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White residue from ginger juice
I’ve been juicing some ginger by grating it and pressing it (by hand). A white residue collects at the bottom of the liquid. What is this?

I believe that this white residue is starch. According to Wikipedia, Ginger is 17.7% carbohydrates by weight, or 14% excluding sugar and dietary fiber.
I let the residue settle and decanted the liquid. The residue behaved remarkably similarly to wet corn starch. From this, I have concluded that it is starch of some form.

good point. You can also test it if you have an iodene antisceptic, the soy sauce colored antisceptic. if you pour it on starch it turns dark blue.


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network with all the interactions of the alpha-synuclein protein
I would like to make a network with all the interactions of the alpha-synuclein protein (in homo sapiens), that is, I would like to visualize the pathways where this protein participates, I would also want to include in the network the genes that encode this protein.
I would like to make this network in cytoscape. Could someone tell me where I can find a guide to do this.
thanks in advance

What have you tried and where are you stuck?

I just started using cytoscape. What I don't know is how to obtain the pathways in which the alpha synuclein participates from cytoscape (or if there is a way to download them and then import them into cytoscape). I would like to create a node with the name snca (which is the alpha synuclein), then, from that node, get the pathways where the alpha synuclein participates, but I don't know how to achieve this

In other words, I would like to build an Enrichment Map, which would show the different pathways in which the alpha synuclein involved is found (both in diseases and physiologically), that is, each node would be a pathway, but each node, too, would be a subnetwork, I should be able to view each of these subnetworks. Excuse me for the question but I am just starting in this field

source: Stack Exchange

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

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How do tree gnarls develop?
What causes the trees to develop gnarls? I can't find anything on internet other than dictionary definitions.
Are they caused by a mold, like the burls?

If I understand what you're talking about by "gnarl", they are usually caused by a bacterium like Agrobacterium tumescens infecting and creating a gall.
Update: Forgot, also in some cases caused by insects.

Thanks, that means that "gnarl" is the same thing with "crown gall" ? By gnarl I meant the most common dictionary definition of it - a rough, knotty protuberance, especially on a tree.

@Ark25 I guess it depends what you mean by "gnarl"- it doesn't appear to be a very specific technical term. Looking at what shows up when you google image search, it shows both extreme twistedness and protuberances. If protuberances are what you mean, they indeed mostly seem to be caused by pathogens such as insects or microbes.

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


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Is there any other sources of hydrogen carriers for the Electron Transport Chain other than the 3 main metabolic pathways?
I am learning about the 4 main metabolic pathways for cellular respiration. I learned that NADH and FADH2 hydrogen carriers are essential in the Electron Transport Chain, because they deposit their hydrogens (which consist of protons and electrons), which are used to power ATP synthase (reattaches ADP and Pi together to form ATP) in the inner mitochondrial membrane. The reduced hydrogen carriers must come from glycolysis, pyruvate oxidation, and the Krebs cycle. I'm wondering if all those pathways were inhibited (and essentially broken completely) let's say, is there any possible way for the Electron Transport Chain to get hydrogen so that it can still work?

Hey welcome to the site!  Just to clarify the start of the question slightly... We generally speak of the electron carriers carrying electrons, not hydrogens... it avoids confusion with the hydrogens which are being pumped across the mitochondrial membrane, which are not the hydrogens attached to NADH, for instance.  In fact, the energy from the electrons on NADH pumps 10 H+ across the membrane (and it only loses one in the process).  Also, NADH actually drops off four electrons, two of which are not associated with a hydrogen at all but rather the pyrimidine rings.

Yes, but you should wait until you meet them in your studies. And you seem to be looking at things backwards. It's not a question of getting the ETC to "work", it's a question of whether there are any oxidative pathways that produce NADH or FADH2 to provide energy for an organism through using the ETC. You're very welcome to this site, but finish reading the Tour (you'll get a badge so that we know you have), and look at the Help on asking questions.


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Mapping of genes: are genes for physically close things close to each other?
I am not sure how to google this but the question is pretty simple: would the genes for the index finger be located "near" the genes for the thumb? Would all genes concerned with the heart be clustered together?

There are no “genes that code for the index finger” as such†, but the question is valid in more general terms.
In short the answer is no (or, at least, not necessarily).
This sort of principle is valid to a large extent in bacteria, where genes that work together are part of one transcriptional unit called an operon. The most famous example of this is the lac operon of Escherichia coli which contains a group of genes all concerned with the utilization of lactose as an energy source for this bacterium.
However in higher organisms, this need not be the case, a good example being the genes for the two chains which associate to form the human blood protein, haemoglobin. Despite their similar structure and probable evolutionary relatedness, the gene for alpha globin is located on chromosome 16, whereas that for beta globin is on chromosome 11.
Nevertheless there are some examples of what are termed gene clusters in higher eukaryotes. These are often genes of a single family that have arisen by relatively recent gene duplication. Over longer periods of evolutionary time they can become separated by recombination. One reason that this may not occur so frequently in prokaryotes is that groups of prokaryotic genes are often transcribed into single so-called polycistronic mRNAs — mRNAs that encode all the proteins of the cluster. This allows expression of the genes to be co-ordinately regulated.
Eukaryotic genes do not normally produce mRNAs of this type, and co-ordinate regulation (e.g. for alpha- and beta-globin chains) is achieved in a different manner. Thus, there is no strong selection against disruption of an ancestral cluster.
As always in biology, there are exceptions. Some eukaryotic genes need to be close together, for example those involved in the class specificity of immunoglobulins (the part that determines whether an antibody of the same specificity if in the cell membrane, or in mucous tissues etc.). These need to be close together for the process known as class switching.
–––––––––––––
† Particular related organs or parts of the anatomy like the different fingers are not encoded by different genes. They are different because of a development programme that determines the fate of precursor cells. That sort of thing lies in the general field of development and embryology, I believe.


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Why do we fall asleep when our eyes are closed?
Why do we fall asleep when our eyes are closed? When we lie down with our eyes closed, eventually we'll fall asleep. Why is that? Is our eyes closing some sort of trigger to our brain that says it's time to sleep?

Why am I sometimes unable to fall asleep, no matter how long I close my eyes? There's your answer: your assumption is wrong.

You're confusing the cause with the effect. The effect is closing of the eyes (and maybe some component of relaxing.) The cause is sleepiness. In other words, when you're falling asleep, the eyes close, not the other way around. You can close your eyes for 24 hours straight, and you might sleep longer out of boredom, but you'll still spend most of the time awake. Closing your eyes doesn't cause sleep.
The reason our eyes close when we sleep is to protect the eyes from drying out and insuring the oxygenation of the corneas.
Take for example truck drivers. They need to keep their eyes open or bad things happen, like death and destruction. Yet, as they tire, try as they might, their eyes flutter closed and off to sleep they go.
Driver drowsiness detection with eyelid related parameters by Support Vector Machine


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Are the first humans black
I have been doing some studies and I believe humans where black before they were white. Black has the ability to create all colors where as white derives from no color so white couldn't have been first. What do you think?

Which one would you consider "the first" in your question?

Possible duplicate of How did some humans evolve to be white?

"Black has the ability to create all colors where as white derives from no color so white couldn't have been first" – permanent black skin pigmentation was an adaptation to constant UV exposure of hairless skin. Our furry hominid ancestors didn't have much of a selective pressure to produce eumelanin (the black pigment) in the skin itself, which is why the logic is not as straightforward as you put it in your question.

Related found (after typing my own answer): https://biology.stackexchange.com/questions/61141/why-did-evolution-make-peoples-skin-dark

Welcome to SE Biology. This is a science site for questions and answers based on scientific principles. It is not a discussion site, so that statements like "I believe" and questions like "what do you think?" are good indications that a question is off-topic. I am afraid that supporting your belief with an argument like "Black has the ability to create all colors where as white derives from no color so white couldn't have been first" cannot be regarded as scientically appropriate here. Please re-read the Tour and the help on asking questions.

It very much depends upon what you call "first humans". Before there was any hairless hominids, the skin under the fur might have been very different from the skin colour of a human 30,000 years ago.
It appears that extant sister species of Homo sapiens sapiens tend to have a light skin under its fur according to a number of popular article I could find (e.g. this and that). Here is a picture of a hairless chimpanzee from the St-Louis (Missouri; USA) zoo

It suggests that the MRCA between humans and chimpanzees had a pale skin but please realize that this claim is based upon my research of poor quality references and it contradicts John's claim in the comments.
It sounds reasonable to me to think that humans skin evolved a darker skin when they started to lose fur and when their skin got more directly exposed to sun. Some human lineages have then "evolved back" lighter skin (see How did some humans evolve to be white?).
You can read more about the evolutionary forces controlling skin evolution in this answer.

chimps actually show both skin tones, as well as a selection of "inbetween patterns" although pale is the most common.  Gorilla on the other hand are far more often dark skinned. s we really don't know what skin tone our common ancestor had.

@John I would love a reference for these claims. I failed to find any good reference so that's why I went with popular sources and a zoo picture only.

