parameter number. This works well in practice and is nice because it's more flexible and a little less convoluted model comparison technique than the LRT. You can additionally use the AIC to compare non-nested models, which doesn't really work for the LRT.
I hope that this helps.


text:
What's the smallest size a human eye can see?
During a biology experiment at school, where we would look at waterweeds under a microscope, my teacher said something about that it's impossible for the human eye to see the cells without a magnifying glass of some sort. So, I saw that as a challenge, and decided to check if I could see the cells. And after holding the leaves really close to my face, I was indeed able to see tiny rectangles.
Since I was curious about it now, I decided to look some more things up. First, I tried to find out the actual size of waterweed cells. Based on this 640x enlarged image, which features cells of 5mm wide and 10 - 15mm long in the picture (which corresponds with of roughly 8µm wide and 15µm long).
I left this in the back of my head for a long time then, but I just looked at my cotton sleeve, and noticed tiny fibers. Not the ones that are woven together, but ones that are standing out from it. So, after looking it up, it turns out that cotton fibers are 10 µm wide.
So, I wondered, what is the smallest size a human eye can actually see? According to a whole bunch of sources all across the internet, it's either 200-400µm, 100µm, or 58-75µm. I also hear 'the width of a human hair' very often, but those can range from 17 to 181µm.

Many plant cells are visible, onion cells being a good example. As to seeing, that depends on your definition of seeing.

Well I mean something like being able to distinguish a shape of a certain size (so in this case a plant cell or a cotton fiber).

Very nice question!
First of all, the 'smallest size' that a human eye can perceive is called visual acuity, and can be expressed in various ways. It cannot simply be expressed by means of size measures, as objects with a fixed size are perceived as smaller when viewed from a distance (perspective). A familiar example is the train track:

Hence, visual acuity has to be measured as a function of viewing distance, i.e,  in degrees of visual view. Measured in degrees, the visual acuity of the average normally sighted person is 1/60 degree, or 1 minute of arc (1 MAR) (Webvision, chapter "Visual Acuity", by Kalloniatis & Luu).
Using the basic structure of the eye and some trigonometry one can deduce the smallest visible size :

The trigonometric formula becomes: $2d \times tan(\frac{\alpha}{2})$ with d being the viewing distance and alpha ($\alpha$) the visual acuity expressed in radians ($\pi \times \frac{degrees}{180}$) (NDT resource center).
Assuming the closest distance an adult can focus (~100 mm) and an average maximal acuity of 1 MAR, the smallest visible size boils down to 29 microns.
A young child can focus at distances down to ~50 mm, and can have a visual acuity of 0.4 MAR, which yields 6 microns.
Note that 1 MAR encompasses 288 microns on the retina, and some 180 photoreceptors. The optical limitations of the eye (such as diffraction by the lens and light scatter by the neural cells in the retina) limits the resolution of the human eye below the theoretical 'pixel limit' of the eye.
So to sum up, depending on one's age, one should be able to see targets in the order of 6 - 29 microns. The lower bounds of this range are indeed in the range of the waterweed cells and cloth fibers you describe.
PS - the values described are representative under ideal conditions, i.e., under situations of abundant lighting and high contrast.

Really well worked out answer. Thanks! The conditions were indeed ideal as you described, there was quite a lot of light, and as you can see in the picture, waterweed cells do indeed have quite a lot of contrast with the cell walls. Great answer!

This might be true under some assumptions ("ideal conditions") but in general the problem is more complex. Visual acuity is about resolving details (eg. ability to recognize a character) and not about seeing or not seeing things. We can for example see stars, even though they are hundreds times "too small" (viewing angle much smaller than 1 min of arc). So, the stars are more ideal than an ideal? Exactly, because they are extremely bright = high contrast. Tiny bright particles over black background would be more visible under microscope, too (with adequate lighting).

Absolutely, this answer is under ideal conditions as specified in the answer. It's all about contrast. Without contrast, no vision.

Excellent work Mr. AliceD

authors: መቓልሕ ምድረ ባሕሪ


text:
Creating a Phylogenetic tree using DNA sequences
I was wondering given DNA samples, how can we create a phylogenetic tree? I mean I don't even want to have actual DNA sequence. Can I generate small samples, like reads of a length 100 or 1000 of nucleotide chains and figure out how they are related.
Suppose we have two reads of DNA samples, for brevity let's have them really small like: AAAGGTCTTGA and ATAAAGTGTA. I can run the sequence alignment algorithm on the strings and I will get {"A", {"", "T"}, "AA", {"G", "A"}, "GT", {"C", "G"}, "T", {"TG",""}, "A"} So basically what alighns then where you have indels or substitutions. I can easily now write up a function that calculates how close are these two strings by giving positive weights for matches strong negative weights for indels, etc. Now I want to know if these two guys with really short DNA are related or no. What is the mathematical measure of relatedness? And after I do Multiple Sequence Alignments and get similar results how do I build a tree from it.

There's a lot of information about creating phylogenetic trees readily available online; e.g. http://evolution.berkeley.edu/evolibrary/article/phylogenetics_07 - can you add more specifically what you're having problems with?

I am looking for rigorous mathematical explanation of how to use multiple sequence alignment of DNA sequences to build a phylogenetic tree. I can find the algorithm anywhere, but exactly how you build a Dendrogram after receiving output from sequencing?

I'd recommend editing any additional details or clarification into the question. I still don't think this answers my question ("what you're having problems with") though - if you have the algorithm (and I'd include this in your question, or a source to it; you may be using the wrong one) then what specifically are you having problems with? If you apply your algorithm to some sample data, what happens?

Sorry, just noticed you're a new user - welcome to Biology.SE. I'd recommend taking the tour (http://biology.stackexchange.com/tour). Questions have to be specific and show what research you've already done, otherwise they won't get answered and may be closed.

Well, that doesn't answer your question of "what I am having trouble with",  because I'm not having trouble with anything except actually finding good sources on how one builds a tree. The link you initially sent was not rigorous enough. Anyway thanks for the welcome. I'll edit the question a bit to give some more info.

@arboviral Done.

This is where it started: http://link.springer.com/article/10.1007%2FBF01734359 presuming you have an aligned sequence to begin with.

