Effects of beer on muscle recovery after exercise

There are a few studies around related to this. One particular study(1) suggests that:

Neural drive is decreased following eccentric exercise leading to a reduction in muscle force. Alcohol may further reduce the already depressed neural drive.
Alcohol also has been shown to affect the innate immune system by altering the activity of a number of inflammatory proteins. A lot of these play "important roles in the damage and repair processes occurring after eccentric exercise". Alterations in these proteins may affect muscle recovery post-exercise.(2).

The second point is maybe what's relevant to the question at hand? They say more research is required though.

Edit (expanded discussion per comment below). The study itself was carried out using 8 (+/-1) standard drinks immediately following a workout. The article(1) itself doesn't hypothesise the point made earlier regarding inflammatory proteins, it rather points to earlier research(2), to suggest this may be the case.

Whether a dose-dependant relationship exists, or the magnitude of it with regards to the alteration in inflammatory proteins is not discussed (except in the context of chronic versus acute alcohol use). Just that the possible effect on inflammatory proteins exists which may affect muscle recovery post-exercise.

Barnes, et al. Acute alcohol consumption aggravates the decline in muscle performance following strenuous eccentric exercise. Journal of Science and Medicine in Sport. 2010. 1:189-93.

Edit
2. Szabo. Consequences of alcohol consumption on host defence. Alcohol Alcohol. 1999. 1999:830–841.


$S_{0.5}$ vs $K_m$ values in enzyme kinetics

This expands my comment on the question to an answer.

If an enzyme exhibits Michaelis-Menten kinetics, then it is valid to define a KM and this equates to the substrate concentration when reaction velocity is 0.5 * Vmax.

However, many enzymes do not exhibit Michaelis-Menten kinetics. One example is when the enzyme shows a co-operative response to substrate concentration. In these circumstances the substrate concentration when reaction velocity is 0.5 * Vmax is still a useful parameter, but there is no KM.

See this paper for a discussion of the kinetic properties of the glucokinase of pancreatic β cells, which acts as the glucose sensor. This enzyme shows positive co-operativity with respect to its substrate glucose, and the authors develop a kinetic analysis in terms of various parameters. These include an S0.5 value for glucose, but a KM value for ATP (presumably because the kinetics with respect to ATP are simple Michaelis-Menten).


How to calculate the effective population size ($N_e$) with overlapping generations?

There are many different ways to do this, depending on what assumptions you make on e.g. stable age structure, distribution of offspring, haploidy/diploidy, population growth etc. As you probably know, there are also two main approaches to effective population sizes, namely ones based on:

the rate of inbreeding ($N_{e,i}$)
the increase in variance of allele frequencies ($N_{e,v}$), and they can sometimes differ a bit from each other.

An early attemp to calculate effective population size with overlapping generations comes from Felsenstein (1971), which is based on life table information. This paper includes several derivations, both on inbreeding $N_e$ and variance $N_e$. As an example, the formula of inbreeding $N_e$ for a diploid population is:

$N_e = \frac{N_1T}{1 + \sum_i^\inf l_i s_id_i v_{i+1}^2}$

Here, $T$ is the generation time, $l_i$ is the survival up to age i, $s_i$ is the survival from age i, $d_i$ is the probability of death at the end of age i (i.e. 1-$s_i$), and $v_{i+1}$ is the reproductive value of individuals in stage i+1. However, this is assuming a constant population size and a stable age distribution, so some rather restrictive assumptions. The paper also includes models for haploid populations, and I haven't gone through it carefully.

A couple of more recent papers that calculate $N_e$ for overlapping generations are Engen et al. (2005) and Engen et al. (2007). These papers use diffusion approximations to derive several formulas for $N_e$ under different assumptions, for age-structured density independent populations. One model for a haploid population in a fluctuating environment is:

$$\begin{align}
N_e & = \frac{N}{\sigma_d^2T} \text{, with} \\
\sigma_d^2 & \approx \sum_{i=0}^k \frac{\lambda^{-2}}{u_i} \left[\left(\frac{\delta\lambda}{\delta b_i}\right)^2\sigma_i^2 + \left(\frac{\delta\lambda}{\delta s_i}\right)^2 s_i(1-s_i) + 2\left(\frac{\delta\lambda}{\delta b_i}\right)\left(\frac{\delta\lambda}{\delta s_i}\right) c_i \right]
\end{align}$$

where $T$ is the generation time and $\sigma_d^2$ is the demographic variance. In the lower equation, $\lambda$ is the deterministic growth rate (dominant eigenvalue), $u_i$ is the proportion of the population in stage i (a component of the stable age distribution), $b_i$ is the expected number of offspring with variance $\sigma_i^2$, $s_i$ is the survival rate of stage i (with binomial variance), and $c_i$ is the covariance between reproduction and survival. You really need to dive into these papers to fully understand how these equations are derived and how the variables are defined (will take up far to much space here). However, at its core, it is using a diffusion approximation on an allele at a selectively neutral locus.