Tanning does not work the same in chimps as it does humans (or gorilla for that matter), chimps can tan to a completely black color, unlike white humans they actually have the genes to produce dark skin even when pale. Since chimps are forest delling it makes sense savannah living humans might evolve a more permanent solution. http://www.sbs.utexas.edu/levin/bio213/articles/skin_deep.pdf

The first (African) humans with low body hair/fur (compared to apes) almost certainly acquired black skin pretty quickly, probably in a simultaneous process with shedding of hair/fur. This is based on genetic evidence, such as the low variation in the "African black skin gene". From Jablonsky and Chaplin (2010):

The near absence in African populations of functionally significant variation in the coding region of the melanocortin 1 receptor (MC1R), one of the major genes regulating human eumelanin production, indicates the action of purifying selection maintaining dark pigmentation under intense selective pressure (Harding et al., 2000; John et al., 2003; Makova and Norton, 2005). The evidence of functional constraint on MC1R in African populations is unusual in light of the high levels of polymorphism observed at other loci in African populations (Makova and Norton, 2005).  [...]
The loss of body hair in humans was accompanied by enhanced barrier functions of the stratum corneum (Montagna, 1981; Elias, 2005), including the evolution of other epidermal keratins (Chimpanzee Sequencing and Analysis Consortium, 2005; Moll et al., 2008), which reduced the skin’s permeability and improved its abilities to resist abrasion and microbial attack. The rapid divergence of genes responsible for epidermal differentiation was one of the most significant results to emerge from the initial comparison of human and chimpanzee genomes (Chimpanzee Sequencing and Analysis Consortium, 2005). Changes in skin pigmentation also accompanied loss of body hair, and multiple lines of evidence indicate that permanent, dark, eumelanin-based pigmentation evolved soon after the emergence of the genus Homo in Africa (Jablonski and Chaplin, 2000; Rogers et al., 2004).

How quickly this (combined process) happened is a matter of some debate, in particular because we don't know how quickly hair was shed (that also depends on the environment of ancestral hominids, which itself is somewhat contentious) or how quickly a (mostly hairless) species would die out (or evolve) because of skin cancers, in the absence of eumelanin protection. Greaves (2014) summarizes:

Early hominin evolution in East Africa at some 2–3 Ma was associated with a dramatic loss of the body hair development that is retained by our primate cousins. Hair growth was retained on the head—the most UVR-exposed part of the body of a bipedal hominin. Some exotic explanations have been entertained for this dramatic phenotypic shift, including avoidance of fur parasites or of catching fire, a response to wearing clothes or an adaptation to an aquatic way of life. But the most likely major adaptive advantage would have been for thermoregulation or facilitation of sweating and heat loss for physically active, hunter–gatherers in the savannah But what colour was the exposed skin of the first hairless hominins? Not black it would seem. The skin of our nearest primate relative, the chimpanzee, is, under the fur, essentially pale or white with melanocytes restricted to hair follicles. The exposed and relatively hairless face and hands are also white in infant chimpanzees of three Pan subspecies (but black in Pan paniscus) and they become facultatively pigmented with age. It has therefore been considered very likely, albeit not unambiguously so, that the first African hominins to discard hirsutism were also white- or pale-skinned. [...]
Rogers et al. [2004] proposed that, around the time that humans first became hairless savannah-dwellers, the now common allele swept to fixation under the influence of potent environmental selective pressure. They calculate a minimum date for the time since that sweep occurred of approximately 1.2 Myr  which is not too discordant with the time estimate of 1.7 Ma when hominins (Homo ergaster or Homo erectus) were believed to have first inhabited the savannah. The adoption of the more open savannah as a habitat may have resulted from climate change and consequential shifts in forestation and faunal species abundance.

With those estimates there's a 500,000-year gap between moving to savannah and getting black skin. What happened in between is a matter of conjecture. (Neither skin nor hair are preserved in the fossil record.) A more recent study (Tishkoff et al., 2017), which further analyzed black skin DNA variation in Africa, concludes:

Skin pigmentation is highly variable within Africa. Populations such as the San from southern Africa are almost as lightly pigmented as Asians, while the East African Nilo- Saharan populations are the most darkly pigmented in the world. Most alleles associated with light and dark pigmentation in our dataset are estimated to have originated prior to the origin of modern humans ~300 ky ago. In contrast to the lack of variation at MC1R, which is under purifying selection in Africa, our results indicate that both light and dark alleles at MFSD12, DDB1, OCA2, and HERC2 have been segregating in the hominin lineage for hundreds of thousands of years. Further, the ancestral allele is associated with light pigmentation in approximately half of the predicted causal SNPs; Neanderthal and Denisovan genome sequences, which diverged from modern human sequences 804 kya, contain the ancestral allele at all loci. These observations are consistent with the hypothesis that darker pigmentation is a derived trait that originated in the genus Homo within the past ~2 million years after human ancestors lost most of their protective body hair, though these ancestral hominins may have been moderately, rather than darkly, pigmented. Moreover, it appears that both light and dark pigmentation has continued to evolve over hominid history.

And speaking of Neanderthals, it is estimated by Lalueza-Fox et al. (2007), based on genetic evidence, that they developed an arrays of skin colors as well, by a mechanism similar to modern humans:

The melanocortin 1 receptor (MC1R) regulates pigmentation in humans and other vertebrates. Variants of MC1R with reduced function are associated with pale skin color and red hair in humans of primarily European origin. We amplified and sequenced a fragment of the MC1R gene (mc1r) from two Neanderthal remains. Both specimens have a mutation that was not found in approximately 3700 modern humans analyzed. Functional analyses show that this variant reduces MC1R activity to a level that alters hair and/or skin pigmentation in humans. The impaired activity of this variant suggests that Neanderthals varied in pigmentation levels, potentially on the scale observed in modern humans. Our data suggest that inactive MC1R variants evolved independently in both modern humans and Neanderthals.

And if the wide-variation of skin color within Africa (from recent studies) confuses you in relation to MC1R's [gene] lack of variation in Africa (it did baffle me for a while), Martin, Henn, et al. (2017) explain it as:

As
  observed previously, we find a strong correlation between absolute
  latitude and average skin pigmentation reflectance caused
  by melanin content. We also observe that populations with lighter
  skin have reduced variation within any given study: populations
  furthest from the equator have narrower distributions, while
  populations closest to the equator have wider distributions.
  These patterns suggest that selection is acting differently at
  different latitudes. In equatorial regions, strong directional selection
  for darker pigmentation has shifted the distribution means in
  some populations to M indices >90, but with wide variances. This
  is consistent with a ‘‘threshold’’ model (Chaplin, 2004) in which
  the protective benefit of melanin needs to meet some minimum
  threshold but with no penalty to darker pigmentation; alternatively,
  diversifying selection could maintain the wide variance.
In stark contrast, pigmentation in far northern European and
  Asian populations has been under directional selection for
  decreased melanin production, reflected by very narrow distributions.

Great answer, though I find the last citation a bit odd: "We also observe that populations with lighter skin have reduced variation within any given study: populations furthest from the equator have narrower distributions, while populations closest to the equator have wider distributions. These patterns suggest that selection is acting differently at different latitudes." The authors seem to ignore that non-African populations (1) have had less time to diversify and (2) have experienced bottlenecks. These factors should've been pointed out when discussing variation in non-African populations.


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Can I simulate a previous and next sequence of my fasta files?
I have fasta sequences of a virus protein that had suffered a mutation in 2008 and this mutant had increase it fitness when compared to wild strain (according to literature).I want to simulate if my sequences were submitted in 2009(I have sequences from 2009 and 2010) or before to mutation and simulate next sequences to verify if the population of mutants really turns majority.
How can I do that?My virus is a RNA -ss. Any tool to use that i can represent it graphically?
Or simplifying..I want to simulate the past and the future sequences of my dataset.

From the description of the model you are interested in, you probably don't need numerical simulations. A simple analytical expectation might be enough. But anyway, if you want to perform numerical simulations (maybe you don't want to assume panmixia), then there are a number of choices to you.
The simulator that you will need needs to be able to implement selection. It hence need to be a forward-in-time simulator and not a coalescent simulator. I personally have experienced with 4 different forward-in-time simulators; SimBit, SLiM, SFS_code and Nemo.
If I am not mistaken, SFS_code won't be able to simulate haploid individuals and will probably be slower than SimBit and SLiM. In SimBit, SLiM and Nemo you cannot do haploid either, however, you can set the cloning rate to 100%, which will essentially be the same as simulating haploids.
To my experience and generally speaking, SLiM and SimBit are faster than Nemo. Because your mutation rate is high and because your recombination rate will be very low (will probably be zero), SimBit should be very very fast and SLiM relatively slow. Also, SimBit will offer much nicer outputs for the statistic you are interested in than SLiM does.
Please note that I am the author of SimBit and I therefore might have a conflict of interest here. Here is the link to SimBit.


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Can I make an population genetic analysis from incomplete protein?
I have an dataset of fasta sequences. This proteins are not complete (My sequences have 700 nucleotides,while complete sequences have 1725 nucleotides)I would like to know if i can make an population genetic analysis from it(like TAJIMA-D test) and similars.

I think it is worth noting, mRNA expression does not equal protein expression.

If the sequences come from (approximately) the same part of the gene, i.e., if they can be meaningfully aligned, in order to build phylogeny.

It very much depends upon what type of analysis you want to perform. For a Tajima's D, all you would need is a single nucleotide sequences in a sample of individuals, so yes you can. Whether or not the sequence came from a gene or any other sequence is not relevant to whether you can compute Tajima's D (although the type of sequence will likely affect the resulting D).


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How to edit manually pdb structures?
I would like to manually movement some b-sheets from a 3D structure of my PDF protein to represent a change in its structure.There some tool i can do that?