There is no single mathematical measure of relatedness.
In the context of phylogeny, you need a model of evolution in order to be able to evaluate some relatedness between your strings of nucleotides. Then you would have various option for how to build a tree so that it reflects this relatedness. See for instance the wikipedia page about computational phylogenetics: https://en.wikipedia.org/wiki/Computational_phylogenetics.
Through what kind of historical process were your sequences generated ?
If there is no historical process underlying the generation of your sequences, then it is not meaningful to build a phylogeny with them. You can still define distances between them and use clustering techniques to build a tree, but that tree will not be a phylogeny.

Now I want to know if these two guys with really short DNA are related or no. What is the mathematical measure of relatedness

First of all this line of thought is dangerous, and creates a lot of room for misrepresentation, which have far reaching consequences when dealing with real DNA samples. Failed marriages are just one example.
Never infer phylogeny from short DNA sequences. You can read up on DNA fingerprinting here, which is used to infer ancestry and used in criminal prosecutions. But I think you already know about this.
Coming to inferring phylogeny on DNA samples where you have real DNA samples. For short sequences (upto 10kb, after that it becomes very time intensive), what you want is a Multiple sequence alignment such as Clustal Omega.
But the other procedure is to align entire genomes. Towards this end, you can check out this article from the UCSC team  on the creation of their phylogenetics track, with the multiz package, the how to is described here. This procedure is based on an existing evolutionary model as stated by @bli. Finally, these alignments were used to generate conservation scores for the template genome (hg18,hg19 etc. etc.) using phastcons. If you go here, you will find the various multiz alignments between different phylogenetic trees, with the pattern multiz*way. Similarly, the inferred conservation from the respective multiz alignments with the use of phastcons and phylop is provided with phastcons*way or phylop*way. The help page for phastcons is here and for Phylop is here


text:
Does tRNA contain parts with double-helix structure?
I read in some place that tRNA has a part which is double-stranded. I wanted to know if that part is similar to DNA (double-helix).
(And I know that tRNA won't contain Thymine)

You need to do some research before asking questions on SE Biology. You can easily find information about tRNA on the internet, e.g. Wikipedia.

It is right that RNA has small areas of complementary base pairing. You asked whether these parts are double helical.
DNA has various types of double-helical conformations. Most popular among them is the B-form, A-form and Z-form. The Watson Crick model is similar to the B-form of DNA. It is right-handed double-helical structure. A-form and Z-form are different from this, e.g,. Z-form has left handed rotation and the number of base-pairs in each helical turn is 12; whereas in B-form it is 11. So, there isn't a single structure of DNA that you can specify by saying the term double helical.
t-RNA has multiple segments that are double stranded, separated by loops (called hairpin loops) and bulges.  These molecules have a complex structure (are not symmetrical like the Watson-Crick model). This confirmation helps in its function. As a general rule, when two complementary base sequences (that may be DNA or RNA) come in close proximity, they tend to pair.
Nascent RNA molecule (single stranded) try to assume a right handed helical conformation, but note that it is very different from double-helix. When two strands of RNA, perfectly complementary to each other come in close vicinity they pair into a right handed double helix(A-form); however these molecules are very uncommon in nature.
Other examples of double strand formation in m-RNA are terminal signal of the transcription process in E. coli.
For further knowledge you may read

Biochemistry by Stryer
Lehninger principles of Biochemistry.


text:
What is the easiest way to measure a human body surface area?
Looking for accuracy, cost-efficiency, and speed: this could provide useful information for determining individual bioinformatic calculations, pertaining to, for example cell type estimation, for personalized medicine regimens. I'm not sure why this question was marked as off-topic and closed.  The biological implications are grandioso.

I am wondering whether enginneering.SE would be better suited for your question.

Walsberg & King (1978) came up with a method for estimating the surface area of an organism fairly cheaply. They were interested in birds, but it would work on people as well. Is this the sort of solution you were after?
http://jeb.biologists.org/content/76/1/185

@kmm - Wouldn't that measure the entire mass of the body? The OP wants just surface area.

I think nomogram method

I think nomogram.I

How cost efficient does it has to be? Do you want these data to be comparable to another study?
Fundamental issues
What do you call surface area?
The interior of the guts (interior of the mouth, stomach, intestins, ...) are typically considered as exterior to the body. I would assume you don't want to consider these things in your measurements, right?
Coastline paradox
By aware of the coastline paradox

The coastline paradox is the counterintuitive observation that the coastline of a landmass does not have a well-defined length. This results from the fractal-like properties of coastlines.

Similarly, for the surface of the human body, the results will be drastically different depending upon the scale of measurement you want to consider. If you want to measure the small bumps of each atom or if you're happy to consider the the skin is a perfectly flat surface, the answer will vary a lot (numerous order of magnitudes)!
So, if you want to make your results comparable to those of another study, then you should definitely copy their methodology. If all that matters is that you can compare the different individual you measure, then you can pick a methodology that works well for you.
Methods
Those are in no way an exhaustive list.
3D scanner
3D scanner is probably the way to go for accuracy.
Approximation of body shape into cylinder and spheres
If you want cheap and inaccurate, you can assume each body part is a cylinder. Consider for example reducing the human body to 4 limbs (which you could further split in two separated by the knee and the elbow), a torso and a head. Just measure the perimeter of each body part and and approximate the surface area.
It might sound like a silly technic but it might well be good enough for your purpose (esp. if you're not comparing your results with another study).
Polyethylene film
Following Walsberg & King (1978) (thanks to @bshane for pointing this paper out), you can wrap people in a polyethylene film and then measure the area of the film. When measuring the area of the polyethylene film, you are going to have to make some approximation into some simplified (or series of simplified) shapes just like I suggested above. Walsberg & King (1978), when measuring birds, assumed the overall shaped was a prolate spheroid with a hemispherical head. You're going to have to try to find what simplified shape ressembles most to the polyethylene film.
Of cours, you might want to eventually consider other fabrics than polyethylene film and you might want to consider different number of fabrics.
You could prepare pre-measured rectangles and triangles of a given fabrics with colors matching a known size. Then you can just stick those fabrics on the individual body and simply add up the size of the shape you add on the body. This sounds like a difficult solution as you will have a hard time to correctly place the fabrics to avoid wrinkles, overlaps and gaps.
Test reliability of your measure
Reliability of your measure
Do not forget to repeat measurement on a few individuals to measure the accuracy of your measure.
inter-raters reliability
If several people are going to work on this project, you should also make sure to test the inter-raters reliability


text:
What is the chemical mechanism by which PcP(c) is converted to PcP(Sc) in Fatal Familial Insomnia
I know the gene PRNP and it's location.  There’s a mutation at codon 178 (asparagine found instead of normal aspartic acid) on the prion protein gene (also found in fCJD) and more uniquely there is a the presence of a methionine at position 129.  My question is how PcP(Sc) converts normal PcP(C) into the mutated destructive form, and whether or not this is a reversible process.