There are many other papers out there deriving effective population sizes, under different assumptions and restrictions, but the ones cited above should be a good starting point, and by looking at citations to and from these you should get a decent overview of the topic. Many textbooks also cover ways to calculate effective population sizes with overlapping generations, e.g. Hedrick (2011) and Felsenstein (2013, free pdf).


Does the speed of electrical impulses through neurones decrease with age?

As @Armatus notes, it is now generally understood that age-related cognitive decline is related to loss of myelination and axon damage (see, for example, Kohama et al, 2012). Myelin degeneration is also likely the cause of some age-related peripheral neuropathies. Loss of myelination (due to inflammation) is also the mechanism behind multiple sclerosis.

Myelin allows for fast and reliable transmission of action potentials by a mechanism known as saltatory conduction. This is necessary to compensate for axons which have small diameters (unlike the squid giant axon) and travel long distances. Degraded myelin will reduce conduction velocity and can result in propogation failures. The links @nico provided in his comment above are useful references.

Kohama, S.G., Rosene, D.L., Sherman, L.S., 2012. Age-related changes in human and non-human primate white matter: from myelination disturbances to cognitive decline. Age (Dordr) 34, 1093–1110.


Do cockroaches eat their shells after moulting?

Yes, it's fairly common for insects to eat their egg casings, shed skin etc. where possible. In a similar vein, as Alex Stone mentions, spiders often eat their webs too. There's generally plenty of valuable protein and nutrients tied up in there, so why not recycle?


Are bronchioles and alveoli covered by respiratory epithelium?

From the nose to the pharynx and larynx: stratified squamous epithelium

From the end of larynx to the bronchi: pseudostratified columnar ciliated epithelial cells.

From the small bronchi cartilage is being replaced by elastic tissue.

Bronchioles: ciliated simple epithelium with club cells (known as Clara cells). Also they lack cartilage and glands.

Alveoli are covered with squamous alveolar cells which form the wall (also known as type I pneumocytes). They also contain great alveolar cells (which produce surfactant) or type II pneumocytes and macrophages.

From http://en.wikipedia.org/wiki/Respiratory_tract , http://en.wikipedia.org/wiki/Pulmonary_alveolus and http://en.wikipedia.org/wiki/Pharynx


How does action potential conduct across a branch in a neuron?

Awesome question!  Thanks for asking it and giving me the motivation to look back at the literature to increase my own understanding!

I do want to point out one issue of semantics before starting.  Just to avoid confusion, I'm going to follow the example of some of the well-known experts in this field and distinguish dendritic spikes from action potentials.  The main difference is that a voltage spike in a dendrite or dendritic spine will experience attenuation (essentially a weakening of the spike) as it propagates toward the cell body, and it is distinct from the all-or-none voltage response of an action potential.  It is generally accepted that action potentials are initiated in the axon (hillock) because that is where dendritic and somatic spikes will ultimately sum to fire the all-or-none voltage response.  I just want to clarify what I mean by dendritic spike when I use it during this explanation.

What happens at the branch? So, to answer your first question, you're absolutely correct that when a dendritic spike occurs, that spike will encounter multiple branch points along the dendrite as it proceeds toward the soma.  When a spike propagates to a branch point, the properties of the branches determine what happen to the spike.  One important property is referred to as the geometric ratio (GR).  This property was described Goldstein and Rall in 1974 (manuscript here for review https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1334570/) and the gist of the equation is that spike attenuation is dependent upon the diameter of the branch the spike is coming from and the branch the spike is going into.  The concept behind this is called impedance mismatch, but that's complex and I won't get into it.  Just know that spike propagation is favorable from a large to a small branch, but is not favorable from a small branch to a large branch.  If you wish to read more about this concept, I'd recommend the Goldstein and Rall paper referenced earlier and chapter 14 of the textbook "Dendrites" by Spruston, Stuart, and Hausser.

Additionally, the presence of leak or voltage-gated channels in that branch can determine how well the spike will propagate or how much attenuation will occur.  This is an easier concept to grasp because the idea is essentially that more channels open when the spike passes through that branch, the more charge leaks across the membrane and causes attenuation of the spike.

All questions regarding preference of flow To sort of attempt to answer all your other questions at once, nothing really forces the spike toward the soma or a specific axon terminal.  There are dynamic changes in membrane potential that may prevent spike propagation down one branch or action potential propagation in a certain direction (like how action potentials aren't thought of as propagating from the axon to the soma because of the hyperpolarization and long inactivation of sodium channels at the area that just fired, preventing what is called backpropagation at that particular time).

