Why do some cells like myofibrils have multiple nuclei?

In the case of myofibrils this is the case because of the syncytial nature of that given structure. It means that in developing muscle tissue after a certain stage the cell divisions become somewhat different in that the final step, being separation of the daughter cells via invagination of cell membranes, does not happen. The nuclei though keep dividing after that. In the end what we have is a big membrane coated structure (basically a cell), with lots of nuclei in it.


What are the metabolic concerns associated with hard water?

Right now, there is no evidence shows hard water can cause any healthy problem.
Previously people were concerned about the high mineral content, such as calcium and magnesium, may cause disease such like cardiovascular disease.
But from the report World Health Organization published in 2003 Hardness in Drinking-water, it says "Although a number of epidemiological studies have shown a statistically significant inverse relationship between the hardness of drinking-water and cardiovascular disease, the available data are inadequate to permit the conclusion that the association is causal. No health-based guideline value for water hardness is proposed."
And the United States National Research Council thinks hard water can supply the calcium and magnesium ions that dietary need. Hard Water Hardness Calcium Magnesium Water Corrosion Mineral Scale
And at the same time, I have to say the filters cannot directly work for hard water because ions cannot be isolate by filter. We can filter the precipitation after boil the water. Boiled water is better because tap water may contain microbes in some country, that will be harm for our health. And also you don't want to feel some "precipitation" while you drinking water. That's it.
For my personal view, I prefer soft water because it tastes a little bit sweet.


Circulating factors affecting human health/longevity

Oxytocin

It has been shown that oxytocin signaling helps in muscle regeneration by activating MAP Kinase/ERK pathway in skeletal muscles.[1]

Oxytocin is also known to promote adult neurogenesis.[2]

Myostatin

It is a paralog of GDF and like GDF prevents hypertrophy of cardiac muscles.[3]

Leptin

Though leptin is not really connected to ageing but it regulates appetite and thereby the metabolism [4]. It may contribute indirectly to ageing by reducing food intake and preventing obesity and other metabolic syndromes.

This review summarizes different soluble small (synthetic) molecules that have potential to induce regeneration.


What’s the name of this insect that looks like a leaf? I found it in Taiwan

It appears to be a Tituria planata nymph.

The adults look like this:

At first I thought it was a treehopper, but it turns out that it was a leafhopper (they are similar but different).

Tituria planata is one of the most common leafhopper species in Taiwan.

https://www.inaturalist.org/observations?place_id=7887&taxon_id=49025&view=species

Image source 1

Image source 2


I think you are asking about cooperativity.  In many biological systems the action of many individuals are coupled together such that when an individual is activated, the others are more likely to act as well.

The classic example of cooperativity is hemoglobin a protein which has four sites to bind oxygen (one in each of four protein subunits).  When there is enough oxygen present for a single binding site to bind oxygen, the other three will have an increased affinity for oxygen.  This makes the binding curve relatively steep.  Take a look at the graph.

When one oxygen is bound, three others also become bound. The more individuals in a cooperative system, the sharper the curve becomes until a very small change in a few individuals creates a very large effect very quickly.  "all or nothing" as you say.

Muscle contraction also is cooperative.  In this case there are many individual muscle fibers, both the myosin thick filaments, and the actin thin filaments.  Each fiber consists of chains of interwoven individual proteins.  When a myosin individual protein acts against a thin filament other myosins in that filament and those on other thick filaments that are bound to the same thin filament will react to the change in tension and also be more likely to activate and pull.  This causes the entire muscle to quickly start to contract together.  Most researches feel that all the various other factors contributing to muscle contraction, calcium release and uptake, other proteins that are associated with the thick and thin filaments etc, are cooperative as well.  A search for 'muscle contraction cooperativity' will give many references like these.


How does the brain regulate its temperature?

From what I can make up from your question is that you assume the blood in the brain is a bad thing in terms of temperature control. It is not. On the contrary, blood circulation is crucial in maintaining brain temperature.

Under conditions where the brain is heating up, which seems the focus of your question, there are mechanisms in place to extract this heat and dissipate it. Ambient temperature is often lower than body temperature, and even in hot climates, transpiration and water evaporation may cool the skin nonetheless.

Indeed, during hyperthermia, venous blood flow from the skin of the face and scalp fed to the dura mater (the brain's membranes) is enhanced and cools the brain (Brinnel et al., 1989). Moreover, blood from the nasal and paranasal mucous membranes flowing to the dura mater cools the brain due to the evaporative cooling of sweat or mucus. The dura mater may also transmit temperature changes to the cerebrospinal fluid compartment (Zenker & Kubik, 1996).

References
- Brinnel et al., Arch Dermatol Res (1989); 281(1): 66-72
-  Zenker & Kubik, Anatomy Embryol (1996); 193(1): 1-13


Can you explain the behavior of this snake?

You're right, snakes rely on scent to capture their prey. The fact that snakes are carnivores may be the one constant among all snake species; absolutely none feed even partially on plant matter. So if plant matter is found in their feces, it must have gotten there accidentally.

Here, check out this post, which answers your question perfectly:

  In most instances, these have been attributed to accidental ingestion, perhaps eaten with a prey item or mistaken for prey by scent...One interesting case was discovered by Harvey Lillywhite in a study published in the journal BioScience in 2008. Studying the scavenging behavior of pitvipers in the intertidal zone, Lillywhite observed cottomouth droppings that were composed almost entirely of seaweed. This was too much plant material to have been eaten secondarily or by accident, and he suspected that there was some other cause for the snakes to be eating it (it certainly wasn't for energy as the seaweed was passing through completely undigested. Lillywhite wondered if it was possible the snakes were feeding on seaweed simply because it smelled like fish. An interesting laboratory experiment proved their hypothesis correct; they offered various plants to cottonmouths, some with fish and others without. The plants without fish were investigated by the snakes, but ultimately left uneaten. Those that did contain fish were quickly devoured. Even when the fish was removed, the snakes ate the remaining seaweed simply because it smelled like their prey!


How exactly do birds get drunk eating fruit? Fermentation should be anaerobic

The insides of fruits are relatively anaerobic environments. Natural fermentation, as in berries that get birds intoxicated, is primarily from yeast, and it seems most likely that humans recognized this process before intentionally encouraging fermentation of anything. Natural fermentation reaching high ethanol concentrations occurs especially when berries high in sugar are frozen and then thawed.

Dudley, R. (2004). Ethanol, fruit ripening, and the historical origins of human alcoholism in primate frugivory. Integrative and comparative biology, 44(4), 315-323.

Fitzgerald, S. D., Sullivan, J. M., & Everson, R. J. (1990). Suspected ethanol toxicosis in two wild cedar waxwings. Avian Diseases, 488-490.


What prevent us from synthesizing a cell from "scratch"?

Designing/engineering functional proteins from scratch is feasible but would take a ridiculous amount of work. Michael Hecht is one person I know of who has done this (example: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0015364). J Craig Venter wants to determine the minimal set of molecular machines required for life, regardless of their origin. This would begin to illuminate which protein functions are absolutely required for life. I believe we could design and engineer proteins de novo to perform all of those functions in theory, but in practice it would be unrealistic. I think the key point to glean from their synthetic cell is the set of functions, not specific genes, required for life. Those functions could be performed by evolved or synthetic molecules.

As a side note, while I believe that to be the overarching goal, they do note the surprising lack of functional characterization in the syn3.0 genome.


What is the meaning of "E-value" in the BLAST search?

You can find the definition here:

  the number of hits one can "expect" to see by chance when searching a
  database of a particular size.

Also read some of the background information given to understand the meaning:

  It decreases exponentially as the Score
  (S) of the match increases. Essentially, the E value describes the
  random background noise. For example, an E value of 1 assigned to a
  hit can be interpreted as meaning that in a database of the current
  size one might expect to see 1 match with a similar score simply by
  chance. The lower the E-value, or the closer it is to zero, the more
  "significant" the match is. However, keep in mind that virtually
  identical short alignments have relatively high E values. This is
  because the calculation of the E value takes into account the length
  of the query sequence. These high E values make sense because shorter
  sequences have a higher probability of occurring in the database
  purely by chance. For more details please see the calculations in the
  BLAST Course. The Expect value can also be used as a convenient way to
  create a significance threshold for reporting results. You can change
  the Expect value threshold on most BLAST search pages. When the Expect
  value is increased from the default value of 10, a larger list with
  more low-scoring hits can be reported.

If you want to understand the calculation I would highly recommend to read the BLAST course

Update
Identity is not the same as the E value
I would try to explain this as easy as possible so let's suppose a sequence:

query sequence: ATGCCGTGC

When you BLAST this (of course your sequence is much longer), you can get the sequence ATGCCGTGC, which will result in 100% identity (NOTE most of the time when you have 100% identity, you have the right sequence but I just picked 100% to make the difference clear. When you have lower percentages of identity the E value is much more important. So when you have 70% identity in different matches e.g. it is important to look a the E value to decide which match is the "right" one for your purpose), now you probably would think that's nice that's the sequence I was looking for. However this would be a mistake! Maybe this sequence is present many times in the database and this is just a random match, this is the point were the E value comes in. Looking at the E value this would be pretty high because you have a short sequence and the chance of matching this in a big database is really high so you can get an E value of let's say: 10. What this means is that you have a chance of finding 10 matches by chance when searching a database of a the current size in other words this match would probably be random! So it's much better to conclude if a match is "significant" based on the E value cut-off than on the identity. Further the choice of the E value is completely yours as said by WYSIWYG♦ as answer to a cut-off question:

  E-value refers to the expected number of random hits for a given
  alignment score. Smaller it is more reliable is your match. There is
  no hard and fast rule for e-value cutoff. You can keep whatever you
  want depending on the level of stringency that you require. But you
  should note that for smaller sequences (< 30nt) there is always a
  higher likelihood of random matches. In such cases it is practical to
  relax the e-value cutoff.

Short simple example

Both of these sequences got the same identity but a different E value. What does this mean? Both of the sequences match the same amount(57%) of the amino acids however based on the E value you can say just as you said that the first match is less likely to be a false positive. I want to note one thing: Always take a look at the biological significance of your result. E.g. you extract proteins from a red blood cell and find proteins with a high E value for red blood cell proteins and a low E value for proteins from nerve cells. It's clear that despite the high E value the red blood cells are probably more biologically significant.


gene transformation from plant to plant - transgenic plant

There are many factors that could influence in the level of expression of a gene, but the two most important are the promoter and the place where your gene has introduce in the genome of de recevier plant.The first one, the promoter depends of the original expression of the original plant, if the expression was high in the original plant, it will be in the receivier plant (if you include the promoter in the construction of the transferred gene and both plant are close in filogenetically).The second one,the place where your gene has introduce in the genome of de recevier plant is a matter of luck, if you use a Agrobacterium, if you gene was introduce in a heterochromatin region it won´t express.