text:
Did my wildlife camera take pictures of coyotes?
A wildlife camera took these photographs of these animals. These are the only pictures my camera collected of them. This picture was taken in the central interior of British Columbia, Canada. The distance from the camera to the subject is a few meters, and the height of the camera off the ground was probably about 50 cm.
I think they're too small to be wolves, and they don't appear to be any of the dogs I've seen in the area. My intuition strongly points to them being coyotes based on pictures, videos, and occassionally seeing them in the light of day. However, I'm hoping that someone can explain why my intuition is right, if it is right.

The small coniferous tree beside the road (left of center) is about 50 cm tall.

While I generally try to avoid cross-posting, I have started pooling my observations into iNaturalist. These photos can be found as Observation 110928801.

They look like coyotes to me.

Could you see if there are any footprints in that path?
It maybe would be clarifying.

That would definitely be useful information, but unfortunately I don't have it

just based on location and probability coyote is a good bet. some way to determine scale would help, such as the height of the short plant on the left side of the picture.

@John I can go back and measure that plant. I'll update the post and tag you when I have that info'.

@John I'd say about 50 cm is the height of that plant.

That makes them barely over 2ft probably less with perspective, that is pretty solidly in coyote size range.

Fantastic! Thanks for your perspective, @John !

 Short Answer
Yes, these are likely coyotes.
 Long Answer
Your options of dog-like carnivores (i.e., Canidae) in BC include the following:

Coyote (Canis latrans)
Grey wolf

British Columbia Wolf (C. lupus columbianus)1,2
Northwestern wolf (C. lupus occidentalis)1,2

Domestic dog (C. lupus familiaris)

Siberian husky
Utonagan
Tamaskan
Alaskan malamute
Northern Inuit dog
Saarloos wolfdog
Shikoku
others...

Red fox (Vulpes vulpes)

Let's start ruling out the unlikely options:
[I'm not trying to be comprehensive, but rather just pointing out the easiest evidence we can see]

Dogs: Many of these might look wolf or coyote like, but it is very unlikely that any of these domestic breeds would be forming wild packs (as is indicated by your image containing multiple specimens). (In fact, it's unlikely you'll even see half of those breeds in BC to begin with!). I'll leave it to you to peruse the links I provided if you insist on examining and comparing their anatomies/morphologies to your specimens.

Fox: Although the largest of the true foxes, this is still a relatively small canine; they typically are less than 20 inches high and 35 inches long [source]. Without some measure of scale, we can't be sure, but your specimens certainly look larger than that relative to the tree in the background. More definitely, however, is that V. vulpes has a tail that is typically half or more of its own body length. The tail also rests nearly to the ground. The length of tail on your specimens do not match either of these length characteristics. Further, the very bushy, white-tipped tail of the fox is not represented by your specimens' only-somewhat bushy, dark-tipped tails. Red fox are also typically solitary hunters (source; unlike your shot of multiple specimens).

I know what you're thinking: "duh, get to the wolves vs coyotes already!!"
See here for a guide to differentiating wolves, coyotes, and dogs

British Columbia Wolf (C. lupus columbianus): According to Goldman (1941)3 and here, this subspecies of wolf is large with a dark (black with some cinnamon brown color) coat. No mention of a light ventral region is mentioned, which is apparent even in your night-camera shots!

Northwestern wolf (C. lupus occidentalis):

Their color does not rule them out -- This wolf subspecies is quite variable in color and can range from all dark, to mottled grey to all white. (see here for visual comparison of some wolf colorations).
This is potentially the largest of wolf subspecies (27-36+ inches tall at shoulder [source] and 4.5 - 7 feet long (nose to tail tip)! [source:i, ii). Your specimen's do not look this large, but they could be juveniles. It would be great to get a height estimate of that plant in your picture or diameter estimate of that tree...
This site suggests that the average pack size is 6-12 wolves, which is significantly more than showed up on your camera (though, obviously this could be coincidence and not a definitive way to rule out this wolf).

This leaves the coyote:

Size: Between the size of the fox and grey wolf: 21-24 inches at shoulder; 3.5 - 4.5 feet long (nose to tail tip) [source].

Color: Gray (or reddish brown), often grizzled, often with whitish/paler throat, chest, and/or belly. Black patches may be seen on the tips of tails and on feet. [[sources: Bekoff (1977)4 and CDFW].

Anatomy/morphology: Bekoff (1977)4 and CDFW compare coyotes to wolves. The snouts are thinner with smaller nose pad on coyote vs wolf, and a coyote's ears are taller and more pointed. Your images, unfortunately, don't really allow us to differentiate adequately.

Behavior: typically less social than wolves and will rarely travel in groups larger than their immediate family. Even still, they often keep their distance and practice more asynchronous activity. However, coyotes are most often observed as lone individuals or in pairs (especially during breeding season)4. This matches your camera's image.

Coyotes are most active in the early evening, but their activity can be variable. Many coyote have become quite nocturnal (especially near humans), but they are known to also be sporadically active throughout the day and showing peaks of activity at dawn and dusk. [sources: Bekoff (1977)4 and Wikipedia. Wolves, though, are also active during evening and night...
Summary
So, based on the the few number of individuals, coloration, and perceived relative size of your specimens, it seems most likely that these are coyote. If you can get us estimates of height and diameter of the foliage in the background, this could be stated more definitively.
You could always go back to the site and photograph and compare the tracks, too (try starting here and here to compare)....
...
Though of course, it could always be a "coywolf" (also see here) :p.