If I remember correctly pymol can edit pdb structures (as well as visualize them).
I've used a student version though (which is a free full version) and I'm not quite sure if its possible to use pymol without buying a license nowadays (though the source code is still open source, so can in principle always install it from that)


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Mutation rate and Evolutionary rate?
What is the difference between then?I have read some jobs that describes analysis about mutations rates and another ones with evolutionary rates.I want to know the diferrence between then.

this answer may be helpful: https://biology.stackexchange.com/a/76988/42697

Mutation rate
A mutation rate is a rate at which mutation occur. It is typically expressed as number of mutations per site (per nucleotide) per reproduction event. In humans, the average mutation rate per site per reproduction is of the order of $10^{-8}$.
When a mutation occurs, it will exist in only one copy in the population. This mutations might, over time, reach a higher frequency and even eventually reach a frequency of 1.0 (we talk about "fixation" then) but most mutations will never reach a high frequency and just disappear in the coming few generations whether or not the selection was deleterious.
Evolutionary rate
The term "evolution rate" has no commonly agreed upon definition. Depending upon your definition the mutation rate will be more or less correlated with the evolutionary rate.
Phenotypic evolutionary rate
Evolution rate might refer to phenotypic evolution. The Darwin (named after Charles Darwin, obviously) is a common unit to measure phenotypic evolution. Other unit of evolutionary rate could consist at measuring how much time it take for the mean phenotypic trait to change by one standard deviation of the original phenotypic distribution.
Genetic evolutionary rate
One can also consider genetic measure of evolutionary rate such as the number of neutral substitutions in a lineage per unit of time. With this definition, the difference between the mutation rate and the evolutionary rate become a little more blurry as the expected substitution rate at neutral site is equal to the mutation rate.
Always cite your source!
You say

I have read some jobs that describes analysis

Please, always include your source. There is no way to give any information about what they mean by these terms without having a look at the original paper.
Related post
The post Are we “more evolved” than present-day bacteria? is somewhat related.


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What is the name of this graph/plot?

What is the name of this graph/plot?

This is a Circos plot, often used for visualizing genomic data, but works equally well for migration and some other applications.

Circos is a software package, and is capable of producing other graphic forms, such as concentric circles.

And there are other software packages that can produce these graphs, like the R-package circlize

Ah, yes you are correct. I've only ever heard them referred to as Circos plots colloquially, regardless of the software used to produce them, but thanks for correcting me!

I think it is Chord Diagram. you can see this for more information.

"A chord diagram is a graphical method of displaying the inter-relationships between data in a matrix. The data are arranged radially around a circle with the relationships between the data points typically drawn as arcs connecting the data." based on the Wikipedia


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Need help designing an enzyme immunoassay (EIA)
I have a FITC-labelled DNA sequence and an anti-FITC antibody, however this antibody is not conjugated with HRP or any other enzyme. I work in a lab with limited finances, so is there a way I could make use of these without having to order an HRP conjugated antibody to be used in a detection assay?

Could you make this a fluorescent assay and simply detect the FITC itself? Do you have a fluorescent plate reader? Or won't the FITC be at high enough levels to detect on its own, and you need the signal amplification of primary and secondary antibodies?

Thanks Matt! I have already done this, i.e. use a fluorescent plate reader to detect the FITC labelled DNA Seq. However, I was trying to find a way to design an ELISA assay without needing a conjugated antibody. But thanks for your suggestion, i'll keep searching!


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Cloning using pET28a and Protein Expression in DH5alpha and BL21
Can someone please direct me to an e-resource or a book that will help a newbie like me learn in depth about Cloning using pET28a and Protein Expression in DH5alpha and BL21. Though I have done cloning using TA vectors, pET28a is very new to me. I need to design primers with restriction sites to insert my GOI into pET28a, so would like to learn how to choose these sites too.

Did you try google? The pET28 systems manual and plasmid maps are easy to track down.
https://research.fhcrc.org/content/dam/stripe/hahn/methods/biochem/pet.pdf
https://www.addgene.org/vector-database/2565/

I did come across these, but they don't answer questions like how the choice of restriction sites will influence your protein. A fellow researcher was asked to justify his choice of restriction sites. If I remember correctly he had used a combination of SacI and HindIII for which a review a reviewer suggested replacing the SacI site as it might cause non-specific proteins to be expressed. It has something to do with codons, but I am not too sure, hence the query!

I can't think of a reason this would matter for this plasmid specifically. It's possible they were concerned about potential coding regions adjacent to your gene of interest, especially if it contained something like a promoter or an extra start codon. If your colleague sequenced their construct, it should be easy enough to spot any unwanted elements upstream of your GOI.

Thanks, i'll read more about this.


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What is the principle behind Microagglutination test (MAT)?
Could some one please help me understand the principle behind the MAT. Also, is it only used to detect leptospira or can it be used to detect other pathogens as well? Kindly share links to papers if possible. Thanks!

Microagglutination tests for the presence of antibodies, which declare themselves by sticking their antigens together into clumps.
Leptospirosis serodiagnosis by the microscopic agglutination test.

The microscopic agglutination test (MAT) is the gold standard for
  sero‐diagnosis of leptospirosis because of its unsurpassed diagnostic
  specificity. It uses panels of live leptospires, ideally recent
  isolates, representing the circulating serovars from the area where
  the patient became infected. A dilution series of the patient's serum
  is mixed with a suspension of live leptospires in microtiter plates.
  After incubating for about 2 hr at 30°C, results are read under the
  dark‐field microscope. The titer is the last dilution in which ≥50% of
  the leptospires have remained agglutinated

You know antibodies work by sticking to things.  If I have antibodies to a thing it is because I have been exposed to that thing, here leptospirosis.  This version of the test uses live leptospires.  If the antibodies stick to them and they clump up, the plate gets clearer.
The good thing about this is that you don't need to know anything about the antigen except it is on the germs, or anything about the antibody except it sticks to the antigen.  Also the germs are big enough to make clumps.  If you have antigens for something small that won't make noticeable clumps (for example a virus, or an individual antigen of interest) you can stick it to microscopic latex beads.  Then just as the leptospires clump up when the antibody sticks to them, the beads will clump up if there is antibody specific to the antigen you have stuck onto them.
Historically the clumping was interpreted by a human with a microscope.  You can do it with an optical density meter too.
This approach is used for lots of different diagnostic tests.  You can find them with google and "agglutination" but those links do not all explain how the test works.

Thank you Willk for explaining it so well..


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Humic Acid and PCR
Quite a few papers claim humic acid is an inhibitor of PCR reactions. I understand this is true when working with soil microbes, but how does it qualify to be a PCR inhibitor in general (i.e when not working with soil micorbes)?

In general, when not working with samples from soil or natural waters, you propbably won't encounter high enough concentrations of humic substances to inhibit your PCRs.
If you're just asking about mechanisms of inhibition, humic acids can interact with polymerase enzymes, with nucleic acids (altering primer annealing and template melting temperatures), and they can partially quench fluorescence in the case of real-time PCR (see Overcoming Inhibition in Real-Time Diagnostic PCR). There are numerous sources in this reference if you want to read more about how these different interactions were characterized in the lab.

The chapter is paywalled, but is available for those who can access Researchgate at https://www.researchgate.net/

Thanks! that is really helpful.


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What are low-accumulating genotypes?
What are low-accumulating genotypes? And how does it differ from high-accumulating genotypes?

Certain organisms and strains of organisms can accumulate certain substances, such as Cd (a toxic heavy metal). If they accumulate those compounds more rapidly and in higher amounts, the organisms can be said to be high-"substance" accumulating organisms, e.g high-Cd accumulating organisms. The genotype which causes being a high-Cd accumulating organism can be described as a high-"x" accumulating genotype, like in this paper. A high-accumulating genotype without reference to a certain substance doesn't make much sense. These genotypic predispositions to accumulation are often used with toxic heavy metals like cadmium and arsenic which persist in the body.
As another "substance" example, high-"x" accumulating organisms is also used in the description of high-plaque accumulating organisms, e.g in this paper.


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Hormones - biotic or abiotic
Are hormones biotic or abiotic? I have tried reading different articles, and  I've found that it is both, but can that be true?

Hormones occur inside organisms as signaling factors, and arise from biological activities, the development or homeostasis of the organism.
Thus, they are considered neither biotic nor abiotic factors in ecology.
(Terrible analogy warning!) It's a bit like asking whether the wheel of a car is petrol- or diesel-based. It makes no sense!
Here's a helpful, comprehensive website.
Biotic (living) factors are living organisms in an ecosystem, that must share common resources or compete in a habitat one way or another. Abiotic (non-living) factors are things like temperature, wind, salinity, etc. that affect individuals or the community of an ecosystem.
As you can see, hormones are just one of many components of a living organism. They aren't living themselves. It's a silly question to ask, as I hope you can see now.


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Possibility a child could genetically be also the child of its grandparent
When two people have sex, it is my understanding their diploid set of chromosomes is randomly split in half to create two haploid sets which unify into the diploid chromosome set of the child. For each parent, there are $2^{23}$ possible permutations or a chance of about 1 in 8 million. Let these two be the grandparents A and B, having a child C
When that child C has a new sex partner D and they make their own child E, in any case E shares half of its genetic makeup with both C and D (i.e., E is the child of C and D). However, with a probability of roughly 1:8000000, when halving C's diploid choromosome set, it always chose the A parts.
This means that E would then have a diploid set A+D, which would make E appear to be both the child of C but genetically could also be the child of A.
Is this understanding (discounting for crossing over and other genetics) generally correct? With a probability that high, if it is possible there should be recorded cases of this (although for all intents and purposes you would never know without sequencing DNA). Are there such cases?

But can we ever discount crossing over? Crossing over is a very consistent feature of meiosis. Perhaps what you say is possible for certain closely linked loci (e.g. HLA haplotypes). But it is hard to imagine for the entire chromosome complement of a sexually reproducing organism.

Welcome to Biology.SE! We encourage you to do some research on your own and then, informed by what you have learned, ask any questions you still have (ideally with references to reliable sources). Since this question "addresses a basic biology concept that may seem trivial to biology professionals", it qualifies as  "homework". In particular, you can't discount crossovers since they are essential. ——— Please take the [tour] and then go through the help pages starting with [Ask] questions effectively on this site. Thanks!

I have found that when learning about a new area starting with a relatively accessible and reliable source like Khan Academy is very helpful. Wikipedia is also generally a good starting point and you can then check their references. Online platforms called MOOCs offer free (or very low cost) courses on a wide variety of subjects — two I am familiar with are Coursera and edX. Finally, textbooks with a good level of detail are also freely available online e.g. from NCBI.