I think a little reading on neuronal cable theory might be of some help with this question too.  This link is to a PDF that gives a pretty brief explanation of this concept https://nanohub.org/resources/20112/download/Lecture1_Passive_Conduction.pdf.  This is a pretty math-heavy field, so beware if math isn't your favorite subject!


Is this a variety of banana slug (Ariolimax columbianus)? (California)

Banana slugs are comprised of three species:

Ariolimax californicus
Ariolimax columbianus
Ariolimax dolichophallus

Ariolimax columbianus, or the Pacific banana slug is known to have black spots, sometimes so large as to cover their entire body (Source). This is certainly the genus of the slug you've shared.

At times, you could be seeing a Black slug which is well-established in Washington and Oregon. I only include this information as I have noticed this species occasionally in Southern British Columbia and wondered if it was the same species as the Banana slug. It isn't. The picture you've attached is not a Black slug (note the pronounced frills on the back of the Black slug specimen)


Do skin mites reproduce on human face during night?

Yes, it is true that these mites exist on healthy human skin, on >98% of people, no matter the country or hygiene.

However, I'm sorry to say that this photograph is not the follicle mite Demodex folliculorum. This image has been widely circulated around the internet as Demodex, but it is actually the head of a silkworm, Bombyx mori. The above version has been modified slightly from the original, but you can see the original here: https://www.sciencephoto.com/media/720736/view/silkworm-head-sem

And here is a zoomed out version of a silkworm: https://macrocritters.wordpress.com/2012/11/26/photographing-down-the-food-web-silkworms-bombyx-mori/

You can find a real close-up of Demodex here: https://www.sciencesource.com/archive/Demodex-folliculorum-(SEM)-SS2367855.html


What is the evolutionary relationship between heme, chlorophyll and other tetrapyrroles?

Note the molecules are not that similar, only one portion is similar.

The similar portion in each molecule is a cyclic tetrapyrroles. Cyclic tetrapyrroles and porphyrins in particular are a fairly common structure in biology they have several functional uses and are fairly easy to construct from existing simpler biological molecules. The fact that they are functionally good at binding metals makes them a reoccuring solution, although not all Cyclic tetrapyrroles bind metals. Many many biological molecules contain Cyclic tetrapyrroles.

Many biological molecules have similar structures to other molecules, especially ones that perform similar tasks, there are only so many shapes you can make with carbon atoms, the shape of a molecule affects its chemical properties and that shape is simple and has useful properties. Heme itself is a functional component of many proteins called hemoproteins, basically whenever they need to bind iron. Another cyclic tetrapyrroles would be cobalamin otherwise known as vitamin B12 which binds cobalt.

Its the same reason hammer handles look very similar even in very different cultures, just attaching a stick is a simple easy to discover solution, so it is the one that gets found most of the time. Functionally that is the shape that works. Or to put it another way they are only so many shapes you can make a wheel.

https://www.sciencedirect.com/science/article/pii/S0167488912001000

http://www.org-chem.org/yuuki/porphyrin/porphyrin.html

https://www.cpp.edu/~lsstarkey/courses/CHM-Lab/PorphyrinBasics.pdf

https://www.mdpi.com/journal/molecules/special_issues/porphyrin_chemistry

To address the updated question. I honestly don't know if they share an evolutionary origin. But given that they are synthesised in different organelles (mitochondria for heme and chloroplast for chlorophyll, I would guess no.


They can pump water over their gills with their opercular flaps (i.e., the structure that covers and protects the gills). This is assisted by the branchiostegal rays. Food would generally not interfere with this process. Additionally, gill rakers assist in keeping food in the mouth.

Some great references for questions like this:

Barton, M., & Bond, C. E. (2007). Bond's biology of fishes. Thomson.

Moyle, P. B., & Cech, J. J. (2004). Fishes: an introduction to ichthyology (Vol. 726). Upper Saddle River, NJ: Pearson Prentice Hall.


Why does replication require primers while transcription does not?

I'm no expert at transcription but from what I can figure out here and here transcription requires a promoter region acting as a template-directed mechanism for allowing transcription, which presumably bear a certain sequence recognised by the RNA polymerase and associated complexes. However DNA polymerase requires primers to guide it to the correct location on DNA.

