Is the Insulin Receptor Considered an Enzyme? Can we consider insulin receptor an enzyme? In other words, does the insulin receptor have enzymatic characteristics?

Yes, the insulin receptor can be viewed as an enzyme, since it catalyzes a reaction -- phosphorylation of tyrosine residues on its substrates. But since the protein has multiple functions, it's probably better to say that the insulin receptor "has enzymatic activity", rather than "is an enzyme".

The notion "enzyme" is not restricted to catalysis of reactions involving small molecules, like those in central metabolism. The IUPAC definition of an enzyme reads: "Macromolecules, mostly of protein nature, that function as (bio)catalysts by increasing the reaction rates". So "enzyme" is a broad term, including catalysis of all chemical reactions. DNA replication, mRNA transcription, protein synthesis and posttranslational modifications all involve enzymes. Those enzymes that act on small metabolites can be called "metabolic enzymes" to clarify their function.


Does common PCR amplify genes regardless of what cells / barriers they are in? I have some understanding of how PCR testing works. What I have always been wondering: how can we be sure that a primer reacts with the targeted gene(s) regardless of where¹ the genes are inside a sample?

¹: Specifically with "where" I mean what cells or other barriers they are inside. In other terms my question is: how can we be sure primers reach every possible target?

In more detail: when targeting a gene that is part of the host organism's own genome things might be quite clear, we can have strong assumptions of "where" the gene is. But when targeting genes that are part of organisms that have a parasitic relationship with the host, a very complex system of  interactions (mostly called "immune system") should govern the (co)existence of host and parasite. These complex interactions might lead to situations in which the parasitic organism is present in a very special, isolated manner / place in the host. To give one example: micro organisms in the cell walls of macrophages that are about to undergo lysis. Can we be sure that common PCR would detect genes of such micro organisms? Why?

EDIT: as pointed out in the comments the perhaps most correct phrasing is, how do the DNA isolation techniques employed guarantee that primers reach every possible target?

There are many methods of DNA isolation, so it is difficult to make broad statements about all situations. Typically, though, during the DNA isolation protocol, essentially all proteins are denatured and removed, as is RNA, cell membrane components, extracellular matrix, etc. If you are using a high-quality well-validated kit from a reputable supplier, your final product should be just DNA. In your hypothetical scenario where parasites have been engulfed by macrophages (BTW, eukaryotes don't have cell walls), as long as the parasitic DNA hasn't been degraded, it will be present at some level after the purification process. As long as it is above the minimum sensitivity of the PCR reaction, any parasitic genes that are being targeted will be amplified.

During the PCR process, the heating step to 95°C completely denatures and melts all the DNA present, allowing the primers unrestricted access to their complementary sequences, where they can then base-pair during the annealing and extension step.


'Big ideas' behind constrained selective breeding (optimization) I'm looking for help in finding the 'big ideas' behind a project I'm beginning and want to make sure I'm not missing anything since I wasn't trained in Biology.
My idea is investigating constrained selective breeding, say, a farmer who wants to balance crop yield with artificially selecting a desired trait (can't select too strongly because this will reduce yield). I've found many models for selection and found Haldane's classic useful but I'm not sure how to incorporate a constraint.

I believe that you are talking about "multidimensional selection".
E.g. you are selecting on not one trait (yield) only but more than one (yield+some other trait). There's quite a bit of work done on this in crop science for example.
See e.g.

here
here
here

Figure 3 from the last talks about this in terms of evolution by reproductive isolation, which is pretty much the same thing except it's natural rather than artificial selection.

The most important thing practically is to try to measure the correlations of the traits in question. If they are positively correlated or uncorrelated, it shouldn't be too hard. If they are negatively correlated then you might have some difficulties due to constraints on the kinds of phenotypes that can exist.
For a recent technical treatment with good references, see here.


Does the sun light have to have *direct access* to fruits to make them sweet? I understand that sun light is needed for fruits to ripen. In the years with less sun light, the fruits are usually not so sweet. So the sun light is needed to make the fruits sweet.

Where and how does the sweetening process occur? Does the sugar in the fruits come from the photosynthesis occurring in the green parts of the plant (leaves) and is then conducted to the fruits, or is the sugar produced in the fruits themselves? Does it matter whether the sun shines directly to the fruits, or is it enough if just the leaves have enough sun light and the fruits become equally sweet even if they are hidden under the leaves?

It would depend if the fruit has the ability to sense or capture light. This depends on chlorophyll mostly, found in cellular organelles called chloroplasts. If the fruit is incapable of capturing light, the light can still have an indirect impact by being captures on leaves, since plants circulate nutrients - this is why roots can grow without having access to sunlight.

Here's a few excerpts from the following paper (https://www.ncbi.nlm.nih.gov/pubmed/11337414):

"To date, molecular regulation of the role of light in fruit ripening has been studied most thoroughly in tomato, and available evidence suggests that light has its greatest impact on pigmentation, with apparently little effect on additional ripening phenomena."

"The green to red color transition typical of ripening tomato fruit is largely due to the developmental transition of chloroplasts to chromoplasts; as photosynthetic membranes are degraded, chlorophyll is metabolized, and carotenoids, including β-carotene and lycopene, accumulate."

In plain English, the pigment that captures and absorbs sunlight for metabolic reasons (photosynthesis) gets transformed into a pigment which absorbs sunlight for other reasons, such as 'looking tasty' or protection from ionizing radiation. Essentially the same thing happens to leaves in autumn - the green chlorophyll pigment, which absorbs the energy of the sun for the plant to grow, becomes the orange carotene, and the leaf slowly dies off.

Interesting sidenote: biological pigments are very important molecules in ecology and in cell biology. Take a closer look: https://en.wikipedia.org/wiki/Biological_pigment

As for your question about how ripening occurs... Google would do a better job than most.


Can one become suddenly ambidextrous? Is it possible to become ambidextrous? i'm 67 and recently and suddenly became ambidextrous about a month ago. I can write w both hands at the same time.

Normally in a right handed person the left hemisphere is considered dominant.  However, a new lesion in the left hemisphere can cause loss of this dominance so it is possible that this has occurred.

Alternatively, if the subject is now able to write using both hands concurrently but different material, it suggests that the two hemispheres are disconnected, the so called split brain syndrome which occurs with lesions of the corpus callosum.  However, ambidexterity is not described in that syndrome.

Anyway, you need to consult your physician.


Difference between NCBI's /genomes and /1000genomes Wondering what the difference is in the data hosted here:

ftp://ftp-trace.ncbi.nih.gov/1000genomes
ftp://ftp.ncbi.nih.gov/genomes/

Also (sidenote), would be interested to note what the difference between ftp-trace and just ftp is. But there is probably over 1TB of data in /genomes, and I assume something of "a thousand genomes" is in /1000genomes, but I'm not sure what data exactly. Would like to know what is i thse two folders, and where the 1000genomes data is exactly. It looks like /1000genomes/ftp/data/ has "sequence data", but I'm not sure how that compares to the /genomes list of species names (and I'm also not sure what "sequence data" entails exactly).

The simplest difference is the scale and range. The aim of 1000 genome project was to provide comprehensive library of human genetic variation. DNA of individuals coming from different ethnic groups, geographic locations were sequenced and the results of the study were published here.

Genomes from NCBI come from different organisms and the number of sequenced individuals differ between species (including human). This database is constantly growing. Detailed description can be found here.


Using proportion or a diversity index when dealing with just two strains I'm looking at diversity maintenance in an experimental system consisting of just two strains. I have count data. I can calculate their proportions and I can use that data in my statistical models, or I can calculate Shannon or Simpson diversity, and use that data.

My question is: When dealing with just two strains, is it really necessary to use a calculated diversity index, or will proportions do just fine?

The benefit of using proportions is that it's a more accessible metric - not requiring any (albeit fairly basic) knowledge of diversity indices and how they alter the data.

Having calculated Shannon, Simpson, and proportions, and run all three through my models, it makes no difference which I use - the data are quite emphatic. My slightly educated instinct is, in this case, to use the most simple metric, but I'm looking for some opinions on the case.

Extended upon the suggested statistic

The proportion $p$ is definitely not a good measure of diversity. A proportion of 0 or 1 both indicates no species diversity as only one species exist. If one species, never reach a frequency of 0.5 in your data it might be reasonable though. Otherwise, something like $p(1-p)$ could be a statistic of interest.

If you're willing to use $2p(1-p)$ (instead of the above $p(1-p)$) as index of diversity, then you could make the point that it can be easily extended to $S$ species with $1-\sum_{s=1}^S p_s^2$, where $p_s$ is the frequency of the species $s$ by analogy to the genetic diversity within populations (see Nei 1973).

Note that these statistic will be sensitive to the sample size. For example, if you sampled only one individual, then this individual can only belong to one species and your measure of diversity can only be zero. You might want to compute an unbiased estimator for this statistic.

Your goal is to reduce the reader's cognitive load

Your goal is to use a statistic that will communicate easily. If your readers are going to be very much used to Simpson's or Shannon's indices, then even a mathematically simpler index might end up leading to an extra cognitive load onto the reader.

IMO, it will be hard to give a more definite answer without knowing 1) who are going to be your readers (which journal are you aiming to publish in) and 2) what are the exact data manipulation and interpretation you are aiming to do based on your results. For example, if it makes the comparisons among graphs easier by using a simpler statistic over a more complicated once, then choosing the simpler statistic is a good idea.

Good statistical analysis

In any case, it is essential that you to try out (and you did) different measures of species diversity and make sure that your conclusion is robust to these different measures and explain in your methods that the chosen statistic won't matter. That will make your point and your choice of statistic much stronger.


