Is there a taxon that "adaptively" radiates faster?

The species from your list have some common traits:

Fast metabolism of themselves, food and fertilizers
sexual reproduction is more complex than crabs and coral
complex ecosystems and fast changing ecology
lots of ecological competition generally advanced compared to ferns, moss and crabs
More Possibility to travel through gene bottlenecks
recent in fossil history
prevalently small
possibility of population explosions
generalist and robust flexible habits and traits like tree nests, live birthing, flying, fangs, close inter-animal relations i.e. with bees,

If you study animals that have sedate speciation habits like platipus, they are limited in many of the common traits from that list.

The animal is therefore mostly dependent on it's ecosystem's stability, richness and opportunities, as well as the specie's own physical wealth, opportunism and stability. Those species have very complex and flexible ecology cycles working over them at a high rate. The rate of change of the ecology is also dictated by the other's organisms ability to radiate in record times, so today's species are in an arms race of the fastest rates of change in predator-prey-reproduction traits, with all the tools of the trade, i.e. fast movement, strong protection, freaky efficient cell-type mutations, locomotive, feeding, protective and spacial habits that by themselves can define the base of the most prevalent recent families.

Some species RNA is more susceptible to change and has to be be counterbalanced with different checks, it's to keep up with the arms race of competition in the complex ecology spaces of most of the planet.


What's the difference between germ line and germ cells?

Germ cells are the cells of germline. Germline is a lineage of germ cells from germline stem cells to gametes. Cells that do not belong to germline are called somatic cells.


Why do I still see a bright light after looking directly at it?

Short answer
The effect you describe is called a negative after image. It can be explained by adaptation effects of the photoreceptors in the eye.

Background

source: Dresden University

Steadily fixate on the black lightbulb for thirty seconds or more and then immediately turn your gaze to the white region on the right. The illusionary glowing white bulb you perceive after fixating on the black picture is called an afterimage.

When the eyes and hence the photosensitive layer therein, the retina, is focused on an image, light-sensitive photoreceptors in the retina respond to the incoming light. In turn, secondary neurons carry the information to the brain via the optic nerve. When photoreceptors are continuously activated, they become desensitized (or fatigued); it is said they adapt to the stimulus (Kalloniatis & Luu, 2012).

Adaptation occurs at multiple stages in the visual system. One of the important processes behind visual adaptation in this example is photopigment "bleaching" due to constant stimulation. The desensitization is strongest  for cells viewing bright stimuli and is caused by the photopigments in the photoreceptors being all used up after continuous activation.

Back to the light bulb illusion above: photobleaching is strongest for cells that were activated by the bright parts of the image, but weaker for cells viewing the darkest part of the figure. Then, when you avert the focus onto the white screen, the least depleted cells (the ones not adapted) will respond strongly, while the adapted cells will be unresponsive. hence, the retina focused on the black light bulb will respond vigurously, while the more peripheral parts of the retina that received the white part of the image surrounding the bulb will be silent. Hence, a glowing light bulb will be perceived in the after image.

This particular phenomenon is called a negative afterimage, in which bright areas of the figure turn dark and vice versa. This will happen when looking for prolonged periods of time to a certain image (this answer), or when looking directly in very bright sources like the sun (as in the question). Positive afterimages also exist, but do not apply to this specific question.

Reference
- Kalloniatis & Luu, Light and Dark Adaptation. In: Webvision (2012). Utah University


Found this bug in my apartment in New York. Does anyone know what it is?

It's probably just the second instar (nymph) of a cockroach (Periplaneta americana).

Compare with this image of the instars:

Source: http://entnemdept.ufl.edu/creatures/urban/roaches/american_cockroach.htm


There is a question on stackexchange that is not only related
Why cytotoxic T cells don't kill dendritic cells when they present antigen?
Your question refers to "some cells" (explanatory text) and "transfected cells". Thus, any cell that takes up the m-RNA-vaccine is referred to. However, any answer must be based on the different types of cells involved in immunization, vaccination and - in killing infected cells, in the end. There are the immune cells which present antigen, there are, as well, the body cells which present.
The explanatory text of your question speaks of "presentation".  The very first paragraph of Wikipedia on "Antigen presentation https://en.wikipedia.org/wiki/Antigen_presentation seems to even limit the term  "presentation" to presentation by immune cells, which would exclude any body cells, like muscle cells transfected with vaccine antigen.  This might illustrate that immune cells are different from body cells in respect of presentation of antigen that becomes target of cytotoxic t-cells.
Both immune and body cells  take up antigene, however the immune cells, by presenting prime naive T-cells, i.e. activate the T-cells. The related question  cited asks about the fate of the immune cells.
As for  the fate of body cells like muscle cells you refer to  in  the last paragraph of your question the video message you link to in your own answer seems to refer to those body cells: transfected cells will simply be killed, but not enough of them to cause harm.
Here are additional remarks, enjoy: I think  it is an open question if the transfection as such would lead to cell death (assuming some failure of cytotoxic T-cells, not doing their job). Think of vaccination that transfects with "attenuated", if not live virus: even if the virus is incapable of - natural - replication  it is unlikely that the host cell whose ribosome machinery has been usurped can keep up its metabolism. I would like to be allowed to mark the following as a new thought of mine: the fact that the antigen is being presented speaks in favour of the cell not being able to survive - which is counterintuitively (how can a cell infected by virus still be able to digest, process, transport and present antigen on its MHCs, at all? Looks like it will stay alive.)
From the very fact of antigen presentation it is possible to infere, in my opinion, that the cell will not survive transfection. Host cells may  have their  own, inner defence mechanisms which are able to beat infection without any presentation of antigen - think of "interferon", but also, nowadays, of double stranded RNA, dsRNA.  A cell that is successful in defeating the virus without engaging the adaptive immune response would cancel out its survival by presenting and attracting cytotoxic t-cells. So it is a possibility that cells in spite of being transfected by mRnA vaccine do not die.  However, that would be considered a vaccine failure.
So with effective vaccination transfected body cells will die.
Caveat: from my reading of textbooks it is a possibility that vaccination might be effective even if body cells are not transfected, but that is beyond your question. Premise of your question is that a cell is being transfected (then what happens?).
A reference on the  possibility  that a "transfected cell" might survive by self defence  would be  "RNA interference",
https://en.wikipedia.org/wiki/RNA_interference
Last not least - see  the video you linked to - there is "B cell immunity".
To my mind it is a possibility that B-cells become transfected by mRNA vaccine - if so, they should present the antigen as an endogenous on to cytotoxic T-cells which makes your question very relevant in respect to B-cells, in my opinion. However, B-cells produce antibodies, considered most important in vaccination. This speaks in favour of B-cells taking up antigen via their B-Cell receptor that should be among cell debris of transfected cells that died - for instance few muscle cells transfected by vaccine. What you need here, in respect of B-cells is authority on either cross-presentation of endogenous antigen by B-cells (if mRNA is being uptaken through membrane, if B cells are transfected) or, authority on B-cells "only cleaning" up dead cell's antigen as part of dead cells, killed by virus or immune cells.
My answer is in line with the answer you already gave -  elaborating on what is in fact causing the cell's death:
"Do mRNA vaccines cause transfected cells to be killed by cytotoxic T cells?"
Yes,  mRNA vaccines cause transfected cells to die.


How do ribosomes interpret stop codons as selenocysteine and pyrrolysine?

Summary
Reassignment of an UGA codon as a selenocysteine codon is thought to involve the interaction of obligatory secondary structure elements called selenocysteine insertion sequences (SECIS) with the dedicated translation elongation factor that presents a unique tRNA aminoacylated with selenocysteine to the ribosome. However the location of these elements and their specificity differ between prokaryotes and eukaryotes. Less is known about the less common reassignment of UAG to pyrrolysine, but some organisms may employ similar mechanisms to those involving selenocysteine, whereas others may almost completely avoid the use of UAG as a termination codon.
Preliminary — Presentation of selenocysteine to the ribosome
Although the question specifically relates to the translation of erstwhile termination codons, it is worth mentioning how selenocysteine (Sec) is presented to the ribosome.
     In most species a specific tRNA — tRNASec — is aminoacylated with serine catalysed the standard seryl-tRNA synthetase. The Ser-tRNASec is then converted to Sec-tRNASec — in Escherichia coli, by the product of the SelA gene.
    Whereas all other charged elongator tRNA species are brought to the A-site of the ribosome by the same elongation factor (EF-Tu in prokaryotes, EF-1 in eukaryotes), there is a specific elongation factor for binding Sec-tRNASec.
Reprogramming UGA codon as a selenocysteine codon
The reprogramming of UGA as a selenocysteine codon requires a stem–loop secondary structure — the SECIS — in both prokaryotes and eukaryotes. However there are differences in how these operate. Quoting from a Nature News and Views piece written in 2007:

In bacteria there is a site-specific recoding signal embedded in mRNA that flags a nearby UGA codon as encoding selenocysteine rather than stop (Fig.b). In other organisms, ranging from worms to mammals, a recoding signal located outside the coding part of the mRNA (Fig.c) signifies that all UGA codons in its mRNA specify selenocysteine (one essential human mRNA has ten UGAs).

The mode of action  of the SECIS elements is envisaged as interaction with the Sec-tRNASec-specific elongation factor, as in this illustration from Ch 13 of Molecular Biology by Clark et al.:

A shortcoming of this cartoon, is that it fails to show that in prokaryotes the SECIS element encompasses the UGA codon. This is important as it may indicate that  termination factors are prevented access to the UGA by secondary structure, which the Sec-specific elongation factor is able to unwind. (In eukaryotes there may UGA codons may also be made inaccessible by local secondary structure.)