EDIT: In light of comments below, according to here and here, RNA polymerase is capable of de novo synthesis meaning it is capable of initiating synthesis of RNA without primers and this is done by RNA polymerase binding to two nucleotides rather than nascent RNA polymer and a single nucleotide. Initiating nucleotide hold RNA polymerase rigidly in place, facilitating chemical attack on the incoming nucleotide. For bacteriophage T7 RNA polymerase, transcription begins with a marked preference for GTP at the + 1 and + 2 positions. DNA polymerase cannot add primers, and therefore, needs primase to add the primer de novo.


Many micro-organisms do not tolerate freeze drying (you should freeze them using cryo-protectants). Apparently Staphylococcus can tolerate it. You don't directly put the dried cells in the medium. First you have to rehydrate the cells using the medium and then transfer them to the culture vessel. See the following instructions from ATCC:

  The preferred method for long-term preservation of bacteria and algae
  is freeze-drying; however, some bacteria do not survive freeze-drying
  well and are frozen instead. For freeze dried cultures, using a single
  tube of the recommended media (5 to 6 mL), withdraw approximately 0.5
  to 1.0 mL with a Pasteur or 1.0 mL pipette. Use this to rehydrate the
  entire pellet, and transfer the entire suspension back into the broth
  tube and mix well. The last few drops of this suspension may also be
  transferred to an agar slant. Alternatively, algal cultures must be
  initiated on agar plates. Please note that anaerobic bacterial
  cultures must be rehydrated in an anaerobic environment; the viability
  of the cells decrease rapidly if the vial is rehydrated in an oxygenic
  environment.

  Incubate cultures under the appropriate conditions. Given proper
  treatment and conditions, most freeze-dried cultures will grow out in
  a few days. However, some may exhibit a prolonged lag period and
  should be given twice the normal incubation time before discarding as
  nonviable.

If you had received the cells from some repository or a vendor, you would have also got an instruction manual on how to revive the cells and what media to use. It is best to follow the specific instructions given in the manual.


How does a woodpecker not give itself concussions?

Various features of brain,skull and beak anatomy help to achieve protection.
A paper was published in PLoSOne in 2011 on this very topic:
Why do woodpeckers resist head impact injury: a biomechanical investigation
There is also a very readable summary on the BBC website. I advise that you read the whole article, but here is a quotation which lists the main findings:

The team's simulations showed that three factors were at work in sparing the birds injury.
Firstly, the hyoid bone's looping structure around the whole skull was found to act as a "safety belt", especially after the initial impact.
The team also found that the upper and lower halves of the birds' beaks were uneven, and as force was transmitted from the tip of the beak into the bone, this asymmetry lowered the load that made it as far as the brain.
Lastly, plate-like bones with a "spongy" structure at different points in the skull helped distribute the incoming force, thereby protecting the brain.
The team stresses that it is the combination of the three, rather than any one feature, that keeps woodpeckers pecking without injury.


Why did we lose our fourth type of cone cell (in the eye)?

Let's be clear most mammals, including basal primates, had only 2 cone cell types. Primates evolved a new cone cell becasue frugivores have a distinct advantage being able to distinguish reds and yellows. The mammal lineage may have never had a fourth cone cell, if they did it was lost early.

Eutherian mammals lost the third cones cells likely because we spend several hundred million years as small nocturnal animals during the dominance of dinosaurs. in small nocturnal animals, there is a strong competition between having more rods for better low light vision vs having cones for color vision, so a loss of some cone cells may have been neutral or even beneficial. There was some work on bats that seemed to contradict this, but they failed to control for frugivory in the bat lineages they studied; So there is an issue with their results. There is a lot of variation in color vision across vertebrates so it is no surprise a consistent selective pressure could have a lasting effect.

source


It is some sort of plume moth, Pterophoridae. I can't tell what species it is, but at least you know more about where to look! Or, hopefully, someone who knows more shows up.

There are probably better places to look, but I found this list of plume moths in Turkey on Wikipedia if you want to compare.


This looks pretty much like a female zebra finch to me (the male have a more prominent feather pattern). See this picture:

These birds are not native in Europe, this is correct. But it is always possible that birds escape captivity (or are released) and the live in countries where they originally not belong. I think this is the case with the zebra finch here. Since the birds are able to live in a pretty broad range of habitats, they can also survive in England.

According to this Wikipedia page (thanks to @Orethrephes) zebra finches which escaped from captivity are quite common in Britain although they usually not survive for long.


Proportion of amino acids from a random polymer of U and C

The first thing to mention is that this is the type of experiment that Marshall Nirenberg performed in order to break the genetic code, and for which he got the Nobel prize in 1968. We stand on the shoulders of giants.

If the ratio is U:C, 1:5, then it is easy to calculate the probability of encountering each of the 8 possible codons composed by just U and C. So for example:

the probability of UUU is (1/6)3

the probability of UUC is (1/6)2*(5/6)

and so on.