Could bacteria and phages be found in tissues? in the literature, there are several studies of the gut virome and microbiome, for instance Reyes et al. on Nature (https://www.nature.com/articles/nature09199?error=cookies_not_supported&code=f5201dbe-3cda-4e09-ba17-688d0352ef81).
Now, it makes sense to find bacteria (hence phages) in stools. However, there are other studies reporting bacteria from tissue, for instance Kostic et al. on Genome res (
Article Genomic analysis identifies association of Fusobacterium wit...
). In this specific case, the identified bacterium (Fusobacterium nucleatum) is an endocellular parasite and has virulence factors that allows it to penetrate tissues. But my question is more general:
Would be normal to identify bacteria and bacteriophages INSIDE human tissues?
In particular, would whole genome sequencing metagenomic experiments be likely to identify bacteria genome from human tissues? And if yes, how can we explain the presence of bacteria inside loci that are supposed to be sterile?
Thank you

Per my comment to the question, here is an answer to the same question asked on ResearchGate:

Whole genome sequencing of human tissue samples often results in reads aligning to bacterial references, and this is actually a method used in the diagnoses of infectious diseases.
Understanding the Promises and Hurdles of Metagenomic Next-Generation Sequencing as a Diagnostic Tool for Infectious Diseases
Identification of low abundance microbiome in clinical samples using whole genome sequencing
However, because pathogen DNA may be present at a much lower abundance than host DNA, it may be difficult to distinguish true infections from contamination and false positives. Steven Salzberg addresses these concerns and offers a computational solution in a recent publication:
KrakenUniq: confident and fast metagenomics classification using unique k-mer counts
"Usually, the vast majority of the reads match (typically 95–99%) the host, and sometimes fewer than 100 reads out of many millions of reads are matched to the target species. Common skin bacteria from the patient or lab personnel and other contamination from sample collection or preparation can easily generate a similar number of reads, and thus mask the signal from the pathogen."

To expand on this answer, it is important to realize that accurate identification of low-abundance organisms in metagenomic reads requires very good reference genomes.  A separate paper from the Salzberg group discusses a startling discovery:  many high-copy repeats in the human genome have been incorrectly annotated as bacterial proteins in the NCBI RefSeq database.
Human contamination in bacterial genomes has created thousands of spurious proteins
This suggests that researchers should be careful when attempting to infer the presence of low-abundance bacteria or phage in human tissue sequencing data, particularly when the presence of those organisms is not corroborated by other analyses.


What is the longest-lasting protein in a human body? Protein life times are, on average, not particularly long, on a human life timescale.
I was wondering, how old is the oldest protein in a human body? Just to clarify, I mean in terms of seconds/minutes/days passed from the moment that given protein was translated. I am not sure is the same thing as asking which human protein has the longest half-life, as I think there might be "tricks" the cell uses to elongate a given protein's half-life under specific conditions.

I am pretty sure there are several ways in which a cell can preserve its proteins from degradation/denaturation if it wanted to but to what extent? I accept that a given protein post-translationally modified still is the same protein, even if cut, added to a complex, etc. etc.

And also, as correlated questions: does the answer depend on the age of the given human (starting from birth and accepting as valid proteins translated during pregnancy or even donated by the mother)? What is the oldest protein in a baby's body and what is in a elderly's body? How does the oldest protein lifetime does in comparison with the oldest nucleic acid/cell/molecule/whatever in our body?

Crystallin proteins are found in the eye lens (where their main job is probably to define the refractive index of the medium); they are commonly considered to be non-regenerated. So, your crystallins are as old as you are!

Because of this absence of regeneration, the accumulate damage over time, including proteolysis, cross-linkings etc., which is one of the main reasons why visual acuity decays after a certain age: that is where cataracts come from. The cloudy lens is the result of years of degradation events in a limited pool of non-renewed proteins.

Edit: A few references:

This article shows that one can use 14C radiodating to determine the date of synthesis of lens proteins, because of their exceptionally low turnover: Lynnerup, "Radiocarbon Dating of the Human Eye Lens Crystallines Reveal Proteins without Carbon Turnover throughout Life", PLoS One (2008) 3:e1529

This excellent review suggested by iayork (thanks!) lists long-lived proteins (including crystallins) and how they were identified as such:
Toyama & Hetzer, "Protein homeostasis: live long, won’t prosper" Nat Rev Mol Cell Biol. (2013) 14:55–61


James Allison's T-cell discovery technique The excerpt on Wired magazine of The Breakthrough by Charles Graeber has the following description of how James Allison found the T-cell receptor.

  Suddenly it seemed so obvious: If Allision could rig up a way to
  compare B cells and T cells, devise a lab experiment that put one
  against the other and let their redundant surface proteins cancel each
  other out, the receptor should be the molecule that didn’t cancel out.
  Essentially, he was looking for a needle in a haystack, and his idea
  was to set fire to the haystack and sift the ashes—or as Allison puts
  it, “pick it out of the weeds.” Whatever was left would be the needle
  he was looking for.

It is a vivid metaphor. But the author does describe in more specific detail how the cancellation actually works. Can someone give such a description?

Well so here's the paper, at least on Dr. Allison's side. Mind you, the discovery is shared by a number of individuals for their combined effort.

So what they did is took mice and immunized them with T-cell lymphoma cells. The mice would essentially produce B cells against the lymphoma cells. And so we know that B cells produce antibodies, and hybridoma methods exploit the B cells to get a lot of that antibody. With their constructed hybridoma library, they could look at reactivity of antibodies produced against the lymphoma cells against the reactivity of the same antibody against other cells. They ended up finding 1/43 antibodies, which they dubbed 124-4O, reacted to their lymphoma line, but not to other cells.

With a pull-down assay, they isolated the receptor and determined it was a heterodimeric glycoprotein. A step further, they looked at both B cells and T cells, then, and were able to detect similar structures in the T cells, but not in the B cells.

Someone correct me If I'm wrong, though.


What was Protein G named after? Protein G (the bacterial antibody binding protein) is often used to pulldown antibodies, for example in chromatin immunoprecipitation (ChIP) experiments.

However, I was unable to find a site describing the history of the said protein, and how the name of Protein G came about, due to the fact that the similarly named (but completely different) G protein came up higher in search results.

What was Protein G named after? For example, Protein A was named after the antibody fraction in which it was found, and G protein was named because it bound GDP and GTP.

As @canadianer mentions in his comment, Protein G is more than likely named after the human group G strain of Streptococci, G148.

  Protein G, a bacterial cell wall protein with affinity for immunoglobulin G (IgG), has been isolated from a human group G streptococcal strain (G148).

Purification and some properties of streptococcal protein G, a novel IgG-binding reagent. - Björck et.al.

Protein G is a  Streptococcus protein that can bind the the Fc portion of the Immunoglobulin G protein. While it is similar to protein A, it was found to have better binding affinities to the different varieties of heavy chain constant regions of IgG molecules.

The G comes from Lancefield grouping. Lancefield grouping was a method that used to be used to classify bacterium. Group G Streptococci could be identified using Lancefield serum type G and that is how they received their classification.


insect paralysis in response to predator echolocation frequency I remember a case in which some insect had evolved an adaptation in response to a predator's (bat's?) echolocation frequency. In mid-flight, it's wing muscles would become paralyzed and it would suddenly drop like a stone directly towards the ground. Does anybody know what this insect is? I thought it was a moth or a mantid.

Many different species react to bat echolocation sounds by altering their flight path or diving. This includes moths and mantids, but also other insects such as crickets, flying beetles, and likely many others as the following list (taken from this review by Miller and Surlykke (2001): How Some Insects Detect and Avoid Being Eaten by Bats: Tactics and Countertactics of Prey and Predator) is in no way exhaustive:

Crickets

"Many bush crickets can hear bats, but few seem to react to bat echolocation. However, the bush cricket, Neoconocephalus ensiger, shows an acoustic startle response during tethered flight in the laboratory (Faure and Hoy 2000). When the insects hear intense batlike sounds with frequencies from 15 kHz to at least 60 kHz, they dive."

Mantids

"Flying mantids (Parasphendale agrionina) react at distances as great as 10 m from a loudspeaker emitting batlike signals at natural intensities. They react by turning or with dives and spiral flight. The responses are all nondirectional, as would be expected from a functionally monaural system."

Flying scarab beetles

"In response to pulsed ultrasound, flying scarab beetles, Eutheola humilis, dropped or flew toward the ground, and walking beetles stopped."

Moths

"Freely flying, unidentified moths exhibit a variety of behavioral responses to bats and to synthetic batlike signals (Roeder 1967a; for recent results, refer to Acharya and Fenton 1992). Moths far from the source often turn and fly away, whereas those close to the source show zigzag and looping flight, power dives, or passive falls."

For a more recent review of the literature for the specific example of moths, I suggest reading Evolutionary escalation: the bat–moth arms race by Hofstede and Ratcliffe (2016).


How to calculate relative fitness from absolute fitness with recapture data? In Grant 1986, it says:

  For this analysis the absolute fitness of an individual id scored as 0 if it disappeared and 1 if it survived, and these scores are then converted to relative fitness values by dividing them by the mean absolute fitness.

Grant, P. R. 1986. Ecology and Evolution of Darwin's Finches

If we have this dataset (for example):

   id x1 x2 x3 x4 x5
1   1  0  1  0  0  0
2   2  0  0  1  1  0
3   3  1  0  1  1  0
4   4  1  0  1  1  1
5   5  1  0  1  0  0
6   6  0  0  0  0  1

Imagine that id is a column of individuals and the rest (x#) are the absolute fitness. Then, how would you calculate the relative fitness? For an example, what would be the relative fitness between x3 and x4?
X1 is the first event of capture. I.e. I went on the field and capture birds for examples and noted who was there (0). I went on the field on X2, X3, etc. and noted which bird was there.

So the dataset is this:

  id   x3 x4
   2    1  1
   3    1  1
   4    1  1
   5    1  0

Lets assume that we caught the entire population so that we know that a 0 is really a dead organism (the estimate is proportional to lifetime fitness). Furthermore, there is no density-dependent selection and the generations are non overlapping.

The survival for next year would be the fitness value of all individual. So in year x4, the absolute fitness would be W = c(1,1,1,0). Then the mean absolute  fitness would be: W_bar = (1+1+1+0)/4 = 3/4. Then to get w, the relative fitness, you divide W by W_bar. w = c(4/3, 4/3, 4/3, 0). Therefor, if you calculate mean(W/W_bar), it should give you 1.

This is as in Endler, J. A. 1986. Natural Selection in the Wild. Princeton University Press, p. 168.