However sequence context effects have also been suggested.
Pyrrolysine
The situation for pyrrolysine (see, for example, Ambrogelly et al. (2007)) differs from that for selenocysteine in that it is synthesised before it is charged to a specific tRNAPyl in a reaction catalysed by a specific pyrrolysyl-tRNA synthetase. The reassignment of UAG to pyrrolysine was thought to involve a change (or almost complete change) in the genetic code of certain archaea in which it is found, with UAG not or rarely being used as a termination codon. However, according to the Nature News and Views article aleady cited, this is not the case for other organisms, in which I am unaware of the mechanism of reassignment.


When is an anatomic entity named "laterale" vs. "lateralis"?

This is actually much more of a lesson in Latin linguistics/grammar.
The root, Lateral, comes ultimately from latus meaning “side" or "flank” in Latin. [Source].

By adding one of these suffixes we instead create an adjective meaning "of the side."

However, the suffixes differ in their inflection (specifically their gender):
Laterale has the neutral, singular suffix of the nominative 3rd declension.
Lateralis has the masculine, singular suffix of the nominative 3rd declension.

 Source: Wiktionary

The difference between the gender-assignment of these suffixes is due to Agreement.

[Agreement] happens when a word changes form depending on the other words to which it relates. It is an instance of inflection, and usually involves making the value of some grammatical category (such as gender or person) "agree" between varied words or parts of the sentence.

More specifically, adjectives must agree with the nouns they modify in case, number, and gender.

Let's break down each of these anatomical structures to understand the agreement-induced inflections [Source]:
os cuneiforme laterale - each of these words is neutral/singular:

os = noun, declension 3, nominative, neutral, singular
cuneiforme = adjective, declension 3, neutral, singular
laterale = adjective, declension 3, nominative, neutral, singular

nervus cutaneus antebrachii lateralis - the 2 adjectives match the  masculine/singular noun they describe:

nervus = noun, declension 2, nominative, masculine, singular
cutaneus = adjective, declension 2, nominative, masculine, singular
antebrachii = noun, declension 2, genitive, singular
lateralis = adjective, declension 3, nominative, masculine, singular

 Note: you see a shift to the genitive case for antebrachii because in this instance this phrase means "Lateral cutaneous nerve of the forearm."

Latin Linguistics  For Dummies:

Declension = category of nouns in Latin and Greek with broadly similar case formation (i.e., they are inflected/conjugated similarly).

Inflection = the modification of a word to express different grammatical categories such as tense, case, voice, aspect, person, number, gender, and mood.

Nominative = The case used to indicate the subject of a finite verb.

Genitive = The case used to express some relationship such as possession or origin. It corresponds roughly to the English preposition "of."


How to calculate the percentage of heterozygous cells based on a gene map

Units of genetic distance

It took me a little while to understand what MU means. I have never seen MU but rather m.u. for map unit, or cM for centiMorgane.

Solving the problem

This problem got me thinking a little bit. My logic is the following:

Probability of being heterozygous at the first locus

You cross two individuals (we'll call them individual 1 and 2)that are both heterozygote at each locus A, B and C (not to be confused with the alleles A and a, B and b and C and c). We'll start with locus A. The probability of being heterozygous at locus A is the probability that individual 2 gives another allele than individual 1. This occurs with probability 0.5. If the three loci were perfectly independent, then the probability of being heterozygous for a given offspring at all 3 loci would just be $0.5^3 = 0.125$... but that would be too easy so let's keep going with the second locus.

Probability of being heterozygote at the two first loci

So let's assume our offspring of interest, got two different alleles for the locus A. The probability to receive two different alleles at the locus B is the probability that either no individual recombine between A and B or both individuals recombine between A and B. It is therefore: $0.2^2 + 0.8^2$. Put together this probability with the probability that the first locus was heterozygote it gives $0.5 \cdot (0.2^2 + 0.8^2)$.

Probability of being heterozygous for all 3 loci

Finally, we can just keep going with he next locus. Let's assume that the offspring have received two different alleles for both loci A and B. The probability to receive different alleles at locus C is the probability that either both parents recombine (between B and C) or none recombine. This occurs with probability $0.1^2 + 0.9^2$.

Put the whole thing together, the probability of a given offspring to be heterozygote (or the frequency of heterozygotes offsprings if you prefer) is $0.5 \cdot (0.2^2 + 0.8^2) \cdot (0.1^2 + 0.9^2) = 0.2788$

Note that calculating the probability that exactly 2 loci are heterozygous would have been a bit trickier.

Good luck for your exam tomorrow!


It looks like a white crab apple or an obscure cultivar of apples, of which there are many.

The best identifier will be the fruit. Because there are not millions of flowers, It looks like there may be less fruit and bigger than a crab apple, although it varies by year.

The fruit of crab apples are small apples which can be used to make jelly. Crab apple trees can grow to 10m, a lot bigger than table apples.

White flowering crab apple:

https://en.wikipedia.org/wiki/Malus

comparison images.


What is this spider spinning a bright green web?

Your image is not the best one, but this is probably the Magnolia green jumper, Lyssomanes viridis.

Here is a better image of it for comparison:

It also matches your location. According to the Wikipedia page linked above,

  the species is native to the Southeastern United States plus Texas, and as far north as Maryland.


Can VMD change its cartoon representation for secondary structure according to trajectories?

As Martin pointed out, VMD doesn't update secondary structure content over the course of an MD trajectory. You can easily do this in VMD by using the SSCache script:

http://www.ks.uiuc.edu/Research/vmd/script_library/scripts/sscache/sscache.tcl

I believe it ships with the newest versions of VMD, actually, but I'm not exactly sure; even if not, you can easily source it:

Extensions > Tk console (in the console, type source Downloads/sscache.tcl (or where ever you've saved the script)). Once you've sourced the script, the easiest thing to do would be to load your topology and trajectory and (in the Tk console) issue the command start_sscache molid, although I advise having a look at the examples and options in the script.


Is this European(western), Eastern(northern) or another Hedgehog?

I think the picture you have is not enough to make a proper distinction between the two species.

By this site, they found some morphological characteristics to make the distinction but:

You have to carefully take pictures of the animal parts
The study nearer Poland has been made in Estonia and those characteristics could be slightly different in other countries.

I did not find another documents about hedgehog populations in Poland but the wikipedia page says both species are living in the country.


What factors affect the rate of transpiration in plant leaves?

The rate of transpiration is very closely linked with the rate of evaporation from the leaf surface.  Increased air movement across the surface of the leaf (e.g. from a fan) increases the rate of evaporation of water from the leaf surface. This happens because water saturated air is moved away; making the osmotic gradient more pronounced.

As for the relative changes from adding an impermeable substance like petroleum jelly to the top and bottom surfaces of leaves, it's a question of plant-anatomy.  In the majority of plants, stomata are only located on the bottom surface of the leaf.  This is to prevent them getting 'clogged' during rainfall and to keep them out of the sun to minimise water loss (again more sunlight on the stomata would increase the rate of evaporation).  Therefore a plant with the underside covered would have a much slower rate of transpiration than one with the top surface of the leaf covered.

Other factors that would affect the rate of transpiration:

Temperature (Positive Correlation)
Humidity (Negative Correlation)
Light Availability Excluding CAM photosynthesis pathway plants (Positive Correlation - the stomata are in the main only open when photosynthesis is occurring, so transpiration is less at night or on a cloudy day in addition to light  heating the leaf surface)
Water content of soil (Positive Correlation)

amount of salt in soil (negative correlation)


Looking at this reference "Frequently synanthropic spiders of Southern California", I would have to say that it is possibly a grass spider. (Please someone correct me if I am wrong).

Beautiful looking specimen!


Most of the dyes used for visualization bind with a much higher affinity to dsDNA. This would be SybrGreen, EtBr (although this will bind RNA as well).
There is a pretty comprehensive website from Life Technologies about Nucleic acid stains that is worth a look.

There is as well a publication on this topic: "DNA Staining for Fluorescence and Laser Confocal Microscopy"


Why some viruses can spread with aerosol droplets, but others some cannot

The question you are asking, if I have it right, is about what is called the "mode of transmission" of a virus.

I won't get into too much detail but there are many many factors that play a role. The main one here (in the example of HIV vs influenza) is which cells the virus infects. Influenza infects cells of in the mouth, throat and lungs. So when it grows and ruptures these cells the virus will be let out in your throat, mouth and lungs. So the way it spreads to other people is coming out of these areas on saliva and mucus when coughed or sneezed in droplets.

HIV on the other hand infects cells of the immune system, which are mainly found in the bloodstream as well as mucus cells (that line reproductive tracks). Again without going into too much detail (because mucoid layers are also found in the stomach etc) HIV can spread only from the location where the cells are located that it infects. Because it does not generally infect cells in the mouth and throat it will not be able to access these areas. So it spreads from blood or sexual intercourse, as that is where the cells of the immune system that it infects are located.


I also had this relevant problem, when I was learning about cartilage tissue.

Generally, cartilage tissue has no blood supply. But it is a traversable  tissue, therefore nutrients, and breathing gases can enter into the tissue without any problem.

But the problem is how does it immunized while there is not containing of lymphatic cells.

The perichondrium, has a presence  of blood vessels. It pulls/stimulate  the necessaries for enter to the cartlage tissue. That's how, the  tissue get immunized.


The principle of staining works in such a way that acidic dyes stain the complementary basic components of the cell hence acidic dye rather than staining lysosome which is acidic will stain the relatively basic components of the cell.


In GWAS we are interested in understanding which SNP has a causal influence on a specific phenotype. At the moment large scale studies are carried out by genotyping arrays. For each SNP we put on the array, we measure the alleles present in the patient.

The cost of genotyping obviously depends on how many SNPs we would like to measure. So the problem when designing a genotyping array boils down to: What SNPs do we put on such an array so that we get most information out of it.