 Citations
 1. Nowak, R. M. (1995). Another look at wolf taxonomy. In Carbyn, L. N.; Fritts, S. H.; D. R. Seip (eds.). Ecology and conservation of wolves in a changing world: proceedings of the second North American symposium on wolves. Edmonton, Canada: Canadian Circumpolar Institute, University of Alberta. pp. 375–397. [see map on p 376 here]
 2. Goldman, E. A. (1941). Three new Wolves from North America. Proceedings of the Biological Society of Washington. 54: 109–113. [See HERE for a map]

 3. Goldman, E.A. (1941). Three new wolves from North America. Proc. Biol. Soc. Wash, 54: 109-13. [available here]
 4. Bekoff, M. (1977). The coyote Canis latrans. Mammal. Spec, 79:1-9.[see here]


text:
Do ants and termites form standoff lineups?
Someone sent me this video. It has a description:

A Termine line (top) and an Ant line (bottom), each protected by its column of soldiers who face each other without attacking.

Here is a screenshot from the video:

Has this behaviour been studied/verified within a scientific context?

Doesn't look peaceful to me - looks like a military stand-off.

@bob1 Agreed! Changed the wording in the title thanks to your feedback.


text:
Are there any eukaryotic algae that degrade EDTA?
EDTA is a frequently-used chelator in molecular biology experiments and medical applications. Buchelia-Witschel et al 2001 discuss certain bacteria have monooxygenases that are capable of degrading EDTA, so there is a basic precedent that living things can biodegrade EDTA.
The chemical properties between eukaryotes and bacteria are sometimes similar, but also frequently quite different. It isn't obvious to me that algae could not degrade EDTA, but has anyone demonstrated that there exists a species of eukaryotic algae that can break down EDTA?

From your question, I assume you're only interested in whether eukaryotic algae can degrade EDTA? "Algae" is term that describes a polyphyletic group, and cyanobacteria (blue-green algae) often get lumped in.

@acvill Thank you for the correction on the taxonomy. Yes, Eukaryotic algae only.

While I don’t have answer for this question I am curious why bacteria/algae degrading EDTA would be important.Any applications?

There may be better options for disposal, but one application might be waste disposal of a commonly-used reagent.

My own interest in this post pertains to the degradation of EDTA confounding the results of nutrient deprivation experiments.

@Galen, your comment on nutrient deprivation experiments suggests that you may be working with / considering a particular algae.  If you provide the name of your organism, you are more likely to get a targeted answer

Thank you @acvill, but this is the question I wanted to ask.

I can find no published evidence for the degradation of EDTA by eukaryotic algae. I found only one publication where algal metabolism of an aminopolycarboxylic acid was tested, specifically whether NTA could be used as a nitrogen source by different marine microalgae.1 Their results, summarized in Table VII, were that the tested organisms could not use NTA as a nitrogen source.

The possibility that NTA could serve as a nitrogen source was investigated using SS-1 medium in which NTA was substituted for the nitrogen. None of the four species of phytoplankton (Table VII) was able to use NTA as a nitrogen source. The slight stimulation shown by S. costatum at 0.073 mg/L N as NTA and I. galbana at 7.3 mg/L N as NTA does not seem to be related to NTA metabolism.

Of course, the organisms listed in the table above are just a small subset of the massive polyphyletic group that can be described as eukaryotic algae.2 Instead of directly asking whether algae degrade EDTA, we can take a naïve approach and ask do any algae encode proteins that are similar to bacterial proteins known to degrade EDTA? Section 5.2 of the review article linked by Galen 3 lists two proteobacterial strains for which the biochemical determinants of EDTA catabolism have been studied: BNC1 (Chelativorans sp. BNC1) and DSM 9103 (Chelativorans multitrophicus). For DSM 9103, a two-enzyme system consisting of a monooxygenase and an oxidoreductase was shown to be sufficient to cleave two acetyl groups from EDTA, though the monooxygenase seems to be the vital component given that the activity of the oxidoreductase could be substituted by paralogous enzyme from the NTA degradation pathway.4
Since that review, additional work has been done to characterize the catalytic mechanism of EDTA monooxygenase (EmoA) and identify structural homologs across bacteria.5 Searching the NCBI Identical Protein Group database for "EDTA monooxygenase" returns 25 hits (as of 6 July 2021) to unique proteobacterial proteins. Multiple sequence alignment of these proteins reveals a highly conserved catalytic core (red regions):

If we then find a consensus sequence from the MSA, we can use that sequence to perform a blastp search against the subset of NCBI nonredundant protein sequences corresponding to taxa annotated as eukaryotic algae by Wikipedia.

Name
Taxonomic Identifier

Mesostigmatophyceae
96475

Chlorokybophyceae
131213

Chlorophyta
3041

Rhodophyta
2763

Glaucophyta
38254

Chlorarachniophytes
29197

Euglenids
3035

Bacillariophyceae
33849

Cryptophyta
3027

Dinoflagellata
2864

Haptophyta
2830

Bolidomonas
91990

Eustigmatophyceae
5747

Phaeophyceae
2870

Chrysophyceae
2825

Raphidophyceae
38410

Synurophyceae
33859

Xanthophyceae
2833

With default blastp parameters, we get a whopping 2 hits, one to a dimethyl-sulfide monooxygenase from the endosymbiotic dinoflagellate Symbiodinium pilosum and one to a protein containing a monooxygenase-like region from Ostreococcus tauri, the smallest free-living eukaryote.6 (The same hits were obtained after blasting each bacterial protein individually to the same algal protein set.)

Description
Scientific Name
Query Cover
E value
Per. ident
Accession

dmoA
Symbiodinium pilosum
87%
1.00E-54
32.21
CAE7149325.1

luciferase-like domain-containing protein
Ostreococcus tauri
82%
1.00E-14
25.72
OUS44788.1

While both of these hits have good query coverage, the sequence identities are low, and both organisms lack (per a separate blastp) good hits to the NADH-dependent FMN reductase needed to supply the FMNH2 cofactor for the monooxygenase, though another enzyme could fill that role.
So, if algae degrade EDTA, they likely do it using enzymes that are structurally different from known EDTA-catabolizing bacterial enzymes, or by a biochemical mechanism yet to be deduced.