Sorry for my bad english, hope I was useful


What is the relatedness of sisters in a haplodiploid system if you mate virgin queens with 1 of their sons

This question may not be duplicate but I would reconmmend reading the answers of this question first How to calculate relatedness in haplodiploid organisms (mainly full sisters and full brothers)?

I think an image is worth a lot of words so here it is:

To calcul relatedness, I used the formula
$ \text{relatedness} = \ce{prop_{mother}} \times \ce{shared_{mother}} + \ce{prop_{father}} \times \ce{shared_{father}}$

The little trick here is that we know the father (son of queen) is unique, so we can focus on either of the two possible son of the queen. This gives us a 0.875 relatedness between sisters with a father being the son of the mother.
I hope it is clearer now.


Does using animal models for medical research also promote veterinary research on these animal types?

Short answer is no. Human disease take a long time to develop naturally, far longer than the lifespan of animals models used (mice, rats or fish).

Also the cost of the drugs far exceed the cost of the animal. There is little point in spending $40k to make a non-commercial anti cancer drug to save the life of a pet mouse who has developed the rare cancer.

Longer answer... some... a bit. Not quite what you expect. There are a few labs that are re purposing anti cancer drugs that failed animal (mouse) trials to treat dogs. It appears that people are willing to spend a fair amount on experimental drugs to save the life of the family dog.

Mice have increasingly been shown to be a poor model for human anti cancer drugs. Their cells grow to fast, their immune system is subtly different from humans. Stuff that work in mice often times are too toxic in human or just don't work. A longer lived animal model is needed. However dogs as experimental animals is too costly to maintain, too much red tape to use and takes too long for cancer progression (You cannot wait 2 years for a cancer experiment to finish).

However a sick family dog with cancer is another matter.


Can sugar be absorbed orally within seconds?

Yes we do. The first part of digestion starts in mouth. Digestion begins in the mouth, where chemical and mechanical digestion occurs. Saliva  produced by the salivary glands (located under the tongue and near the lower jaw), is released into the mouth. Saliva begins to break down the food, moistening it and making it easier to swallow. A digestive enzyme (amylase) in the saliva begins to break down the carbohydrates (starches and sugars).


Is better healthcare a bane to the long-term survival of the human race?

Gene frequencies (frequencies of each allele at a given loci) in populations are affected by many things divided in to systematic and dispersive processes. Systematic process (migration, mutation, selection) affect gene frequencies in an often quite predictable manner and strength. Dispersive process (Random drift, differentiation between sub-populations, uniformity within sub-populations, increased homozygosity) are random in their direction and only predictable in their amount. Your question focusses on the systematic process called selection.

In a large population (if we ignore dispersive processes for the time being) the gene frequencies will reach an equilibrium called Hardy-Weinberg equilibrium. This means, with constant rates of the systematic processes, each locus will reach a point of stable equilibrium. Mutation and migration bringing more variation, and selection reduces it (over simplified - sometimes selection maintains variation, see my answer about why variation exists here). The frequency of each allele at equilibrium will be where these effects balance out. If selection becomes weaker against one allele it will become more frequent in the population.

By using medicine etc. to cure diseases we reduce the selection acting on a deleterious mutation thereby putting the gene frequencies in to a state of flux during which they move to new equilibrium points. So in short the answer is yes, modern medicine is decreasing our genetic fitness by allowing deleterious mutations to become more common.

I raise the question, will we ever be able to cure all genetically caused disease? In my reckoning, no, at least certainly not in our lifetimes.

It only represents a real risk if we suddenly are unable to cure diseases. This would increase selection against that allele and the gene frequencies would move back towards the equilibrium point which existed before the selection was reduced. What this means for the population is the loss (death) of those with the low fitness genotype.

The best reading you can do on this is the opening chapters of Falconer and Mackay's Introduction to Quantitative Genetics. Also read about Hardy-Weinberg equations.

HTH


Why is one phase of a plant dominant (Alternation of generation)?

Short answer:

Many of the evolutionary developments in plants developed in the sporophyte life stage. Because increased fitness of these increasingly sporophyte-"dominant" plants would result in a greater survival and reproductive success, these plants became more dominant than the more-limited gametophyte-dominant early plants.

In other words, natural selection of the fittest plants (which happen to have sporophyte traits increasingly adapated for living on land) likely led to the dominance in the sporophyte life stage.

Long Answer:

I would suspect this is due to the evolutionary advantages that are associated with the sporophyte life stage in land plants. Multiple advantages developed in the sporophyte stage of plants as they evolved from simple gametophyte-dominant bryophyte ancestors to the vast array of much more successful and dominant ferns, gymnosperms, and angiosperms.

 Shift from gametophyte-dominated life cycle (in bryophytes) to sporophyte-dominated life cycle. Source: Cengage Learning (2016)

Evolutionary developments that have occurred across almost 500 million years in land plants include the development of stomata, true leaves, pollen and seeds, and flowers and fruits. Each of these developments occurred in the sporophyte life stage.

Each of these evolutionary traits had significant effect on the success of the various plant types that possess them, as can be inferred from the increasing success and dominance of increasingly more evolved plant Divisions.

Each of these sporophyte-focused evolutionary traits increased the reproductive success of plants (therefore swaying increasing advantage toward plants possessing increasingly sporophyte-dominant life cycles) due to their ability to increase plant size, growth, and success living in arid (i.e., terrestrial) environments.

stomata allow for increased gas exchange with more limited water loss (note: these were necessary with the development of cuticles in sporophytes of early bryophytes as well)
vascular tissue2 allow plants to grow taller/bigger/more complex while still receiving water and nutrients from the soil
true leaves allow for greater energy capture and growth (and therefore increased competitive advantages)
pollen and seeds allow for greater dispersal and removes/limits the constraints of aridity on fertilization
flowers and fruits increase pollination and seed dispersal

Reducing the gametophyte stage to small cells within larger sporophyte plants allows this otherwise water-necessary process to occur in the dry conditions of living on land. As such, the pollen-bearing plants (i.e., gymnosperms and angiosperms) have been very successful in colonizing vast terrestrial ecosystems.

Another evolutionary advantage proposed, (e.g., see Bernstein et al. (1981)1 and Michod & Gayley (1992)3) is that the diploidy of the sporophyte stage allows masking of the expression of deleterious mutations through genetic complementation. From Wikipedia:

  Thus if one of the parental genomes in the diploid cells contains mutations leading to defects in one or more gene products, these deficiencies could be compensated for by the other parental genome (which nevertheless may have its own defects in other genes). As the diploid phase was becoming predominant, the masking effect likely allowed genome size, and hence information content, to increase without the constraint of having to improve accuracy of replication. The opportunity to increase information content at low cost is advantageous because it permits new adaptations to be encoded.

However, this proposal has been more recently challenged by Szövényi *et al. (2013)4 because evidence suggests that selection isn't more effective in haploid vs. diploid lifecycle phases of mosses and angiosperms.

For further reading:

"Evolutionary history of plants" [Wikipedia]
"Evolution of Seed Plants" [Lumen Learning]
"Evolution of Seed Plants" [OpenStaxCollege]

 Citations

1  Bernstein, H; Byers, GS; Michod, RE (1981). Evolution of sexual reproduction: Importance of DNA repair, complementation, and variation. The American Naturalist. 117 (4):537–549. doi:10.1086/283734

2  Lucas, W.J., Groover, A., Lichtenberger, R., Furuta, K., Yadav, S.R., Helariutta, Y., He, X.Q., Fukuda, H., Kang, J., Brady, S.M. and Patrick, J.W., 2013. The plant vascular system: evolution, development and functions F. Journal of integrative plant biology, 55(4):294-388.

3  Michod, RE; Gayley, TW (1992). Masking of mutations and the evolution of sex. The American Naturalist. 139 (4):706–734. doi:10.1086/285354

4  Szövényi, Péter; Ricca, Mariana; Hock, Zsófia; Shaw, Jonathan A.; Shimizu, Kentaro K. & Wagner, Andreas (2013). Selection is no more efficient in haploid than in diploid life stages of an angiosperm and a moss. Molecular Biology and Evolution. 30 (8): 1929–39. doi:10.1093/molbev/mst095


List of species recently found of economic value

I can only answer for plants. The short answer is no, there is no central source of information of that kind.

You are basically asking about the sub-classifications economic botany and ethnobotany, which cover the economy and human uses of plants respectively. Neither field is particularly fast-paced, so you could simply keep up to date with the latest issues of journals like Economic Botany or Ethnobotany.

One key area of development for new valuable crops is in crop wild relatives, but there is no particular publication you should watch for that. Keep an eye on the Crop Trust as they will shortly be releasing, in collaboration with us (the Millennium Seed Bank) some reports and data about this.

If I wanted to keep up to date with all those things over time, I would set up some saved search alerts on Google Scholar for key search terms like 'emerging crop', 'new crop', 'crop wild relative', etc. Just perform a search on Scholar and then click the envelope button to the right of the search field to create a recurring search alert.

One other potential source of information would be to check FAOSTAT each year. You can examine global and local data about area under cultivation, yield, investment, commodity value, etc. of most crops. If something new were to emerge as a big player, it would appear on there.


Why does the definition of a "gene" require it to have a product?

TL;DR - It's just a scientific convention. Whether it is good, broad enough etc is another question.

The question is a bit like asking why in mathematics, the set of natural numbers does not contain negative numbers. The reason is simply because it was defined like that as it was considered a useful working definition.

As you noted the definition of a gene has radically changed, from being defined by the oberved phenotype, to a molecular definition agnostic to functional consequences.

Maybe your interpretation of the old definition of a gene (as an inheritable, modifiable piece of genetic information) is correct, but modern nomenclatures tend to focus on what one may call defining "minimal functional categories". You could define a gene as a group of all the sequences that lead to a given phenotype, sure, but for a complex phenotype like "being tall", you would have to include a huge part of the genome in your definition of the gene (growth factors, transcription factors, cellular metabolism, all of it!!). Pretty much anything could be a gene of some kind given a certain context. And if everything is a gene, well, then the definition is too broad and not really useful.