To answer that question it is important to realize that SNP alleles are not independent. SNPs that lie in the same haploblock are highly correlated. So measuring one of these SNPs is often sufficient to predict the alleles of other SNPs that are in so called linkage disequilibrium. When designing a genotyping array, we probably do not want to measure all of those highly correlated SNPs, because we gain very little information. Instead may want to measure just few of them and then impute (predict) the alleles of the correlated ones from population study data.

So back to the GWAS analysis. From the SNPs on the designed genotyping array we now predict SNPs that are associated with the phenotype. Say after our statistical analysis we found an associated SNP. But from the design of our array we know that there may be highly correlated SNPs that we did not measure. All of them will also be statistically associated. If we do not impute, we may come to the wrong conclusion that we already found the true causal SNP. In fact, there may be many and finding the correct is often very difficult.


What does it mean for a hormone to be "active"

Biological activity has a range of definitions, but in this case I believe it to mean that the T3 hormone is produced at a rate 3-5x that of T4. This can be measured with fluorescent tags. Typically, fluorescent tagging uses a reactive derivative of a fluorescent molecule, called a fluorophore. The fluorophore selectively binds to a specific region or functional group on the target molecule and can be attached chemically or biologically. Using technologies, such as microscopes, fluorescence can be viewed/imaged; this allows scientists to count, track, and/or monitor molecules over time.


These are shelf (or bracket) fungi. They love dead or diseased wood, and are as common in woods as dirt.

Yours are kind of in rough shape (it looks like they're experiencing some problems of their own.) It's hard without examining a specimen to determine exactly which it is.

Some are edible, some are beautifully colored, some are hard, some soft, some plump, etc. But they are all shelf fungi.


I wrote to Carl Zimmer, and he replied within two hours. That's pretty cool!

So I thought I'd put everything I learned about this here as an answer with all the links, and if you guys have more to add, feel free to post more answers.

My Original Email:

Currently I'm reading She Has Her Mother's Laugh, which is a fascinating book, and I'm thoroughly hooked. However, while reading it, I came across this passage that troubled me. The bit in question is in Chapter 2: Traveling Across the Face of Time.

  "It also meant that the inheritance of acquired traits - taken as a
  fact by Hippocrates, Lamarck, and Darwin alike - was impossible."

From what I've read on the subject over the years, almost unanimously people agree that Darwin opposed the idea of Lamarckian heredity. I thought that Darwin's idea is that small modifications arising from random mutations (although he didn't know about DNA and genes at the time), if beneficial, were retained and accumulated over the generations resulting in new species. But is it not the case? Did actually Darwin subscribe to the idea of Lamarckian heredity?

I did a little bit of research on this, and found some conflicting and confusing information.

To start with, I've read Darwin's Origin of Species and though I remembered him opposing the idea, so I searched through the book again and found the following para.

  Thus, as I believe, the wonderful fact of two distinctly defined
  castes of sterile workers existing in the same nest, both widely
  different from each other and from their parents, has originated. We
  can see how useful their production may have been to a social
  community of insects, on the same principle that the division of
  labour is useful to civilised man. As ants work by inherited instincts
  and by inherited tools or weapons, and not by acquired knowledge and
  manufactured instruments, a perfect division of labour could be
  effected with them only by the workers being sterile; for had they
  been fertile, they would have intercrossed, and their instincts and
  structure would have become blended. And nature has, as I believe,
  effected this admirable division of labour in the communities of ants,
  by the means of natural selection. But I am bound to confess, that,
  with all my faith in this principle, I should never have anticipated
  that natural selection could have been efficient in so high a degree,
  had not the case of these neuter insects convinced me of the fact. I
  have, therefore, discussed this case, at some little but wholly
  insufficient length, in order to show the power of natural selection,
  and likewise because this is by far the most serious special
  difficulty, which my theory has encountered. The case, also, is very
  interesting, as it proves that with animals, as with plants, any
  amount of modification in structure can be effected by the
  accumulation of numerous, slight, and as we must call them accidental,
  variations, which are in any manner profitable, without exercise or
  habit having come into play. For no amount of exercise, or habit, or
  volition, in the utterly sterile members of a community could possibly
  have affected the structure or instincts of the fertile members, which
  alone leave descendants. I am surprised that no one has advanced this
  demonstrative case of neuter insects, against the well-known doctrine
  of Lamarck.

If you read the last few sentences, Darwin says "accumulation of numerous, slight, and as we must call them accidental, variations,..." which to me indicates he's referring to random mutations in genes even though he didn't know what genes were. Furthermore, a little later, "For no amount of exercise, or habit, or volition, in the utterly sterile members of a community could possibly have affected the structure or instincts of the fertile members, which alone leave descendants." and "I am surprised that no one has advanced this demonstrative case of neuter insects, against the well-known doctrine of Lamarck."

To me this clearly indicates he was opposed to the idea of Lamarckian heredity. Could you please explain a little bit more about your stance? I'd love to hear the different viewpoint and the reason(s) behind it.

Since I was curious about this, I posted the same question on StackExchange biology forum.

One of the users pointed me to an article by Michael T. Ghiselin who seems to be in agreement with you regarding the matter.

While some of his points about textbook authors misunderstanding Lamarckian heredity sounds fair and valid, I can't quite see how he came to the conclusion that Darwin agreed with Lamarck. The passage he points to (Lamarck was the first man whose conclusions ...") doesn't say anything about Darwin agreeing with Lamarck about acquired traits being passed down the generations.

In Wiki page about Pangenesis, the following is mentioned just as you have in your book.

  "Darwin thought that environmental effects that caused altered
  characteristics would lead to altered gemmules for the affected body
  part. The altered gemmules would then have a chance of being
  transferred to offspring, since they were assumed to be produced
  throughout an organisms life.2 Thus, pangenesis theory allowed for
  the Lamarckian idea of transmission of characteristics acquired
  through use and disuse. Accidental gemmule development in incorrect
  parts of the body could explain deformations and the 'monstrosities'
  Darwin cited in Variation."

Which indeed supports your viewpoint, but to me that seems like a direct contradiction to Darwin's own words in Origin of Species.

So which of it is true? Or, since Pangenesis theory came after he published Origin, did he change his mind later?

Carl Zimmer's reply:

  Thanks for your email. I've also had to struggle to work out Darwin's
  views about Lamarck--they were complicated and changed over his
  career. Basically, the more Darwin thought about heredity, the more he
  warmed up to the possibility of the inheritance of acquired
  characters. I've attached a few good papers that may be of interest.

  Best wishes, Carl Zimmer

Papers Referred by Carl Zimmer:

Gemmules and Elements: On Darwin’s and Mendel’s Concepts and Methods in Heredity.

Lamarck, Evolution, and the Inheritance of Acquired Characters.

orgThe Early History of the Idea of the Inheritance of Acquired Characters and of Pangenesis.

I haven't read those papers yet, will do so when I find time and hopefully it will shed some more light on it.

So, I'm still not quite sure about the issue, but I do think Carl Zimmer's point of view is fair enough, that it is possible Darwin's views evolved over time. Particularly, given that nobody knew what exactly even heredity means at the time, it's possible that Darwin thought it cannot just be ruled out. Besides doesn't seem like he was out campaigning for the idea, rather allowed for it with his Pangenesis hypothesis.

After all, Darwin's theory was about how small modifications accumulated over time leads to species change, not how those changes occurred in the first place. So, while it turned out to be untrue, if there was a mechanism in which experiences could be stored in the unit that transmitted heredity, you can probably incorporate that into Darwin's theory of evolution. That probably was a line of thought Darwin had.


How does the brain immediately know when one's thirst has been quenched?

There are two different aspects to or drivers of thirst, osmolality and hypovolemia, with plasma osmolality elevation being the more potent stimulus of thirst.
As stated by @arboviral (and supported by his link), not a lot is known about the mechanisms of immediate thirst satiety. Much more is known about the mechanisms causing thirst, and the mechanisms of return to normal serum osmolality over 120-180 minutes after rehydration than those immediately satisfying it.

Most of the research on this is old (pre-2000). In one often-cited very small study 1, swallowing and esophageal reflexes (or as the authors termed it, “oropharyngeal metering”) had a significant effect on relief of thirst, that is,

  the act of swallowing helps to regulate the volume of fluid ingested by providing an integrated signal proportional to the cumulative ingested volume.2

Study participants were all mildly dehydrated. One (control) group was allowed to drink ad lib. Another was allowed to drink ad lib but as they did so, the fluid was simultaneous extracted from the stomach by a nasogastric (ng) tube already in place. Interestingly, the difference in the amount of fluid ingested between these two groups (N.B. ad lib) was only 15% (the latter group drank a bit more.) The third group was given fluid by ng tube only.

The very act of swallowing fluid boluses induced satiety. However, plasma arginine vasopressin levels (involved in the regulation of fluid intake) dropped rapidly during the first 5 minutes of drinking in both ad lib groups, but not in those given fluids via ng tube, so it is not only mechanical receptors that are involved in quenching of thirst.

This confirmed an older study which demonstrated the importance of the rapid drop in plasma vasopressin in hydration before major changes in plasma osmolality occurred.3

Given the lack of extensive research and the relative immediacy of thirst suppression, The most likely explanation is a combination of sensory modalities and reflexes (mucosal wetness, temperature of rehydration fluid, mechanicoreceptors in the oropharynx, esophagus, etc.), and some rapid hormonal changes - e.g. in AVP - are responsible for limiting the amount we drink when thirsty.

1 Regulation of fluid intake in dehydrated humans: Role of oropharyngeal stimulation
2 Influence of age on thirst and fluid intake
3 Acute suppression of plasma vasopressin and thirst after drinking in hypernatremic humans


Is it possible to produce bread using only microorganisms?

It looks like that yeast has been engineered to produce starch.

First of all, the authors had to knock out the genes responsible for the glycogen metabolism pathway (both glycolysis and gluconeogenesis). Then they introduced eight genes known to belong to the amylopectin biosynthetic pathway. The authors succeeded in having starch granules in yeast.