Is the number of legs in myriapoda determined entirely by the genome? Myriapoda (comprising, among others, millipedes and centipedes) can have hundreds of legs (Illacme plenipes having up to 750 legs). Interestingly, the number of legs (or leg pairs) appears to differ not only between myriapod species, but even between individuals of the same species:

  Body light cream-colored, thread-like, extremely narrow and long (max. width: ♂ 0.55, ♀ 0.64; max. length: ♂ 28.16, ♀ 40.40). Adult individuals with 84 – 192 segments, and with 318 – 750 legs (VMNH paratype ♀ with 192 segments and 750 legs, more than any other organism known on Earth).

  Source: "A redescription of the leggiest animal, the millipede Illacme plenipes, with notes on its natural history and biogeography"

This raises the question: Is the number of legs in myriapoda determined entirely by the genome, or do environmental factors play a role in precisely how many legs eventually develop?

Note: Myriapoda generelly hatch with very few legs, and more legs develop during successive moults. Some examples can be found here.

Three factors that influence the number of legs are:

1) Sex : In some species of myriapoda, the females have been found to have more leg segments than males (reference) eg: Himantarium gabrielis

2) age

  Growth is by adding segments and legs with successive molts
  (anamorphic), and myriapods continue to add additional segments and
  legs after they have reached sexual maturity (reference).

3) Working of a "segmentation clock" (set of genes) that periodically adds segments (reference).


Where I can find information about the study of animal vision? I read a lot of articles on the internet about how dog see the the world. But I can't find the source of these study. I want to know how did scientist setup the experiment to find out animal vision. I don't know which keyword should I use to search for the information on the internet. Can anybody please send me some research paper about the problem?

Color vision was shown to be dichromatic (sensitivity to two color peaks) by Neitz et al. (1989). For a broader review of color vision of mammals, see Jacobs (1993).

Literature Cited

Jacobs, G.H. 1993. The distribution and nature of colour vision among the mammals. Biological Reviews 68: 413-471.

Neitz, J. et al. 1989. Color vision in the dog. Visual Neuroscience 3: 119-125.


Sodium levels and blood pressure Why does low Na+ levels cause hypotension (low blood pressure).
  Alternatively, why does excessive intake of Na+ cause
  hypertension or high blood pressure?

From what I understand, there are two different mechanisms at play. Renin secretion from kidneys occur during low salt levels in blood and hence, under low sodium concentration, high amount of Renin secretion from kidneys should actually raise the blood pressure, via the Renin-Angiotensin loop, constricting the arteries. Under high sodium concentrations, low renin secretion should be accompanied by low pressure. If the osmotic entry of water into blood raising the blood volume and thus pressure, plays a role here, will not, on increase of blood volume, decreased Vasopressin increase urinary output and thus control the blood volume, not allowing it to cause any body changes? Why isn't the first effect (renin) significant here as compared to the second (osmotic entry).

Renin angiotensin is for regaining homeostasis. If you have lost salts, blood pressure and blood volume will decrease due to loss of fluids. But the renin angiotensin mechanism will try to undo this by stimulating uptake of Na+ and water. But still, they can not fully compensate for loss of Na+, INTAKE of Na+ is NECESSARY.

By the way, the main stimulus for triggering renin angiotensin is Blood volume and pressure rather than Osmolarity of blood. In conditions like hemorrhage where blood loss occurs but there is no change in osmolarity, Renin-Angiotensin will still respond (but ADH(vasopressin) will not as ADH responds only to change in osmolarity.)     —  biogirl


Why is Cacao a stimulant? As I write this I've just eaten a fair amount of cacao (not cocoa) powder and it's given me quite a buzz. I've googled the effects and the Internet seems to think it's somehow psychoactive although it's hard to find a reliable source for the details.

Some sites describe the ceremonial preparation of cacao but I'm wondering if someone here knows the actual science behind the effects of cacao.

Can you please help me understand the underlying processes better?

Cacao and products made from cacao contain a number of bioactive compounds. As far as the subjective sensation of a "buzz", there are three related alkaloids present in cacao that may account for this effect. Theobromine is present in the highest concentration, but is probably less potent of a stimulant. It's not well researched. Theophylline has a number of effects, and is well studied. It has CNS stimulant effects, with a similar mechanism of action to caffeine, as a competitive antagonist at adenosine receptors. Caffeine is also present in cacao. The stimulant effects of caffeine are known generally, but if you'd like to read more about the pharmacology this is a good review.


If due to some reasons a small portion of our neurons die, how does our body remake them? There are various reasons such as intake of neurotoxins, and possibly traumatic injuries, that can cause neurons to die.
After several hundreds, or thousands of our neurons die, how does our body remakes them?

Evolution set us humans up to not regenerate our neurons on the whole, which is a pity since we can regenerate our liver cells, so it's not an impossible feat. If neurons die because you drank too much, banged your head, or just because they got old, they are gone. If this death is accelerated like in Alzheimer's disease, you can literally see holes in the brain autopsy. That said, we now know that in a few exceptional areas of the brain, neurons do regenerate. It's not many cells but it's something. The two areas known to date are the dentate gyrus of the hippocampus (DG below) and the subventricular zone of the lateral ventricles (LV). See the green zones in the image below.

From Ernst & Frisen PLoS Biology 2015 (CC-by)
In those areas, like elsewhere in the body, new cells are made from stem cells, that is infant-like cells that have the potential to become many cell types. If we could only understand why some cells regenerate and others do not, we might be able to promote this. Alas, our current understanding is woefully incomplete. We'll need more research # # # before we can dream of regenerating our neurons.


Why is it beneficial for trees to grow that tall? Why is it beneficial for trees to grow that tall?

This sounds like a kid's question Mom, Dad... why are trees so tall?

Costs and Benefits

There are some obvious costs:

Need much Carbon and other nutrients
maintenance cost
energy cost (for growing, to bring water (and nutrients) up to the higher leaves, etc...)
Sensitivity to wind
etc...

Potential benefits I can think of:

Competition for sunlight
Better dispersal
protection against predation
Some birds (maybe especially those that are potentially good seed dispersers) prefer to land on high trees (avoiding the cost of regaining altitude). Being high attract these birds which eat the fruits and disperse the seeds.

Are there other potential benefits?

Specific to palm trees

I am currently in Bali, Indonesia looking at palm trees that seem to have a very low competition for sun light (the forest is not dense, the light is intense), there are no tall predators (but being tall might be a protection against climbing predators as well and there are indeed monkeys and other potential climbing predators) and looking at coconuts, papaya and banana I can hardly think that being tall helps to disperse further away (seeds dispersal by barochory). So, why (evolutionary reasons) are palm trees so high?

Beside the reasons you've given I would add environmental disturbance (e.g. flooding) as a evolutionary factor that could explain selection for taller individuals. Many palms also inhabit and thrive in disturbed habitats (Frangi & Lugo, 1998; Salm, 2005), which indicates that this could potentially (i.e. speculative) be an important factor for this taxon. Palms also have the combination of tall height and a relatively low "wind profile" (relative to many other groups of trees), which indicates that they are adapted to exposed/disturbed habitats.

This, along with the reasons you've already given, seems to cover the most obvious evolutionary reasons for selection on tree height. However, these will naturally trade-off against negative effects on e.g. fecundity from investing and maintaining biomass.


Can viruses be reintroduced to humans from ice sheets? A comment on reddit mockingly say

because the ice is melting away due to global warming, this new virus came out of the melted ice sheets

It has 60+ upvotes and no one replied
Is it possible for viruses that are frozen in ice sheets to reinfect humans when they are melted?

Yes, pathogens have been shown to survive freezing temperatures under ice and still remain potentially dangerous after the melting of ice.
This has not only happened for relatively simple molecules such as the ones that viruses are made out of, but it has also even happened with larger pathogens such as bacteria (anthrax outbreak in 2016).
Unlike high temperatures which can cause molecules to dissociate due to excess kinetic energy, freezing temperatures usually preserve molecules and even cells such as with the aforementioned anthrax outbreak. This means that viruses can survive cryogenesis.
In 2015, researchers in Tibet discovered 28 'new' species of viruses in (melting glaciers).
These sort of discoveries lead many to believe that with global warming, previously unknown viruses could appear once again.
As for SARS-CoV-2, it seems that melted ice sheets are unlikely to be the source of such a virus considering (among other reasons) that the virus was first noticed in Wuhan China, a place lacking permafrost.


Discrete vs Continuous Replicator Dynamics The replicator eqn in the case of discrete non-overlapping generations and asexual reproduction is given by the discrete replicator eqn:
$$x_i(t+1) = x_i (t)\frac{f_i(t)}{\bar f (t)}$$
where $x_i$ is the frequency of the strategy $i$ and each individual playing strategy $i$ produces $f_i$ copies of itself in the next generation. $\bar f$ is the average fitness. Thus
\begin{equation}
\Delta x_i = x_i(t) \frac{(f_i(t) - \bar f (t))}{\bar f (t)}
\end{equation}
In the case in which $x_i$ is a continuous variable, we have the continuous replicator eqn given by
$$\dot x_i = x_i (f_i - \bar f)$$

Question: I suppose we should be able to derive the continuous replicator eqn from the discrete replicator eqn by evaluating the discrete replicator eqn for $\lim_{\Delta t \to 0}$. How can I do this derivation?

To make this derivation, it is better to focus on the population size of a strategy as opposed to the frequency of a strategy. Let $N_i$ be the population size of the strategy $i$. Strategies in the replicator dynamics change exponentially. The equation for continuous exponential growth is $\dot N_i = f_i N_i$. Since $x_i=N_i/N$ and $\dot N = \bar f N$, calculating $\dot x_i$ by using the quotient rule gives us the continuous replicator eqn.