That is why a bottom-up definition is more useful: define the gene as small units that may or may not infuence certain phenotypes, and then define the phenotype as the result of all the genes involved.

Many things that can be inherited and that exist in various forms are the product of distant pieces of DNA, some coding for proteins, others for regulatory RNAs, so the scientific community decided to define the gene as the "minimal functional unit" of genetic information. That does not mean that each gene is unaffected by other factors (just like for example a "cell" is a functional unit but must be considered as a part of a broader context to understand how it works), so genes may interfere with each other, overlap etc.

It's also important to emphasize that the gene is not only the protein/RNA coding sequence, but also includes promoter regions, untranslated ends and many other regulatory elements that can also have inheritable phenotypic consequences. So pretty much anything functional is part a gene; enhancers are a special case, and a recent challenge to this contemporary definition of a gene, because they challenge the previously accepted notion that genes are structurally continuous and therefore spatially well-defined.


What does it mean to combine in-solution capture of chosen RAD tags to target sequencing reads to desired loci?

http://www.genetics.org/content/genetics/202/2/389/F1.large.jpg?width=800&height=600&carousel=1

Schematic overview of the new RAD protocol and RAD capture (Rapture) method. (A) RAD tag isolation. Two wells are depicted in each of two different plates. Genomic DNA is digested with a restriction enzyme and ligated to biotinylated well barcode adaptors (yellow and blue bars). (B) RAD tag isolation and library preparation. DNA from each well is pooled platewise, mechanically sheared, and incubated with streptavidin beads. Following washing, DNA is cleaved from the beads leaving the well barcodes. Finally, a library preparation is performed where a unique plate barcode is added (red and purple bars). (C) Rapture. Multiple plate libraries are pooled, hybridized to biotinylated oligonucleotide baits corresponding to the targeted RAD tag loci, and captured to produce the final library enriched for the loci of interest.


Hardy-Weinberg law gives the frequencies of genotypes from allele frequencies: $p^2 + 2pq + q^2$

Because the allele "T" is dominant, the frequency you're directly measuring through the experiment is the addition of $p^2$ and $2pq$.

$$p^2 + 2pq = 150/215 ≈ 0.6976$$

solving for $p$ (and replacing $q$ by $1-p$) gives:

$$p = 1 - \sqrt{\frac{13}{42}} ≈ 0.45 $$

The frequency of heterozygotes is given by:

$$2p(1-p) ≈ 0.4950 $$

And the number of heterozygotes in a population of 215 individuals is given by:

$$215 \cdot 0.4950 = 106.43$$

That sounds very alike what you wrote in the comments!


I assume you are asking if E. coli can survive extreme pH conditions and according to this study E. coli K-12 W3110 survives at pH 1.2 – pH 2.0 under low oxygen. This study cultured different strans overnight and exposed to pH 2.0 for 2 hours before diluting 1:80,000 and 1:400,000, under anoxic and aerated conditions. Dilutions where then plated allowing colonies to grow up overnight at 37 oC so this study measured survival as far as I can tell. E. coli can demonstrate robustness and E. coli survives and grows 'accustomed' at pH 7-8 based on the paper here. However it would be great if you clarified your agar pH and the E. coli genetic background you are interested in if you have a specific question about its particular limits (growth or survival).


Rosalia alpina (common name: Rosalia longicorn)

See here: Rosalia longicorn

The link I added shows a picture of an identical Alpine Longhorn Beetle. This longhorn beetle occurs mainly in the European Alps and the neighboring countries (e.g. Germany, Hungary, and France). Minimum height is around 700m above sea level. The adults are active from June to September. The color varies between blue and grey, the head is characterized by one spine on each side of the head. The number of black dots on the elytra varies.


How can individual strands of coat hair have only a single color when considering codominance?

The terms dominance/recessivity, additivity, co-dominance, incomplete dominance, etc... express a relationship between genotypes and phenotypes but the mechanism behind this relationship is not being defined by these terms.

There might be a large number of mechanisms associating genotypes at a given locus with phenotypes. There is, in general, no reason to expect a mechanism of relationship of dominance at a given locus to be similar to that at another locus. In other words, there is no general mechanism leading to dominance/recessivity, additivity, co-dominance, incomplete dominance, etc...

On top of that, the actual mechanism in specific cases leading to particular genotypes - phenotype association are often unknown and I don't think we have good knowledge about what mechanisms could be more common or even how to categorize such mechanisms.

You might want to have a look at the posts

Why are some genes dominant over others? What is the mechanism behind it?
Evolution of dominance

or some papers such as

Llaurens et al. (2009)

In general, it appears suggested that one needs to know the specifics of the biological pathway to be able to predict dominance relationships and gene interaction network (see Llaurens et al. (2009)).


Those are collembolans. The common name is "springtail". Several groups of collembola are semi-aquatic. They are not larvae, but adults. There is a nice paper on aquatic collembolans here: https://link.springer.com/chapter/10.1007/978-1-4020-8259-7_36


By what mechanism can two chromosomes fuse?

Chromosome fusions (as well as fissions and other changes) usually take place during cell division or pairing, therefore, it's very unlikely that two chromosomes will fuse in an already fertilized egg. If they do such, this egg would probably be aborted.

One factor that may lead to chromosome fusion is loss of thelomeric sequences (telomere = chromosome extremity) (O'Sullivan and Karsleder, 2010) - but probably not in an fertilized egg.


Trigger allergic reaction in non-allergic people?

Is there a proven way to trigger an allergic or allergic-like reaction in non-allergic people?

No.

At least, not that I've ever heard of. Allergic reactions, as you point out, are an overactive immune system response to an allergen (anything that causes the reaction). What determines who is allergic to what is a mix of genetic and environmental factors, and because an allergic reaction is categorized as an error on the immune system's behalf, inducing an allergic reaction is essentially finding a way to force the immune system to make a very, very specific error.

What can (and does) happen is the development of new allergies. Almost all allergies are acquired after birth, and repeated exposure to substances can lead to the appearance of new allergies - one case that stuck out in my head was a woman developing an allergy to a copper IUD that she tolerated well when it was first put in place.

I am not aware of any ability to predict what substances will cause the development of a new allergy. Both the unpredictability and inability to induce allergies comes down to how the immune system reacts to antigens (foreign substances). The machinations of the immune system's response to an allergen or antigen is fairly complicated, and at any point in the chain of events the potential for an overreaction is there. That's probably another question, though, and a much longer answer.


Perplexing by the way of estimating the shared or different genes between humans and chimps

Even though I am not sure I understand your question completely, I want to try to explain to you the genetic difference between humans and chimpanzees. I will work through your questions:

We share 98.5% genes with chimps ,so there is about one percent difference .It means we can approximately differ from them by one base pair every hundred base pairs on average.

It is correct that humans and chimpanzees differ approximately every 100 nucleotides in their DNA sequence. This is does not mean that 98.5% of the genes are shared. It means that humans have about 98.5% (more precisely about 98.8%, The Chimpanzee Sequencing and Analysis Consortium, 2005) sequence identity with chimpanzees, disregarding indels. The main part of this difference (about 85% of the 30 - 35 Mio. different nucleotides) comes from fixed differences: a fixed difference is a position in the haploid genome in which every human has, say, a G and every chimpanzee has a T. Accordingly, the site is different between the two species for every individual of the respective species. The smaller part (about 15%) comes from sites that are either variable (SNP) in humans or variable in chimpanzees, or in both (there is even a small number of very old variants, i.e. shared alleles between humans and chimpanzees, e.g. see Azevedo et al., 2015). Note that the estimates get better the more individuals of the respective species are sequenced and compared.

A human receive 3 billion base pairs from each of parents ,and has six billions in total .Do the math by using one hundred to divide six billions and then it equals sixty millions.
Due to that, The person have ,on average ,sixty-million-nucleotide differences with a chimp.
However,the professor of the online courses I attended said it's thirty-million-letter differences.
Dose that mean it's counted on haploid?Why?

Yes, it is counted on the haploid genome. I would not make any sense to count it on the diploid genome. When looking at my last paragraph you might see why: Remember, we are looking at (i) fixed differences (both copies of a diploid human genome differ from both copies of a diploid chimpanzee genome) or (ii) at variable sites. Assume you find one site in one haploid genome that fulfills either (i) or (ii). If you then look at the same site in the other copy of the genome you already know that the position is either (i) or (ii). This means that it has already been counted as a difference. If you counted it again, in the worst case, over the complete genome, you would count everything twice. In general, you would strongly overestimate the absolute difference.

Also ,I'm not sure that about one percent difference is calculated on one base pair or one nucleotide every thousand base pairs or nucleotides .Because he mentioned the both.

I do not understand this. I had a brief look at the video and heard him talking of one nucleotide per thousand. He compares the sequence difference between human-chimpanzee (98.8% overlap, approx. 1 per 100, about 1.2% fixed differences and SNPS) and human-human (99.9% overlap, approx. 1 per 1000, 0.1% SNP), pairs. Note that in the last case we cannot look at fixed differences as there are no fixed differences between human populations. The 0.1% difference are the percentage of SNP in the human genome.


Fine permanent fluorescent marker for blue light

I have had this problem before... this is what I ended up using:
https://www.amazon.com/Invisible-Blacklight-Marker-Blue-Yellow/dp/B004C89M9Q

You may have to write on a tape or something so that the ink doesn't come off. (Ink may come off if you incubate plates)


How do baby animals that primarily subsist on cellulose get their initial gut flora?

I would not expect this to be any different than other animals - they get the flora from the environment. Key components of the environment for newborns are:

Birth canal
Den / living quarters
Skin / fur of mother, especially near the teats
Diet
Feces of family members (animals sniff this and aren't so clean)


How to Calculate how many times an enzyme would cut a plasmid

There are $4^4$ 4-cutters, which means that given a 4 cutter, you expect it to cut once every 256 bases on average. $7300/256 = 28.5$, so you expect 28 or 29 cut sites in the plasmid. This is identical to what you calculated.


These regions, if unannotated, are simply called intergenic regions.

Sometimes, if a large section of chromatin is regulated by an enhancer/silencer/locus control element, then there are boundary elements that demarcate this chromatin region and prevent the spread of the chromatin state to neighbouring regions.