Here's a Table that gives all of the values and demonstrates that the answer is (D).


Douglas & Gunter (1946) The Taxonomic Position of Corynebacterium acnes. J Bacteriol 52: 15-23.

This is the paper, in which Corynebacterium acnes was proposed to be transferred to the genus Propionibacterium as Propionibacterium acnes, and it contains a detailed description of the cultivation procedure.


Why does Anti-A antibodies make type-A blood type clump?

When an antibody meets its antigen it will bind it. So if you add anti-A antibodies to A blood, these antibodies will bind the red blood cells. Because of its Y-shaped form, each antibody can bind two epitopes. These can be located on the same red blood cell, or on two different. If they are located on two different, this leads to cross-linking of two blood cells. Since this happens many times, the blood cells will clump. The whole process is called hemagglutination.

See the image (from here) for illustration:


To have a drug travel to a specific place in the body can you just coat the drug in extracellular signals recognized by target cells?

Yes you can, in principle, coat the drug particle with ligands such that it will be targeted to specific cell types. This is being considered and actively worked upon for development of targeted drug delivery. However, you should also bear in mind that doing these kinds of coatings would be costly and it would greatly increase the cost of the drug.

At least, in the case of cystic fibrosis or any other disease of the lungs, an easy method of restricting the drug to lungs would be by taking the drug in the form of aerosol, using inhalers.

  Mechanism of inhalers.   Picture source: http://www.globalasthmareport.org/management/inhalers.php


Why are there few tetrapod dinosaur carnivores?

Most of the dinosaur predators were derived from a common ancestor. The body plan was efficient, and the theropod predators speciated widely (including birds). But statistically, the number of therapod predators are not independent observations, but linked to each other through ancestry. Similarly, most mammalian predators (Carnivora) are linked to a common ancestor. Both forms worked, but there is not evolutionary pressure to evolve toward a particular form; only pressure to favor successful accidents. So it is an interesting question, but it could be that the predator niche was filled by bipeds in dinosaurs and quadrapeds in mammals.


What am I looking at in Franklin's Photo 51 of DNA?

Well, you said that you...

  ...have a lot of trouble connecting the photo to the diagram.

And that's quite excusable: interpreting that X-ray image is actually very complicated .

All the quotes and images in this answer, except the bulleted list further down, come from this paper (Lucas, 2008), which explains that historic picture in details.

Full disclosure: I always though that that image depicted the X-ray going longitudinally (that is, along the main axis) through the DNA. However, it goes transversely:

  When filamentous macromolecules are packed in a fibre along a fixed direction, the X-ray intensities diffracted by the fibre fall on the observation screen along approximately straight and equidistant lines, the so-called layer lines, perpendicular to that direction. This important concept was introduced by Michael Polanyi in 1921 for the X-ray study of cellulose. (emphasis mine)

These are the so called layer lines (I'm keeping the original paper's legend in all images):

Then, still according to the paper, Crick and others started to hypothesise how would be the X-ray diffraction in a monoatomic helix:

  In 1952, Cochran, Crick and Vand developed an analytical theory for X-ray diffraction by a monoatomic helix. The immediate interest of their theory was to give a transparent, analytical expression of these amplitudes at a time, in 1952, when computers, if at all available, were barely capable of a brute force calculation of the total diffraction intensity.

This is the X-ray diffraction pattern in a monoatomic helix, which is quite important to understand the famous DNA diffraction image later on:

Thus, with that theoretical background, we can understand our famous image:

In this image, a X-ray diffraction image of A-DNA (left) and the more common B-DNA (right) show a periodic pattern in the layer lines.

The diffraction patterns can be interpreted as follows (source):

The layer line separation reveals the value of the polymer repeat period. The decrease of over 20% of the layer line spacing when going from A to B implies an increase of that much in the period: P = 2.8 nm for A-DNA and 3.4 nm for B-DNA.
Look at the sharp, discrete spots observed near the center of the
diffraction pattern for A-DNA along the first few layer lines, these
suggest the crystalline order in the fibre. In the high-humidity
B-DNA pattern, these crystalline spots are absent, suggesting that
the extra water molecules must have invaded the space between the DNA
molecules, freeing them from being locked into crystallites.
The thick arcs at the top and bottom of the B-DNA pattern are found at approximately 10 layer line intervals from the center, implying that B-DNA had 10 repeating units within one period of 3.4 nm. These are produced by the scattering of X-rays by the equidistant, nearly horizontal flat bases separated by 0.34 nm. The A-DNA pattern lacks these big smears, suggesting that the bases in A-DNA are not horizontal, and the number of base pairs per helical period is closer to 11.
The central cross in B-DNA represents the Saint Andrew cross expected
from a helical molecule. The large radius r (1 nm, indicated by the
meridian angle of the cross) and the absence of intensity in the
meridian diamonds indicates that the phosphate backbone is at the
periphery of the helix. This cross appears to be absent in A-DNA,
however, this is due to destructive interference from some of the
inclined base pairs.