Want to know which insect it is? I often found it in washroom. Is it dangerous and how to control it This insect found in the wash room.  Its so fast and disappears once someone enters the washroom.  Is it dangerous?  Whats name of this insect.  How to control this insect

That is a house centipede, Scutigera coleoptrata https://en.wikipedia.org/wiki/Scutigera_coleoptrata
They are harmless, unless you are a cockroach or termite. People find them unpleasant and creepy, because they run fast, but they are predators that prey on pests in your home.
https://www.familyhandyman.com/pest-control/heres-why-you-should-never-kill-a-house-centipede/


How to get phased haplotype from vcf file? Although I'm reading all days and nights, still confused in this area. I have hundreds of BAM files (from whole-genome sequencing of a given human population). I was wondering how I can obtain phased haplotype from raw vcf file? I read also about GATK, but didn't find a clear answer to my question. Please kindly tell me if this tool is suitable for this goal?
Would you please introduce me some practical guides and pipelines that explain such an analysis step-by-step?

Thank you for your help

You should be able reformat BAM files to VCF files with PGDSpider.

I find using specific softwares for this type data formatting tends to be error prone and not practical when having a large number of files to reformat, so I personally like to simply read about the format standard and write the code to reformat myself but that's very much a matter of personal preference.


Find protein homologs with BLASTp I'm trying to find homologs of a set of proteins using BLASTp. I'm working with custom databases.

I'm using evalue of 0.00001 as threshold.

I would like to filter queries having hits with >90% identities. Because BLASTp output is based on HSPs, I cannot filter by %identities/query, only by HSP.

I would like to know how to do this and also if I'm following a reasonable strategy.

Here is an alignment example: qcovs=100 but qcovhsp lower.

qseqid           sseqid         pident length mismatch gapopen qstart   qend  sstart    send   evalue bitscore qcovs qcovhsp
HPNK_01698      HAPS_0519       81.88   596     75      5       630     1225    615     1177    0.0      889    100     49
HPNK_01698      HAPS_0519       49.17   301     115     8       84      366     201     481     2e-56    214    100     23
HPNK_01698      HAPS_0519       53.64   261     61      6       436     684     616     828     6e-49    191    100     20
HPNK_01698      HAPS_0519       46.61   251     62      3       332     510     584     834     6e-46    181    100     15
HPNK_01698      HAPS_0519       53.27   214     79      4       1       194     1       213     1e-45    180    100     16
HPNK_01698      HAPS_0519       55.96   218     60      8       550     764     643     827     1e-40    164    100     18
HPNK_01698      HAPS_0519       51.56   225     61      7       516     731     642     827     1e-38    157    100     18
HPNK_01698      HAPS_0519       49.57   230     77      6       484     713     643     833     1e-37    154    100     19
HPNK_01698      HAPS_0519       57.89   76      26      1       364     433     760     835     1e-13   76.3    100     6

Code used

Make database

makeblastdb -in $Hparasuisfastadatabase -out H_parasuis_strains_gb_ALL.fna_databaseBLAST -dbtype prot -parse_seqids

Run BLAST

blastp -db H_parasuis_strains_gb_ALL.fna_databaseBLAST -query 'out_2.fasta' -out HPNK_selected_vs_H_parasuis_strainss.tblastn -evalue 0.00001 -outfmt "6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qcovs qcovhsp" -max_target_seqs 50

Thanks, Bernardo

First of all, if you want 90% identity, you can discard this hit. None of the HSPs pass that threshold. What's more, since you're working with proteins, there are no splicing issues involved and you should be able to get a single HSP spanning most of the query and subject sequences. Assuming, of course, you have a true homolog.

In your output, I see many small, overlapping HSPs, most of which have low identity. I can't be sure without seeing the sequence but it's a safe bet that what you have there are low complexity/repetitive regions and that's why you have so many separate HSPs. The only half way decent one starts at position 630 of the query sequence and is only 595 residues long, less than half your query protein. Either you have a very diverged N-terminal region or your HSP is just a conserved domain. Again, I would need to see the actual sequence alignment to be sure but that doesn't look like a true homolog (assuming your species are reasonably close which they must be if you're using a 90% identity threshold).

So, always assuming that your species are close enough to expect decent homologs, I would simply ignore the shorter HSPs and deal with those that represent more than, say, 80% of my query's length at >=90% identity. Shorter hits will most often be conserved domains or repetitive/low complexity regions. The thresholds you choose depend on the species you are studying.

If your species are not that close, don't use BLASTP at all. Instead, you can use something like hmmer. Collect a set of homologs from various species for each of your query proteins, build a matrix using these and use that matrix to search your database. You could also use Selenoprofiles which uses a similar approach.


Energy flow in an ecosystem What are the mistakes in here?

The image below shows a part of the energy flow diagram in an ecosystem.

1. Part P of the biomass of the primary producers does not get consumed. Give a concrete example of how this energy can get to the decomposers.

I think this energy can get to the decomposers, because the biomass simply dies. E.g. dead animals, dead trees, etc.

2. The amount of energy that is represented by R may end up with the decomposers in different ways. Name three ways.

I can come up with natural cell dead (dissimilation after assimilation).

3. The energy that is released through dissimilation, is not only released in the form of heat. What can this energy 'Q' be used for by the organism?

I think this energy (e.g. dissimilation of glucose) could be used for movements by the organism.

You are right but a small correction: P represents primary producers. So the answer is just dead plants or tissues such as broken branches.
Death, excretion because of turnover (which includes urine or other metabolites), tissue turnover (shedding of dead cells, molting etc)
Maintenance, locomotion, repair, immunity


What structural features make a molecule a potent opioid receptor agonist? For instance, take morphine. It is used as a baseline for measuring the potency of opioid agonists. Its structure looks like this:

But then, take heroin, around three times as potent, its structure is this:

6-Monoacetylmorphine is around 30% more potent than heroin itself:

If, however, the acetyl group is bonded to the top instead of the bottom, the resulting substance is less active than heroin.
U-47700, a research chemical, has a totally different structure but has around 7.5 times the potency of morphine in animal models:

Desomorphine is 8-10 times more potent as morphine:

Oxymorphone is 10 times as potent as morphine:

The common explanation that I've heard for these substances being potent opioid agonists is that they mimic the endorphins produced in the human brain. However, Beta-Endorphin is 18-33 times more potent than morphine, and its structure is thus:

That looks absolutely nothing like any of the substances mentioned above. How can molecules with structures so radically different from those of endorphins mimic the action of endorphins? And how come U-47700 has a potency similar to the more powerful morphine analogues but looks nothing like them?

But then, there's Fentanyl:

100 times more potent than morphine, making it a more powerful Opioid receptor agonist than the body's own endorphins, and its structure looks nothing like them or any of the other ones so far. Other analogues are even more potent. So what is it about a molecule's structure that makes it a potent opioid? The only structural similarity that I can spot is the presence of a tertiary amine group in the structure. And how does U-47700 have a potency similar to the more powerful morphine analogues?

Specific parts — moieties — of an agonist molecule bind to the receptor protein, causing the receptor to change shape, which in turn initiates a signaling pathway inside the cell.

Some agonists are better at causing the receptor to change to its "optimal shape" for relaying signal. These are called "full agonists".

Other agonists cause a partial change in receptor shape and reduced signal. These are called "partial agonists". The strength of the resulting signal is called "efficacy".

In addition to differences in efficacy between agonists, the parts of the agonist that bind to receptors can have a different chemical makeup and so create a different environment of electrostatic attractions and van der Waals forces between atoms in the agonist and in the atoms in the binding site in the receptor. Agonists that bind more strongly will stay bound to the receptor longer. These are said to have higher "affinity". Agonists that bind more weakly have lower affinity.

It is affinity and efficacy together that determine potency, or how well an agonist works.

In a biological environment, as well, these agonists swim in a pool with other agonist and antagonist molecules. For example, your agonist of interest might do a good job of changing a receptor's shape, but if it binds weakly, it will "pop off" the receptor quickly — perhaps too quickly to trigger the necessary signal. Another, different agonist molecule comes in and binds more tightly, but it doesn't create as strong a signal. Or perhaps a passing antagonist molecule floats around and pops onto the receptor, stopping any signal from that receptor and also keeping any agonist molecules from binding again for a while.

The population of agonists, antagonists, and receptors all change the number and kinds of binding events that take place, which determines whether you get the signal you want, as strong as you want it.

While the opioid molecules you are looking at have different numbers and varieties of atoms, it is the molecule's shape and electrochemistry that matter. Most specifically, it will be the part of the agonist molecule that actually binds to the receptor that matters most: how well it causes a conformal change in the receptor, and how strongly it binds.

These molecules could look very different from one another, but ultimately it is the part of the molecule that binds to the receptor, and how well it binds, that matters.

I don't know about the receptor-ligand kinetics of how different opioids bind to pain receptors, specifically, but hopefully there is some terminology in this answer that helps with further research.


In a human, what non-germline cells have the highest/lowest mass? I'm just curious which cells are largest/smallest in the human body other than sperm/ova.

Large cells: adipocytes, the cells that store fat are possible the largest in humans, because after puberty they rarely divide and just enlarge if a person gains weight. A typical adipocyte is about 0.1mm in diameter, but may be a lot larger. Another good candidate is a megakaryocyte, which is a gigantic (up to 0.1mm), cell with multiple copies of its genome producing blood platelets. Depending on your definition of a single cell, also myocytes may be considered large cells, because during development several cells fuse to form one muscular fibre. Additionally, myocytes gain weight if a person excersises and strengthens muscles.

Small cells are more common, and it is difficult to point to some consistently smallest cells. The smallest kind-of-cells are definately platelets and erythrocytes (red blood cells - they are anucleated, thus may not be considered "real" cells). Generally, the part that determines the minimum size of the cell is the nucleus, so any cells, that are more-or-less metabolically inactive may be just a tiny bit larger than the size of their nucleuses. These may be for example polar cells, the tiny cells, that carry the excess of genome during meiosis in women and die soon afterwards.

If you are interested in all kinds of strange cells inside yourself, you might start by browsing this beautiful, though longish list.


Do pharmacodynamics and pharmacokinetics of the drug in animals are the same as in human? I'm trying to understand the purpose of different clinical trial phases, and the following question comes into my mind :

  Do pharmacodynamics and pharmacokinetics of the drug in animals are the same as in human?

The reason I asked this question is that our professor said that pharmacodynamic and pharmacokinetic studies are performed during pre-clinical studies (in animals), as well as during clinical trial's phase 1. The answer I was thinking about is that animal's physiology cannot be the same as human's physiology, thus, pharmacological profils should be different.