You could start with this yeast strain to produce your bread. You'll probably need to at least dry them in order to reduce them to some sort of flour (probably a hard one, since the yeast would be rich in proteins); otherwise, some means to purify starch grains from yeast should be devised. Then you'd mix them with water and regular baker yeast and maybe you could obtain a bread-like food!


That's some kind of mole cricket (Gryllotalpidae).

According to this website there's only three species found so far in Romania:

Gryllotalpa gryllotalpa
Gryllotalpa stepposa
Gryllotalpa unispina

It's most likely you've encountered a specimen of the first species as it's the most common and widespread one in Europe, Gryllotalpa gryllotalpa:


Warburg effect in lung carcinoma, the logic?

As far as I can tell, there are no data to support your hypothesis. Someone correct me if I'm wrong.

There's actually some data to support that aerobic glycolysis occurs in lung carcinomas despite their proximity to oxygen (ref). There's even an article on the prolifertative pathway behind lung microvascular endothelial cells (ref), alluding to this requirement for anaerobic glycolysis in rapid proliferation.

T cells exhibit a similar pathway, switching to aerobic glycolysis in the effector phase where T cells are rapidly expanding (see clonal expansion). The general trend is that the Warburg effect lends itself to the rapid accumulation of biomass, supporting rapid cell division, instead of energy production to support cell survival (ref). You could rationalize it like cancer is already a failure in cell death, and so the cancer cells need to worry more about carbon compounds that support rapid division. There was also a paper by Cai et al. in 2014 that described their hypothesis behind aerobic glycolysis in cancer, whereby the Warburg effect helps tumors resist anoikis and thereby promote metastasis (ref).

It may be that the Warburg effect is a means to a required end for cancer cells, but as far as I know there's is no correlation between proximity to oxygen and incidence of Warburg metabolism. There are papers out there that continue to ask: is the Warburg effect a cause or consequence of cancer (ref? My thought is that cancer cells will undergo the Warburg effect at a given stage regardless of histology or behavior because it's beneficial.


Variance in Fst in the infinite island model

From Lewontin and Krakauer 1973, the ratio

$$\frac{F_{ST}(d-1)}{\bar F_{ST}}$$

approximatively follows $\chi^2$ distribution of degree $k=d-1$. Here $d$ is the number of demes (number of islands), $F_{ST}$ is the random variable of the $\chi^2$ distribution and $\bar F_{ST}$ is the average $F_{ST}$ that is $\bar F_{ST} = \frac{\sum F_{ST}}{n}$, where $n$ is the number of $F_{ST}$ values.

The variance of a $\chi^2$ distribution is $2k$, therefore

$$var\left(\frac{F_{ST}(d-1)}{\bar F_{ST}}\right) = 2d-2$$

Taking $\frac{d-1}{\bar F_{ST}}$ out of the ratio, the variance of $F_{ST}$ becomes

$$var(F_{ST})=\left(\frac{d-1}{\bar F_{ST}}\right)^2(2d-2)$$

, which simplifies into

$$var(F_{ST}) = \frac{2(d-1)^3}{\bar F_{ST}^2}$$

The above expression is probably the most interesting result but one could go further and express the variance independently from the mean (by replacing $\bar F_{ST}$ by Slatkin 1991 expectation for $\bar F_{ST}$ in a finite island. It yields to

$$var(F_{ST}) = \frac{2(d-1)^3}{\left(\frac{1}{1+4Nm(\frac{d}{d-1})^2}\right)^2}$$

, which again "simplifies" into

$$var(F_{ST}) = \frac{2 \left(4 d^2 m N+d^2-2 d+1\right)^2}{d-1}$$


Has there ever been an attempt to create nutritionally tailored food for adult human consumption?

Yes.  Rob Rhinehart has developed what he refers to as "a food substitute intended to supply all of a human body's daily nutritional needs, made from powdered starch, rice protein, olive oil, and raw chemical powders" which he calls Soylent.
It was developed and tested largely in 2013, crowdfunded late 2013, and is expected to start shipping in 2014. Tests of at least 30 days with no other food intake have been performed, though this is being marketed as a food and not a medical aid of any sort, so does not require FDA testing.
Even if this didn't exist, there are numerous "complete" human diet foods meant for medical use.  These are mentioned in an article about soylent:

Jay Mirtallo is a professor of pharmacy at Ohio State and the immediate past president of American Society for Parental Enteral Nutrition, which focuses on the science and practice of providing food to patients through both intravenous injections and feeding tubes. His main concern with Rhinehart's plan is that he's making the concoction himself, rather than buying it from reputable suppliers.
"He basically made medical food," Mirtallo says. "If he wanted to switch to a liquid diet, those are already available."
Indeed they are. Companies like Abbott Nutrition and NestléHealthScience sell dozens of medical food products, intended to be used by patients under medical supervision and administered intravenously, orally, or through an incision in the stomach.
"They’re very complex products, in terms of making sure you get them in a form that’s palatable but that stays in a form that’s bioavailable to the body," Mirtallo says. "Some of the products are very difficult to get into a liquid form and may lose their potency when they do that, or could interact with other substances that keep them from being absorbed completely."

Rob appears to be attempting to make a consumer version, and so far reports are positive.
But even if you don't trust Rob's mixture, there are countless medically complete nutritional foods that are FDA approved, though not generally available at the supermarket.


The Wikipedia pages on Blood-testis barrier and Sertoli cells have some information relevant to your question, with some academic references included.

You could also search for reviews on Sertoli cells on Google Scholar - several of the first returned results seem relevant, if you are able to access them.


What's the biggest obligate anaerobic organism discovered till now?

There are three worms which have been found in the sediment of the mediterranean seafloor, which not only live without oxygen but also do not tolerate the exposure to oxygen. They belong to the metazoans, for more details see either the report (reference 1) or the original article (reference 2). They reach a size og about 1mm.

References:

Scientists discover first multicellular life that doesn't need
oxygen
The first metazoa living in permanently anoxic conditions


Do brain cells shrink during REM sleep or a lucid dream?

For those who have access, here's the full text.

Having read the paper, I can't give you a good answer.  This study was performed in mice, not humans, and "sleep" was achieved in a few ways.  Mice normally sleep most of the day, so it was easy to get mice sleeping normally.  They also induced "sleep" through anesthesia (incidentally, they saw these same results in anesthetized mice, which I think is quite interesting).  The state of "sleep" was confirmed mainly through the measurement of brain waves, in particular the delta wave.  Delta waves correlate to deep sleep, and that's what they measured.  Panels D and G from figure 1 (below) basically confirm just that - the mice were asleep - and that's all we know so far.  More exciting research to be done!


How does salt concentration affect chromatin compaction?

I think the 'whereas' part is excessive and misleading - I'd say "Higher concentration results to more condensed state".
So charge of DNA + solution will be the same but it will be distributed differently:

$$ D^{-} + nK^{+} + nA^{-} \rightarrow (D^{-}+mK^{+}) + (n-m)K^{+} +nA^{-} $$


Can I compare Shannon-Wiener diversity indices between plots of different sizes (area)?

Theoretically the index for two habitats of different areas are not directly comparable. Consider the species area relationship. You see more species when you look over a larger area. So you will potentially see a different index just from considering a larger area, and not necessarily because there is something different about the habitats.

Borda-de-Agua et al tackle this head on. They use fractals to account for differences in area when comparing diversity indexes. It's complicated and I won't try to put the methods here because I don't understand them. Definietly worth looking at yourself though.

I would also consider using species abundance distribution (SAD) curves for comparison. They give information about diversity but don't sum it up in one nice number like indexes. I feel they are area invariant too. Since if you keep increasing the area of your sample, the total species will only increase from relatively few rare species, while abundances will increase mostly with the abundant species. This will not change the slope of the curve but just the intercept (My hypothesis, not sure if it's actually true). Checkout a good comparison of SAD's and diversity indexes in the intro to McGill et al.

References

Borda‐de‐Água, L., Hubbell, S. P., & McAllister, M. (2002). Species‐area curves, diversity indices, and species abundance distributions: a multifractal analysis. The American Naturalist, 159(2), 138-155. Link

McGill, B. J., Etienne, R. S., Gray, J. S., Alonso, D., Anderson, M. J., Benecha, H. K., Dornelas, M., Enquist, B. J., Green, J. L., He, F., Hurlbert, A. H., Magurran, A. E., Marquet, P. A., Maurer, B. A., Ostling, A., Soykan, C. U., Ugland, K. I. and White, E. P. (2007), Species abundance distributions: moving beyond single prediction theories to integration within an ecological framework. Ecology Letters, 10: 995–1015. Link


Are there proteins that stabilize mRNA:RNA polymerase or mRNA:ribosome complex?

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

There are other possible problems with very long mRNAs:

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

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


Your first definition is correct: a homologous trait is one that can be mapped unambiguously between two samples. What establishes the homologous relationship depends, of course, on the trait or feature being mapped.

For example, one way sexually homologous traits can be identified is by tracing the physical development of a male vs. a female organism. If precursor x becomes y in a developed male and z in a developed female, then y and z can be referred to as homologues.


When you blow through your nose while holding it closed to "depop" you ears, why do you sometimes feel it in your eyes?

The sinuses connect the ear, nose and throat as well as the tear ducts of the eye. The pressure created when holding your nose and blowing is transferred to any area it can get to; in this case, the eyes and ears.


Strength of Female Chimpanzees vs. Male Chimpanzees?