Finally, this is an image better relating the double strand structure of the DNA (both A and B DNAs) with the X-ray diffraction image:

Reference:

Lucas, A. (2008). A-DNA and B-DNA: Comparing Their Historical X-ray Fiber Diffraction Images. Journal of Chemical Education, 85(5), p.737.


How are bioremediation microbes removed from the environment once they perform their function?

Usually you have to introduce conditions which hinder multiplication of the microbe such as altering the pH and emperature. This is hard to achieve in a large environment, alternatively, you can introduce a recombinant of that microbe which posses a survivability threat to the initial microbe.


What (open) software there are for simulating behaviour of human (cortical) neurons?

No, not in the neuronal level. Because IMHO this is an ill-posed problem.

EEG combined with MRI won't have the resolution to do solve the inverse problem of neuronal network simulation. There are at least hundreds of thousands of neurons that would be involved in your specific behavioral task, and you have at most a few hundred electrodes. You'll have to make grossly simplifying assumptions about how the brain works to start any inference. You might learn something, but the claim would be somewhat weak.

Personally, I wouldn't work with EEG if you are interested in the mechanistic details of the brain. It is a very useful signal for some cognitive investigations (especially for humans), but the current technology lacks the spatial resolution.

Yes, in the coarse scale network level. (response to @sets' feedback)

If you don't care about micro-scale dynamics of the neural network, and want to model some population-wide activation that changes EEG, as @sets commented, there are various source localization & dynamical system models that you can use. I am not an expert on this, so see @sets' answer, or hopefully someone else will answer in detail.


It's unclear how costly wax production really is, here are a few resources with some guestimates and studies:

http://www.honeybeeworld.com/diary/articles/fdnvsdrawn.htm

http://www.beesource.com/forums/archive/index.php/t-260700.html

http://www.bushfarms.com/beesharvest.htm#expenseofwax

But doubtless wax is costly to make. So it seems totally reasonable that your bees are actually collecting wax. But my guess, if they were doing this in October, is they drawn by the smell and were poking around for something besides wax per se, like sugar or pollen bits in the wax.

I just saw honey bees buzzing around a compost pile on a warm day in January, focusing on orange peels and used coffee grounds. They would masticate both grounds and peels with their mandibles, but to my eyes weren't actually collecting anything (they didn't bring it to their mouths with their tongues nor pack anything on their corbiciculae). I suspect they will pick at lots of things when there's not a strong pollen or nectar flow, maybe for medicinal reasons or the like, or perhaps more "for something to do."


Enzyme inhibitors, reversible or irreversible?

These modes of inhibition are reversible. They describe a particular modification of the working of the enzyme (change of affinity, of maximum velocity, of both) due to the reversible binding of an inhibitor to a particular site.

On the other hand irreversible inhibition stops the enzyme from working through an inhibitor that binds irreversibly : no more transformation of substrate occurs after that. One cannot define a velocity, affinity, or IC50, for a non-working enzyme, thus "competitive", "non-competitive", "uncompetitive" are not useful concepts in that case.


The ELISA kits are calibrated to meet the requirements of
  internationally certified laboratories such as Center for Biologics
  Evaluation and Research (CBER) associated with FDA. In most cases, the
  presence and specific concentration of an antigen is the target for
  the measurements. Such measurements take place through the absorbance
  measurement. According to the Beer-Lambert law, concentration of a
  compound in a solution is proportional to the quantity of light
  absorbed at its specific wavelength and at a constant optical
  path-length. Following absorbance measurement, a simple calculation
  leads to the concentration of the analyte via equation below:

  A = εlc

  where A is absorbance of the solution at the particular wavelength, ε
  is the molar absorptivity, l is the path length or deepness of the
  solution (which for common spectrophotometers is 1 cm or less if a
  well plate reader is employed), and c is the concentration of the
  substrate in the reaction solution (M or mol/dm3). Majority
  of ELISA microplate readers correspond to this method for the
  calculation of the analyte concentration. Introducing standard
  solutions with known analyte concentrations can be the basis of such
  calculations for the instrument. Measurement unit are typically
  expressed in gram per liter (g/l), mol per liter (mol/l) and copies
  per milliliter (copies/ml) for nucleotides, plaque forming units per
  milliliter (pfu/ml) for infectious viruses, colony forming units per
  milliliter (cfu/ml) for cells and phages, as well as unit per
  milliliter (u/ml) for specific enzymes. These measurement units can be
  also reported over time (i.e. ng/ml/h). Relative units (RU/ml) or
  equivalent units (Eq/ml) are normally used if no international
  standard references exist for the conversion.