I just want to know more about this and feed my curiosity.

Drug metabolism in humans is different than animal models.

Here is one example: http://www.ncbi.nlm.nih.gov/pubmed/17125407

The link is a review where they compare different animal models to humans with a focus on CYP enzymes commonly involved in drug metabolism.  While they describe similarities, they note:

  CYP2C, -2D and -3A show appreciable interspecies differences in terms of catalytic activity and some caution should be applied when extrapolating metabolism data from animal models to humans

Essentially your instincts that different species have different pharmacology is accurate and based on the many genetically-based differences in the drug processing machinery.

Similarly, there are differences in drug metabolism between humans with different genetic backgrounds, which is the basis for pharmacogenetics (https://en.wikipedia.org/wiki/Pharmacogenetics).

More recently, differences in gut microbes have also been raised as contributory factors (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3529681/).  In this case, identical twins with different diets could conceivably have different pharmacokinetics.

  Tying this all back, the reason human drug trials are needed is because animal and tissue culture models are necessary, but insufficient to predict drug efficacy or safety.


Was iron important for the first life on Earth? Some ions or compounds are thought not to have become involved or important in the metabolism of living organisms until some time after certain mutations took place. For instance, early life is thought to selectively allow calcium ions through its membrane, but eventually also evolved the ability to selectively allow sodium ions, specifically through a mutation that lead to a change in the composition of a channel protein from glutamine to lysine.

Currently, iron is involved in oxidations involving molecular oxygen, such as in cytochromes and clearly holds a key role in modern life, despite that free iron or even ferric compounds are rarely accessible. From my understanding, iron most likely became incorporated into the metabolism of microbes after during/after aerobic organisms had developed, but this does not rule out the possibility that iron was involved earlier on. So, I am wondering if iron was involved in early life, and details on how would be appreciated.

Cyanobacteria require iron for photosynthesis and can be found as fossil stromatolites dating back to 3.5 billion years ago.  Stromatolites are layered structures made up of cyanobacteria and sediment.

Source: https://en.wikipedia.org/wiki/Stromatolite

Modern stromatolites can be found at Shark Bay in Australia, Chetumal Bay in Belize, and Laguna Bacalar in the Yucatan Peninsula.

Cyanobacteria are also believed to have evolved into the first microbes to produce oxygen by photosynthesis, which was a catalyst for the Great Oxygenation Event which occurred around 2.45 billion years ago.


How to filter out the antibody light chains into kappa and lambda types on the PDB website? The antibody light chains can either be kappa or lambda types. I am searching homologous sequences on the RCSB PDB website. Is there a way to filter out these two types?

The instructions are as follows.

Go to the RCSB PDB Webite.
Under the search bar, click Browse by Annotations.
Click the blue tab labeled SCOP.
Click the white arrow next to the folder titled All Beta Protiens.
Click the white arrow next to the folder titled Immunoglobulin-like beta-sandwich (...).
Click the white arrow next to the folder titled Immunoglobulins (Superfamily).

This is where it splits, based on your specific search.

For Light Chain, Constant Domain:

Click the white arrow next to the folder titled C1 set domains (antibody constant domain-like).
For Kappa Type, Click the folder (not the arrow) titled Immunoglobulin light chain kappa constant domain, CL-kappa.
For Lambda Type, Click the folder (not the arrow) titled Immunoglobulin light chain lambda constant domain, CL-lambda.

For Light Chain, Variable Domain:

Click the white arrow next to the folder titled V set domains (antibody variable domain-like).
For Kappa Type, Click the folder (not the arrow) titled Immunoglobulin light chain kappa variable domain, VL-kappa (...).
For Lambda Type, Click the folder (not the arrow) titled Immunoglobulin light chain lambda variable domain, VL-lambda (...).

This should get you to the list of proteins that you are looking for.


How do you find probabilites of carrying an X linked recessive disease in a blank pedigree given only one phenotype? In the above pedigree, the black square indicates the male
affected with Hemophilia which is a X-linked recessive trait. What is the
probability that the proposed child, which is a male (indicated as ?), will carry the disease?

I started off by trying to figure out the probability that the sister has an X chromosome carrying the disease, which I got as 10/16 (Since the mother must be XcXc or XcX and the father either XY or XcY; possible genotypes are XcX, XcX, XcXc, XcXc, XcX, XX, XcXc, XXc)

I then repeated the procedure for each generation and got the final answer as 9/16. However, the correct answer is 1/8. How do you obtain this? Are my assumptions flawed?

Source: https://biolympiads.com/wp-content/uploads/2016/08/inbo2009-Q.pdf

Assume the disease is rare. The parents of the affected male are thus probably X/Y and Xc/X. Their daughter (the sister of the affected male) has a probability of 1/2 of being a carrier (Xc/X). Her daughter also has a 1/2 probability of being a carrier. Her son has a 1/2 probability receiving the mutant X chromosome. Therefore, the probability that the son is affected is 1/2 * 1/2 * 1/2 = 1/8.

I can’t right now, but I’ll try and make it clearer with a figure tomorrow.


Are turkish hazelnuts edible? The title of this question is Google suggestion verbatim, when I tried to write it. Yet, I found no answer specifically for Turkish hazelnuts.

Turkish hazelnut is a tree and it's fruit looks different from normal hazelnuts growing on hazelnut shrubs.

One such tree is planted close to my house, so I was wondering if I could eat all the nuts on the ground.

First off all let me say something about this tree.

Turkish hazelnut (Corylus colurna) is a slow growing pyramidal tree approximately around 25 m (82ft) tall, with a stout trunk up to 1.5 m (4 ft 11 in) in diameter. Leaves are deciduous, rounded, 6-15 cm long and 5-13 cm across, softly hairy on both surfaces. Corylus colurna is actually the largest species of hazel. The name Korylos (korys) comes from Greek and means helmet. The tree is dioecious.

Nuts are about 1-2 cm long, surrounded by a thick, softly spiny and bristly involucre (husk) 3cm diameter. The nuts are born in cluster of 3-8 together. The nuts mature in September and are edible, with a taste similar to common hazel (Corylus avellana). The nuts have a very hard shell, approximately 3 mm thick, which it makes them a little hard to open. Nut production is very irregular and occurs every two to three years.

Source

https://en.wikipedia.org/wiki/Corylus_colurna
http://www.missouribotanicalgarden.org/PlantFinder/PlantFinderDetails.aspx?taxonid=277856
https://www.chicagobotanic.org/plantinfo/tree_alternatives/turkish_hazelnut


Do electromagnetic waves outside the visible spectrum have any role in photosynthesis? Does photosynthesis occur only in the visible spectrum? If there is a way to completely remove all other electromagnetic bands from the sunlight, including infrared (IR), will photosynthesis continue to occur?

Is there any known plant (algae, fungi, bryophytes, pteridophytes, gymnosperms and angiosperms) that utilises the other wavelengths for photosynthesis?

Photosynthesis is nearly all visible light. There's usually not enough UV and thermodynamics more or less rules out infrared. Chris covered that pretty spectacularly, but that nearly is significant.

There is some evidence that certain kinds of fungi can use gamma rays or other ionizing radiation for energy, but they're not particularly common. Melanin is used instead of chlorophyll, but apparently 'tooling up' for radiosynthesis(is that a word?) is fairly hard on the cell. It was assumed the melanin was a protective pigment(like it is in humans) and produced only when necessary(also like humans), but these particular ones were discovered growing in the Chernobyl reactor and apparently evolved to continuously produce melanin and absorb ionizing radiation for food. They don't stop producing melanin even on the verge of starvation. Other melanized fungi live in high-radiation environments, like mountaintops and the poles, where there is more UV and cosmic rays and things.

Fun fact aside: Fossilized melanized fungal spores turn up at the K-T boundary when the earth had no magnetic field and therefore higher radiation flux at the surface (see paper linked above and Hulot & Gallet, 2003 for more).


Latency differences between our senses I would be interested in knowing how long it takes between the moment something touches our skin and the moment something is activated in the brain. Also how long does it take in total until we subjectively perceive something? And I would like to know whether this is dependent on which part of the body was stimulated (same latency for foot and neck?), or if it is dependent on the nature of stimuli (heat or mechanical). I would also be interested in that latency in the auditory and visual system. I tried searching for that but could not find what I needed due to the lack of the correct terminology.

Given your questions taken together and your comment It would be interesting to know how much delay the brain can handle in understanding causality and associating events I think you are interested in intersensory asynchrony. A well-known example where two stimulus modalities are perceived as separate while they are in fact coming from the same event is a thunder heard after lightning. This is caused by the fact that sound travels at a speed much slower than light, and hence a thunder can lag lightning by seconds.

source: NASA
In many instances, however, one event generating perceptions across stimulus modalities are actually perceived as synchronous while they are in fact offset in time due to differences in physical characteristics of the stimuli. Taking the storm as an example - when it is far away, the thunder is perceived as separate because the thunder lags by seconds. But when the thunderstorm is close enough, the auditory crack and visual lightning are actually perceived as synchronous, while in fact they are still offset because of the sound travelling so much slower than light.
So the question becomes, as you rightfully ask, what are the margins in which stimuli across modalities can be asynchronous while perceived as one event? In other words, what is the window of integration?
A review by Vroomen and Keetels (2010) describes various psychophysical studies that have examined what the minimal asynchrony is between two stimuli of different modality that lead to a synchronized percept. The following values are for simple stimuli, such as tactile taps, auditory beeps and visual flashes.