Let's start with a word of caution: on the internet, the terms macroevolution and microevolution (especially together) are usually used primarily in creationist rhetoric. As such, it is usually best to avoid them, especially when talking to a lay audience. The main mistake creationists perpetuate when thinking about micro-vs-macro evolution, is that the two are somehow different and distinct physical processes. This is simply not the case, they are both just evolution. The scientific distinction between the terms, comes not from the physical world around us, but from how we choose to talk about it. When a biologist says "microevolution" or "macroevolution" they are actually signaling what kind of questions they are interested in asking, or what sort of tools they plan on using.
Verbal and empirical theories
In verbal and empirical theories, the micro-macro distinction is usually one of timescales. A person in the macroevolutionary paradigm, usually asks questions above the level of individual species, as Evolution 101 writes (emphasis mine):

instead of focusing on an individual beetle species, a macroevolutionary lens might require that we zoom out on the tree of life, to assess the diversity of the entire beetle clade and its position on the tree.

Empirically, macroevolutionary answers to these sort of questions are usually ones that don't have access to detailed evolutionary histories or direct experiment. Instead, the method tends to be ones that use geology, fossils, and back-inferences from broad differences/similarities of existing species. As such, most macroevolutionary theories tend to be descriptive, instead of predictive. Most of paleontology can be classified under the macroevolutionary paradigm.
If someone explicitly says that they are looking at microevolution then this usually refers to a contrasting methodology that tends to be heavy on direct experimental manipulation. Most importantly, microevolutionists tend to have access to rich and detailed evolutionary histories. As such, it is not surprising that you saw a study that identified itself as microevolutionary while looking at different species of Drosophila. The fruit fly is one of the widest used model organisms, and as such the study you are referring to probably experimentally manipulated populations of fruit flies and collected a rich dataset of genetic changes in the generations of fruit flies that they studied.
Of course, most studies are at intermittent levels, and no this isn't called meso-evolution (except by silly people). If you are not clearly using the macroevolutionary nor the microevolutionary paradigm but still looking at evolution, you would just simply say 'evolutionary' without any prefix.
Formal and mathematical models
For formal and mathematical theories, the micro/macro distinction is also one of methods used by the theorist not of the underlying domain they are studying. Mathematical modeling of evolution falls into two broad categories: frequency-dependent and frequency-independent models. Frequency-dependent models almost never make the micro-macro distinction. Although you could argue that dynamic models in evolutionary game theory are micro, and static equilibrium concepts like ESS are macro, but I doubt theorists would endorse this view. As such, I will focus on the frequency-independent models.
For frequency-independent models, they key concept is the fitness landscape -- a way to map each genotype to a fitness. If the model tracks a population (a distribution over vertices in the fitness graph) on a static fitness landscape, then it would typically be just called an evolutionary model (the word microevolutionary is seldom used explicitly in this field). However, if the authors assume that mutations are extremely rare and thus any mutant goes to fixation before a new one arises then they can use Gillespie (1983, 1984) to replace the population by a single "typical individual" occupying one vertex of the fitness graph. At this point, the model becomes macroevolutionary and the underlying rule for calculating fixation probabilities would be the microevolutionary component. This approach is often also coupled with an alternation of change in fitness landscape followed by mutations and a selective sweep. Fundamentally, though, macroevolutionary models are just convenient (or tractable) approximations to a real underlying evolutionary dynamics. This should never be forgotten.
Strange example bridging the gaps
Finally, it is important to stress that the macro- and micro-evolutionary paradigms are not necessarily exclusive and do not have to correspond to a difference in timescales! This is best done with an example of a respected theoretical study that mixes everything together. Kauffman & Weinberger (1989) used their newly developed NK model or rugged fitness landscapes to study maturation of the immune response (Tonegawa, 1983). The developed a macroevolutionary mathematical model because they used Gillespie's trick to replace a population by a typical individual by abstracting away from the underlying microevolutionary calculation of fixation probabilities. However, their model was studying evolutionary dynamics within the human immune system (so timescales of days to weeks) and was tuned by parameters gathered by empirical microevolutionary studies that tracked individual nucleotide changes (Crews et al., 1981; Tonegawa, 1983; Clark et al., 1985). Lastly, the study results can be used to inform a question typical of verbal macroevolutionary theory: Are there any examples of sudden leaps in evolution?.
As such, the above study used a formal macroevolutionary model, informed by empirical microevolutionary work, to help us understand a question typical of verbal macroevolution while looking at a physical process that operated on the incredibly short timescale of days to weeks. No wonder people are so confused by the micro-macro "divide"!

References
Clark, S.H., Huppi, K., Ruezinsky, D., Staudt, L., Gerhard, W., & Weigert, M. (1985). Inter- and intraclonal diversity in the antibody response to influenza hemagglutin. J. Exp. Med. 161, 687.
Crews, S., Griffin, J., Huang, H., Calame, K., & Hood, L. (1981). A single V gene segment encodes the immune response to phosphorylcholine: somatic mutation is correlated with the class of the antibody. Cell 25, 59.
Gillespie, J.H. (1983). A simple stochastic gene substitution model. Theor. Pop. Biol. 23, 202.
Gillespie, J.H. (1984). Molecular evolution over the mutational landscape. Evolution 38, 1116.
Kauffman, S. and Weinberger, E. (1989) The NK Model of rugged fitness landscapes and its application to the maturation of the immune response. Journal of Theoretical Biology, 141(2): 211-245
Tonegawa, S. (1983). Somatic generation of antibody diversity. Nature 302, 575.


What fraction of human cells gets infected during a viral infection?

After trying to answer this for a while, I've reached the conclusion that the information needed to answer it satisfactorily doesn't exist. I'll go ahead and summarize my findings here for you though, hopefully it's informative.

Different viruses infect different cells, and different people's immune systems are more or less successful at limiting infection, so the number of cells that get infected also varies a lot. A useful way to narrow your question might be "What is the maximum fraction of virally infected cells that can be survived?". All infections will vary between this maximum and 0%, with milder disease being associated with lower % cells infected.

I looked at Ebola as a candidate because it can infect a wide variety of cell types and a lot of research has been published on it.

From J Virol. 2004 Apr; 78(8): 4330–4341.:

  Notably, 20 (91%) of 22 nonsurvivors had viral loads that reached ≥10^8 RNA copies/ml within the first 8 days after the onset of symptoms, thus suggesting that 10^8 RNA copies/ml can be considered an approximate threshold that predicts a fatal outcome with a positive predictive capability of >90%.

So, for about 3 L of blood serum in a person, that's 3 * 10^11 copies of the viral genome in the blood as a threshold for survival. I couldn't find useful information about viral load in organs and other tissue, but since blood has been claimed to have much higher concentration of virus than saliva or other fluids, we can assume this number is a significant portion of the total load. I'll assume about a third, so we get 10 ^ 12 genome copies. Some or even most of these RNA genomes might not be incorporated into viral particles but counting actual viral particles is very difficult, so no-one has published a correlation between Q-RT-PCR results and infectious particle counts as far as I could find.

Next, we need to estimate the number of progeny viruses that each infected cell produces. In Clin Diagn Lab Immunol. 2002 Jan; 9(1): 19–27. , researchers got about 2 * 10^4 PFU from wells containing 2-4 * 10^6 alveolar macrophage cells each. In these wells, 50-60% of the cells had detached from the walls, compared to 0 in non-infected wells. This implies that, for macrophages at least, about 100 infected cells make enough virus to create one plaque in the assay they used. In J Virol. 2015 Jul 1; 89(13): 6773–6781. about 1000 particles per PFU are reported for the 12th passage of virus in their Vero cell assay system, so 10 virus particles per infected cell. We estimated 10^12 virus particles earlier, so that implies about 10^11 cells were infected. That's a similar number as estimated for HIV in the comments of that question you linked. At about 3 * 10^13 human cells in the body, that's about 0.3 % of all cells.

Each of the studies I've linked here is quite different from the other, so plugging results from one into the other is introducing a lot of error, but hopefully the process was informative.


As pointed out in the answers above, your blood pressure increases transiently as you exercise because more oxygen and nutrients are consumed by your muscles. However, regular exercise increases your "cardiovascular reserve"-this means your body will be able to function well at lower blood pressures. However, caution must be taken in hypertensive patients whose blood pressures are already up the roof. In these patients, gentle exercise and life style modifications (diet, stopping smoking, weight loss etc) may reduce their blood pressures over time. This will also reduce their risk of cardiovascular catastrophes in the future.

So whilst exercise will reduce blood pressure, it is not recommended as a first line of life style modification in hypertensive patients because exercise itself is stressful initially and stress is also bad for a hypertensive patient. Hence, exercise is good but gentle exercise is recommended for hypertensive patients. But again I doubt gentle exercise will do maximum good to your cardiovascular system because the type of exercise that helps lower your blood pressure is one that makes you sweat (e.g tread mill, etc). But once the blood pressure of a hypertensive patient is under control, then they can safely exercise and this should help lower their blood pressure long term. I hope this makes sense.


Why genes are assumed to follow multivariate normal?

Usually if something is not expected to behave according to some scheme then the measured values for such a parameter are assumed to be normal. It is not just with gene expression but with all types of measurements like dimensions of an object, luminosity of an electric bulb, range of a bullet etc.
In any measurement, the random error is modeled using normal distribution. I don't have a very intuitive explanation for why random errors follow normal distribution but mathematically it comes from the central limit theorem.

Now each gene is a variable and measurement of each gene suffers some random error; so a multivariate normal distribution is used.

When we dismiss a null hypothesis in a t-test or z-test what we are actually doing is dismissing our parsimonious notion that a sample is drawn from a given normal distribution. This means two things:

The sample belongs to some other normal distribution (different $\mu$ and $\sigma$)
The sample follows some other distribution

But a t-test will never be able to point out the exact reason. All it tells you is that the sample is not from some given normal distribution.


Taq Polymerase's stability at high temperature

Well, the evolutionary reason is the one you mentioned: the thermostability of Taq allowed its bacterium to colonize high-temperature environments. That, however, does not say anything about the molecular reasons for this difference.

I found this very good answer on Quora. The end of the post has several references for its claims.

Long story short, the stability of a proteins folded state depends on the difference of free energy $\Delta G$ between the folded and unfolded states. However, $\Delta G = \Delta H - T \Delta S$, which means the difference in free energy can either come from a bigger difference in enthalpy (for example, salt bridges, hydrogen bonds, hydrophobic interactions and such in the folded state) or a smaller difference in entropy (for example, a low number of "not-rigid" amino acids like glycin , or a high release of water from folding).