I searched for "chimpanzee strength" on PubMed and followed links from

O'Neill et al. PNAS 2017 "Chimpanzee Super Strength and Human Skeletal Muscle Evolution"

to

Bozek et al. PloS Biology 2014 "Exceptional evolutionary divergence of human muscle and brain metabolomes parallels human cognitive and physical uniqueness" where I found this figure:

which shows pulling strength normalized by individual body weight, with circles for females and diamonds for males.  In fact, the Excel spreadsheet given in supplementary materials shows there were two males and three females, tested in three sessions; in each session weight and pull strength were listed. The ratios of max pull to weight for males were 3.44 and 3.49, for females were 2.9, 3.14, and 3.25 (one of the males was quite small ...)

O'Neill et al. give a few other references, you could follow those as well and see what you come up with.  (I found that "chimpanzee strength" gave better results as a search term than terms including "sexual dimorphism", which tended to be swamped by more general discussions of the evolution of dimorphism in apes.)

@jackaidley notes in a comment that

  extreme caution is needed in interpreting these results for Chimp sexual dimorphism, quite apart from the very low number of individuals, there is a significant problem with these measurements - as the paper itself notes "The inability to control nonhuman primates' motivation hinders the exact quantification of the muscular strength difference between humans and nonhuman primates, even in a highly controlled experimental setup." This problem equally applies to male/female differences.

In other words, male chimpanzees might pull harder at least in part because they are more motivated than females in this particular setting, rather than because of intrinsic differences in strength. (That may or may not matter for what you are trying to do with this information.)


How does the lymphatic system filter blood?

The spleen is an integral part of the lymphatic system, and it is a major filter of blood.

The way this is done is by removing old, distorted, or antibody-coated red blood cells from circulation.

Interestingly, the spleen is enlarged with many leukemias and lymphomas.


The colour of aquatic plants found deep under oceans

You're right that certain wavelengths of light are more capable of penetrating deeper depths of water. However, it turns out, blue light typically travels to deeper depths than all other visible wavelengths of light (and red light does not travel deeply at all).

See my previous SE answer for more details about plant coloration due to this phenomenon.

So that leaves the question of why are these plants red?
Plant life itself cannot exist without the ability to photosynthesize, so actually no plants exist beyond a few hundred meters down. And in fact, many of these plants are still green (e.g., see here and here).
However, some red algae represent an example of a red "plant" (not actually a plant, but quite closely related) that can live fairly deep. According to the University of California-Berkeley:

red algae are red, "because of the presence of the pigment phycoerythrin; this pigment reflects red light and absorbs blue light. Because blue light penetrates water to a greater depth than light of longer wavelengths, these pigments allow red algae to photosynthesize.

Interestingly, this 'reddening' phenomenon is shared by many deep sea organisms, though more often for the sake of protection from predation.
Red light does not reach ocean depths, so deep-sea animals that are red actually appear black and thus are less visible to predators and prey.
See this NOAA page for more:

All objects that are not transparent or translucent either absorb or reflect nearly all of the light that strikes them. When struck by white light (containing all colors), a red fish reflects red light and absorbs all other colors.
The deeper you and the fish go, the less red the fish will appear, because there is less and less red light to reflect off of the fish. At 100 meters, red light does not penetrate and, at this depth, a red fish is difficult, if not impossible to see. Instead, the fish appears blackish because there is no red light to reflect at that depth and the fish absorbs all other wavelengths of color.


As you noted, karyotypes are prepared from cells halted in metaphase or prometaphase.  In either of these phases, the chromosomes will have two chromatids bound together at the centromere with the chromatid arms lying parallel to each other.

The difficulty in seeing the chromatids in most karyotypes is due to the way they are prepared. Most karyotypes are prepared using Giemsa staining which causes contrasting bands to appear on the arms.  These bands have patterns characteristic for each chromosome type and thus allow the chromosome images to be identified and sorted into pairs.  Between the stain, lighting and exposure used to show the banding, the images don't show the separate forms of the chromatids well.

With alternative staining and lighting, the chromatids can stand out (all images from commons.wikimedia.org):

Still, in typical karotype photographs of sufficient resolution you can at times make out the separate forms of chromotid arms, or perhaps the "pinched waist" shape where the centromere draws the chromotid arms together:

So, yes, a normal human karyotype has 92 chromatids present; they just tend not to be apparent due to the preparation done to show banding.


When running a gel, what could cause a standard to run "faster" than usual?

If the salt content or pH is different than "normal," run time can be affected. After all, you're applying an electric current to the sample(s) and ion concentrations and pH are sensitive to that current. Of these two, salt/ion levels are more likely to have an effect as your samples/markers are likely to be adequately buffered.


Can multiple antibodies bind the same antigen?

Not only is it possible for multiple antibodies to bind a single antigen, when that happens, it's more likely to trigger a full immune response.

Here's a description of the concept from a company that sells antibodies for research. To help understand the quote, you'll need to know that the portion of an antigen that an antibody binds is called an epitope.

  Because an antigen can have multiple different epitopes, a number of antibodies can bind to the protein. When two or more antigen binding sites are identical, an antibody can form a stronger bond with the antigen than if only one of the antibody’s sites is bound. Antigens with multiple identical binding sites are called multivalent, and antibodies are able to bind it more strongly.

Here is an article that describes, among other things, the importance of clustering in B-cells when multiple B-cell receptors bind to a single multivalent antigen.


A simple question about amoeba (Naegleria fowleri)

I have studied somewhere that Naegleria can turn into cyst forms when the environment or the atmosphere becomes harsh to protect itself.

This is true, and can happen for a multitude of reasons, including extreme temperature (hot or cold), lack of food, and overcrowding.

  Is it possible for Naegleria to survive boiling temperature if the water is boiled for 15 minutes at 100 degree celcius by converting into cyst form and enter the nose of human and kill the human?

In short: no, this is not possible. According to a publication by the Environmental Research Center, & U.S. Environmental Protection Agency:

  At 51, 55, 58, 63, and 65°C, Naegleria survived about 30, 10, 5, 1, and <0.5 min, respectively.

That being said, just by going off the figures from the publication, there's no chance that Naegleria would survive for 15 minutes in 100°C boiling water.


Tl Dr: In the rosalind example they are showing the 3 reading frames that stem from the definition of a reading frame(non-overlapping triplets), not an example of open reading frames. Open reading frames are non-overlapping triplets between a start codon and a stop codon.
Its important to step back and understand what is meant by reading frame first.  It has been determined experimentally that translation occurs in non-overlapping triplets.  That is to say that if the ribosome starts reading at position n it will translate n, n+1, and n+2 into an amino acid, then it will start at the next non-overlapping position which is n+3.  For example, in the sequence: ABCABCABCABC if you start at A it will read ABC ABC ABC.... But if you start at B you will have an equally valid reading frame by the definition of a non-overlapping triplet and it will read BCA BCA BCA.  If you start at C it will read CAB CAB CAB.  What would happen if I start at the next A? ABC ABC ABC... See how you are back in the first reading frame?  No matter how long a given sequence it will only have 3 reading frames.
Why does that matter if they all start at an AUG?  You don't know which AUG translation starts at.  Not all AUGs are actually used as the first codon in translation, in fact, most are either internal to a translated protein or not translated at all.  An open reading frame is any sequence starting from an AUG to a stop codon.  An open reading frame can be within another open reading frame. E.g: AUGCTAUGGCGAUGUUAGAUGA. All of the valid open reading frames are:
    AUGCTAUGGCGAUGUUAGAUGACUAACUAA

    AUG CTA UGG CGA UGU UAG
         AUG GCG AUG GUU AGA UGA
               AUG UUA GAU GAC UAA
                      AUG ACU AAC UAA


Rabies is a viral disease that causes inflammation of the brain. It is adapted to the nervous systems of vertebrates particularly mammals. Arthropods are too different for lyssaviruses to survive in their body.


What do the negative and positive signs mean in the rotation angles of DNA helices?

The signs refer to the direction of rotation. Anticlockwise (right handed helix) is positive whereas clockwise (left handed helix) is negative. The direction of rotation is observed from the side where the helix is ascending towards you (perpendicular to the ascent of helix).

In other words, curl your right hand and extend the thumb out. When viewed from above (thumb points towards you), the fingers curl anticlockwise. This is a right handed (RH) helix. Conversely, you can use your left hand to understand left handed (LH) helices such as that of Z-DNA.

  Obtained from http://biomedapps.curtin.edu.au/biochem/tutorials/prottute/helixfigures.htm. Which, in-turn is modified from Lehninger et al (1993) Principles of Biochemistry 2nd Edition Worth Publishers

   This figure depicts a RH helix. Taken from Wikipedia.

These definitions can be defined in a converse sense too and you would find many web sources that do like that. For example, if you look from below your hand such that the thumb points away from you, then the fingers curl clockwise. However, in physics and mathematics, positive angles are conventionally measured anti-clockwise. So my definition here would be suitable with respect to your question.


Why is it difficult for germs to jump species?

The main reason is the biochemistry of two unrelated species are different. The more closely related two species the more similar things like the hormones and signaling molecules will be and inversely the more distantly related they are the more different those things will be. Even highly constrained molecules like hemoglobin may still pick of mutation that get carried on, and things like an immune system actually favor variation evolutionary speaking. Even within the same species these things vary, and this is often cited as a possible reason sexual reproduction exists at all, to make each individual slightly different, so there is less chance of catching disease or parasites from others.
Animals have many many barriers to infection, getting pass all of them is hard and tends to only occur with organisms that evolve alongside that animal and thus have been selected to get pass that specific combination of barriers. Getting past them all if you have not been evolving along side it is incredibly unlikely, some diseases manage it be cause there are billions of them, and if you run billion upon billion of random keys through a lock some may fit just due to chance. this is also why disease transfer is more likely the more closely related species are. SIV/HIV easily jumps between great apes because they are very closely related (and the insist on eating each other) the chances of a dog catching it are astronomically unlikely because the chances of the viruses surface proteins matching receptor proteins of a dog is basically only chance as there has been no evolutionary pressure to alter it.