Source: Enzyme-linked Immunosorbent Assay (ELISA): From A to Z (SpringerBriefs in Applied Sciences and Technology) by Samira Hosseini, Patricia Vázquez-Villegas, et al. | Dec 31, 2017; ISBN-13: 978-9811067655; ISBN-10: 9811067651. Page 65.

Contrary, Units Per Millilitre (U/mL) are normally used when there are international standard references, agreed upon by scientists and doctors.

Please find the following information on Arbitrary Units (AU):

  The WHO Expert Committee on Biological Standardization (ECBS) is the
  scientific body that establishes WHO biological reference standards
  which serve to define an internationally agreed unit to allow
  comparison of biological measurements worldwide. WHO International
  Standards (IS) for biological substances are recognized as the highest
  order of reference materials for biological substances and they are
  assigned potencies in International Units (IU). International
  Standards are used to quantify the amount of biological activity
  present in a sample in terms of the IU, allowing comparisons between
  assays from different laboratories.  Calibration of assays using
  System of Units (SI)units is not appropriate given the variability of
  the specific activity of a biological material. Use of an arbitrary
  unit makes it possible to better define parameters such as the
  analytical sensitivity of tests or clinical parameters such as
  protective levels of antibody.

Usually, arbitrary units are defined within a particular study. For example:

  A human serum pool positive for anti-dsDNA IgG antibodies (DNAPo) was
  prepared by mixing equal parts of serum from 10 patients with SLE who
  had a positive CLIFT test ≥ 1:640. The DNAPo was arbitrarily designed
  as having 100 arbitrary antibody units (AU) per ml. Artificial serum
  standards containing different AU/ml were prepared by diluting the
  DNAPo with phosphate-buffered saline (PBS) containing 0.1% Tween 20
  (PBS-T). A human serum pool negative for anti-dsDNA IgG antibodies
  (DNANe) was prepared by mixing equal parts of serum from 10 healthy
  persons who had a negative CLIFT test and no clinical evidence of SLE.
  The DNAPo and DNANe pools were used as positive and negative controls
  in the ELISA and CLIFT reactions.

Thus, the U/mL figures are comparable, while RU/ml or AU/ml are not.


How could pharmaceutical metabolizing bacteria be used to synthesize them?

In principle yes, because almost all reactions exist in an equilibrium and enzymes are catalysts (meaning they affect mainly the activation energy for a specific reaction).

The best approach is to purify the enzymes in question (probably not even from their original species, but from another transgenic bacterium), since this reduces your total systems from two different organisms and all their enzymes and metabolites to a very specific set of two enzymes and a handful of metabolites.

But ...

The natural equilibrium of the reaction might favor the more basic metabolites.
Even if you speed up the generation of "P", that won't help much if only a fraction is converted in the first place. Many biological systems deal with this problem by coupling unfavourable steps with hydrolysis of high energy molecules (like ATP), but linking to this to a given enzyme would be a very challenging process.
The enzyme(s) might not be overly specific for "P".
Many enzymes that remove or break down 'unwanted' products can act on multiple similar molecules. This make sense for cells, since its much more efficient to have one enzyme taking care of multiple 'bad things'. However, this makes reversing the process tricky since your reaction will yield multiple products of which only one is interesting. Optimising the enzyme to be more specific is relatively straight forward using in vitro evolution though.
'Basic metabolites' is not a clearly defined thing.
Many pharmaceuticals are pretty complex, and enzymes that break them down likely literally chop it into two pieces. Whether these pieces are actually easy to obtain chemicals that can be used as input for an enzymatic reaction is a different question. Depending on the complexity of 'P' you might have to use a pretty large number of enzymes (all of which may come with the above mentioned problems) to be able to start from reasonably basic molecules.


Controlling for phylogenetic signal - what is statistically appropriate?

I have a tentative answer to my question.

In the publication for the R package phylosignal (Keck, 2016), they state:

  To test the presence of phylogenetic signal, the null hypothesis is that trait values are randomly distributed in the phylogeny. Another null hypothesis might be that trait values follow a Brownian motion model but it is less often used and implemented.

So it would seem there are indeed two null hypotheses, however it seems disingenuous to not test for both, especially since it is relatively simple to do. And of the two, should you for some reason be limited to one, testing for $\lambda = 0$ (or the log-likelihood of a general linear model) should be your first choice.