While enthalpy is often the reason for stability of folding, it seems like for Taq polymerase the difference actually lies in an unusually small difference in entropy between the two states.

Edit: here is one of the best references of the post. Open the link above to find more. https://www.ncbi.nlm.nih.gov/pubmed/24174290


This is common sea buckthorn, Hippophae rhamnoides. The blueish leaves and the orange berries are unmistakable.

It occurs mainly in coastal regions, but is also cultivated throughout the world. The berries are processed to all kinds of food.
In their natural habitat in Northwestern Europe, the berries serve as a food source for migrating birds that fly from Scandinavia to the UK or the Netherlands. However, because the berries are often rotting and contain alcohol in september, sometimes drunk birds can be observed.


Why does the Vas Deferens detour over the pubic bone?

The vas deferens following this seemingly odd path is just a result of the natural course of our evolution.  Millions of generations ago, our ancestors were quadrapedal (walked on all four limbs).  Similar to if you were to crawl on your hands and knees today.  In this position the pelvis is rotated forward by ~90 degrees from its position in humans currently.  The natural position of the testicles in this position would be to hang straight down due to gravity.  Which means the vas deferens would have a straight path up in front of the pubic bone.
Generation after generation, our taller and more upright ancestors were able to survive slightly better than their less upright siblings.  Meaning that many of the more quadrapedal siblings died while their more upright siblings survived and reproduced.  This process has continued for millions of generations, leading to our current homonid upright posture.
The path of the vas deferens was established long before our ancestors ever began this slow transition toward standing vertically.  So the vas deferens still follows the same path that it always has, but now our testicles have moved down because we stand vertically.  Which leaves the vas deferens with its seemingly odd route of going forward over the pubic bone and then down to the testicles.
Evolution is a continuous incremental process over the course of millions of generations.  There has been no point at which the vas deferens could be 'unplugged' and rerouted around the pubic bone.  So it now just loops in front of the pubic bone and then to its destination.
Richard Dawkins used the word "mistake" in his description because there is not necessarily an advantage to that particular route of the vas deferens(there may even be small disadvantages), but there was a LARGE advantage to our ancestors standing upright.  So as we evolved to stand more upright, the testicles moved down appropriately and the vas deferens just ended up where it is, simply because that's where it always has been.  Not really a mistake, just the way it worked out.
Hope this is helpful.


So, at core (it seems) you are interested in the rate of recombination events and the magnitud of their occurrences. This would be the only source I can think of that would give you information about the 'composition' of one chromosome (other than brute-force sequencing).

As you seem to know, recombination is an important factor that 'shuffles' some specific areas of the chromosomal DNA during meiosis (process needed to produce gametes). There is a technique developed by T. H. Morgan almost a century ago, that measures the rate of this 'recombination' events by comparing how two (or/and more) particular loci co-ocurr across generations (when recombination occurs, it is more likely to break co-occurrences of distant loci, as opposed to close loci). Thus, we can use this distance measure as a measure of recombination. In fact, in honor of Morgan, the units used to measure recombination rates are called 'centiMorgans', and abbreviated cM. A cM is the probability for a particular loci (it can be a gene or a smaller sequence) to co-occurr with another loci (to be 'linked' to). Thus, is a relational measure: every particular sequence is related to another, and by transitivity (if A and B are close, and B and C are also close, then A and C will probably be close -this is a generalization-). When done in chromosomal or genomic scale, we can build linkage maps.

In modern times, there's quite more resolution, from whole genome studies at gene/chromosome resolution, and even higher resolutions (remember that a 'loci' can be any unit of your interest in the DNA).

Being all that said, recombination rates, and thus the probability of inheriting certain DNA features from parents/ancestors based on recombination, vary quite a lot. There are many 'hot-spots' and other regions seem to be 'protected' from recombination (for instance, regulatory DNA). According to the work I cite here, the probabilities of recombination can vary within about an order of magnitude (the most likely recombination occurring at 10 times higher probability than the less likely), but this varies a lot.

I hope this helps a bit. I am still not sure what do you mean by "biological factors", but for sure recombination is one of the important factors influencing the identity of each chromosome across generations (others are, of course, point and chromosomal mutations).


Is there any evidence that supports glycolysis in mitochondria or chloroplasts?

A search of UniProt returns no reviewed, mitochondrially encoded genes involved in glycolysis. There are some unreviewed entries, but a brief perusal of the associated publications suggests to me that these have all been incorrectly annotated (as one may expect from unreviewed entries).

Interestingly, Rickettsia prowazekii, an obligate intracellular parasite and the modern species thought to be most closely related to mitochondria, also has no glycolytic enzymes:

  Genome sequences of organisms enjoying an endosymbiotic lifestyle are at risk. The activities of homologous nuclear genes may render genes of the endosymbiont expendable and as a consequence they become vulnerable to obliteration by mutation. Good candidates for such purged genes in Rickettsia and mitochondria are genes required for amino-acid biosynthesis, nucleoside biosynthesis and anaerobic glycolyis. These and other genes would have been deleted when an ancestral genome first lived in a nucleated cell. Once genes essential to a free-living mode are lost, the endosymbiont becomes an obligate resident of its host.

Andersson SGE, Zomorodipour A, Andersson JO, Sicheritz-Pontén T, Alsmark UCM, Podowski RM, Näslund AK, Eriksson A-S, Winkler HH, Kurland CG. 1998. The genome sequence of Rickettsia prowazekii and the origin of mitochondria. Nature 396:133-140.


How far can we go in engineering our own evolution?

How far could we go towards engineering a space-durable human species?

I think this question is likely to get closed as off-topic. It is extremely hypothetical and would be a better fit on WorldBuilding.SE. But here is my messy attempt to answer this question.

Assumptions

So, I guess in your question, you assume that we know everything about how our genotype (≈the genes of an individual) make up the phenotype (≈how we look basically). So, I will try to give a short answer to this question by assuming that we were to know exactly what would happen if we were to add the sequence ATTTAG in the somewhere in the alpha-globulin gene or the sequence CCTATA somewhere in the regulatory sequence of the lactase gene. It is important to understand that today we are very far from having such knowledge. I also assume that we are able to recreate the environments we need to develop the new thing (human2.0 if you want) that we would have engineered. For example if we make up humans that need to grow in a different environment than the womb. By the end of the answer, I will pretty much assume that we know everything that can be known about organic chemistry and how chemicals process genetic information. Note that to the point we're doing the drastic modifications I will be talking about, it will be a philosophical issue to decide whether the new species we created is a human or not (it may even come to the discussion whether the new thing we created is a living thing or not).

Resistant to evolution as a species or only resistance as an individual?

Are talking about a species that would definitely be resistant to survive in these extreme environments or are you talking about a bunch of individuals surviving in this extreme environments. The difference is "evolution". Species evolve and not necessarily in the right way. High radiation may yield to high mutation rate and therefore to mutational meltdown (decrease in mean fecundity of the population through time yielding to extinction of the species).

General Limitation

With these very extreme assumptions, then we are only limited by physics. We could not add wings of the size of the wings of a warbler to a big animal like human and hope that it is going to fly.

Resistance to radiation

I know little about this subject but will try to say some things that may eventually help you. We cannot really make our DNA insensitive to radiation (although we may eventually make our DNA slightly more stable) because radiation will alter matter. It is a physical process that cannot be altered. We may create a lot more of tumour suppressor genes to protect ourselves from developing cancer. Eventually we could create a species that would be able to synthesize (and carry) a concrete shield around its body to protect itself from radiation. But it might eventually be easier to code genetic information with something else than DNA! Eventually made up of quartz because quartz is relatively insensitive to radiation. We could indeed create a whole new form of life.

High radiation yield to high mutation rate and high mutation rate may likely lead a species to extinction (mutational meltdown) but not necessary directly kill the first generations of this made-up species.

Extending lifespan

This sounds "easier". We may "just" target the alleles (=variant of a gene) that are known to diminish the human lifespan, to slightly (but probably considerably) increase human lifespan. Again, I assume that we can know these alleles and can replace them with better variants. However, evolutionary processes will just recreate those genes, just because there always has an equilibrium between the rate at which new deleterious mutations appear and selection purging the genome from these mutations. Moreover, at an age where reproduction has already occurred selection is less important and it allows for deleterious allele that are expressed late in the age of the individual to eventually spread in the population.


Genomic distribution of tRNAs in eukaryotes

In Drosophila, at least, some are clustered (e.g. these), but there are many tRNA genes in eukaryotes, so overall they are dispersed. You might find the Genomic tRNA Database useful, and the paper describing it.


Why are archae and bacteria grouped into separate domains?

Reducing your question to its fundamental misunderstanding

Similarly one could ask "Mammals and birds are vertebrates. Why are they grouped into two separate taxa?" (Bacteria and Archea include all procaryotes unlike my example). Or even better, one could ask "procaryotes and eukaryote are all alive. Why are they grouped into two separate taxa?" At the end of the day, your question boils down to why do we group lineages together?.

Why do we group lineages together?

Population tend to diverge through time. For this reason we end up with a multitude of different lineages. As humans we can enjoy grouping lineages as we like to. One might group all yellow flowers together for example. However, a grouping which is of particular interest is one which represent evolutionary relationship. Insecta for example represents a group that include all currently living insects and all of their ancestors until the most recent common ancestor of all insects (that is the individual toward which all currently existing lineages coalesce). Such groups are called monophyletic.

You should have a look at this post where I made an intro to phylogenetic.

In short, to answer your question,

  [W]hy are [Bacteria and Archea] grouped into two separate domain?

Because they represent two separate lineages.

Phenotypic differences

Of course, different lineages often have distinct phenotypes which makes their grouping intuitive to a naturalist. For Bacteria and Archae, it takes some knowledge in molecular biology to tell how different they are phenotypically. This document from Colorado University does a good job in introducing the similarities and differences between Bacteria and Archae


Intermediate of Krebs cycle that can form Chlorophyll?

The standard treatment of this topic is covered in Section 24.4.3 of Berg et al., available freely online:

Succinyl CoA is a precursor for porphyrin in mammalian cells by condensing with glycine to form δ-aminolevulinate as shown in this diagram from that book:

This reaction is catalyzed by mitochondrial δ-aminolevulinate synthase.