What are the evidence that all life today descended from a common ancestor (LUCA), and which organisms (if any) challenge the concept?

It is quite likely (but impossible to study) that the first stages of life originated more than once. However, the evidence that all currently existing life originated from the same population is very strong.

On the most basic level, many of the basic building blocks of life, RNA, DNA, amino acids are chiral, which means they come in multiple forms, but for the basic processes in nature, always the same one is chosen. For example, the DNA double-helix is almost always right-handed in nature,
see also here. If there were multiple origins of archaea and bacteria, we would expect some to pick another form, if not other molecules. In addition, many crucial proteins such as DNA- and RNA- polymerases are very similar between all species, the probability that they would have originated independently is simply minuscule.


GMO stands for Genetically Modified Organism.

Grafting a plant to a rootstock of another plant is a form of plant husbandry -- a farming technique -- but this isn't changing the DNA code of either of the plants. Sort of like you receiving a donated organ wouldn't be the same as Genetically Modifying you. When a desired trait arises in a plant this is a way to keep reproducing it without having to wait for cuttings or seeds to grow.

There are some plants such as seedless watermelon that are reliably made by crossing two seed species. You take two species of watermelon, Species 1 and Species 2. Both of these are normal -- they make seeds. When you cross them, you get seeds that grow into AB hybrids, seedless watermelons. These AB are infertile, so you have to cross A and B again each year to get more seeds to plant.

Species 2 has twice the number of chromosomes as 1. They are able to cross and make seeds that grow. But when the [1x2] plants grow and try to form seeds, those cells have an odd number of chromosomes. The process of making seeds and pollen -- meiosis -- has a step when half of the chromosomes go into one cell and half in another. Because there is an odd number, the process never finishes and you get no seeds.

to demonstrate how this doesn't work, lets look as a simple example.
Species 1 has two chromosomes [A and B]. It has two copies of each chromosome in all of its cells (one from each parent), and just one copy in its sex cells

Species 1 Normal Cells [AA BB] and sex cells [A B]

Now, Species 2 has a different set up. It has twice the chromosome number
Its normal cells are [aaaa bbbb] and its sex cells are [aa bb]

this means that when you cross pollen from one with seed from the other, you get
[aaA bbB] or [AAa BBb]

When these crosses try to make seeds, its impossible to halve the triplet chromosomes.

True GM technology is used to do things such as, in olives taking the genes that cause oil production to occur, and copying them many times. This gives you more oil production.

Or, in tomatoes -- when you pick a tomato the cells of the skin start expressing a gene that makes the skin soften and break apart. This facilitates seed dispersal. GM deletes that gene, making the fruit bruise less, reducing need for packaging and quadrupling the shelf life.


Do sea sponge cells transfer nutrients to each other?

yes choanocytes handle the bulk of sponges digestion and transfer nutrients to other cells. in some species Amebocytes also perform a similar function.


How can a recessive allele propagate until they manifest?

The short answer is: Because of genetic drift. If a mutation does not influence the fitness in heterozygote individuals, then its frequency varies only through genetic drift while it reaches some high enough frequency. If by chance, the frequency of this new allele achieve a high enough level so that the homozygous for this allele given by $x^2$ where $x$ is the frequency of the new allele is high enough to have considerable effect, then the selection coefficient $s$ between the recess if homozygote and the two other genotypes matters.

Hope that helps you. Don't hesitate to ask further more accurate question if you need more details. Or let us know if this does not answer your question.


Why is FAD, rather than NAD+, reduced in the succinate dehydrogenase reaction of the Tricarboxylic Acid Cycle?

In general, $\ce{NADH}$ and $\ce{FADH2}$ are coenzymes. The structure of the main part of an enzyme determines which coenzyme or which prosthetic group will work with the concerned enzyme. Unlike most other TCA cycle enzymes, Succinic Dehydrogenase involves the participation of $\ce{FAD}$ rather than $\ce{NAD}$ and that is a consequence of its specific structure.

Another possible reason might have to do with the energy gap available. The conversion of succinate to fumarate liberates less free energy as compared to other oxidation reactions. Ususally, the steps which are coupled with NADH reduction have a free energy change of about $\Delta G=-100$ to $-150~ \pu{kcal/mol}$ but the conversion catalysed by succinic dehydrogenase has a free energy change of $ -80~ \pu{kcal/mol}$ (rough figures) and hence releases less energy. It therefore might not be feasible to couple with $\ce{NADH}$ reduction but it would be favourable (thermodynamically) to couple with an easy-to-reduce (since the reduction potential is higher and hence reduction is more favourable) and hence less energetic molecule $\ce{FADH2}$.

Apart from structural and thermodynamic requirement, one last possible reason might be that succinic dehydrogenase is a part of the mitochondrial membrane and involved in the Electron Transport Chain. Here, flavin molecules are excellent electron transporters (in comparison to nicotinamide based reductants) and therefore it might be more profitable to use a flavin based reductant to later facilitate easy electron transport and proton transfer.
Again all these are probable reasons and I dont know for sure.


How have single-celled organisms learned to survive?

Nothing more than physics

Simple physics

If you drop a rock, the rock will fall to the ground. If there was a table underneath, the rock would stop its fall when reaching the table. Clearly the rock response to the environment but the rock does not need to be conscious (whatever you mean by "consciousness") of anything in order to response to the environment.

Explanations for a programmer

As you are a programmer, you will agree that when you do std::cout << 3+2 << std::endl;, nothing more than physics is happening so that the value 5 followed by a newline gets printed to the screen. It is relatively complex though. So complex as it may feel quite magical before having spend many hours learning about computer engineering.

You understand that if you write a, say, boids program, all that is happening is just a bunch of addition and multiplication (eventually a power). The program does not need to be "aware" of what it is doing (nor do the individual agent) in order to respond to the change of behaviour of a predator.

You could even write a self-learning algorithm and you would still accept that there is no consciousness involved but only very simple operations.

Biology

Living things are pretty much the same. They are relatively complex (more like a computer than like a rock) but essentially the same. Also, the response tot he stimulus is often (not always) adaptive as it has been selected. But at it's core there's nothing more than physics involved!

More info

If you want to learn more about the mechanism of living things in general, you might want to just open an intro book or have a look at an intro course such as Khan Academy > Biology for example.

You can as well pick a simple response to an environmental stimulus and study its mechanism. How about having a look at chemotaxis for example!

Note that you might also want to follow a short and introductory course to evolutionary biology such as Understanding Evolution for example.


What is the convention for log notation in biology?

There is a reason for prevalent usage of log2 and log10 compared to loge in biology and other experimental sciences. Usually while doing measurements we are generally interested in fold changes and we generally talk in the sense of two-fold (or multiples of two) or ten-fold.

Doubling is a common phenomenon at least in case of growth and so a two-fold change would represent an event of doubling. log2 gives you the extent of "doubling". Even in computer systems memory is always in the exponents of two (because of binary); so even there log2 would make sense.

In biology you would generally not see a ten-fold change but in physics it is relatively common. Most values are represented in scientific notation which basically uses an exponent of 10. Also the metric units are in multiples of 10. Here log10 would be more suitable.

loge has a special mathematical importance i.e. it can be denoted as a series. The euler's number has other important properties too. Therefore in most theoretical studies, both in biology and physics, loge is preferred because it makes more mathematical sense.

To sum up, experimentalists prefer log2 or log10 because they are better for representing measurements whereas theoreticians prefer loge because of its mathematical importance.

W.r.t the old paper that reports log-log relationships, the base of the logarithm wouldn't make any difference to the slope. But most likely it must be log10 (I think back then log10 was the default log; log2 would be much more suitable for lengths and weights of organisms compared to log10 because these measures would not vary in multiples of 10).


How to dialyze large amounts of precipitated enzyme solution (ammonium sulfate)?

Ammonium Sulfate (AS) precipitation is used to concentrate protein. After AS precipitation, you would centrifuge the sample to pellet the protein and then resuspend it in a small volume of water or buffer. You can then dialyze this small volume.

There are other methods of concentrating large volumes of protein, including chromatography and ultrafiltration.


Should I dilute DNA with water or elution buffer?

I would generally recommend using the elution buffer (which is typically Tris-EDTA buffer) or TE-Buffer, as the pH and the conditions stabilize the DNA for a longer time, which is especially interesting when you want to store your DNA.

It also dependent on your downstream application - when they recommend diluting the sample in water (or another buffer) I would follow this advice. In my experience TE buffer is perfect for all molecularbiology applications (cloning, digestion, amplification, PCR, etc.). The EDTA will of course complex bivalent cations, but since the concentration in the buffer is only 1mM and the DNA solution is usually diluted further in PCR or digests, this plays no role in real life applications.


Do all different types of neurons serve different roles?

Short answer
Neurons were (and still are) often identified based on their histological features (e.g., by Cajal). In turn, the structural hallmarks of neurons are often related to their specific functions.

Background
The question is broad and difficult to answer without restricting to some example types of neurons. As you say, about 10,000 types of neurons have been identified. Their roles are often obscure. I will restrict the answer below to a few examples of neurons with relatively well characterized histological features and relatively established functionality.

Fig. 1. Examples of neuronal cell types. A. Purkinje cell B. Granule cell C. Motor neuron D. Tripolar neuron E. Pyramidal neuron F. Chandelier cell G. Spindle neuron H. Stellate cell.source: Ferris (2012)

The Purkinje cell (Fig. 1A) receives large amounts of input in the cerebellum, integrates it and sends it away to deeper layers. Hence, as an integrator, it has an elaborate input side (large dendritic tree) and a single output axon. The basic  function is similar to the pyramidal cell in the cerebrum (Fig. 1E) and their appearance is hence similar.