One of the hallmark features of meiosis is the formation of chiasmata, that is the crossing over and exchange of genetic information.
These structures are generally necesssary for a normal meiosis to happen and you should never get a cygote without any crossing over. Therefore, taking this into account, the probability of your scenario is 0.

I've been thinking some more about crossing over, and think it might not be an issue at all. In the 1:2^23 case, even with crossing over, the DNA would be mixed with some other, but would generally still appear to have both the actual parent and the grandparent as possible parents. Or would the crossing over make this always distinctively clear?

The crossig over in meiosis in the child (C) of the grandparents (A and B) means that all of the chromosomes will be a mixture of A and B. Therefore, the information to the subsequent child E cannot be purely A or B. There may be instances that on a particular chromosome where crossing occurs in a perfectly homologous region making it invisible. But for this to happen on every chromosome A and B would have to be perfect clones.

I think this 1:8000000 should be further diminished twice by the chances to get "intact"(no cross-over) chromosome from A to C and from C to E. So the final probability is far less.


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Analysing bird diversity data across 2 sites and 2 time periods - Ordinal data. Shannon's and Jaccard?
I am looking for the best way to analyse the data below to measure and compare the bird diversity between 3 sites and across 2 time periods - basically are they different from each other and have they changed over time.
I don't have count data from 1992 so have applied the categories used in a historical publication to my own data. I was planning to use Shannon's on the table below and Jaccard's using presence/absence data. Is that the most appropriate, and will these give me p values? If not what further tests would be needed?

Welcome to Biology.SE! Please take the [tour] and then go through the help pages starting with [Ask] questions effectively on this site. In particular, since this question appears to be entirely statistical in nature, it would be a better fit for [CrossValidated.SE]. Thanks!

I’m voting to close this because it is a purely statistical question and so belongs on [statistics.se].

Update: see also answers here.
I would strongly recommend reading more about species co-occurrence analysis, e.g. here and here. This is a big field where a lot of work has been done on its statistical analysis and interpretation.
You can apply any metric that you want for a a basic idea of correlations in the data, and for that the simple distances are a reasonable place to start (I would probably just look at a correlation matrix or do principal component analysis to start, but it's a matter of taste). But you will likely want to use something more sophisticated to account for multivariate structure in the data.
In terms of statistical inference, e.g. p-values, you will need to do something more than those metrics. Distance measures are just metrics, they don't apply tests and they don't have a null hypothesis per se. The easiest thing to do would be to apply an empirical null distribution by generating a large set of permuted datasets (where you shuffle the labels of the rows and columns), compute the metric for each permuted dataset, and then use that distribution of metrics as a null distribution. That's still a fairly naive analysis, but it is at least accounting for some of the structure of the data.
I think that your best bet is to go read some more about how people have analyzed this kind of data in the past, and use some of those tools. If you are comfortable with R, the vegan library is a good place to start, it has already implemented a lot of the basic analyses you might use.
Update on ordinal data:
For helpful background on ordinal data analysis, I would suggest reading this paper. As for transforming ordinal data to ranks, that is fairly simple as long as it's ok for the method for you to have ties in rank.
I am also assuming that you have data that look like this, in addition to the table that you screenshotted:
Site |  Species  |  Amount   |
A    |     X     |  common   |
A    |     Y     |very common|
A    |     Z     |    rare   |
B    |     X     |    rare   |
B    |     Y     |   common  |
B    |     Z     |very common|

You can turn this into ranks:
Site |  Species  |  Amount   |
A    |     X     |  2        |
A    |     Y     |  1        |
A    |     Z     |  3        |
B    |     X     |  3        |
B    |     Y     |  2        |
B    |     Z     |  1        |

Thanks for your comment. I have read the links that you supplied and would like to produce rank abundance graphs as shown in the paper. As I mentioned I only have ordinal frequency data (very common, common etc), so how do I transform this so that I can attribute a numerical frequency to each species?

Ah, just from looking at the table I guess I didn't understand very well what you meant. See update.


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Monitoring fish growth
I am new to experimental design and would like to perform the following experiment but I am not sure if my setup is correct or not so far. My idea is below.
Let's say you want to optimise the growth conditions for the small fish. You have been using a standard type of food that has worked reasonably well. But now, three new brands of fish food have become available and you want to conduct an experiment to test which food works best and whether it is worth changing to one of the new food types.
Based on previous experiments, you reckon that you will need 10 replicates to have a good chance
of finding meaningful differences. There are aquaria and tanks of different sizes available so you are not constrained by the equipment.
For this experiment, I assume there will three treatments (the three new brands of fish food). I will call them A, B and C. Since we are testing three different brands of fish food, I will replicate the brands of fish food three times so there will be nine tanks used in this experiment in total. I will label them from 1 to 9. Each tank will randomly receive different food brands of food every day.
But now, how will I find meaningful differences in the growth of the fish if I apply different treatments randomly on the tanks? Also, how many fish do you recommend I use for this experiment and how often should I monitor the growth of the fish? Or do you have another suggestion of how I could set up this experiment, help. Your help will be much appreciated. Thanks in advance.

If this is a homework question, please consider using the 'homework' tag, and consult the homework policy here (https://biology.stackexchange.com/help/homework). It will help people give more suitable answers. If you are actually designing this experiment in real life, it would help if you included information about the species and the purpose for which they are being raised.

This is a good experiment!

3 types of food: ok
9 tanks: ok
Apply different treatment randomly - no!  You are right - no way to tell with random.  It is like comparing McDonalds to vegan diet - you have to stick with your diet to be able to tell a difference in the end.  So Tank 1,2 and 3 get food A.  Tank 4,5,6 get food B.  Tank 7,8,9 get food C.  Each tank gets only that food type.
You need to add the same weight of food to each tank.  More food = more weight.  I am not sure how much is a reasonable amount; it depends on your fish.  Get out what looks like a reasonable amount and weigh it.  That is how much food each tank gets each day.  You could weigh a bunch of quantities and set them aside - maybe in an old egg carton?  That will make it easier to feed the fish each day.
Have the same number of fish in each tank. 3?   Ideally fish are same species and all about the same size to start because different size fish might not all gain weight at the same rate.  I am picturing 11-cent goldfish.  3 per tank makes 9 / food type and 27 in all which is good.
You can gauge growth by fish weight.  When it is time to weigh the fish, fill a bowl with aquarium water, put it on the balance and zero the balance.  Now you will get the weight of whatever you add to the cup because balance is ignoring weight of cup and water.  Sweep a fish out of the tank into the bowl and weigh it.  Then dump it back in and repeat with next fish.

If you can tell the fish apart each one can be weighed separately and I think should also be named with a name that starts with the letter of its tank because that is fun.  For example TankB4 will contain Burr, Black Beauty and Buzz.  Tank C7 will have Clyde, Caesar and Coco.

Keep track in your lab notebook of each fish and its weight
Take a photo of each one when you start and one when you are done.  This will make a great poster.

You can weigh fish every day or maybe every 3 days if you think handling them is too stressful.
You can track weight for each fish, average weight for each tank, and average weight for all 9 fish (3 fish / tank x 3 tanks / food type) over time.  You will graph each of these.  You can use Excel or do by hand.

If a fish or 2 die your data will still be good.
Good luck!  I hope you will come back to this idea and post an image of Aloysius, Angel and Aesop, the fish from tank A1.

It would still strengthen the design for treatments to be randomly assigned among the tanks, ideally in a way that is blinded to the person weighing the fish (you keep a key to which tank gets which treatment, decode after data collected). This helps guard againt measurement bias and other possible confunding variable, like where the tanks are located in the room, or the order in which they are fed. You'll also want way more than 3 fish per tank, depending on the stage of development they are starting at. Some fish have very low rates of survival to adulthood.

Per your prompt, there are three new brands of fish food, but...

You have been using a standard type of food that has worked reasonably well.

I suggest you include the "standard" fish food as a control, so that you will have four treatments in all.  Besides that, the experimental framework suggested by Willk is spot-on.