Auditory and tactile stimuli: 80 ms
Auditory beeps and visual flashes: 25 - 50 ms
Visual and tactile stimili: 35 - 65 ms

Note that these lag differences are relatively large, given the speed of peripheral neural transduction (see the other answer of Nandor Poka below). Moreover, the window of integration between more complex stimuli can be much greater. For example, the window for speech and visual information can be as large as 203 ms.  Such large windows of integration point toward higher processes playing a role in the brain.
Note that only temporal lags below 20 msec are expected to go unnoticed because of hard-wired limitations on the resolution power of the individual senses,
Hence, Vroomen and Keetels (2010) argue there must be higher processes at work in the brain that actively synchronize percepts that are offset in time, but seem to belong to one and the same event. One such mechanism is referred to as temporal ventriloquism, which means that one perceptual modality is actively shifted in time to match it with another. This effect is most pronounced in visual stimuli, in that a visual percept is actively adjusted in time to match a sound or tactile stimulus. Likely visual percepts are shifted preferably by the brain, because the visual system is the slowest of all the senses.
Reference
- Vroomen & Keetels, Att Percept Psychophys 2010; 72(4): 871-84


Help me to identify this stream insects this insect I found on a stream in the southern region of Bangladesh. Its dorsal part is fully black and head and thorax is yellowish with black spot. Body size approx. 5-6mm, limbs 8-10mm

Attempt at an answer

I think it is a water strider. There are 1700 species of water strider. Some of them are easy to eliminate (the marine ones typically) but I am not really able to give you more info from your picture.

To make sure, it is a water strider, can you please tell us
- How long are the legs (hard to tell form the picture)?
- If it was alive, was it standing on water? What can you tell us about its behaviour?

What insects live in the water?

If it is a stream insect, it could be...

a mosquito larvea
a mayfly larvea
a odonate (dragonfly or damselfly) larvea
a water strider

Here are my thoughts for each of them

a mosquito larvea

No, mosquito larvea don't any limbs

a mayfly larvea

I can't see the long cerci

a odonate (dragonfly or damselfly) larvea

Again I can't see the cerci
Also, although, it is often hard to see, it does not appear to have a fancy detachable mouth piece.

a water strider

We can't tell the length of the limbs from the picture. Were they really long?
If it was alive, was it standing on the water?


Why have a placebo control group when testing a new drug if existing drugs can be used? It is general practice to compare a new treatment against a sham treatment (placebo), and then use those results to compare efficacy of the new treatment (call it B) to an existing treatment (call it A), at least when existing treatments are available.

However, it seems reasonable and more ethical to use A and B together in a study, rather than giving one group a sham treatment. In other words, instead of a placebo group and a treatment group, why aren't there two treatment groups, one with A and one with B? Statistical noise such as regression to the mean, natural progression of the disease, etc would all show up in both groups, so if B gave better results than A, we should be able to say directly from that result that B works better than A, and we would be doing so in a more direct way than comparing the effect sizes of B vs placebo to the effect sizes of A vs placebo.

You are correct. For serious diseases, it would be unethical to withhold the existing treatment (Treatment A) for a placebo. Treatment A is called "standard of care", and new treatments are compared to this.


What diurnal red & black moth is this? Seen today on a hot summer day in Stockholm, Sweden. Length around 25 mm.

Looks like a six-spot burnet (Zygaena filipendulae)

Diurnal moth of the Zygaenidae family.

Description:

has a wingspan of 30–40 mm (1.2–1.6 in)...The fore wings are dark metallic green with six vivid red spots (sometimes the spots are merged causing possible confusion with other species such as the five-spot burnet). Occasionally, the spots are yellow or even black. The hind wings are red with a blackish fringe. The larva is plump and hairy with variable markings, usually pale green with rows of black spots.

Habitat:
meadows, woodland clearings, sea-cliffs and rich, grassy/flowery areas.
Range:
common throughout Europe, June - August.
Sources:
wikipedia


Terminology for quantitative response of T cells to antigen complexes In the article, there is a statement which is:

  Although DCs are
  remarkably efficient in evoking T cell responses with few antigen–
  MHC complexes (1–100 per DC) (1–3), they must first
  encounter a T cell with appropriate antigen specificity (one in
  105 to 106).

What does the part  "Although DCs are remarkably efficient in evoking T cell responses with few antigen– MHC complexes (1–100 per DC) (1–3)" mean?
What is the significance of the value "1–100 per DC"?
What does evoking T cell responses with few antigen–MHC complexes mean?

It means that a dendritic cell only needs between 1 and 100 antigen–MHC complexes on its surface in order to activate a T cell.


Father with mutated mtDNA- why isn't his offspring at risk? Mothers transmit their mitochondria (and therefore mtDNA) to their offspring and fathers don't. Lets assume that father had a mutation of the gene that encodes mtDNA, would then be his offspring at risk? Why?

I also found the following statement:
"The current genetic advice is that fathers with mtDNA mutations are at no risk of transmitting the defect to their offspring."

How can that be true? Is it because of gene silencing?

Thank you in advance!

...would then be his offspring at risk? Why?

No. Generally speaking, fathers do not pass on their mtDNA (Mitochondrial DNA).

Why? Because the mitochondria present in oocytes (egg cell) is the mother's, as every oocyte directly inherits the mother's mitochondria when they are made in the reproductive organs. The mitochondria that the sperm from the father carry to the egg do not enter the egg cell or are destroyed in the process.

It's also worth mentioning that, in general, mtDNA does NOT reside in the nucleus of cells, but in the mitochondria itself. It is not condensed during cell division, it is not spliced during Meiosis II, and it does not undergo recombination with another cell's mtDNA. Instead, when a cell divides, each cell takes about half of the mitochondria present in the cell and maintains them. That way only the mitochondria present in the cell before division will be inherited by the daughter cells, and thus only the maternal mitochondria present in oocytes (egg cells) before sperm instigate cell division will be inherited by any offspring.


What are cranial nerve nuclei? I am having a really hard time trying to understand what cranial nerve nuclei are. I have been reading/watching videos and I keep hearing, "that is where the cranial nerves make connection with the brainstem." Also, I keep hearing that some cranial nerves may have multiple nuclei and some nuclei may have multiple nerve synapsing there; however, this information doesn't really help me understand what they are. Are they a processing center? If so, doesn't information get processed in the cortex?
So the main question is: What is a cranial nerve nucleus and what is it's function? What does it mean by "where nerves make connections"?
I am attaching the image which I've been studying. You may refer to that when answering this question.

Nerves (including cranial nerves) are constituted of neurons, and other supporting cells and structures (e.g. myelin sheath and extracellular protein matrix). Neurons are highly specialized cells that have a particular cellular anatomy:

As you can see, the cell body (or soma) contains the nucleus, and projects it's axon towards other structures in the body, sometimes more than a meter away! Cranial nerves (like other nerves) are a collection of the axons, not the cell bodies themselves. The cell bodies reside together, in the brainstem, forming what we call nuclei. So that's simply what they are.


Why is a heritability coefficient not an index of how "genetic" something is? On his blog, Eric Turkheimer writes:

  [T]aken as a number, a unit of analysis, heritability coefficients are
  funny things to aggregate on such a massive level.  What exactly are
  we supposed to make of the fact that twins studies in the
  ophthalmology domain produced the highest heritabilities?  Should eye
  doctors, as opposed to say dermatologists, be rushing to the genetics
  lab because their trait turns out to be more heritable?  No.  Whatever
  else a heritability may be, it is not an index of how "genetic"
  something is.  It is not, for example, a useful indicator of how
  successful gene-finding efforts are likely to be.  If nothing else,
  differences in reliability of measurement are confounded every
  heritability tallied here.  My point is this-- although it's nice to
  know that on average everything is 50% heritable, it's hard to attach
  much meaning to the number itself, or especially to deviations from
  that number, to the fact that eye conditions have heritabilities
  around .7 and attitudes around .3.  Having two arms has a heritability
  of 0.

As I understand this, one reason Turkheimer believes heritability coefficients are not an index of how genetic a trait is is that they are confounded by varying levels of measurement error. So, for example, maybe the relatively low heritabilities in skin conditions compared to eye conditions are because there is more measurement error in relation to skin conditions.

Turkheimer implies that there are other reasons why it's not appropriate to say a heritability coefficient is an index of "how genetic" something is. What are those other reasons?

Rather than discussing what heritability is not through wordy sentences, let's just talk about what heritability is. There are two "types of heritability":

Heritability in the broad sense
Heritability in the narrow sense.

I will discuss a few concepts and slowly introduce the concept of heritability in both senses.

Phenotypic trait

The phenotype is the consequence of the genotype on the world. In brief, a phenotypic trait is any trait that an individual is made of!

Quantitative trait

A quantitative trait is any trait that you can measure and ordinate, that is any trait that you can measure with numbers. For example, height is a quantitative trait as you can say that individual A is taller than individual B which is itself taller that individual C.

Variance of a quantitative trait

In a population, different individuals can have different values for a given phenotypic trait $x$. Because we are talking about quantitative traits we can calculate the variance of the trait in the population. Let's call this variance $V_P$ such as

$$V_P=\frac{1}{N}\sum_i (x_i - \bar x)^2$$

In the above equation, $x_i$ is the value of the phenotypic trait $x$ of individual $i$. $N$ is the population size (there are $N$ individuals in the population) and $\bar x$ is the average phenotypic trait $x$ in the population.

$$\bar x = \frac{1}{N}\sum_i x_i$$

What is causing phenotypic variance

Why would a population display any phenotypic variance? Why wouldn't we just look exactly the same? What explains these differences?

For some traits, we see very little variance. To consider the example the OP gave in the post, the number of arms in the human population shows very little variance. However, there is quite a bit of variance in terms of the number of IQ, in terms of height or of weight.

There are two (main) sources of variance that are underlying this phenotypic variance. The first one is the genetic variance and the second one is the environmental variance. We will call the genetic variance $V_G$ and the environment variance $V_E$.

If in a population, people vary a lot in terms of how many hamburgers they eat, then there is a non-negligible $V_E$ underlying the phenotypic variance $V_P$ for weight. If in a population, there is a lot of variation of genes affecting weight, then there is a non-negligible $V_G$ underlying the phenotypic variance $V_P$ for weight.

By the way, a gene (or another non-coding sequence) that is polymorphic (i.e. has more than 1 allele in the population) and which explains some of the variance in the phenotypic quantitative trait is called a Quantitative Trait Locus (QTL). A locus is a sequence (of any length) on the genome.

Math reminder

Variances of uncorrelated variables can simply be added! For simplicity, we will assume for the moment that we are considering uncorrelated variables. As a consequence, we can express the phenotypic variance $V_P$ as a sum of the phenotypic variance that is due to environmental variance $V_E$ and the phenotypic variance that is due to genetic variance $V_G$

$$V_P=V_E+V_G$$

This equation is slightly simplified and this will affect the below calculations. See the section Other sources of phenotypic variance for more info.