References

Erickson SJ, Maloney TE, Gentile JH. Effect of nitrilotriacetic acid on the growth and metabolism of estuarine phytoplankton. J Water Pollut Control Fed. 1970 Aug;42(8):Suppl:+329+.
Keeling PJ. Diversity and evolutionary history of plastids and their hosts. Am J Bot. 2004 Oct;91(10):1481-93.
Bucheli-Witschel M, Egli T. Environmental fate and microbial degradation of aminopolycarboxylic acids. FEMS Microbiol Rev. 2001 Jan;25(1):69-106.
Witschel M, Nagel S, Egli T. Identification and characterization of the two-enzyme system catalyzing oxidation of EDTA in the EDTA-degrading bacterial strain DSM 9103. J Bacteriol. 1997 Nov;179(22):6937-43.
Jun SY, Lewis KM, Youn B, Xun L, Kang C. Structural and biochemical characterization of EDTA monooxygenase and its physical interaction with a partner flavin reductase. Mol Microbiol. 2016 Jun;100(6):989-1003.
Palenik B et al. The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation. Proc Natl Acad Sci U S A. 2007 May 1;104(18):7705-10.

Fantastic work! Thank you for putting in your valuable time to search through the literature.


text:
How much does dry moose scat weigh and vary in dry weight?

I am in the early stages of designing a study involving a moose (Alces alces) population.

One of the important objects of measure are their moose pellets, which I want to take a battery of measurements on including their dry weight.

I recently picked one up off the forest floor and subjectively it felt quite light.

Originally I had intended to buy a ~$100.00 weigh scale with 0.01g precision.

If the pellets are too light to be detected by the balance, that is a obviously problem.

But I am also interested in variation, so it would also be a problem if most of the variation among pellet weights occurs below the precision of the balance.

Has anyone published statistics on the weight and variation in weight of moose pellets?

Why individual pellets rather than pellet groups? But 0.01 g is quite small...

@BryanKrause If I measure aggregates of pellets then I cannot study their individual variation. Why I care about their individual variation is particular to the study questions.

@BryanKrause The precision of 0.01 g is quite small, subjectively. I want to know ahead of time, if possible, that I have sufficient precision.

MacCracken and Van Ballenberge, 1987 report that pellet size varies a bit by sex and age; male pellet dry weight (all measurements are dry) averaged 1.9 grams (range 0.9-2.9), female 1.3 g (range 0.6-2.0), yearling 0.8 g (range 0.6-1.7).
Unfortunately they do not report SDs but instead SEs of 0.01 for every group; we can try to reverse engineer the SD but with one significant digit there isn't much precision to work from. SE = SD/sqrt(N), their N were 34/36 for the male/female adult groups, so let's just base it on 35...
0.005 * sqrt(35) = 0.030 g
0.015 * sqrt(35) = 0.089 g
The SD could be somewhere in that general range, but based on the range I suspect it may actually be much higher, and that they used a larger N for calculating the SE (perhaps many pellets per group). The other possibility is that the range is mostly due to outliers. As a rough estimate from the ranges, you could guess the SD is around 1/6th of the range (if normally distributed, 99.7% is within +/- 3 SD), with ranges around 2.0 to 1.4, that would give you a SD around 0.2 to 0.3 grams.
They used a scale with 0.01 g precision; seems sufficient for this work. Dry weight is certainly the standard for this sort of thing, so you may want to look at equipment for drying as well.

MacCracken, J. G., & Van Ballenberge, V. (1987). Age-and sex-related differences in fecal pellet dimensions of moose. The Journal of wildlife management, 360-364.

(+1) Thank you for (1) finding a citation and (2) clarifying whether it was dry/wet weight.

Nice use of the definition of standard error of the mean, btw.

@AgnesianOperator I think likely my original estimates for SD based on SE are very low; I suspect they put in measurements for many pellets per group and calculated their SE across all of them, so the N=35 is wrong. I think my new estimate based on the ranges will be closer to what you actually observe.

Thanks, I appreciate that update to your answer. Regrettably, I can only upvote your answer once. I will read the study to understand their statistics in detail. On the face of it I am thinking of using a random intercept model to allow different piles of scat to be from possibly-different individuals whose age/sex might differ.

@AgnesianOperator Yes, I would certainly suggest mixed effects models for something like that. I don't think you'll find much use in the stats for this particular paper, as although they are doing some interesting stuff for 1987, statistics has come a long way, particularly in how biologists use statistics.


text:
Order of iNaturalist taxon levels
Purpose
I want to take a collection of iNaturalist observations and build a tree diagram of the taxonomic levels that occur in that sample.
Background
Most of us have seen diagrams like this

or like this

that illustrate some of the taxonomic ranks that biologists assign organisms into.
I am looking at the data export options for iNaturalist and I see that there are more possibilities than conveyed in these simple diagrams. I am also unfamiliar with precisely what these added levels are telling us. I could ignore these additional levels, but I would rather not if they are biologically meaningful. I have tabulated these export variable options in the order that I am guessing they belong in, alongside the similar names mentioned on wikipedia.

iNaturalist
Wikipedia

taxon_kingdom_name
Kingdom

taxon_phylum_name
Phylum

taxon_subphylum_name
Subphylum

taxon_superclass_name

taxon_class_name
Class

taxon_subclass_name
Subclass

taxon_superorder_name

taxon_order_name
Order

taxon_suborder_name
Suborder

taxon_superfamily_name

taxon_family_name
Family

taxon_subfamily_name
Subfamily

taxon_supertribe_name

taxon_tribe_name
Tribe

taxon_subtribe_name
Subtribe

taxon_genus_name
Genus

taxon_genushybrid_name

taxon_species_name
Species

taxon_hybrid_name

taxon_subspecies_name
Subspecies

taxon_variety_name
Variety

taxon_form_name
Form

Questions

Is there a total order on the iNaturalist taxon levels? If not, what are the exceptions?
Do I have the taxonomic levels in the right order in my table?

Wikipedia has another table with some more of these levels: https://en.wikipedia.org/wiki/Taxonomic_rank#Terminations_of_names

I am not interested in software recommendations along the lines of "just put your data in <software_name> and it will figure it out". I have software solutions. I want a conceptual understanding of the (possibly partial or total) order.