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Question about manifestation of an X linked disease (homework help)
I am doing some practice exams and came across this question. Perhaps the answer is a lot simpler than my reasoning, but after discussing it with others, I still disagree why the answer is clearly and simply what it is, based on the information given. I apologise that this is quite long.
-- Question starts below --
During very early development of mammalian females, one of the two X-chromosomes normally present in each somatic cell becomes deactivated. Deactivation is random, with maternal and paternal X-chromosomes equally likely to be deactivated. All cells deriving from this cell carry the same deactivated X-chromosome, creating local patches of tissue with either maternal or paternal features.
Anhidrotic ectodermal dysplasia (AED) is an inherited skin condition caused by the recessive allele (s) of a gene that has its locus on the X-chromosome. In some individuals with this condition, sweat gland distribution is patchy, with some areas lacking glands. In other individuals with this condition, all sweat glands are absent.
Q) Which one of the following is consistent with the information given?
A. Patchy sweat gland distribution is found in males and females but is more likely in males with the condition.
B. Patchy sweat gland distribution is equally likely in both males and females with the condition.
C. Only females with the condition exhibit patchy sweat gland distribution.
D. Only males with the condition exhibit patchy sweat gland distribution.
--/ End of question /--
The correct answer is C.
I would like to know why you should be able to tell that only females will manifest or show patchy sweat gland distribution. My reasoning is, individuals with the condition must have the recessive gene. Since it is an X-linked condition, and females have 2 X chromosomes, they must have 2 copies of the recessive gene (little r) to get this condition. Thus, females must be homozygous recessive, or rr. (Males will be just r)
If we define the phenotype of having the recessive gene, we say that it causes the individual to either have patchy sweat glands or no sweat glands at all.
The thing is, they do not tell you under what conditions a person will have patchy sweat glands and when they will have no sweat glands at all.
As an X linked disease, think haemophilia, they should either have the skin condition or they don't: in other words, even though females can be carriers, carrier females will not exhibit the symptoms and the question talks about all humans with the condition (either males or females).
How* the disease manifests isn't made clear, so how are you supposed to know that of those who have patchy sweat glands vs those that have absolutely no sweat glands, females are those who have patchy sweat glands?
*For example: Angelman and Prader-Willi syndromes are both results of errors on the same region of chromosome 15, however, the important how that explains who gets PW and who gets A is, the phenotype depends on whether it was maternal or paternally inherited due to imprinting. By telling you this, if the question told you that males tend to get PWS and females tend to get Angelmann, told you some characteristics of either syndrome, then told you "given that a specific gender is more likely to get a specific symptom", you can determine whether males or females are more likely to have which disorder (PWS or A) based on the symptoms.
The question doesn't have this sort of information. It doesn't tell you how a person tend to get patchy or missing sweat glands and it doesn't tell you anything about incomplete dominance, co dominance, mosaicism etc.
Since both males and females can have the condition but it is X linked, why is it not A, but C? Why can't males have patchy sweat gland distribution too?
One answer I got while discussing this was, the information tells you about random inactivation of the X chromosome. For me, this only further rejects the idea that only females can be patchy: Suppose a female is a heterozygous with XRXr. One X chromosome gets inactivated at random. Let's pretend XR gets inactivated, leaving Xr. This is the recessive gene.
Now, we know the recessive gene produces the skin condition, with could be either patchy or completely absent sweat glands. Sadly, it does not tell you the exact symptoms of the recessive gene, see my point and analogy about PWS and A above. Since it doesn't tell you, I can only assume that like all other X linked diseases, having the recessive gene will cause the individual to show symptoms of that disease, end. of. story.
Usually when you have the recessive gene, you have the disease. Either have or not have. Thus an XRXr female with the first X inactivated will have the recessive disease, and fully show this (no sweat glands at all).
If she was XRXr with the second X inactivated, she would show a normal phenotype and have fully functional in ordinary quantity sweat glands.
Similar case with the males: They either have, or not have the faulty gene due to the fact that they only have 1 X chromosome.
Now, during my discussion with others, someone said, if you assume one copy of AED X (Xr) + one copy of normal X (XR) = carrier, then there is no way to explain how anyone could have patchy glands. They either have normal glands or no glands at all.
But I think this is exactly the point. The question doesn't ask you how anyone could have either patchy or totally absent sweat glands. Why does patchy glands exist? Where is the condition of its existence? It's not told to you in the question. The normal train of thought (at least for me) is therefore, a female either has it or they don't. No where does the text mention anything to do with mosaicism. IF the question TOLD you that AED is inherited the way it is etc, then stated that females have patchy sweat glands while males have none at all, and asked you "what is the most likely reason for this", then you can say mosaicism.
This direction of reasoning would work much more logically.
Otherwise I just don't see this.
Would appreciate input from someone specialising in genetics.

"Since it is an X-linked condition, and females have 2 X chromosomes, they must have 2 copies of the recessive gene (little r) to get this condition." Your reasoning here is wrong. Actually, to have a patchy phenotype, the female must be heterozygous! Therefore, the cell patches where $X^r$ was inactivated exhibit normal phenotype, and cell patches where $X^R$ was inactivated are missing the sweat glands. Men cannot exhibit patchy phenotype because we can only have either $X^r$ (missing glands everywhere) or $X^R$ (normal phenotype everywhere).

@Domen you should write a formal answer

@Domen Thanks but I know that, given the statement, "men cannot have patchy, only females have patchy", then the reasoning is because men only have one X. That's fine. However, in the REVERSE, if they say "some people have patchy, some people have none at all - why?" how do you determine that females have patchy and not men? It makes no sense to me that if a female has XRXr, why would she be patchy, rather than completely normal? Do we assume codominance? I'd like to use hemophilia as a comparison and example -->

@Domen <-- Correct me if I'm wrong: the dominant version of a gene dominates the phenotype, eg in hemophilia, even if the female genotype is XHXh, the female will have a completely normal phenotype because the normal version of the H dominates over the h. Why should AED be any different?
And, if we bring random X inactivation into the picture, does that mean if the X carrying the normal H in a female carrier gets inactivated, then the female will have only the recessive little h, Xh, left, making her fully hemophiliac?

If the disorder was an autosomal recessive disorder (and follows the Mendelian dominant/recessive pattern), then your reasoning is correct. Both men and women would either have the disease or not have it at all. If a person has a heterozygous genotype, then they will not have the disease. Only if they have a homozygous recessive genotype will they show any characteristics of the disorder. This is clear for an autosomal recessive disorder. However, for X-linked disorders, things are a bit more complicated. For men, there is no concept of recessive/dominant alleles. They either have the gene or they don't. For women, we need to consider X-inactivation.
When we talk about X-inactivation, the main thing is that it is random. That means that approximately half the cells in the body will contain one copy while the other half will contain the other copy. Some cells will have the recessive allele and some cells will have the dominant allele. In the case of color-blindness, this means that approximately half the cone cells will be defective and the other half will not be. Most of the time, the brain learns to ignore the signals from the defective cells. However, there are cases when the number of defective cells are more than the number of normal cells (due to random inactivation), and the woman experiences Tetrachromacy. You cannot compare these disorders to Angelman and Prader-Willi syndromes because those are on autosomal chromosomes and these are on the sex-chromosomes. You are right that the textbook does not mention anything about which individuals have patchy sweat glands and which do not have them at all. However, the textbook states that the recessive allele on the X-chromosome causes the condition. This means that we can assume both of these things:

The trait is not a polygenic trait - That is, it is only caused by the gene on the X-chromosome
The trait follows Mendelian dominant/recessive pattern

Although we cannot be extremely sure of either of those, we can assume it for sake of this problem. Since the allele is able to cause a complete loss of sweat glands, we can arrive at the conclusion that the recessive gene causes the loss of sweat glands. The reason some women have patchy sweat glands is because of X-inactivation, and the areas in which the recessive copy is activated will have no sweat glands. On the other hand, the areas where the dominant copy is activated will have normal sweat glands. I think that the source of confusion here are the terms dominant and recessive. Although we say that the trait is "recessive", there is no concept of dominance and recession for X-linked traits. For women, half the cells are deactivated whether or not they contain recessive traits. For men, the X-chromosome is used whether or not they contain recessive traits. This means that the even if a dominant gene (in the X-chromosome) was recessive, it would have no consequences on the phenotype. In women, the phenotype (for X-linked traits) is always random (or rather, it's different for each cell - some sweat glands have one phenotype while others have a different phenotype).
Regarding your question on why men can't have patchy sweat glands, consider this. A man would either have the defective copy of the gene or a normal copy of the gene. Normal copy would result in normal sweat glands, so we have to deduce that the defective copy would result in patchy sweat glands. However, this would mean that women with homozygous recessive would still have patchy sweat glands. Hence, nobody would have complete loss of sweat glands. Since the question clearly states that some people have patchy sweat glands while others have a loss of sweat glands, we have to assume that the defective allele causes a complete loss of sweat glands. This makes sense as men would have no sweat glands, homozygous recessive women would still have no sweat glands, and heterozygous women would have patchy sweat glands. Because this is the only answer that makes sense, this must be the correct answer.
Like you said, this isn't guaranteed to be true, especially if the trait is polygenic. However, it's the most plausible answer (and the other three are definitely false), so I think that the question is put in a fairly good manner. Since they have mentioned the trait to be X-linked "recessive" (like I mentioned before, there really isn't a concept of recession for X-linked traits), we can assume that it follows the Mendelian pattern, and making this assumption, arrive at the right answer.
Hope I answered your question!

Thank you for this comprehensive answer. You are right in that my confusion was from the terms dominant and recessive and the lack of this in X-linked traits. I also see why the Angelman and Prader-Willi syndromes are a different case. Thanks!


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Is there a resource to query gene expression similarity? Stratified by sex?
We developed such a resource. The editors of "Bioinformatics" (at OUP) rejected the paper on the grounds that we did not run a comparison with "state-of-the-art" [similar] resources. Can someone help me find similar resources?
Link to our own resource, ExFiles.

There are some similar resources, which at the same time do not mix the ingredients in the same way as your tool. Hope that pointing out the respective differences will help your rebuttal or resubmission! e.g.:

A similar web service is part of flyrnai, where tissue-specific expression can also be compared based on sex, and genes can be queried by the similarity of their gene expression profile to other genes.
A resource which has some conceptual relatedness regarding sex-specific similarity in gene expression, and which is also based on the GTEx dataset, has recently been published in the form of tables by Gershoni et Pietrokovski, BMC Biol, 2017.
A very nice and broad resource of high-quality gene expression profiles on individual tissues, but also on response to stimuli, is EBI GXA. Though the web interface only allows to query sex-specific expression profiles, computationally minded people could readily compute (dis)similarity between samples after downloading the (well-structured) data dump of EBI GXA.
A similar tool in terms of the visualization of sex-specific expression patterns is part of gtexportal, though it seems limited to displaying a single gene.


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Linkage between chromosomal loci and the Hardy-Weinberg principle
Referring to the Hardy-Weinberg principle, which is always stated with respect to a particular locus on the chromosome, if we observe that two different loci are either always together in Hardy-Weinberg equilibrium, or never together in Hardy-Weinberg equilibrium, can this give us any information about the linkage between the two loci?