On the other end of the spectrum is the spindle neuron (Von Economo neuron, Fig. 1G), which is believed to receive simple input and generate simple output and is built for speed. Less integration allows for faster responses. Hence, its dendritic tree is much less elaborated than the Purkinje cell (cf. fig. 1A) (Allman et al., 2011).

As a last example: the Chandelier cell (Fig. 1F); these neurons have axons that form elaborate collaterals that are believed to form local inhibitory connections in the cortex to keep (excessive) stimulation in check; hence their output is more elaborate, with relatively simple input. Dysfunction is believed to be associated to epilepsy (excessive stimulation) (Inan & Anderson, 2014).

References
- Allman et al., Ann N Y Acad Sci (2012), 1225: 59–71
- Inan & Anderson, Cur Opinion Neurobiol (2014), 26: 142–48
- Purves D et al. (eds.) Neuroscience, 2nd ed. Sunderland (MA): Sinauer Associates (2001)

Source
- Ferris, 2012, Sci Am blog


Why isn't the sticky bud plant more widespread

As already pointed out in comments, Galium aparine is actually very widespread, according to wikipedia it occurs in "a wide region of Europe, North Africa and Asia from Britain and the Canary Islands to Japan. It is now naturalized throughout most of the United States, Canada, Mexico, Central America, South America, Australia, New Zealand, some oceanic islands and scattered locations in Africa."
Because it occurs on moist and rich soils, it doesn't occur everywhere. It is unable to grow on dry and nutrient-poor soils (like deserts and steppes). But it is also absent in frequently mown grassland.


Weird gelatinous substance in highland bog

So the One show did a magazine piece on this the other day; a scientist did some DNA analysis of some of it.

The conclusion was that it's frog jelly (full of frog DNA). Apparently frogs have glands full of the components of the jelly in sacks in their bodies, when the sacks come into contact with moisture it expands massively (same process that allow them to produce frog spawn). When a frog is eaten (by a bird or fox) the jelly expands on contact with any moisture. The animal eating the frog either vomits it up or doesn't eat it in the first place and voila.


Is it possible to create a restriction map using python?

First I'll answer this question on the assumption that you are learning or about to learn python (perhaps because you have heard it's become popular in bioinformatics) and you wish to know whether it can be applied to this problem. The answer is: almost certainly, yes. Python is a programming language of the type that can be used to automate the solution of this sort of problem, just as C, C++, Java, Perl could.

How would you go about it? You would first solve the problem by hand, analyse the logic of your solution and then try to devise a general algorithm based on this logic that can deal with a variety of cases. Then you would code this in python (or whatever). The program would have to deal with inaccuracies in real values of fragment lengths (your data are obviously contrived) and the possibility of more than one fragment of the same length (see below).  If you used python and wanted graphic output you would need to use a library that provides graphics as python does not come with graphics like Java does. I gather there are some. However this is certainly not the easiest of programming tasks for a beginner, as you'll see when you try to analyse the logic of manual solution. And of course the data might be insufficient to provide a unique solution.

However as this is a homework assignment you may also be wanting to find a program to check your answer and perhaps be unsure of how to go about it. I don't know of any packages to do this — it's perhaps not 'cutting edge'. So how to go about it?

What I would do is start with the single digests and draw a set of diagrams for each of the possible arrangements of the restriction sites in each case (pencil and paper work well here). Then I'd look at the simplest double-digest, identify unique fragments that were also present in the single digests and thus not cut by the second enzyme, and again draw diagrams of the possibilities for the pairs of restrictions sites. Be careful; the HindIII/BamHI double-digest doesn't add up to 900, so there must be two fragments of the same size here. When you have all the possibilities drawn out, there is presumably only one final restriction diagram that is consistent with all the data.

On second thoughts, devising an algorithm for solving this general case would be quite a demanding task. If you are starting programming I'd tackle easier algorithms first. And an effective algorithm might adopt a brute force approach rather than the semi-intuitive way we solve these by hand. For example your algorithm might generate all possible maps for each of the the three single digestions, and then generate all possible maps for the double-digestions, together with a calculation of the size of fragments the double-digestions would generate. These would be compared with the size of the actual fragments from double-digestions, eliminating those possibilities that don't fit. Coding it would involve writing methods/functions (or whatever they are called in python) to generate these maps, storing the coordinates of the sites in a data structure such as an array, and writing other methods to make comparisons etc.


Well, what you seem to be suggesting is "Did life evolve twice on Earth?"

Your original question has an answer: Probably yes. It's not unlikely to think that the original cell evolved into two different paths and then one went extinct. However, that doesn't address LUCA. If we found fossil evidence of what we thought was LUCA, and then fossil evidence that LUCA had a genetic cousin - all that would do is push the application of the term "LUCA" on more evolutionary step backwards until both shared a common ancestor which would then be called LUCA. You can do this indefinitely until all life originates from a single cell, with countless offshoots which have gone extinct.

If you mean was there a whole other type of life - one that did not originate from LUCA and existed - then the best answer we have right now is "No." All life we know of, no matter how different and old, is still based on RNA/DNA and proteins. Fossil evidence supports this premise until about 3.5 BYA.

If there was an 'alternate' construction of life, we have not found the fossil evidence for it, and might not know what it was if we did. If the alternate form didn't utilize cells, we might not be aware we had it. If it did, but utilized different metabolisms or structures for proteins and storage - that evidence would be long gone by now.

Our best bet for answering if life can evolve differently than what we have today is out among the stars.


Question: Annotation search in scientific literature

Well here's my take.

Too bad there is no open access to this article.

I see that they only had statistics for the top 50 annotations, which implies a fairly manual process - you are going to have to read a bunch of articles or abstracts to decide directly whether the software is right.

In this case, the authors are using a weighted latent semantic indexing method, which have started with a bunch of articles for each gene.  In this case they would need to only look over the data they put into their training.

That might not answer your question though.  If you wanted to search through the literature to confirm annotations generated by a different method...you could create a semi automated process perhaps like this:

Do a rough search from NCBI in an automated fashion (I use biopython) and then read every article for validation of the Gene Ontology result.

Its possible you could search for the gene and the GO term if to get a more focused result, load them into a database or just create a set of files with the data.  I would build a little website that would let me scan through the abstracts with links to the complete articles (pubmed supplies DOI numbers) and just check 'yea' or 'nay' on whether confirmation happened.

There's other potential options on finding articles for search.  the Genbank record for the gene often has a few references in it. One might use pubmed's referencing feature, where you can start at these confirmed articles that describes the gene in question and use pubmed to show articles that have referenced that first article.  I'm not sure if this is accessible by API, but you can build an article graph by crawling the bibliographic references.

For anything that was neither directly contradicted nor confirmed you would have to go back to google and look until you found something.  Clearly there are genes for which you will find no reference at all, and you will have to avoid these for confirmation.

I'm not sure if they checked for any biases here. If something like interpro2go which generates GO mappings from protein domains was used to generate the annotations, you would want to take care that you weren't just rediscovering the references which helped generate the annotations in the first place.


Let's start with a short comment. You say [..]evolution is nowadays pretty much analyzed through phylogenetic trees[..]. Many evolutionary biologist make extensive use of phylogenetic methods but a good part of evolutionary biologist (most of them I would say) do not work directly with phylogenetic methods.

I am not sure I understand your question but I hope the following may help you either to understand the issue or to edit your question. Note that I am NOT a phylogeneticist (not sure this word exist).

What is the definition of "Node" in Phylogenetics

I think your definition of a node might be unusual. In phylogeny, a node is the most recent common ancestor shared by two sister lineages.

Time in a phylogenetic tree

Also, when you look at a phylogenetic tree, the axis along which lineages diversify represent the time (real time in years). Now, we use different methods in order to make estimation of this real time such as the rate of neutral substitutions. You may want to read about molecular clock

  each successive node has taken time to develop?

I am not sure what you mean by "develop" here. Develop usually means the process by which a given individual/organism change phenotypically through its lifetime. See Developmental Biology.

  average time it takes for any organism to loose or acquire a certain trait

Again, I am not sure whether you talk about development or evolution here as you talk about "any organism" and not about "any population". In any case, an average time for a population to acquire a given arbitrary trait is a concept that 1) is valid only for discrete trait, 2) that is very dependent on the kind of trait you want to consider, 3) that will varies a lot from one given trait to another and 4) is extremely dependent on when you consider being the starting point of the evolutionary process toward the creation of the given trait. Therefore, it doesn't makes much sense.

Currently used "standard evolutionary time unit"

However, you can use similar concept for quantitative traits. The Darwin (d) is a unit of evolutionary change (invented by Haldane and named after C. Darwin). It is defined as the e-fold (e≈2.7) in a mean of individual's trait in a population over million of years.

Similarly, the Haldane is a unit of evolutionary change (I don't know who invented it and it is obviously named after JBS Haldane) and correspond to the number of standard deviation (of the distribution of individual trait in the population) change in the mean of individuals trait in a population per generation. The Haldane unit is therefore dependent on the selection pressure, the additive and non-additive genetic variance and environmental variance (therefore the heritability) and the mutational pressures.


Does a top heavy population grow, decline or stabilize?

Your question leads to the field of structured population models, and I am afraid the answer is: It very much depends on the exact Leslie matrix!. The population may go extinct, may remain at stable size or may grow indefinitely (theoretically speaking), may increase in size and then go extinct, etc…

This is at least true if you make the assumptions that the rates that govern transitions between age/stage classes (survival, growth, reproduction) are  stable over time. Your scenario is explicitly dealing with transitory population dynamics, i.e. it is asking about the population fluctuations that will happen before the age-structured population stabilizes (assuming it does stabilize at some point).

To understand what will happen to your population you can model your population using a Leslie Matrix.