To quote from that reference:

  Two molecules of δ-aminolevulinate condense to form porphobilinogen, the next intermediate. Four molecules of porphobilinogen then condense head to tail to form a linear tetrapyrrole in a reaction catalyzed by porphobilinogen deaminase. The enzyme-bound linear tetrapyrrole then cyclizes to form uroporphyrinogen III, which has an asymmetric arrangement of side chains. This reaction requires a cosynthase. In the presence of synthase alone, uroporphyrinogen I, the nonphysiologic symmetric isomer, is produced.

This is illustrated in Fig. 24.35 of that section, which also shows that uroporphyrinogen I gives rise to protoporphyrin IX, the precursor of haem:

As regards the synthesis of plant chlorophyl (which also contains a porphyrin ring), the review quoted by March Ho contains the following statement:

  The porphyrin ring with its conjugated double bonds is assembled in the chloroplast from eight molecules of 5-aminolevulinic acid…

(5-aminolevulinic acid is the chemically preferred name for δ-aminolevulinic acid.)

However the review goes on to explain that although 5-aminolevulinic acid synthesized in plant mitochondria uses the same pathway as mammals, that produced in the chloroplast and used for the synthesis of haem that gives rise to chlorophyl uses a different pathway, termed the C5 pathway, in which — as March Ho stated — glutamate is the precursor:

I suspect that the person who set the question is an animal or biochemist who thought he would try a variant of the standard question “which intermediate is a precursor of haem”, and ventured into waters where he was out of his depth.


Why do plants not have fat cells, and store their reserves in vacuoles instead?

Short answer
Parallel evolution can generate similar traits in unrelated organisms based on unrelated structures.

Background
First off, the question is based on a few misconceptions:

First, you are making an apple-and-pear comparison; Fat cells are cells - vacuoles are organelles. Fat cells store fats in liquid droplets, called adiposomes, which are lipid-rich cellular organelles that regulate the storage and hydrolysis of neutral lipids and are found largely in the adipose tissue. Vacuoles have a variety of functions, namely 1) contribute to the rigidity of the plant through hydrostatic pressure; 2) store nutrient and non-nutrient chemicals and 3) break down complex molecules. The latter two functions are quite reminiscent of the function of lipid droplets in animalia.
Plants do not store energy reserves in fats like animalia do. Instead, plants store starch, which is not a hydrophobic lipid, but a hydrophilic glucoside polymer.

Apart from these specifics, the question can be tackled more generally - unrelated organisms can solve the same 'problems' (like storage of energy rich compounds) through parallel evolution (wings in bats and birds for example). Why does a bat wing contain fingers and a bird's wing doesn't? Because they developed the ability of flight through different, parallel adaptations.


Identifying internal structure of dissected prawn

It is, indeed, the alimentary canal. It is actually this canal that it's important for one to remove when "de-veining" shrimp, rather than the vein itself, which lies immediately dorsal to the digestive tract.

In the future, I might suggest doing a little more research prior to asking the stack exchange, as frequent users of the site do like to see evidence of effort on the part of the user to find their own answers. A quick search for "alimentary canal prawn" or "arthropod anatomy" would likely have turned up your answer.


Why are Merkel cells innervated by an axon, and not a dendrite?

There is nothing out of the ordinary with the Merkel cell, as it functions in the same way as photoreceptors and hair cells (see Further Reading #1).

Fig. 1. Merkel cell. Source: Gallery for Share.

The Merkel cell depicted in Fig. 1 is a specialized receptor neuron. It has no axon. It relies on a secondary sensory neuron (a somatosensory afferent) onto which it synapses to direct the touch signal to the brain.

Note that neural information flow goes from dendrite to axon. An axon terminates into a synapse. The Merkel cell, which does not have an axon, features a synapse directly onto the dendrite of the secondary neuron (See Further Reading #2).

Further Readings
1. Can axons act as receptors?
2. What are the functions and differences between axons and dendrites?.


Are Young Girls Stronger than Young Boys?

Here's a study comparing performance in swimming. Before age 8, there's no difference; from age 11-12, there's some small difference, after age 13, there's a large difference.

https://www.eurekalert.org/pub_releases/2012-05/iu-wsg052512.php


The main governing principle is very simple and one of the most elegant mechanisms in biology. It is based on concentration gradients.

Briefly, certain cells secrete signaling molecules (called morphogens) that are detected by other cells. These molecules then activate the transcription of genes in the target cells. Since the molecules in question are produced by a subset of cells, their concentration varies according to the distance between the receiving cell and the producing cells. Different concentrations of the signalling molecules have different effects and this allows the formation of really quite complex patterns.

This can be illustrated very well using Wolpert's classic French Flag Model:

So, different concentration thresholds of the morphogen result in different patterns of gene expression. This simple system can give extremely complex results (such as yourself, for example). It gets more complex when there are multiple morphogens acting in tandem. Say morphogen1 is produced by cells on the left and morphogen2 by cells on the right, cells in the middle will have varying concentrations of both morphogens and will react accordingly:

Just about every pattern formation during development boils down to differential concentrations of diffusing morphogen molecules, it really is a wonderful example of how simple systems can give rise to extremely complex results.

On an unrelated note, this has absolutely nothing to do with evolution. At least no more so than any other biological process. If your friends do not accept evolution, I don't see how they can use this as an argument. Even if a god created humanity in his or her image as these people tend to believe, babies are still born and they still develop in the womb. As far as I know, even the most fundamentalist Christians do not believe in a mechanic god that puts babies together in the womb from some kind of IKEA kit. Even if humans sprung fully formed from the hands of this god, individual infants are still formed out of multiple cells. There is therefore a need for a mechanism that can coordinate this process, whether this mechanism evolved or was designed is irrelevant, it is still there and extremely easily observable (images taken from here):

The image above shows the locations and concentrations of various morphogens in a fruit fly embryo. You can see the concentration gradients clearly.


How to create a collection of anonymous sequences for teaching and testing?

There are a couple key words in this question - anonymous and teaching. Yes, NCBI is a source for sequence data, but it is not anonymous (it is annotated, which means a student could also find it and copy/paste that annotation without doing the actual analysis). Notice, I am not assuming that the request is for human data. Now, if anonymous human data are needed, most available sequence data are anonymous but there remains that annotation issue: If it is annotated already, what will they learn?

A good alternate source for some human genome data would be from Complete Genomics. They have released anonymized (de-identified) data for at least 69 subjects. The question asks about 1000 sequences, but how large? This is an important consideration. Other details are also lacking in the question.

Another source may be the 1000 Genomes data, also human. If you're interested in plants, there are sequence data out there from ~98 different Arabidopsis thaliana accessions/cultivars/strains.


If an antiserum was raised in a buffalo by injecting it with monkey red blood cells, what would be the full name for this antiserum?

its actually Anti-Monkey RBC Buffalo Antiserum as the Ab formed is Anti-monkey RBC Buffalo Ab.


This looks like either a young or rather hungry Theridiid spider.  I am not particularly experienced with Eurasian spiders, but as far as I know, the only Widows you're likely to come across in Karachi are either the Redback (Latrodectus hasselti) or the relatively non-problematic Brown Widow (Latrodectus geometricus).  This does not appear to be either of these - the front pair of legs is nice and long, but the abdomen is too plain and the bands at the leg joints are not particularly dark brown and contrasting.

Young female Brown Widow from Bug Guide:

Male Widows, both Black and Brown, tend to have small, rather stripy abdomens, and as adults, will have enlarged ball-like structures at the end of the face-mounted pedipalps.

Male Black Widow from Bug Guide:

That just leaves one of the medically unimportant Theridiids as the most likely candidate, and that's where I unfortunately cannot help any further.  Still, the rule of thumb is that unless you identify it as a problem biter, it's not worth worrying about.  The trick is to look at its underside.  If it shows a red hourglass, it's a problem - if not, no worries.
Judging by the photos, I'd go with "no worries", unless you can come up with a clear image of something troublesome.

I include references to support my claims regarding the distribution of Widows in Pakistan:

https://pdfs.semanticscholar.org/f7c7/3ec7c79da6b3b685e4f05649069c4717803e.pdf

http://eprints.iisc.ac.in/15097/1/First_report_of_the_brown_widow_spider%2C.pdf


How do plants intake minerals through their leaves?

They absorb minerals through pores in the leaves known as stomata, this is some what similar to the way a medicine patch is absorbed on human skin except for the fact that the human skin has sweat pores instead of stomata.

Here is a animation of them opening and closing on a tomato plant provided by Wikipedia:

sources:

  A nice review by Wocjik (2004) (from the comments)

  http://www.wisegeek.com/what-is-foliar-feeding.htm

  http://en.wikipedia.org/wiki/Foliar_feeding

  http://cronodon.com/BioTech/Plant_Transport.html


A PCR Master Mix is just a way to speed up your pipetting. Instead of preparing 10 different reactions of 20ul each (for example) you prepare one reaction of 200ul and then you split it into 10 tubes. This also reduces distraction errors (it is less likely to forget to pipet once 100ul that 20 times 5ul). Also, less plastic (pipet tips) is used.

Some tips:

Given the error of some pipettes, it's generally good to count for an extra reaction every 10. For example, if you need to run 10 PCR, make a Master Mix for 11.

If you are planning to run the same PCR on different templates (which is often the case) then you don't need to add the template in your Mix.

Negative and positive controls should always be included.

The negative control of a PCR reaction is often done by substituting the template DNA with water. The negative control should not show any amplification band. This guarantees that you don't have DNA contaminants in your mix and that you primers do not amplify each other.

A positive control is a reaction that is expected to work. In this case, if your positive control shows amplification but your sample don't, you know it is not because of some component in the reaction behaving unexpectedly (Polymerase, buffer, etc..).

Let's make a practical example.

Let's say I want to amplify the same target sequence from 10 different DNA samples.

The 1X reaction looks like something like:

**PCR 1X**

Template     1ul
Primer F     1ul
Primer R     1ul
H2O         10ul
MgCl2        2ul
dNTPs        2ul
Buffer 10X   2ul
Polymerase   1ul

Now, considering the positive and negative controls, and 10% extra volume, I would prepare a Master Mix for 13 reactions. Of course, the template must be excluded.

**Master Mix 13X:**

Primer F     13ul
Primer R     13ul
H2O         130ul
MgCl2        26ul
dNTPs        26ul
Buffer 10X   26ul
Polymerase   13ul

Now aliquot 19ul in the 12 tubes, add 1ul of the right template to each of them (in the C- just add 1ul H2O) and run it!