Linkage is a physical reality
Linkage refers to the physical presence of several loci on the same chromosome. Two loci that are said to be in close linkage mean that they are locataed relatively close to each other on the same chromosome.
Linkage disequilibrium is a statistical reality
If two alleles at two different loci are found on the same haplotype than expected by chance (that is more often than the product of the respective frequencies), then there is an statistical association between these two loci. We refer to this statistical association as linkage disequilibrium (abbreviated LD). If there is absence of statistical association, then the two loci are independent and we talk about linkage equilibrium.
Let's consider two bi-allelic loci. We will call the two loci A and B and the two alleles A0, A1 and B0, B1 at loci A and B, respectively. Let $p_{AB}$ be the frequency of the haplotypes that contains both A1 and B1. Let $p_A$ and $p_B$ be the frequencies of the alleles A1 and B1, respectively. Then LD between the loci A and B (called $D_{AB}$) is defined as
$$D_{AB} = p_{AB} - p_Ap_B$$
There are other related mathematical definitions of LD than $D_{AB}$, such as $D'_{AB} = \frac{D_{AB}}{D_{max,AB}}$ and the correlation of coefficient among the two loci $r_{AB}$.
Linkage disequilibrium and Hardy-Weinberg equilibrium
You will note that, knowing whether or not each of the two loci are in H-W equilibrium (or respecting the assumptions of H-W equilibrium; see this post) tells you nothing about LD among these two loci. For example, two loci can both be at H-W equilibrium but be in LD (or in linkage equilibrium). Similarly, two loci can both be at H-W non-equilibrium but be in LD (or in linkage equilibrium).
Linkage and linkage disequilibrium
Various things can affect LD. In theory, there is no need for linkage to have linkage disequilibrium. A strong epistatic interaction for early life survival between two loci (whether or not they are in the same chromosome) can eventually cause  LD. However, most of the cases of known LD probably concern pairs of loci found in tight linkage. There is a diversity of processes that lead to LD and reviewing them would be outside the scope of this answer.


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difference between animal and plant durring organogenesis
i would like to ask about organogenesis of animals and the relationship with plant as we depends on it. me i do not understand why we are totally different with the plant but all of our body made comes from plant.
please help me to understand

Our development is determined by our genes, encoded in our DNA. This is what makes a human who eats a salad develop differently from a pig, butterfly, or even bacterium eating that same salad.
To understand how all of this works, you can start by studying introductory biology. A nice way to start might be by searching youtube for "introductory high school biology", and selecting a video sponsored by a university. You can also find many free introductory textbooks online.
Then, you will begin to understand the secrets of how life works!


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Programming Languages for Bioinformatics
Good day everyone,
I am very much interested in math, biology, and programming; ideally I would like to study bioinformatics and, as such, was wondering what languages are typically used by bioinformaticians? Particularly for fields of bioinformatics relevant to biochemistry, virology, and immunology, what are the most useful programming languages to know, as well as their strengths and limitations? (i.e. in what setting would it be better to program in C++ than Python, is R good to know and if so why, etc.)
Thank you in advance.

I believe many relevant pointers can be found over at https://biology.stackexchange.com/questions/15292/basic-programming-and-bioinformatics. In short: If you're expecting to do data analysis and visualisation, R. Anything else where you work on the biology itself, Python. Everything else is unpredictable; you might end up doing modelling in Mathematica or C++, or developing plugins for FIJI in Java.

Hi welcome to Bio.SE! This question is unfortunately off-topic here, and likely will be closed. However, in addition to the already linked post, a related discussion exists on our Meta site that might also interest you: What program(s) do biologists use for statistics?

The languages currently popular for bioinformatics work are Python, Java, R, Perl, and BASH, though the use of Perl is gradually declining. Note that Python has become the most popular language in a general context, so it's natural that it's the most popular in bioinformatics too. Of course 'most popular' doesn't mean 'best'. Pick a computer language and I'll point you to some publication that uses that language in a bioinformatics context (perhaps excepting COBOL, and RPG).
Some bioinformaticists spend most of their time performing analyses using existing software, perhaps using a scripting language like Python or BASH to 'glue' together existing programs or to control the submission of jobs to a computational cluster. Scripts and packages using the R language are often used in these analysis pipelines to perform sophisticated statistical analysis and visualizations.
Other bioinformaticists are developing new algorthims. In these cases computational speed can be very important, so languages like C, C++, or Java are often used. However, newer languages like Go, Julia, and Rust are also used, according to the taste of the researcher. New machine learning tools are often prototyped using MATLAB.
If your interest is primarily biology (biochemistry, virology, and immunology) then you'll want to focus on learning the biology, and supplement it with Python, BASH, and R. If your interest is primarily in developing new algorithms, then focus on statistics, and computer science. You'll still want to learn Python, BASH, and R, but you'll also want to add something like C++ or Java. The good news is that once you've learned 2 or 3 computer languages, you can pick up additional languages much more easily.

Good resources for getting started: https://www.biostarhandbook.com/ and https://software.broadinstitute.org/gatk/best-practices/ . I very much recommend biostar handbook as they have you work with manageable datasets in a class-like format.


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Help Finding Specific BlaZ Gene Type Sequences on Genbank
I am doing an undergraduate research project that involves blaZ gene typing for different strain types of Staphylococcus aureus bacteria; for reference, here are some of papers on this topic that explore resistance abilities of the blaZ gene and four different beta-lactamases that can be produced:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3421557/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC188454/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC106017/
Right now, I need to be able to align blaZ gene sequences in plasmids (or chromosomal regions, when looking at type B blaZ genes) of different S. aureus strains to the four blaZ gene type (A, B, C, and D) reference strains as mentioned in the first paper. The first paper provides the following strains as "reference strains": type A, PC1; type B, 22260; type C, RN98; and type D, FAR19. However, I can only find the nucleotide sequence for PC1 and the partial sequence for 22260 on Genbank; when I search for RN98 or FAR19, no relevant results come up. Even though these gene type sequences should only be distinguishable by several SNPs, it's important that I have references for all four for the most accurate strain typing.
In the last paper, I noticed they provide some of the differences between all four blaZ gene type nucleotide sequences, but not in a "from start to finish" order (i.e. the numbering above the nucleotides is not sequential and the sequence comparison length overall is less than 846, which is the length of the PC1 coding regions). Please advise, as after countless searches on Genbank I still do not have the specific sequencing information I need for each blaZ type.

The sequences from that first paper probably weren't submitted to any database. If you want those specific sequences, you could try contacting the corresponding author.

You might have already tried this, but how about doing a BLAST search using one of the sequences you do have, and perhaps limiting the organism to Staph a.
More info:
The below article did a similar comparison and mentions types RN9 and FAR10, perhaps these are related (or  misspelled originally)  See below.
"Real-Time PCR Assay for Detection of blaZ Genes in Staphylococcus aureus Clinical Isolates" at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3993521/
mentions:
"type A, PC1(pI254) and NCTC 9789; type B, 22260 and ST79/741; type C, 3804(pII3804), RN9(pII147), and V137; and type D, FAR10"
In any case I highly recommend going to your original article of interest, and use the "cited by" function to find more recent papers.

Thank you! I tried this and a list of S. aureus strains with significant alignments were produced. Unfortunately none of them contained the reference sequence strain types as mentioned in the paper, i.e. RN98 or FAR19, but another paper I found gave some information on the types of differences (and number of differences) between amino acid sequences in each one, so I might be able to look into that as well for deducing sequence differences.


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How does heat stress kill plants?
It is known that many plants typically die when exposed to temperatures of 40 to 50°C (I believe, these figures relate to exposure times of ~1–2 hours). When a plant is heated, what is the mode of action at the cellular level that results in the death of the plant? What are the main mechanisms involved?
P.S. I have read that at high temperatures plants synthesize heat-shock proteins, so I am not sure if this plays a role in the death of the plant. Another obvious effect following heating is the damage/denaturation of some chlorophylls (but a plant should be able to survive the loss of some photosynthesis; e.g. detaching leaves will not kill a plant).
Any explanation is greatly appreciated.

High temperatures cause 'global' damage, i.e. damage to pretty much the entire plant, everywhere, in every cell. One of the most major mechanisms is denaturations of proteins in the cell; proteins misfold, aggregate, lose their structure and function, and cannot sustain the variety of metabolic and other activities in the cell. Heat shock proteins are proteins responsible for stabilizing or re-folding proteins that denature. They are expressed as a last line of defense, which can only partial protect or rescue the plant from accumulating proteinaceous debris and temperature-related damage.

And perhaps I should add that if you cause such widespread protein dysfunction, you obviously and adversely affect cell signaling, transport of metabolites, the ability of the cell to regulate its own behavior, or multiply, or generate energy, regulate water content, fight off microbial pathogens, let alone sustain the catabolic, long chain of reactions required for photosynthesis and growth. The list is very long if you go into molecular detail. And on the cellular level, plant cells survive by maintaining gradients and compartments which require a positive energy balance to upkeep.


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Why isn't Fluorine, or Neon, the final electron acceptor in cellular respiration?
I'm a Chemistry student learning about periodic trends. I know that in (many organisms') cellular respiration, oxygen serves as the final electron acceptor due to its high electronegativity.
However, applying the periodic trends, fluorine is more electronegative than oxygen, and the noble gas neon even more so than fluorine. Why aren't either of these the final electron acceptor? I know that in some organisms, the final electron acceptor is sulfur. But I've never heard of it being fluorine or neon. Why?

One of the main reasons that modern(!) biology uses oxygen as an electron acceptor is availability.
Around 2.45 billion years ago, oxygen (O$_2$) started being built up in the atmosphere (which actually killed off a lot of the lifeforms/bacteria at that point). Since then, oxygen consuming lifeforms were able to establish themselves. Before that, most organisms probably used mainly (elemental) hydrogen as electron acceptors.
Apart from not really being available in the atmosphere, there are other reasons why fluorine or neon don't make for good biological electron acceptors:

While elemental fluorine (F$_2$) is indeed extremely electronegative, this makes it so reactive that it:
a) could not be controlled by biology [the reactivity of oxygen is why it killed so many bacteria in the first place] and
b) just does not occur (or at least remain in) in the elemental state in nature (there is no measurable F$_2$ in our atmosphere).
Neon (and other noble gases) are in theory also quite electronegative, actually so much so, that they never* occur without their electrons and therefore don't react at all.