Leslie matrix

The Leslie matrix describes the age-specific survival and fecundity of individuals in a population. Shortly speaking, a population dynamic model using age-specific survival and fecundity looks like this:

Renaming the matrix and the two vectors you see above it gives: $n(t+1) = M \cdot n(t)$, where $n(t)$ is the vector of number of individuals in the population at each age and $L$ is the Leslie matrix, where the first row gives the fecundity of each age ($f_2$ is the fecundity of the individuals of age class $s$ where the number of individuals is a given time is given by $n(t)$) and the lower diagonal gives the survival probability ($s_2$ is the probability of surviving from age 2 to age 3).

So, basically to answer your question, define your Leslie matrix $L$, define $n(0)$ and iterate (left multiply your vector $n(t)$ several times) to see how the number of individuals change through time.

Leslie Matrix and Linear Algebra

The right leading eigenvector of $L$ gives the stationary distribution and the associated eigenvalue gives the growth rate $R$. If $0<R<1$, then the population size decreases through time if $R>1$, the population size increases through time. $R$ cannot be lower than zero (see Perron-Frobenius theorem to understand why). Many biologically interesting features can be extrapolated from the Leslie matrix such as the average lifespan or wether you'd better to introduce individuals of a age 1 or of age 2 (for examples) in a natural population in order to prevent its extinction. The sensitivity of the population to change in parameter (such as an increasing mortality rate at lower age due to a new predator for example). You will find more information on the Leslie matrix on the wiki article and on many other websites.

Other ways to model using a Leslie Matrix

One can also model age-structured population using right matrix multiplication (unlike the above example) and use row vector rather than column vector. The Leslie matrix may also be a function of various variables such as the population size. One can also extend the Leslie matrix to incorporate different class of individuals in the population (different genotypes or different sexes for examples).

Books

Charlesworth (1994) and Caswell (2001) are both excellent books to go deeper into the subject of structured population models, but may be challenging if you lack some background knowledge in linear algebra or population biology.

Hope that helps!

EDIT - R simulation

It's been a time I haven't programmed in R. So, I took the advantage of your question to have some fun and write a quick and dirty code. Below you'll find a R code that allows you to see how the age-specific population size varies depending on your initial vector $n$ and the Leslie matrix. I also add some instructions in order to change the number of age classes (because my code is not very flexible).

install.packages("ggplot2")
require(ggplot2)

######## Parameters

n = c(2,23,40,300) # initial age-specific population size. MNodify as you which. You can as well change the number of age classes

L = matrix(c(0,0,1,2,0.8,0,0,0,0,0.8,0,0,0,0,0.3,0), byrow=T, ncol=4) # Leslie matrix (Left multiplication). Modify as you wish but make sure it should always be a Leslie matrix. You can modify the number of age classes

nb_generations = 100 # Number of generations you want to compute

############

n_t = data.frame(age1=rep(0,nb_generations), age2=rep(0,nb_generations), age3=rep(0,nb_generations), age4=rep(0,nb_generations), Generation=1:nb_generations) # It is a data frame to keep track of age-specific population size changes through times. You will have to add more `ageX` columns if you want to simulate more than age classes

# print (n)
for (generation in 1:nb_generations){
    n_t[generation,1:4] = n
    n = L %*% n
#   print (n)
}

n_t2 = reshape(n_t, direction='long', idvar='Generation', v.names='Age_Specific_Number_of_Individuals', varying=c('age1', 'age2', 'age3', 'age4'), timevar='age') # Just to reshape the data for convenience!

ggplot(n_t2, aes(x=Generation, y=Age_Specific_Number_of_Individuals, color=factor(age))) + geom_line() + scale_color_manual(values=colorRampPalette(c("black", "grey"))(4)) # change the last number of this line according to your number of age classes.

You can see that there are important osciliations in the first 30 or so generations just as expected. Then, the population more or less stabilizes. You also see that due to a survival probability that is different from 1 for each age class, there is necessarily at equilibrium more individuals in age class 1 than in age class 2 and more in age class 2 than in age class 3, etc...


Short answer
Some genes on the X chromosome escape X-inactivation. Two copies of these genes are needed for normal development. These genes are also present on the Y chromosome. Hence, healthy males and females both have two copies of these genes. In Turner's, the SHOX gene seems to be one of the culprits, which is needed for normal skeletal development.

Background
It appears that both of the X chromosomes are needed for normal development in females, as some genes on the X chromosome escape X-inactivation. Many of these genes are located at the ends of each arm of the X chromosome in areas known as the pseudoautosomal regions. Although many genes are unique to the X chromosome, genes in the pseudoautosomal regions are present on both sex chromosomes. As a result, men and women each have two functional copies of these genes. Many of these genes are essential for normal development (source: Genetics Home Reference).

Which genes on the X chromosome are responsible is not yet known, but a gene called SHOX seems to be involved, which is important for bone development and growth. The loss of one copy of this gene likely causes short stature and skeletal abnormalities in women with Turner syndrome (source: NIH), see Fig. 1.

SHOX is a transcription factor of the homeobox family. It acts during early embryonic development to control the formation of the development of the skeleton. It plays an important role in the growth and maturation of bones in the arms and legs source: NIH).

Fig. 1. Some developmental malformations seen in Turner's syndrome. source: University of Chicago

FYI - I wrote an answer before checking for duplicates. I think my answer does add additional information (the SHOX gene is not mentioned in the linked question's answer


Contacted technical support and received the following response:
"Unfortunately, the information you requested is proprietary and we will not be able to share the images of excitation-emission spectra of EZ-Vision® Bluelight DNA Dye and the chemical structure of the dye. "


The best explanation I have come across so far is that male sex hormones have an adverse affect on longevity.

The effect may simply be that a high testosterone level suppresses the immune response against a variety of infections.

There was a recent study about korean eunuchs from a few hundred years back, that found that "The average lifespan of eunuchs was 70.0 ± 1.76 years, which was 14.4–19.1 years longer than the lifespan of non-castrated men of similar socio-economic status."

That's a pretty hefty difference and a strong argument for testosterone as risk factor for an earlier death. This would also vibe well with reports about increased risk of cardio-vascular death in steroid users.


Pen and paper game to explain the theory of evolution

Not sure if this qualifies as pen-and-paper, but one game I am familiar with to highlight natural selection goes something like this:

Materials:

Creatures - Small paper pieces of different colors from construction paper - hole-punch remnants work great, or you could cut squares

"Habitats" - Large, colorful surfaces. Whimsical maps work well for this, maybe certain posters, the important thing is to have some diversity.

Gameplay:

This is ideally done in multiple groups, each group should start with an equal ratio of creatures of at least two different colors.

One or more people spread the Creatures around the Habitat.

Another student (or multiple) act as the "predators". They sit/stand above the board and close their eyes; then they open their eyes, and harvest the first Creature they see. Close eyes again, and repeat until 1/2 of the Creatures have been eaten.

Count up the paper pieces and note the number of each remaining; for the next Generation, the Creature makeup should be 2X the number of remaining pieces: so if there are 30 Blue and 20 Red remaining, the new generation is 60 Blue and 40 Red.

Repeat over a few generations - the result should be that different habitats lead to different proportions. You can also change things up by suddenly shifting the habitat!

You could possibly include genotypes and recessive traits for an older group (the version I suggested is effectively haploid parthenogenesis), these might be harder to keep track of.

(I don't have a citation for this game but if anyone knows it, please provide in the comments!)


Combining gene expression data from two species

This is the FlyBase page for the example gene: Dsim\GD10095. There, you have a section "orthologs", linking to OrthoDB. So my suggestion is: Find the list of synonyms for D. simulans on FlyBase (perhaps here?), download the Drosophila section of OrthoDB, and finally find the 1:1 orthologs.


Can the human eye distinguish colors in the periphery?

It depends to some degree on what you define as peripheral vision. Until relatively recently, color vision in the peripheral field has been thought to be substantially less developed than color vision in the central field.

Most estimates of peripheral color perception place the limit of trichromatic (RBG) vision at no more than 30 degrees from fixation; the visual field was thought to be completely color blind at about 50 degrees of eccentricity.

Within the last 30 years, an increased understanding of the changing spatial scale in the peripheral field has led researchers to reevaluate what is believed about peripheral function. Virtually every measure of peripheral color perception can be improved by using a suitably large stimulus in the peripheral field.1

Here is a schematic of distribution of cones in the retina. While cones are decreased in number at 60°, they do exist.2

Here is a schematic of angles on peripheral vision.2

Previous measured sensitivity curves farther into the periphery than thought. In one paper, detection thresholds for colored disks showed that color vision, in particular color vision mediated by the L – M cone-opponent channel (luminance or blue–yellow colors), persists up to at least 50 deg. The larger the color disc, the better the peripheral color vision.[3]

  The range of eccentricities over which red–green color vision is still possible is larger than previously thought. Color stimuli can be reliably detected and identified by chromatically opponent mechanisms even at 50 deg eccentricity. Earlier studies most probably underestimated this range. Differences could be caused by technical limitations and the use of stimuli of non-optimal size. (Emphasis mine) In agreement with previous studies we found that the decline in reddish-greenish L − M color sensitivity was greater than for luminance and bluish-yellowish S − (L + M) signals. We interpret our findings as being consistent with a functional bias in the wiring of cone inputs to ganglion cells (Buzás et al., 2006) that predicts a decrease but not a lack of cone-opponent responses in the retinal periphery.

1 Color vision in the peripheral retina
2 The Retina: Rods and Cones
3 Color perception in the intermediate periphery of the visual field


Cancer cells and their ability to avoid the immune system

There are several types of cancer immune evasion.
Such as releasing immunesupressive cytokines, downregulation of MCH and comprising mutiations which are not immunogenic.
If the cancer at a tissue is evading immune system by immunosupressive cytokines it may not be affected from auto-immune attack.
However if the cancer is not attacked by the immune system just because it is not "different" enough from the nearby healthy tissues, then at this time auto-immune attack to the tissue may technnically attack the cancer also.