We can now talk about heritability!

Heritability in the broad sense

Heritability in the broad sense $h_B$ is defined as the fraction of phenotypic variance $V_P$ that is explained by genetic variance $V_G$. In the equation, it gives:

$$h_B=\frac{V_G}{V_P} = \frac{V_G}{V_E+V_G}$$

Heritability in the narrow sense

Heritability in the narrow sense $h_N$ makes one further trick. We have to consider that the genetic variance $V_G$ that is underlying the phenotypic variance can itself be decomposed into a sum of variances. The variances that we like to consider the additive genetic variance $V_{G,A}$ and the dominance genetic variance $V_{G,D}$.

The additive genetic variance is the genetic variance that is due to additive interaction between alleles. The dominance of genetic variance is due to non-additive interactions between allele.

We can now define the heritability in the narrow sense $h_N$ as the is defined as the fraction of phenotypic variance $V_P$ that is explained by the additive genetic variance $V_{G,A}$. In the equation, it gives:

$$h_N=\frac{V_{G,A}}{V_P} = \frac{V_{G,A}}{V_E+V_G} = \frac{V_{G,A}}{V_E+V_{G,A}+V_{G,D}}$$

In the special case, when all the genetic variance $V_G$ is exclusively done through additive interactions, then $V_{G,D} = 0$ and $V_{G,A}=V_G$ and therefore $h_N=h_B$

Interpretation of the heritability

If all of the phenotypic variance is due to genetic causes (and regardless of whether there is a lot or a little variance), then $h_B=1$. If all of the phenotypic variance is due to environmental variance, then $h_B=0$.

So what does a $h_B=0.3$ means?

It means that 30% of the phenotypic variance is explained by genetic variance and that 70% of the phenotypic variance is due to environmental variance.

So, what if there is no phenotypic variance in the population? if $V_P=0$, then the heritability is undefined (as dividing by zero is undefined). However, in general, we tend to think that there is always a tiny bit of environmental variance and most people would just go on saying that heritability is 0 when $V_P=0$.

What will affect the heritability?

A measure of heritability is true for one population, in one environment.

If you change the population, add a few mutations for example, you might well create a polymorphic locus that is causing some phenotypic variance. If you put the same population in another environment, you could suddenly have more or less phenotypic variation due to environmental variance. Typically, if you measure heritability in the lab in a controlled and constant environment, then you will likely overestimate the heritability (as you underestimate $V_e$) compared to the same population that is living in a very heterogeneous environment.

What heritability is not!

If a trait has low heritability, it does NOT mean that it is (or is not) an adaptation. It only means that there is no genetic variance that explains the phenotypic variance.

Why do we care about heritability?

If there is no genetic variance for a trait, it means that the only way this trait can change through time is by changing the environment (or by creating a non-zero genetic variance through mutations). If there is a non-zero genetic variance and if there is a difference in fitness between individuals having different trait value then, the trait is under natural selection.

The most commonly used index of heritability in the heritability in the narrow sense $h_N. $Why do we care about $h_N$?

Let $\bar x_t$ be the mean phenotypic value of the trait $x$ at time $t$. One generation later, that is at time $t+1$, the mean phenotypic value is $\bar x_{t+1}$. Let's define the response of selection $R$ as the expected difference between these two quantities, that $R=E[\bar x_{t+1} - \bar x_t]$. Let's define the strength of selection $S$ and the heritability in the narrow sense $h_N$, then

$$R=h_N \cdot S$$

As a consequence knowing $h_N$ allows us to predict the effect of selection on a given trait.

This equation is called the breeder's equation (see this post about its interpretation).

Other sources of phenotypic variance

Saying $V_P=V_G+V_E$ is a little too simplistic. In reality, there are other sources of phenotypic variation such as variance due to epigenetic changes $V_I$ and variance due to developmental noise $V_{DN}$ for example. It is also sometimes very important to consider the covariance between any pair of such variance. So, the equation would more correct if stated as

$$V_P = V_G + V_E + V_I + V_{DN} + COV(V_G, V_E) + COV(V_G, V_I) + COV(V_G, V_{DN}) + COV(V_E, V_I) + COV(V_E, V_{DN}) + COV(V_I, V_{DN})$$

Note that everyone is free to further decompose any of the above variance into a sum of variances as we did above for the genetic variance. For example, the environmental variance $V_E$ could be decomposed into the sum of the phenotypic variance due to variance in temperature $V_T$ and the phenotypic variance due to variance in precipitation $V_{\text{precipitation}}$ assuming the other types of environmental variances are negligible.


How do genetic chimeras with different blood types not die? If a person is a chimera and has two different blood types in his veins, how does he not die? Shouldn't the immune system attack one of the blood types?

  In 1953 a human chimera was reported in the British Medical Journal. A
  woman was found to have blood containing two different blood types.
  Apparently this resulted from her twin brother's cells living in her
  body. More recently, a study found that such blood group chimerism
  is not rare.

Since these cells are there by the development of the immune system, the immune cells recognize them as self. By blood transfusion with incompatible blood type, the immune system recognizes the blood cells with different histocompatibility antigens (A,B,Rh etc...) as non-self, that's why it attacks them.


book on human sexuality evolution i am interested in Lynn Margulis' book Mystery Dance: On the Evolution of Human Sexuality.

i don't find it in libraries and it's too old and i am too poor for paying 20 euros for a 1991 book.

Does anybody know a more modern book about human sexuality evolution?

Wild connection by Jennifer Verdolin is good, but it really depends on what you are looking for is there a specific area you are trying to learn about?

The Third chimpanzee might interest you, it compares human behavior to the other chimpanzee species, sex is only a small part but it it=s there.

I a can warn you against a book I used ot like,sperm wars by Baker, A lot of the research is his own and there have been a lot of issues with people being unable to replicate many of them, so a lot of it untrustworthy which is a shame becasue I really liked his writing style.


Why do ribosomes associate with rough endoplasmic reticulum but not other membranes? As far as I am aware, all eukaryotic membranes consist of a lipid bilayer. Smooth endoplasmic reticulum (ER) and rough ER are distinguished by the presence of ribosome on the rough ER surface, but are apparently otherwise similar. So what causes ribosomes to associate with the membrane of rough ER but not that of smooth ER. Or do ribosomes convert smooth ER to rough ER by binding to their membranes?

Peptides that are destined to be either secreted or included in the cell membrane have a signal sequence that binds a protein called Signal Recognition Particle (SRP). The SRP will in turn bind to translocons--basically peptide tunnels in the RER membrane. You need to have this interaction between the translocon and the SRP in order to have stable ribosome attachment. That's why you won't have ribosomes attaching to other membranes. After attachment the ribosome will continue translating mRNA through the translocon into the RER lumen.

Now here's where things get a bit dicey. If the resulting structure in the RER lumen is largely hydrophilic then it will stay in the RER lumen where it will later be targeted to wherever it needs to go based on other signal sequences.

However, if there are large stretches of hydrophobic residues, then those don't like to be in the aqueous environment of the ER lumen. Ergo they will incorporate into the RER membrane. Thus they become transmembrane proteins. A good rule of thumb is that if you have 10 hydrophobic residues in a row, then there's a good chance that is going to get incorporated into a membrane.

Once protein transcription is complete the ribosome will no longer be associated with the RER and will re-enter the pool of free ribsomes (ready to pick up a new mRNA).

EDIT: In response to question in comments about why translocons are found in RER and not in other membranes

You can certainly ask the question, but you're getting close to falling into that beautiful genesis of basic science research: something I call the 'why-hole'. I searched around, and I don't think we have a full understanding of membrane organization throughout the cell, though I would defer to any cell biologists who have a better understanding. We do have some idea of the mechanisms. What follows is a stitching together of hypothesis and some facts I was able to find.

It stands to reason that since the translocon is a complex of transmembrane proteins, its parts were at some point synthesized by a ribosome through a previous translocon. That would get it into the RER membrane. Now in order for a piece of membrane (or anything within the ER lumen, for that matter) to get to its final destination it has to have another signal sequence. This signal sequence will be recognized by unique signal recognition protein. This will localize the protein to a part of the RER that has several proteins that will form a vesicle (one of which being clathrin) and a protein attached to it called a v(esicle)-SNARE. Once the vesicle buds off it will go through the cell until its v-SNARE runs into a t(arget)-SNARE. This interaction will ultimately lead to fusion of the vesicle into the target membrane (e.g. lysosome, plasma membrane, etc). I double checked all of my facts here, where you can read more about it.

All of this is to say that if a transmembrane protein doesn't have a signal sequence it won't get targeted for transport through this system and should stay in the endoplasmic reticulum.

At this point it would be good to remind everyone that the ER--both rough and smooth--is a continuous structure with the RER located closer to the nucleus and the SER closer to the cell membrane. So it would be reasonable to ask why translocons stay in the RER and doesn't diffuse into the SER. That's where we get into more emerging science. From what I gather there are many proteins that are involved in the strutrue and organization of the ER. You can look into "the ability of NDPK-B to form microdomains at the membrane level" or reticulon2, but I think there's still a fair amount of work to do in this field. I encourage you to look into these resources, and if this is something that you're interested in consider doing some research. There is some evidence that membrane dysfunction is associated with neuronal disease, so any advances would be well received.

As always, keep asking questions!


What Biology topics are linked to the Nitrogen Cycle? What other educational topics are linked to the Nitrogen Cycle? I am a Science teacher, and I need to organise my lessons. Thanks!

The nitrogen cycle is a highly biological process. Nitrogen gas is ~75% of the earth's atmosphere and N2 is the most chemically stable form of Nitrogen for conditions in the Earth's crust.

Nitrogen is one of the four most essential and universal elements to terrestrial life (Carbon Hydrogen and Oxygen being the others).  Nitrogen fixation, the name given to the various processes which make chemically reactive forms of Nitrogen available to living things. Ammonia (NH3 / NH4+) and Nitrites / Nitrates (NO2-, NO3-) are chemically unstable compared to N2 and the conventional biosynthetic processes in the cell can use any of these forms to make proteins, nucleic acids and other biologically active molecules necessary for life.