*It's somehow possible to form noble-gas compounds, but it requires very specific chemical reaction conditions, that mostly occur under controlled man-made conditions (and are not good for biological life forms).

I suspect another reason is simple availability.  Oxygen is the most abundant element in the Earth's crust, fluorine is relatively rare (> 0.1%), and tightly bound in compounds.

There are instances of fluorine gas found in nature (within certain minerals) but they are pretty rare. YouTube and press releases 1, 2

Note that oxygen was available to organisms well before the GOE, merely in bound form.

Yeah, try breathing F2, it won't be fun.

Availability and applicability.

Availability.
In the beginning, there was CO2. It was abundant in the atmosphere, and later, the oceans.
Fluorine and neon weren't, and so respiration evolved around what was (and is) available.
Ref.: Paeloclimatology / History of the Atmosphere.

Applicability.
The other point about oxygen is that it works rather beautifully both ways. Chloroplasts can easily split up CO2 and H2O into glucose and O2 with a bit of sunlight. Hemoglobin can combine both O2 and CO2 with just a little difference in partial pressure. Mitochondria can run through the citric acid cycle without getting destroyed in the process.
Once fluorine has taken hold of another atom and formed a molecule, it will be pretty hard for an organism to make it let go again, and if it does the fluorine will want to react with something, anything really, whether that's good for the organism or not.
On the other end, neon doesn't want to react with anything.

So while chemically there's a point to be made for the more energetic oxidizer, evolution / an organism is not "interested" in the energy content alone. The substance must be available, and the process must be somewhat sustainable. Oxygen ticked those boxes, fluorine and neon didn't.
Even rocket scientists, who are really looking for the most energetic compounds they can get their hands on, dropped the idea of fluorine as a propellant because it's not safe to handle in uncombined form. There's a lesson in there.

Availability isn't the fundamental issue with neon. Even if half the atmosphere were neon, we wouldn't be using it in respiration.

@DavidRicherby: You could say the same about fluorine. Both factors -- inavailability and chemical unsuitability -- apply. Either would be enough as an explanation on its own.

I think the rocket scientist example is flawed, because these rocket scientists happen to belong to a biological system that has evolved using oxygen. Reasoning after the fact of evolution under oxygen availability is not telling that evolution under predominant fluorine availablity couldn't have lead to (other, functioning) biological systems. To point out another example: H₂O is really a very aggressive and corrosive solvent. But we are perfectly adapted to deal with it and use it, and thus it is fine for us, whereas, say, H₂S is not. However, already if you'd ask some  sulfur-oxidating...

... bacteria, their "point of view" will differ considerably.

@cbeleites: Which on the one hand takes us right back to "availability" -- H2O was on this planet long before life was, so naturally life evolved to be tolerant of H2O. Predominant fluorine "availability" would mean abundance of fluorine compounds, and you'd still face the problem of breaking those up for an organism to actually do anything with the fluorine (that doesn't destroy the organism). I'm open for a demonstration of a viable "breathing cycle" corresponding to photosynthesis / citric acid cycle that works with fluorine.

@DevSolar: of course. As modern chemist I'd start by guessing cell walls of teflon to contain HF solution. I.e., while of course I cannot say how nor whether things would have evolved, I'd expect a fluorine-based biology to be all in all more "harsh" (in a similar sense as that our oxygen-based biology uses more harsh chemistry than the sulfur based (nowadays) niche biological systems), but then the whole organisms would probably be more "fluorinated" and thus able to cope with fluorine/HF. (Also, on the one hand the difference in electronegativity between oxygen and fluoride is less than that

between sulfur and oxygen, but at the other hand, there's the step from divalent to monovalent which would be a major difference). Also, the aggressivity may be offset to a certain extent because we may be looking at systems that work in much colder temperatures: 20 °C is the boiling point for HF, melting point is below -80 °C. Stepping down a few 10s of °C may be slowing down reactions to a suitable pace. Who knows? Summary: HF/F₂ based biology would surely look quite diffrent from "our" biology. But that doesn't mean it cannot work.

@cbeleites: ...but not on Earth, or anything even remotely similar to it. So... as far as the answer to the OP's question is concerned, is there anything that would "need" editing?

@DevSolar: no, sorry: I like your answer - no need to edit. The point of my comment is just that the alien rocket scientist (or combustion engine engineer) from the HF-planet may think fluorine a totally normal oxidizing agent... (and may have trouble if that bit of H₂O they have gets into the HF in their system...)

The atomic radius of fluorine is just slightly larger than that of carbon.  When a fluorine atom bonds to a carbon atom that is part of a carbon backbone, the fluorine atom covers up not only the C-F bond but also the adjoining C-C bonds.  This makes it impossible for biological enzymes to access these bonds to break them, and is why fluorinated compounds are biologically inert.
This is the reason why we fluoridate water and toothpaste; bacteria have no enzymes that can break down enamel that is formed with fluorine!  It is also why teflon (repeating units of -CF$_2$-) is not biodegraded yet saturated fatty acids (repeating units of -CH$_2$-) are easily biodegraded.
All elements that are used biologically have ecological cycles where they are reused for other purposes.  Because fluorinated compounds can't be broken down, such an ecological cycle would rapidily come to a halt.  Therefore, fluorine has an evolutionary disadvantage over other elements.
I agree with the other answers that neon can't be an electron acceptor because it won't form into compounds.  I disagree with their "oxygen first" argument; evolution doesn't care which mechanisms evolve first.  If a fluorine metabolic pathway had been more effective than oxygen's, its pathway would eventually surpass the earlier-evolved pathway.  Furthermore, there are plenty of trace minerals (e.g. selenium) that are used by life.

Evolution does somewhat care about which mechanism evolves first: it tends to seek a local optimum, not a global optimum.  If the benefits of an oxygen pathway and a fluorine pathway are similar, and both are a major advantage over whatever came before them, evolution is likely to get "stuck" on whichever one evolves first.

@Mark True, but then you get something like the evolution of photosynthesis, and everything gets thrown out of whack planet-wide. It's not inconceivable that the same thing could happen with something like fluorine, if it were available. The availability argument is much stronger - regardless of utility, life can't use it if there's no source for it. The proportions of elements in life are pretty close to the proportions of biologically available elements (e.g. including the "no enzymes" argument) in Earth's soils and oceans.

Neon just does not work as an electron acceptor. It is that inert that there are currently no known Neon compounds at all.
Fluorine would work in principle, but it is rare compared to oxygen and its strong reactivity makes it a very dangerous substance in elementary form. So it seems very natural that life chooses Oxygen and not Fluorine.

Of course, oxygen is also a horribly dangerous substance - it's just that evolution created life that's able to cope with it. Even then, it's a balancing act and oxygen (and oxygen compounds) are responsible for quite a few cell deaths and cancerous growths :P The Great Oxygenation Event killed of almost all life on Earth's surface/oceans. But granted, coping with fluorine would be even worse, and perhaps impossible (under standard pressure and temperature).


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Why exactly would a big, tropical, dangerous spider not thrive if imported to a "cold" climate location such as Sweden?
Whenever I have put forward the idea that somebody could capture a bunch of disgusting, huge spiders from Australia, fly them to Sweden and just let them out in the woods nearby houses, and they will naturally replicate in huge numbers in no time and take over the place...
... then I get a reply such as: "They would not survive for long because they are out of their natural habitat."
What does that mean exactly? Would they not be the "spider kings" of this new place where all other spiders are smaller and weaker? Would not the imported spiders, as disgusting as it is to think about, rule and spread? Not die out at all?
Why exactly would such monsters not be "fit" for Sweden? If the problem is that it snows here but not in Australia, then what makes it unable to dig itself into the ground like the other spiders (I assume) do? And hibernate?
I don't understand why they would die out and not thrive, but I certainly hope that they would quickly die because even the native spiders here are gigantic and disgusting IMO, and I would simply not be able to live in Australia with its monster spiders... nor would I ever go on vacation there, for this reason alone.

Downvoted primarily because spiders are not "disgusting",

welcome to the biology SE! Please try to maintain neutral language in your questions and avoid opnion-based "facts" (like saying spiders are "monsters").

I believe this page from Khan academy explains pretty decently why temperature is important to living organisms:
https://www.khanacademy.org/science/biology/principles-of-physiology/metabolism-and-thermoregulation/a/endotherms-ectotherms
It comes down to biochemistry. Temperature affects the molecules that make up our bodies and do everything in it. At very low temperatures water freezes and that messes up everything in the body for obvious reasons (molecules can't travel around and react with each other in solid ice the way they can in liquid water; ice takes up more space than water so the process of water freezing will break cell structures...). At very high temperature complex proteins will tend to lose their shape which is vital to their function, meaning they become useless. And beyond those two "hard" limits, living bodies use chemical reactions to run (essentially you could argue a living body is a very large set of varied chemical reactions), those chemical reactions are usually helped around by complex molecules called enzymes, and enzymes often have certain temperature ranges they work best in. This further narrows the range of temperatures various organisms can tolerate or thrive in.
So I believe this addresses one aspect of your question - why tropical spiders might not do well in a cold climate. The enzymes in their bodies are just optimized for a higher temperature than that. Moreover, temperatures in Sweden fall below freezing and cold-resistant species tend to evolve specific adaptations for that that aren't just behavioral but also chemical. The abstract of this paper discusses different such strategies in insects (I assume spiders would be similar), which involves either chemical adaptations to avoid bodily fluids freezing, or adaptations to survive them freezing:
https://journals.physiology.org/doi/abs/10.1152/physrev.1985.65.4.799
So native Swedish spiders probably have adaptations of this kind that tropical spiders don't, and they're not adaptations that can arise in a single individual or even a few generations.
You bring up another question however, which is why can't tropical spiders adopt behavioral adaptations, like digging in the ground or hibernating? Well, for one thing "hibernating" is as much a physiological adaptation as a behavioral one. You don't just improvise the body slowing down that way by lying down for a nap. But more to the point, it isn't clear you can just improvise novel behaviors either, especially if you're a spider. Many behaviors like "burrowing to avoid the cold" are hard-wired evolved responses that organisms that haven't evolved them are going to do spontaneously. I don't know if the burrowing behavior you describe in spiders specifically is of that type. This paper makes the point that while spiders (like other arthropods) are traditionally thought of as having completely hard-wired behavior, more recent thinking shows they do display more flexible behavior as well:
https://www.sciencedirect.com/science/article/pii/B9780124159198000033
Still, arthropods do show more such instinctive, hard-wired behavior than, say, mammals do. So while it's not impossible that some such spiders would happen to burrow in a way that avoids the cold (that's another point - they'd have to burrow at the right time, in the right places, in the right way... things that might not be obvious), and if they didn't have huge physiological barriers to surviving the cold even while buried, and enough survived this way to reproduce and multiply and weren't outcompeted by native spiders who weren't freezing their cuticles off this whole time... Then I suppose they could spread, but that's a lot of "ifs" and I think a tiny minority of introductions of tropical species would satisfy them, if any.