I'll just point out that these rank-based definitions are highly problematic and not really consistent with current thinking (despite their prevalence on the web). https://biology.stackexchange.com/questions/55457/why-is-traditional-rank-based-taxomomy-considered-by-some-as-logically-inconsist

Too echo what @kmm says, these other middle/intervening levels of the hierarchy exist precisely because it doesn't quite work to build a hierarchy like this. When you want to refer to some relevant group, you sometimes have to come up with a new name for it.

This may be a better question for the iNaturalist forums.

I believe this is the correct order, based on this question:

kingdom
phylum
subphylum
superclass
class
subclass
infraclass
subterclass
superorder
order
suborder
infraorder
parvorder
zoosection
zoosubsection
superfamily
epifamily
family
subfamily
supertribe
tribe
subtribe
genus
genushybrid
subgenus
section
subsection
complex
species
hybrid
subspecies
variety
form
infrahybrid

Note that the hybrid ranks, biologically, break the otherwise strict hierarchy.
This FAQ may also be helpful.


text:
Are glands in human made up of muscles?
I came across this idea while reading an excerpt of my textbook $-$

Muscles move eye lid , tongue, beat the heart, popel food through gut, discharge wastes, squeeze out secretion from the glands etc.

But I don't understand how this happen ? Does glands really squeeze out hormones?

No they are not made of muscles. Glands are modified epithelial tissues. Glands are basically of two types Endocrine and Exocrine glands.

Endocrine gland

It is a gland that lacks a duct system. The secretions are directly
   poured into the bloodstream. They secrete hormones through exocytosis mostly.

Exocrine gland

It is a gland with a duct system. The secretions(sweat, sebum,saliva
   etc) are transported onto a surface of the body (e.g skin or lumen of GI tract) by
   ducts.Exocrine glands may be controlled by muscles at the duct level.
There are three types of exocrine glands based on the process of secretion of chemical substance at a cellular level$-$

Apocrine gland

It is a gland in which the apical portion of the secretory cells are distintegrated to release the chemical. e.g- Apocrine sweat gland

Holocrine gland

It is a gland in which the secretory cells are completely disintegrated to release the chemical.e.g.- Sebaceous gland

Merocrine gland

It is a gland in which the secretory cells remain intact and the chemical is
released through exocytosis.e.g.- salivary gland

Reference:

Wikipedia
ncbi


text:
What is nastic movement due to growth?
I have read in my biology book that

"nastic movement is the movement that occur due to difference in the rate of growth on two opposite sides of a plant organ" ,

but I can't visualize how this movement take place and how the rate of growth cause this movement?

Think about say the left side of a shoot growing more than the right side. That way the left side will be longer and will be pulled back by the right side. This causes the shoot to turn to the right. By controlling the differences in the rates of growth in the two sides (using plant hormones) the plant can control exactly how much the shoot will turn.
Most nastic movements however, are not directional. For example, in Thigmonasty, or the movement due to touch, of the Mimosa pudica (or the touch-me-not), cells at the base of the leaves pump out K+ ions and Cl- ions, while taking in Ca+2 ions, thus create a osmotic gradient and lose water. This loss in water results in a drop in turgor pressure of the leaves and they droop.
https://en.wikipedia.org/wiki/Thigmonasty#Thigmonasty_in_the_Fabaceae


text:
Vo2 of the brain is 21% of Q=13% or of the entire blood?
I am reading now the book which states that the Q (blood flow per unit of time) of the brain is 13% while the Vo2 (relative Oxygen used per unit of time) is 21%. Now my question is if the Vo2 of the brain (21%) is taken from the entire blood or from the part the brain gets (Q=13%)?  It is not clear on the book. I've attached the picture with the explanations.

What do $Q$ and $Vo2$ stand for?

Q stands for blood flow per minute, and Vo2 stands for relative Oxygen used per minute - as it's written on the chart.  I can assume that the letter Q is for Quantity and Vo2 is for Volume of o2 (oxygen).

TL; DR 21% of the whole body.
The VO2 numbers add up to 100%. You are looking at the way six major compartments of the body split the oxygen consumption. The brain takes 21% of the whole oxygen brought into the body by the arteries.
The Q numbers add to 100% too. The brain takes 13% of the blood coming out of the heart.
The picture is meant to contrast high-flow and high-metabolism organs. The kidneys take 20% of all the aortic blood, but use only 7% of the oxygen consumed in the body. The coronaries take 4% of the blood leaving the ventricles, but use a large 11% of the oxygen take in by the whole body.

Thank you. Now I understand it (I also calculated all the vo2 in the chart and I got 100% and that proofs your things). But now I don't understand why there is not a relation or correlation between the Q and VO2, it means that it can be Q of 4% while the vo2 is 11% (almost 3 times than the Q!). Then my exact wondering is how could it be that a very small Q can supply a big quantity of vo2. By the way,  What does it mean "TL; DR" (in the beginning of your answer)?

TL:Dr means "too long ; didn't read", a frequent rebuttal on the Net for those who make too long an en explanation. It has been taken up in preemptive defense by those who write long explanations, like me.
The heart and brain use a lot of oxygen because they do intensive work, which require intensive burning of energetic substrates. The kidney is not a burner, but it needs a lot of blood in order to do its job - filtering -, effectively. Neither organ takes up all of the oxygen in the blood, but heart muscle and brain are making more of an effort compared to the kidney.

Thank you for your comment. I think that I didn't explained my question well. I've been asking, how could it be that a small amount of blood (e.g. in the heart 4%) can supply a big quantity of oxygen (vo2=11%)?

When blood leaves the brain or the kidney, it is not fully depleted of O2. Blood leaving the left heart has a hemoglobin saturation with O2 of nearly 100%. (Almost every Hb molecules carries the maximum, four O2 molecules.) Blood returning from the body through veins, mixed in the right atrium, has a saturation of about 75%. (That is, the average Hb molecule has three O2 molecules still bound to it.) Roughly speaking, the various organs take up only one of the 4 molecules of O2. Brain is striving a bit harder; brain venous blood saturation is about 70%. Kidney vein Hb O2 saturation is ~80%.


text:
Fastest observed biologically evolution rate?
Fastest possible evolution rate?
What is the theoretically quickest evolution rate?
For example, I could calculate this as the number of offspring an E. coli colony can generate at once * (time frame/time per generation) * mutations per generation, and then assume every organism on earth had this same evolution rate.
Is there a generally accepted value, perhaps based on mass of organism?