Ammonia and similar compounds like Urea come from the biological byproducts of  living cells - waste.  These are readily recycled in the soil by plants and microorganisms and I imagine some animals and insects.  Nitrogen is a limiting resource for living things and most biomes are pretty efficient at recycling reduced nitrogen from primary (NH3) secondary (NHR2), tertiary (NR3) and quaternary amines (NR4).

But the biosphere has a great demand for bio-useful nitrogen and in most places on earth in deep water and soil microorganisms can break down N2 directly into nitrates/nitrites. This happens deep underwater or underground because the enzymes which perform this activity are delicate and ruined by O2.  This process is entirely performed by anaerobic microorganisms. In recent decades, the ecological importance of subterranean microbial communities has become better appreciated for the complex structure and ecological roles it plays.

An interesting side note to this is that up to the early 20th century, the growth of life and human civilization was really limited by the intense labor required to grow food.  While hydrogen oxygen and carbon are essentially free - obtained from water, atmospheric oxygen and CO2 by plants, Nitrogen was the limiting factor in many cases to growing more crops or animals for food.

The Haber process, which converts nitrogen and hydrogen gas under high temperature and pressure into ammonia over a catalyst was such an important discovery - it allowed the industrial preparation of effective fertilizers and was instrumental in creation of the green revolution.


Proof of Darwinism( fossil records) What logic do anti-darwinists provide for refuting the proofs( by proof I mean the fossil records) of darwinism ?

Is it the missing links?

If yes then , what do they make of the fossils which are present?

Note : I'm not being hostile to the anti-darwinists, I'm just very curious.Also I really want  to see it directly from an anti-darwinist (i.e. answer from an anti darwinist highly preferred)

If there's any problem in my question please inform me.

I'll try to approach this topic academically and undogmatically since skepticism is essential to scientific discourse. However, bear in mind that as of $2009$, $97\%$ of scientists believed in evolution (Pew Research Center, 2009). Although I can't find how many scientists were surveyed, nor what disciplines they studied.
I've not given many citations, since I'm questioning the counter-arguments to evolution, and not necessarily providing my own specific claims. I'll also avoid discussions in comments since this could be a contentious topic.
1. Missing Links
In theory, if there aren't fossil records forming a complete chain of species from early hominids to humans, then this could cast doubt on the theory of evolution. But the fossil records are convincing and there are many of them (Wikipedia, n.d.). Also, if there truly is a missing link and there is no relation between humans and early hominids, then why do their skeletons look so similar? And why did humans suddenly come into existence? Is there a better rational or empirical explanation?
2. Skepticism of Existence of Fossils
One could propose that there are no fossil records, or that they have been faked. But why would anyone want to make any fake fossils or lie about them? What possible conflict of interest would they have, and why would this benefit them? Why would tens of thousands of scholars want to support this theory? How does this explain how scientists are still discovering new fossils? If anything, a paper that shows that evolution is false would be groundbreaking and give instant publicity to whoever publishes it.
3. Inaccuracy of Techniques: Radiocarbon Dating
A variety of scientific techniques have been used to garner information about the fossil records. One such technique is radiocarbon dating, which tells us how old the fossils are. If this technique is inaccurate, we could be wrong about the fossils and they might not form a chain that leads from early hominids to humans. But the technique is broadly accepted. Also, how would this explain the fossils - why would humanoid fossils with such odd shapes occur at random points in time? The shapes of these fossils don't look like typical human mutations.
4. Skepticism of Age of the Earth
Another counter-argument proposed to evolution is that the age of the fossils doesn't allow for their beliefs of the age of the earth. But the age of the earth is accepted by the geological community to be ca. $5\;\mathrm{bn}$ years old (Wikipedia, n.d.).
5. Summary and Micro/Macroevolution
A related counter-argument to evolution that I'd like to cover, but isn't directly related to the accuracy of fossil records is that of microevolution vs. macroevolution. Some people argue that we can only see evolution on minute scales, and not on large scales. But then if a population does have small-scale changes, and there is a higher probability individuals with certain adaptations surviving, then wouldn't these micro-effects sum, over millions of years, to produce large-scale effects?
In my opinion, the counter-arguments for the fossil records of the fossil records for evolution are rather flimsy, which suggests that evolution is the best explanation we currently have for the evidence we've observed.


IgA complement activation Recently, I have been reading Janeway's immunobiology and had a question on immunoglobin A. I read that IgA activates the complement pathway using the Fab fragment of the IgA. How does IgA do that? I can't seem to find an information on that in  the book or online.

I found some reports on it (like reference 1) but there is an oddly little amount of publications on this topic. then I found this review in Mucosal Immunology (reference 2, interesting to read) which doubts this activation. It says:

  Interaction with complement

  IgA lacks the residues identified in the Fc regions of IgG or IgM that
  bind to C1q, and consequently IgA does not activate the classical
  complement pathway. Although several papers have reported activation
  of the alternate pathway by heat-aggregated, denatured, or
  recombinantly generated IgA, this seems to be essentially artifactual,
  and intact native IgA antibodies complexed with antigen inhibit
  complement activation induced by IgG or IgM antibodies. This
  effect is also replicated by Fabα fragments generated by cleavage of
  IgA1 antibodies with IgA1 protease. It is telling that mixed
  aggregates of heat-denatured IgG and IgA activate the alternate
  pathway in proportion to the content of IgG, and that C3b becomes
  covalently linked to the IgG heavy chains, not to IgA. Intriguing
  reports that IgA antibodies promote complement-dependent lysis or
  opsonization of encapsulated bacteria probably also arise from
  facilitation of alternate pathway activation by bacterial
  polysaccharides

It names three papers to underline this (which are number 45-47 in the reference list of the article), which can be found as references 3-5. So the question here is not only how the mechanism looks like, but also if this is real or an artefact.

References:

Activation of complement by human serum IgA, secretory IgA and IgA1
fragments.
Structure and function relationships in IgA
Anti-inflammatory activity of human IgA antibodies and their Fabα
fragments: inhibition of IgG-mediated complement activation
IgA blocks IgM and IgG-initiated immune lysis by separate molecular
mechanisms.
Activity of human IgG and IgA subclasses in immune defense against
Neisseria meningitidis serogroup B.


How do I clean phenol contaminated RNA without losing any of the sample? I recently extracted RNA from developing plant leaves for the first time, as part of a very long and intensive experiment. The samples were extremely precious because of the amount of effort that went into obtaining them (harvesting thousands of miniscule leaves, one from each plant, to get the required mass).

I extracted the RNA with TRIzol and chloroform. Nanodrop showed excellent yield as you would expect for actively growing young tissue, but some of the samples had really low 260/230 ratios. I know this suggests phenol or salt contamination, but what can I do to clean the samples without losing any of the precious RNA? And how can I avoid the contamination in the future?

You can clean up phenol by washing with choloroform, and then doing an isopropanol precipitation followed by a 75% EtOH wash (let me know if you'd like an exact protocol).

To avoid contamination (and sample loss), you have to be meticulous in your pipetting (which you'll get better with practice). You can always use those phase-lock tubes which basically jams a solid gel between the aqueous and organic phases so it's a lot easier to pipette.


Is this is a parasitic wasp, which I found in Blackpool Lancashire? A wasp I photographed on my van window in Blackpool just before it attacked me.
Is it true that it’s rare to see them in the UK?

Yes it is ! If you look closely at the wing patterns, they are characteristic of the Ichneumonidae family, as you can see below*. This family contains thousands of species which all are parasitoids. Species identification is very tricky for this family, but the patterns on the abdomen (2nd and 3d strips are yellow, the others are black, yellow spot at the tip) and on the tibia (yellow strips) are very characteristic of the species Ichneumon stramentor, which parasitizes caterpillars.

Based on the width of the abdomen, this is probably a female (ovipositor is not visible for this species).

It's actually quite common in the UK (they live in habitats such as hedgerows or woodlands), but not so frequently seen on windows!

*Image source : DrawWing.org

Derived from: Comstock J.H.
(1918) The wings of insects. The Comstock Publishing Company, Ithaca, N.Y.


What value type would a chromosome position be in a database or form? I wanted to create a tool for some fields like SIFT, Phenotype, etc..so for example I know Phenotype will have "Text" values or SIFT will have some determined values from a drop down list...but what about Chrom Positions? what are some valid sample values for that? so I can know what type I can use for it.

Since it sounds like you are the one designing the database, you can make this a number of ways. The simplest is probably to reduce it to two variables, likely two decimals.

See this hemoglobin example for a chromosomal locus example.

There are N chromosomes (23 for humans, if you like, sex chromosomes can be treated as a pair).
There are 2 chromatids per chromosome.
The part of the chromatid is either p or q (short or long arm).
Then there is the location on the portion of the chromatid (eg, 15.5).

Chromatid can be easily represented as a decimal, where the integer portion is the chromosome number, and the decimal portion corresponds to the chromatid and arm.

The chromosomal locus can be another decimal, such as 15.5 for the example above.

This is of course one way, and there are many other ways you could do this.


How does hemoglobin-free blood transport oxygen? Snails... We have some kind of huge garden snails appearing lately (read since the last 10 years or so). Try as one might, it's impossible to avoid them when driving. These snails do not appear to have red blood - I doubt they have blood at all. Wikipedia tells me blood's red colour is due to haemoglobin - which serves to transport oxygen.

How do snails (and any other animals that do not have red coloured blood) get oxygen? Or, do such animals need no oxygen at all?

Snails, like most molluscs, have a protein called hemocyanin dissolved directly in the hemolymph ("blood"). Hemocyanins are copper-containing metalloproteins: the binding site for a single O2 molecule contains two copper atoms. Unlike hemoglobin, where reversible oxygen binding is accomplished without a change in the oxidation state of the Fe(II) atoms in the heme prosthetic groups, in hemocyanin the copper undergoes a transition from colourless Cu(I) in the deoxygenated state to blue Cu(II) when oxygenated.

Incidentally, there are certain species of fish that manage with little or no hemoglobin - icefish.