There are cases in which you may want amplify different targets sequences from the same DNA. In this scenario, you want to include the template in the Master Mix, since it is the same for all the reactions, but you want to add the primers separately since each reaction will contain a specific set of primer to amplify a specific sequence.


How to edit/insert new gene after cutting with CRISPR/cas9

Cas9 is used to create a double-stranded break (DSB) in genomic DNA. The cell can then use homology directed repair (HDR) to repair the break. The cell can also use non-homologous end joining (NHEJ) to repair DSBs, but this isn't useful for inserting specific sequences. In HDR, a sequence homologous to either side of the break site is used as a template so that the break can repaired error free. Naturally, the homologous template could be a homologous chromosome or replicated copy of the broken chromosome.

However, consider a situation where you introduce DNA into the cell with regions of homology to the break site and whatever other sequence(s) you wish to insert/delete/change. In this case, the break can be repaired using this exogenous DNA as a template and your changes will then be incorporated into the genome.

There are other methods of introducing site-specific DSBs to the genome, such as TALENs. The big advantage of CRISPR/Cas9 is that site recognition is performed by RNA rather than protein (it is much easier to synthesize small RNAs than to engineer large proteins).


How plausible would it be for animals to normalize the birth of monozygotic twins in their reproductive cycles?

As a whole, your question is opinion-based, speculative and not really suitable for Bio-SE.  However, several of the building blocks that you describe have a reasonable biological foundation, which might be useful to know.

For instance:

There seems to be a genetic component To monozygotic twinning (see e.g. ghr.nlm.nih.gov). This means that the trait could, very hypothetically, be selected for, and a population with a higher level of monozygotic (identical) twins could develop (ignoring other possible detrimental effects). Different frequencies of MZ twins is also know from castle breeds and other species.
The level of shared genes is the driving force between kin selection. This is part of the explanation  for the reproductive system in social bees (and other eusocial insects), where (usually) a single queen reproduces and her workers (all sisters), which are 75% related to eachother due to the haplodiplod genetic makeup, all work towards the common "goal" of producing new sister queens (which will then be 75% related to all the workers).
This gives some (again, very hypothetical) basis for your idea that "...they shared the exact same DNA there would be less ingrained concern over which gets to spread its genes".
Monozygotic twins often describe an especially strong sibling bond, which lends support (again, very hypoythetically) to your idea of "...expect them to coordinate efforts in the acquisition of food and evade potential predators.". There is also some research to back this up (e.g. Fortuna et al. 2011. Twin relationships: A comparison across monozygotic twins, dizygotic twins, and nontwin siblings in early childhood), but there are also studies that indicates that monozygotic twins show similar sibling behaviors as other types of sibling pairs (see e.g. Mark et al. 2017. Using Twins to Better Understand Sibling Relationships). This is not a field I know well and there is most certainly many more relevant sources to look at.

The introduction to Mark et al (2017) contains many references on MZ twins that should be relevant to your "thought experiment", e.g.:

  ...Fraley and Tancredy’s (2012) later work, which suggests that twin children rely more heavily on their co-twin for safety and security than do non-twin siblings.


How to secrete a recombinant protein from E. coli?

According to the 2012 iGEM Kyoto team:

  The Twin Arginate Translocation pathway(TAT) is [an endogenous] secretion system in E.coli. This system can carry proteins that have torA signal amino acid sequences at N terminal. TatA, TatB, TatC and TatD compose Tat complex on inner membrane. Tat complex recognizes torA signal peptide and then it transports protein (with torA) from cytoplasm to periplasm. In addition, protein that has passed through TAT pathway cut off the torA signal. Proteins which are secreted by this system have no tag that obstruct the activation of the protein.

The team created a cassette with the TAT genes to increase the expression of the pathway and also included the Kil gene, which perforates the outer membrane, possibly aiding the external diffusion of the secreted protein. They noted, rather tongue-in-cheek, that

  Needless to say, function of outer membrane as membrane is essential for E.coli to survive. In other words, overexpression of Kil causes cell death. In this reason, we must find suitable amount of expression.

Note: according to their lab notebook, the team never observed extracellular GFP via confocal microscope (search that page for 'confocal'). So. Beware :)

Their project page also includes some references to the following literature:

Tracy Palmer and Ben C. Berks "The twin-arginine translocation (Tat) protein export pathway"
J. H. Choi. S. Y. Lee "Secretory and extracellular production of recombinant proteins using Escherichia coli"
G. Miksch · E. Fiedler · P. Dobrowolski · K. Friehs "The kil gene of the ColE1 plasmid of Escherichia coli controlled by a growth-phase-dependent promoter mediates the secretion of a heterologous periplasmic protein during the stationary phase"
Brad A. Seibel* and Patrick J. Walsh "Trimethylamine oxide accumulation in marine animals: relationship to acylglycerol storage"


If you use PCR, the products from the reaction will be linear pieces of DNA. Yes, you'll have two templates that are circular after the first denaturation, but when you anneal the primers and perform elongation you'll be making linear fragments. The exponential nature of PCR means that these linear fragments will far exceed the template after several cycles.

PCR can also be tuned to be selective for shorter fragments. If you use the proper protocol for PCR of the entire plasmid, your PCR products will be a complete copy of the plasmid that is linear.


What cell types produce immunoglobins and where are those cells found?

First: B cells do not produce antibodies, but rather they synthesise the B cell receptor. This may sound a bit picky, but is not, since the B cell receptor is anchored in the membrane of B cells and not secreted. Its this receptor which recognizes antigens and causes the production of highly specific antibodies (including somatic hypermutation and all related processes).

Antibodies are produced by plasma cells (and that basically the only thing they do), which are differentiated from activated B cells which have undergone the selection process on the given antigen.
IgM is the first set of antibodies produced by the mature B cell, later on this often changes to IgG (different heavy chain, same antigen recognition site) by class switching.

The secreted antibody is IgA, which is found basically in all body fluids and is also secreted into the different mucosas of the body. It has two subtypes: IgA1 and IgA2 with A1 mostly present in the serum and A2 mostly secreted (see: "IgA subclasses in various secretions and in serum." for details).
IgA is made by plasma cells which are located in the lamina propria adjacent to mucosal surfaces. For details see thesetwo papers:

Intestinal IgA synthesis: regulation of front-line body defences.
The polymeric immunoglobulin receptor (secretory component) mediates
transport of immune complexes across epithelial cells: a local
defense function for IgA.

B cells (and also plasma cells) are found in the blood, they are then called peripheral cells. B cells have to be present outside the lymphatic tissues (not only bone marrow but also lymph nodes and the spleen) to be able to get in contact with antigens. Upon contact with an antigen they move into the adjacent lymphatic tissue.
You will not find IgG mRNA in the blood, as this will trigger immune responses. And it has no function outside of the cell.


I would do this through the Reactome database. Searching for "AKT Signalling" returns, among other things, an entry for PI3K/AKT Signaling in Cancer (Homo sapiens) (REACT_147723). Clicking on that link will take you to the pathway's page, and if you click on "Disease" (under "Locations in the PathwayBrowser"), you will be shown a link to the pathway browser page of that particular pathway.

Once there, click on the "Molecules" tab, select "Proteins" and you will get a list of the proteins involved in the pathway and their UniProt accessions. There, you can click on the "Download" link and retrieve this list:

MoleculeType    Identifier  MoleculeName
Proteins    P31751  UniProt:P31751 AKT2
Proteins    P31749  UniProt:P31749 AKT1
Proteins    Q9Y243  UniProt:Q9Y243 AKT3
Proteins    P16220  UniProt:P16220 CREB1
Proteins    Q12778  UniProt:Q12778 FOXO1
Proteins    P98177  UniProt:P98177-2 FOXO4
Proteins    O43524  UniProt:O43524 FOXO3
Proteins    P98177  UniProt:P98177-1 FOXO4
Proteins    O15530  UniProt:O15530 PDPK1
Proteins    Q92934  UniProt:Q92934 BAD
Proteins    P46527  UniProt:P46527 CDKN1B
Proteins    P38936  UniProt:P38936 CDKN1A
Proteins    Q00987  UniProt:Q00987 MDM2
Proteins    P60484  UniProt:P60484 PTEN
Proteins    Q8WWG1  UniProt:Q8WWG1 NRG4
Proteins    P10721  UniProt:P10721 KIT
Proteins    Q9HCT0  UniProt:Q9HCT0 FGF22
Proteins    P15498  UniProt:P15498 VAV1
Proteins    P05230  UniProt:P05230 FGF1
Proteins    Q15303  UniProt:Q15303-1 ERBB4
Proteins    P08620  UniProt:P08620 FGF4
Proteins    P42338  UniProt:P42338 PIK3CB
Proteins    O14944  UniProt:O14944 EREG
Proteins    O15520  UniProt:O15520 FGF10
Proteins    P21802  UniProt:P21802-3 FGFR2
Proteins    Q13480  UniProt:Q13480 GAB1
Proteins    P55075  UniProt:P55075-1 FGF8
Proteins    O00459  UniProt:O00459 PIK3R2
Proteins    Q9UEF7  UniProt:Q9UEF7-1 KL
Proteins    P09619  UniProt:P09619 PDGFRB
Proteins    Q8WU20  UniProt:Q8WU20 FRS2
Proteins    Q86Z14  UniProt:Q86Z14 KLB
Proteins    O00329  UniProt:O00329 PIK3CD
Proteins    P11487  UniProt:P11487 FGF3
Proteins    Q9GZV9  UniProt:Q9GZV9 FGF23
Proteins    P42336  UniProt:P42336 PIK3CA
Proteins    P42081  UniProt:P42081 CD86
Proteins    P35568  UniProt:P35568 IRS1
Proteins    P04626  UniProt:P04626 ERBB2
Proteins    Q02297  UniProt:Q02297 NRG1
Proteins    P12034  UniProt:P12034-1 FGF5
Proteins    P56975  UniProt:P56975 NRG3
Proteins    Q9NP95  UniProt:Q9NP95 FGF20
Proteins    Q02297  UniProt:Q02297-10 NRG1
Proteins    P22607  UniProt:P22607-2 FGFR3
Proteins    P21802  UniProt:P21802-1 FGFR2
Proteins    Q15303  UniProt:Q15303-2 ERBB4
Proteins    P62993  UniProt:P62993-1 GRB2
Proteins    O60258  UniProt:O60258-1 FGF17
Proteins    P11362  UniProt:P11362-1 FGFR1
Proteins    P15391  UniProt:P15391 CD19
Proteins    Q9Y4H2  UniProt:Q9Y4H2 IRS2
Proteins    Q99075  UniProt:Q99075 HBEGF
Proteins    P21583  UniProt:P21583-1 KITLG
Proteins    P31371  UniProt:P31371 FGF9
Proteins    P27986  UniProt:P27986 PIK3R1
Proteins    P22455  UniProt:P22455 FGFR4
Proteins    P04085  UniProt:P04085-2 PDGFA
Proteins    P06241  UniProt:P06241 FYN
Proteins    P01127  UniProt:P01127 PDGFB
Proteins    P21802  UniProt:P21802-5 FGFR2
Proteins    Q06124  UniProt:Q06124 PTPN11
Proteins    P21781  UniProt:P21781 FGF7
Proteins    O95750  UniProt:O95750 FGF19
Proteins    P21860  UniProt:P21860-1 ERBB3
Proteins    O14511  UniProt:O14511 NRG2
Proteins    P01133  UniProt:P01133 EGF
Proteins    O76093  UniProt:O76093 FGF18
Proteins    P21802  UniProt:P21802-18 FGFR2
Proteins    O43320  UniProt:O43320 FGF16
Proteins    P04085  UniProt:P04085-1 PDGFA
Proteins    P10767  UniProt:P10767 FGF6
Proteins    P06239  UniProt:P06239 LCK
Proteins    Q92569  UniProt:Q92569 PIK3R3
Proteins    Q6PIZ9  UniProt:Q6PIZ9 TRAT1
Proteins    Q9UEF7  UniProt:Q9UEF7-2 KL
Proteins    P35070  UniProt:P35070 BTC
Proteins    P16234  UniProt:P16234 PDGFRA
Proteins    P33681  UniProt:P33681 CD80
Proteins    P22607  UniProt:P22607-1 FGFR3
Proteins    P09038  UniProt:P09038 FGF2
Proteins    P10747  UniProt:P10747 CD28
Proteins    P00533  UniProt:P00533 EGFR
Proteins    P55211  UniProt:P55211 CASP9
Proteins    P22736  UniProt:P22736 NR4A1
Proteins    Q6ZVD8  UniProt:Q6ZVD8 PHLPP2
Proteins    O60346  UniProt:O60346 PHLPP1
Proteins    Q9UKV8  UniProt:Q9UKV8 AGO2
Proteins    Q9HCE1  UniProt:Q9HCE1 MOV10
Proteins    Q9UPQ9  UniProt:Q9UPQ9 TNRC6B
Proteins    Q9HCJ0  UniProt:Q9HCJ0 TNRC6C
Proteins    Q9UL18  UniProt:Q9UL18 AGO1
Proteins    Q8NDV7  UniProt:Q8NDV7 TNRC6A
Proteins    Q9H9G7  UniProt:Q9H9G7 AGO3
Proteins    Q9HCK5  UniProt:Q9HCK5 AGO4
Proteins    Q96RU7  UniProt:Q96RU7 TRIB3
Proteins    Q5T1C6  UniProt:Q5T1C6 THEM4
Proteins    Q96B36  UniProt:Q96B36 AKT1S1
Proteins    P49841  UniProt:P49841 GSK3B
Proteins    P49840  UniProt:P49840 GSK3A
Proteins    O15111  UniProt:O15111 CHUK
Proteins    Q9BPZ7  UniProt:Q9BPZ7 MAPKAP1
Proteins    Q6R327  UniProt:Q6R327 RICTOR
Proteins    Q9BVC4  UniProt:Q9BVC4 MLST8
Proteins    P42345  UniProt:P42345 MTOR
Proteins    Q9UBS0  UniProt:Q9UBS0 RPS6KB2
Proteins    P49815  UniProt:P49815 TSC2


Note: This is based on literature searches I've done a while ago out of general curiosity. I'm in no way an expert on human reproduction.

First, I'm not sure if you are asking about evolutionary reasons or the  developmental causes for for a difference in sex ratio. Here, I will focus on the developmental causes.

There is much evidence for a male bias in human sex ratio at birth (secondary sex ratio), and the ratio have been shown to change over time, e.g. as in 20th centrury Europe after WWI and WWII (see data in Gellatly, 2009),in relation to environmental or parental factors (see e.g. Jacobsen et al, 1999), or between ethnic groups.

A bias in sex ratio can be due to (at least) three different mechanisms (Irving et al (1999)):

deviations in X/Y-ratio of sperm
selection of sperm within the female reproductive tract
biases in implantation success and/or survival for embryos of different sex

I guess that success of fertilization/zygote formation could maybe be considered a separate step (between 2-3). From what I've seen, there is little evidence for explanation one, see tests in e.g. Irving et al (1999) and Graffelman et al (1999). Graffelman et al (1999) also tests for an effect of male age, but do not find support for this idea.

A problem is that it is extremely difficult to study these factors under natural conditions (practically and ethically). Interestingely, there is also evidence for an excess loss of male fetuses in recognized pregnancies (Ingmarsson, 2003, Boklage, 2005), and this would indicate that the sex ratio close to conception is even more skewed towards males than the sex ratio at birth. This is even more curious with the background of an unbiased sex ratio in sperm (explanation 1 above). However, it is very problematic to study what happens in the first part of pregnancies, since a number of weeks have usually passed before the pregnancy is even recognized. I haven't found any papers reporting the human primary sex ratio (sex ratio at conception), which could be affected by factor 1 & 2 above.

Even so, the most likely explanation seems to be a strong bias during embryogenesis, as mentioned in Boklage (2005):

  The usual excess of males is present through pregnancy beyond clinical recognition, but not present at fertilization. The difference, therefore, must arise between fertilization and clinical recognition, through a preferential loss of females during embryogenesis.

The same paper also points out that fewer than 25% of natural human fertilizations go full term (probably even less), and 2/3 of these fail before the pregnancies have been clinically recognized. By back-calculating from the secondary sex ratio and the excess of failed male pregnancies, the sex ratio close to conception has been estimated to 125-135 male:100 female (Pergament et al, 2002))

The exact causes for such a bias during is not entirely known from what I've seen. However, Boklage (2005) mentions a couple of different explanations. For instance, among mammals in general, the earliest stages of embryogenesis proceeds more rapidly in male embryos:

  Since some of the products of these earliest stages are signals from one part of the embryo to another, or from the embryo to the maternal physiology, signals which are required for continuation and maintenance of the pregnancy, the establishment of viable, clinically recognizable pregnancy is correspondingly more efficient in general for male embryos (Krackow, 1995; Kochhar et al., 2003).

The same issue is also mentioned in Ingvarsson (2003):

  Studies in recent years indicate that sex differentiation begins at conception. The SRY gene on the Y-chromosome is already transcribed at the 2-cell stage and triggers growth acceleration in the XY embryos. This accelerated growth is believed to be important for the male embryo as it allows complete testicular differentiation before the levels of oestrogenic hormones become too high as pregnancy progresses.

Boklage (2005) also mentions genomic imprinting and chromosome X–Y segregation as possible explanations for a bias (see references in paper for more).

So, in summary; there is evidence for an male bias in secondary sex ratio (sex ratio at birth), even though there is usually a excess of failed male fetuses in recognized pregnancies, which means that the male bias must be even higher very early in pregnancies. There is some evidence that this might be due to a higher success of embryogenesis of male fetuses, but the evidence isn't strong and the issue is difficult to study.

  The evidence provided by these results is indirect. That is unlikely to change. Ethical, technical and financial considerations argue against the destruction for karyotyping of statistically sufficient numbers of products of natural human fertilizations. Granted, any bias of the sort we seek to discover and understand might, after all, operate differently, or not at all, in sham fertilizations of hamster oocytes, versus oocytes from artificially induced human ovulations, versus naturally fertilized human oocytes. The best evidence we have, or are likely to have in the foreseeable future, indicates that the consistent excess of males observed in human births does not originate in a consistent bias in spermatogenesis or in fertilization.
  (Boklage, 2005)

Finally, it should also be mentioned that the bias in sex ratio at birth can be partially due to sex-selective abortion and infanticide, but to what extent is not entirely known (see Wikipedia: Sex-selective abortion).

You might also find this related question on the heritability of sex ratio interesting: The gender of offspring of Twins?.


How do gain-of-function alleles help with problems caused by genetic redundancy?

Genetic redundancy can be a problem when studying knockout or hypomorph mutations of genes, or analogues such as exogenous RNAi. The redundancy can consist either in incomplete knockout (the genome contains more than one copy of the gene but only one copy is mutated) or functional redundancy (different genes doing more or less the same thing). If you try to figure out what a gene does using a loss-of-function allele, the other copies or redundant genes may weaken or completely mask any phenotypic manifestation of the lost gene function.

Gain-of-function alleles can be useful in such a situation. A gain of function means the gene product does something different, or something additional to its normal function. Even if there are "healthy" other copies of the gene or redundant genes around, the modified function of the introduced allele will interfere with the normal behaviour of the molecular pathways it partakes in, and thus cause a phenotype.

A classic example are constitutively-active alleles. Imagine for example a tumour suppressor with a mutation that prevents it from being degraded (but imagine you don't know this gene is a tumour suppressor yet). Let's say there are other tumour suppressor genes around, so if you knock out your gene of interest, the cell behaves as normal - loss-of-function isn't useful to study what the gene does. Now, if you introduce this constitutively-active mutant variant that doesn't get degraded, it causes too much suppressive activity and drives the cell into complete cell cycle arrest, regardless of the other redundant genes being around. Based on this phenotype, you could infer that the gene is probably a tumour suppressor.


Why use embryonic neurons to study protein knockouts/mutants in long term potentiation?

Some suggestions, there may be more:

1) The knockout may not be viable to adulthood (the animals die). Perhaps heterozygotes are viable, but to test the full knockout you need a homozygote.

2) Even if the knockout is viable to adulthood, the brain may develop adaptations to the knockout that aren't specific to the knockout itself: up- or down-regulation of certain channels, for example.

3) Plasticity is upregulated in developing brains compared to adult brains, so the effects may be more pronounced in the young tissue.

4) Younger tissue cultures better - you can keep cells alive longer, more easily.

Also good to note is that undifferentiated stem cells tend to be "neuron-default" - that is, if you just culture embryonic stem cells and you don't do anything special to them, they will tend to generate neuron-like cells. In the adult, neurons are always going to be alongside other types of cells: glia, vascular tissue, etc.