Without an explanation for how you are defining "rate" I think this question is fairly meaningless and I suspect it might be rooted in a misunderstanding of the term "evolution".

@BryanKrause Fair point.  I can describe what I mean in terms of a simplistic model.  If we use a binary string to represent DNA, there is a space of finite length binary strings that we can explore through mutation, crossover and selection.  From an initial starting population, and using biologically plausible parameters, what proportion of the space can be explored within a certain time delta?  Is this rephrasing clear enough?  If so, I'll replace my question.

I sometimes like to share this comic in circumstances like this - I don't mean any offense, but I think it sums up the problem nicely if you approach it with an open mind. My point is that a simplistic model will never capture "evolution" because evolution is an incredibly broad theory encompassing many separate parts. For one, although it's reasonable to consider finite length binary strings, biology won't produce genomes of the same length, so you need to consider strings of varying lengths. You very quickly get to a combinatorial explosion.

And although people often talk about "random mutation" the mutations in biology aren't purely random. You often have duplication of entire segments creating multiple copies of the same protein which can then diverge from each other. Further, mutation is only one tiny part of evolution, you also have selection, which is incredibly dependent on environment, and non-selective changes like genetic drift.

@BryanKrause I'm not saying the model is comprehensive, but there surely must be a way to mathematically model evolution rate according to some definition or another in a field over 150 years old.  All I was doing is offering one simple model to define rate, I'm sure someone has invented a better one.

@BryanKrause I've updated my question with an explanation of how "rate" is defined.  Can it be removed from hold?

As Remi.b already pointed out, if you allow fitness and phenotype to vary sufficiently, you could state that the rate of evolution is effectively infinite: complete in one generation. So no, I think it is still a poorly defined question.

I happened to notice this previous question in the sidebar of a question unrelated to yours, but I thought it might be of some use to you. The question is on selection but the answers get a lot more broad. You won't find a precise answer to your question but it might help you as a starting point to how people have attempted to model relatively abstract evolutionary processes. Like I was alluding to with my cartoon link, though, this is an entire field of work.

@BryanKrause, is an infinite evolution rate scientifically plausible?  I'm doubtful.  Anyways, I've simplified my question per Remi.b's suggestion.  Can it be taken off hold?

See How fast is fast? Eco-evolutionary dynamics and rates
of change in populations and phenotypes I think this what you were asking about

Theoretically, it really depends how crazy you want your model to be! Also it depends what you mean by "evolution rate".
Let's consider a very simple and quite crazy model. Let's assume that by evolution rate you mean the difference between mean phenotype between one generation and the next. Let consider a trait (which heritability is $h_N = 1$) which takes only two values $x_1$ and $x_2$. In a given generation, half the individual carry the trait value $x_1$ and half the trait value $x_2$. If the relative fitness of $x_1$ and $x_2$ are 1 and 0 respectively, then the evolution rate is simply $| \frac{x_2 + x_1}{2} - x_1 |$. Allow $x_2$ and $x_1$ to be very big values and the rate of evolution will be very fast. In the most extreme case, $x_1 = \infty$, then the evolution rate is simply infinite.

The model needs to incorporate mutation so we don't need to assume the traits already exists in the population.  It also helps if there is biological plausibility to the model.

@yters How is there no biological plausibility to the model? For mutation, simply do not assume x1 and x2 are held by half of the population.

@yters The amount of biological plausibility is up to the one that answers. My point was to highlight the question made little sense for this reason. If you ask "In theory, ...." you have to get ready to get an answer that is not practical. Instead you could ask "What is the fastest fastest evolution ever observed?" But then and again, you will really need to define what you mean by "evolution speed".

@Remi.b, what is a good definition of "evolution speed"?  I have no preference in the matter, I just want a good working definition that has evidence behind it.  Not being a biologist, I do not know the correct technical terms that will get an answer, so asking me for the correct technical terms is not something I can provide.

Talking about genetics, some may like to use the rate of neutral substitutions as a measure of the speed of evolution. Talking about phenotypes, some may like to use the Darwin (unit) to measure speed of evolution. Some may like to talk about speciation rate. In all of these cases, you cannot really ask for a theoretical limit without specifying exactly the basis for the model you wish to consider. You can ask though for observed evolution speed. Coming to phenotype, you'll have to specify whether plasticity counts as evolution or not.

I note that the question was edited/changed substantially since this answer was given, making it look off-topic now, although it was addressing the original question "What is the theoretically quickest evolution rate, that maintains biological plausibility?", not the current one, which asks about observed evolution.

Sure, we typically ask OP not to change their question after it has been answered. So, I edited the question back to what it was when I answered. If the OP has another question he/she must ask it on a different post. Thanks @Fizz for highlighting this issue.


text:
Could there be latent infections in the human population that we don't know of?
Herpes simplex 1 virus and toxoplasmosis are common infections that that many of use have (65% and 10% of americans, respectively). Both of these are chronic, latent infections that can occasionally cause problems (i.e. in HIV patients) but most of the time don't.
Could there by many other such common, latent infections in the human population that we don't know of? Do we have any idea of how many there would be?

Given the right (or wrong, depending on the perspective) circumstances, even your body's own microbiome can turn against you - a human body cultivates thousands of bacterial colonies that, if something disturbs their balance (e.g. antibiotics), can cause severe metabolic disturbances

@Sty: The micro-biome normally doesn't infect people, i.e. it's technically "outside" of the body.

@KevinKostlan From wikipedia Gastroenteritis, also known as infectious diarrhea, is inflammation of the gastrointestinal tract [..] Gastroenteritis can be due to infections by viruses, bacteria, parasites, and fungus.

@Remi: Key word "normally". Gastroenteritis seems to be a more "typical" infectious disease, which represents pathological conditions (unless most people who are actually "infected" show minimal symptoms and "don't" have the disease, where "infection" means infiltration into tissue).


text:
Oregon - Tree Identification
Western cascade forest, definitely was planted. In a clearing in the forest along with some western red cedars. I couldn't find anything that would match it when looking for native Oregon trees.