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Identification of a lifeform
There's a video I found on Facebook and I'm unable to figure out what the creature featured happens to be.
Adding images that have been taken from the video itself, apologies in advance since they're not high qualify images.

Can anybody shed any light on what it is?
The video was shot near Ratan Babu Ghat which is situated along the bank of Hooghly river, Kolkata, West Bengal, India. Here to be precise.

This is a polyclad flatworm.

Here is a video of notoplana vitrea moving similarly to the one in the video that you linked:
https://www.asturnatura.com/especie/notoplana-vitrea.html
Here is a gallery of polyclad flatworms observed in India:
https://inaturalist.ca/observations?place_id=6681&subview=grid&taxon_id=52318
A number of the images in this gallery look similar to the one in your video, but very few of them are identified beyond this order taxon of polyclad flatworm.


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Does using CRISPR/Cas9 knockout need to add donor DNA in the process?
I am new to this technology and don't quite understand how it works. Hope someone can give some suggestions! Thank you.

Short Answer:
No, for CRISPR/Cas9 knockout you do not need to add donor DNA.
Little bit more detail:
CRISPR/Cas9 allows you to cut at a given position (defined by the gRNA). This will lead to double strand breaks (DSB).
If you do not add any donor DNA, the cell's DNA repair mechanism will try to repair the double strand break. Since these CRISPR/Cas9 mediated DSBs usually have a blunt end it is really hard for the DNA repair mechanism to fix it. If will often introduce an error, for example by missing a basepair (or several) leading to a frame-shift mutation. This DNA repair mechanism is called Non-Homologous End Joining.
If you do add donor DNA it will contain both the sequence you want to introduce (say a green fluorescent protein) and homologous sequences on both sides of the double strand break. In other words, if you want to cut the sequence ATCGCA exactly in the middle you will get ATC and GCA. You want to insert AGA. The donor plasmid must then have the sequence ATCAGAGCA. As the cell now has the homologous regions, it will use the Homology Directed Repair mechanism.
The sequences are for illustration only - in reality a gene is usually several hundred basepairs and it is recommended to have much longer homology regions.


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Can CRISPR be used in editing primary cells? How is the transfection efficiency?
Sometimes people wanna use primary cells to do gene-editing because the cells normally have more interesting characteristics, but can we really do so?

The most efficient CRISPR-based editing of primary cells that I've read about used a technology called prime editing, developed in 2019.
Search-and-replace genome editing without double-strand breaks or donor DNA (PDF download)
Normal CRISPR-Cas9 protocols involve generating double-strand breaks (DSBs), and then either introducing a template for homologous repair or allowing the cell to repair the break without a template, leading to insertions or deletions at the lesion site.  Off-target cutting is common, so normal CRISPR-Cas9 produces undesirable or lethal mutations in a majority of transfected cells.
Prime-editing uses a catalytically impaired Cas9 (generates nicks instead of DSBs) fused to a reverse transcriptase.  The fusion protein is guided to its target by a prime-editor gRNA ("pegRNA") that contains a normal targeting domain at its 5' end and a template for reverse transcription at its 3' end. A nick is generated by the Cas9 on the exposed strand (i.e. the strand not bound by the gRNA), and the 3' portion of the pegRNA partially hybridizes to the the 5' side of the nick, providing a free 5' end and a stretch of ssRNA for DNA polymerization.  Resolution of the resulting "flap" by endogenous nick-repair mechanisms results in editing at the target site without DSBs or exogenous DNA template.
This method was assessed for its efficiency in several cell types, including primary mouse cortical neurons, which are post-mitotic and terminally-differentiated.  The authors observed the desired edit of DNMT1 in 7.1% of cells, with 0.58% indels on average.  Cas9 nuclease editing of the same cell type with the same lentivirus transfection system resulted in 31% indels, on average, an error rate more than 50 times that of prime-editing.

Theoretically, CRISPR technology is not restricted by species cells, but primary cells have limitations of passage and monoclonal jumping, so it is generally cell lines that are used for gene editing.


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Do people with higher body hair growth (eg. Women with hirutism) need more protein?
Hair is protein. Does that mean that the body of a woman with hirutism is using more than usual protein to make hair and thus she needs more for building and repairing muscles?

Welcome to Biology.SE! Please take the [tour] and then go through the help pages starting with [Ask] questions effectively on this site. This seems likely to be true, but I would guess the extra amount is trivial (think about how little your hair grows each day). If you really want an answer to this I suspect [medicalscience.se] might be a better choice (please don't repost — instead request migration). ——— We also expect you to do some research on your own and then, informed by what you have learned, ask any questions you still have (ideally with references to reliable sources). Thanks!

Not significantly.  Most people should have roughly 50g of protein a day, and most diets provide plenty.  Larger people require a little more,  and activity levels are another factor.
Hair grows fastest on the head, but when talking about "hirsutism" you are referring to body and facial hair, not head hair.  Now consider shaving all the body hair, the total mass is pretty trivial, and the increase in mass per day is a fraction of a gram. Now consider the difference between a "hirsute" and "less hirsute" person, and there the extra protein requirements disappears into triviality.  One extra lentil a day, not something worth measuring.
So the amount of body hair someone has naturally is not a factor that needs to be considered when calculating the protein requirements. In fact, one doesn't need to calculate protein requirements at all, unless you are on a very strict diet (in which case you will likely be under the care of a physician.)


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If there is such known "Mitochondrial Eve", does it means that all the mitochondrial dna in everyone's body is same?
P.S. I know not that much, just some basics, but this question really interested me :)

Hello and welcome at Biology SE!
Can you edit your question and define 'same'? It is important to clarify what do you mean by this term. Since every cell in your body can acquire mutations independently and this is also true for cell organelles like mitochondria, in the strict sense of same = identical, then the answer is no. Based on the above logic even two mitochondria can have different DNA due to individual unique mutations.

The mitochondrial genome has a nonzero mutation rate, though estimates differ according to method. Estimates are approximately $10^{-8}$ per bp per year on average, and the mt genome is $\approx20,000$ bp. So we can do some math and expect that many (or most) people experience heteroplasmy, which is to say that they carry various different mitochondria (in the same person).
Note also that you inherit multiple mitochondria from your mother, not just one.
So- no, not everyone has exactly the same mitochondria, even if we can trace them all back to a single hypothetical human.

The M.DNA mutates about 1% in a million years, and because our Mt-Eve is 150,000 years old, our mitochondria are 0.15% different, or 99.85% the same globally, and 99.95% the same in Eurasia.
The variation of mitochondrial DNA between different people can be used to estimate the time back to a common ancestor, because mitochondrial DNA accumulates mutations at the rate of approximately one every 3,500 years per nucleotide. They can measure the differences in a tree:

Good answer, but I think it would be improved with some reference for those figures and a link for the tree.


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Examining antibody - target interactions in PyMOL
I'm a student currently looking at antibody responses against a viral target protein of interest.
I have my own, annotated PyMOL session of my protein and I also have .pdbs of crystallised antibody structures that bind to this protein and rougly know whereabouts it binds. I would like to know if it is possible to combine these two structures into a single session to observe or at least overlay the two structures together. Many thanks!

You might find a PyMol expert on this list. If not try SE Bioinformatics or even Chemistry.

I don't think PyMOL has built-in functions for that; you might have to use a docking software like AutoDock. I'd also recommend asking on Bioinformatics SE as David suggested

Multiple structures may be read into a given session using the following for each pdb:
File -> Open

In the narrative below, the reference antibody has the name AAA and the second antibody has the name BBB. The heavy and light chains have pdb chain IDs A and B, respectively. Adjust those as needed.
Whole molecule overlay
Make visible the reference and the moving molecules.
From the menu item next to the BBB molecule name (right panel), select:
A -> align -> to molecule (*/CA) -> Object: AAA

Antibody's variable domain overlay
I think it may be more helpful to align the variable domains than the whole antibodies or Fabs. Typing the following on the command line is a temporarily adequate way to align proteins:
align BBB and chain A+B and res 1:109, AAA and chain A+BL and res 1:109


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Why Vibrio Cholera produce its toxins?
I was wondering, why V. Cholera synthesize toxins which induce in humans diarrhea? I know that diarrhea is protective mechanism for humans, to get rid of bacteria. But why V. Cholera just stop producing it? What is its function in bacteria? In which bacterial metabolism it is involved?

Which of these questions do you most want to see answered? One of the questions you ask is very interesting to me, but I would not be interested in answering them all. Can you narrow this down to one (maybe two) question(s)? Thanks.

What is its function in bacteria?

Then may I  suggest you edit your question to basically just that? Because that is a very interesting question. Also, it would help if you included any research you've done to answer the question. Thanks! :)