Nice pictures! +1


text:
How can we decide at which level does natural selection apply?
Here is a passage from The Selfish Gene

If selection goes on between groups within species, and between species, why should it not also go on between larger groupings? Species are grouped into genera, genera into orders and orders into classes. Lions and antelopes are both members of class Mammalia, as are we. Should we then not expect lions to refrain from killing antelopes, 'for the good of the mammals'? Surely they should hunt birds or reptiles instead, in order to prevent the extinction of the class. But then, what of the need to perpetuate the whole phylum of vertebrates?

So shouldn't it eat the other animals? Also in the next line, it says about vertebrates that since reptiles and birds are vertebrates, the lion should go for invertebrates.
It sounds illogical, but what stops lion from eating his "relatives", or from sparing a deer? And till what extent is the term "relatives" valid?
Is the question is too broad, please give a source where I can get an answer.

It would be helpful if you could cite the actual passage. It is a bit unclear when reading your post whether the subject is lineage selection or group selection (aka kin selection).

It feels to me that the title of the post ask another question from the content. "What level applies" is different from "How does selection at this level make sense". Generally speaking evolutionary biologists assume that selection at level higher than variance in individual's fitness within population is often negligible but I don't think there is much data and much of a good argumentation for the extent of the validity of such assumption.

@Remi.b so individual fitness and that of close ones determined by Hamilton's equation. And that would also explain the lion example as they are too distantly related to outweigh the advantage.

I did not say that. I said I would need a clear quotation from the book to be able comment on it.

@Remi.b Actual statement in next comment.

If selection goes on between groups within species, and between species, why should it not also go on between larger groupings? Species are grouped into genera, genera into orders and orders into classes. Lions and antelopes are both members of class Mammalia, as are we. Should we then not expect lions to refrain from killing antelopes, 'for the good of the mammals'? Surely they should hunt birds or reptiles instead, in order to prevent the extinction of the class. But then, what of the need to perpetuate the whole phylum of vertebrates?

I haven't read the book, but these questions seem rhetorical. It seems like Dawkins is setting the stage for an answer, which I suspect he gives later.

the follow up to the portion being discussed is that while those other form may exist they are swamped by gene/individual level natural selection. genes that benefit the species at the expense of individuals can't compete with genes that benefit themselves or individual lines at the expense of the species.

Natural selection favors genotypes that produce phenotypes that survive to reproduce.  To the extent that natural selection drives the instincts that drive predation, the (essentially tautological) answer is that, "It has proven selectively beneficial for that particular genotype to eat whatever it's eating."
Indeed, nature has shown us a variety of solutions to these selective pressures.  Cannibalism is not hard to find.  In the extreme case, mothers from diverse species are known to kill and eat their own offspring, which at first glance may seem like the height of failure when it comes to propagating one's genes.  And yet we can hypothesize a number of selective advantages of that behavior.  (E.g., the mother can produce large litters when food is plentiful, and then adjust for shortages to shore up a smaller litter when food is scarce.)
We also know that there are selective pressures against eating similar animals.  For example, pathogens that kill an animal are likely to afflict its genetic relatives, and so that could drive the aversion of some carnivores (including humans) to eating close relatives.
There are plenty of examples of evolved symbiotic organisms.  Alligators don't eat plovers, and sharks don't eat remoras.
But it is hard to imagine selective pressure for altruistic behaviors that don't benefit a particular genotype.  E.g., if there are mammals to be eaten, the lions that eat them will survive and the ones that don't will die.  If the entire class Mammalia is under pressure, population dynamics trump evolved behavior: If prey become scarce, predators will become scarce.  But if any predators survive, it will be the greedy ones.


text:
What species is this white thin shell? Santa Cruz, California
These shells (see in green circle) are empty inside which look like very flat marble donuts/coins.
The small ones are like dollar coins; big ones can be cup coaster-sized.
It has beautiful five-fold symmetry (see the top one) on its back and a whole on its ventral part. Must be related to sea urchin?
What's more interesting is that some of them (see the one at the bottom) have a tiny shell of a different kind anchored on them.

In your second photograph, are you referring to the white conical shell or the little black thing?

sorry for not being clear. Trying to refer to the same thing - flat white shells

They are definitely sea urchins, specifically a type of sand dollar called the Eccentric Sand Dollar, which is found all along the Pacific coast of of the US. Here's what they look like when they're alive:

They're filter feeders partially buried in the sand.
If you're referring to the white, conical shell attached to the sand dollar, that's a barnacle, probably a California Barnacle, but the species is easier to identify alive (or newly dead) than bleached and eroded (to me, anyway.)

you are amazing!

@Code42 - Thanks, but I just happen to like sand dollars and I'm good at searches. :)


text:
Why do immunoglobulin heavy chain genes sit next to the telomere?
I just noticed that IGH genes are the last group of genes at the end of Chromosome 14.
Is there a reason / selection pressure to explain why that is?
Below is a snapshot of the UCSC genome browser. As indicated by the red box at top right, there are no more genes further towards the telomere.
Regarding the fact that IGH genes have to go through DNA recombination, it superficially counteracts the telomere's mission to safeguard chromosome stability. Then is it merely a coincidence or reflecting selection pressure?
And why aren't light chain genes sitting at the end of a chromosome?


text:
A creature from Mediterranean
I have recently encountered this red thing on one of the beaches of east Sardinia. It was pretty close to the coast (around 20 meters) and has sticked to one of my fins.

My question is: how is this creature called? Is it dangerous to have skin contact with it?

Nice picture! I do not know what it is, but I would not touch it.

How big is it? The perspective makes it hard to tell. (And I agree, very nice picture!)

It was about 15x15 cm.

This appears to be an anemone, with the pedal disc towards the camera. I can't offer a positive ID, but would hazard a guess at genus Actinia.
Anemones sting, and the stings of anemones range from completely harmless to humans to highly toxic. In the absence of a positive ID, I would avoid poking it.

Thank you! Based on your answer, for me it is mostly similar to this one. Some of the photos here look very similar too, although I didn't see those blue dots near the tentacles.