Another issue was with corPagel from the package ape. It requires an initial value to estimate Pagel's $\lambda$ (Unless you fix the value, of course). Convergence is not guaranteed, and sometimes requires some fine-tuning of the initial value. This set off a bit of a red-flag, so I collected the median lambda from 5000 subsets of my data (I am using a different family/phylogeny/dataset, where the $\hat{\lambda}$ model is preferred over a Brownian, but not $\lambda = 0$).

I think this is stronger argumentation for a phylogenetic signal, albeit a weak signal.

#output of comparing lambda=0, lambda=1, and median lambda

                 Model df      AIC      BIC    logLik
pglsModel_0          1  3 636.9876 643.6899 -315.4938
pglsModel_1          2  3 681.9784 688.6807 -337.9892
pglsModel_MEDIAN     3  3 636.4812 643.1836 -315.2406

#output of comparing median lambda to the estimated lambda from the full dataset

                 Model df      AIC      BIC    logLik   Test   L.Ratio p-value
pglsModel_MEDIAN     1  3 636.4812 643.1836 -315.2406
pglsModel_FULL       2  4 637.7024 646.6388 -314.8512 1 vs 2 0.7788265  0.3775

By the evidence of the distribution of lambdas, and the median model's AIC, BIC, and log-likelihood, we can argue in favor of the median model. Residuals also look normal and random. Confidence intervals should also be obtained. But, in reality, here, the difference here between a weak phylogenetic signal and none at all are vanishingly small.


Questions about insects eating flour and living in the cracks of a wooden cutting boards

This is a species of psocid (of the family Psocoptera, AKA booklice).

Species are best differentiated by their abdominal structure and antennae. Without a better (more magnified) image and info about the OP's location, identification to species is not possible. Though its small size will definitely narrow the options.
If I had to guess based on the limited detail (and my limited knowledge), I'd say it's a species in the genus Liposcelis or related genera.  See Oklahoma State's ENTOPLP ID key for examples and info. I've included 2 random images of Liposcelis species for reference: the (TOP) is Liposcelis brunnea and (BOTTOM) is Liposcelis corrodens.
You can find info about prevention and removal from Oklahoma state, King's College London or the Orkin man.
UPDATE: Though I, myself, cannot identify this psocid to species, according to this KCL article, Liposcelis bostrychophila is the most likely candidate. [See here for ID notes].

From the KCL article:

Liposcelis bostrychophila is the principal psocid pest species in the UK and in Europe. This single species, which is mainly an inhabitant of households, is responsible for virtually all of the psocid related complaints in the UK (Turner & Ali 1996)...Recent estimates are that 30% of households contain this species.
Several other small (about 1mm long), flattened, wingless liposcelid species (eg. Liposcelis corrodens, L. pearmani and L. brunnea) can become quite common in industrial sites, particularly in the summer months but are uncommon in domestic premises.


What is the length and number of the parts of a conformational epitope?

I found this paper that analyzes 111 antigen-antibody x-ray crystal structures.

They find that in their dataset that "the large majority of Ag structures (86.5%) have CRS ranging from 20 to 229 aa." CRS stands for contact regions span which they define as "the minimum contiguous amino acid sequence containing all CRs of an Ag or Ab and represents the size of a complete structural epitope or paratope, inclusive of CR and the minimum set of supporting residues required for proper conformation." further on they mention that "The average and median CRS of all 111 Ag structures is 125 and 95 aa, respectively." and "the majority of the structures do indeed have epitope CRs that are clustered together in short, contiguous stretches ranging from 2 to 12 aa, longest contiguous sequence of CR being 5"

I hope this helps.


Since you didn't get the right answer to #6, let's review the basis for this model.

The basis of the model is precisely the statement in #6: an individual channel has a defined conductance when it is conducting, and if all of them are conducting then the total conductance is the single-channel conductance times the number of channels (conductances in parallel add).

Confusion comes from the fact that, in the terminology used here, an "open" channel is not necessarily conducting. The probability that a single channel is conducting is the product $r^{n_1}s^{n_2}$.

$r^{n_1}$ describes the "activation" process, while $s^{n_2}$ describes the "inactivation" process. These are two separate processes, with the activation process now known to be driven by voltage-dependent changes in the configuration of transmembrane domains of the channel protein, while the inactivation process is a charged part of the protein inside the cell that more slowly moves to block the channel when the cell is depolarized. See this summary by Clay Armstrong, who contributed much to our understanding of these processes.

So in the terminology here, a channel can be "open" (in terms of the $r$ process) but still "inactivated" (through the $s$ process) and thus be non-conducting. It can also be "closed" (in terms of the $r$ process) and "inactivated", in which case it is also non-conducting.

The way this is modeled is that $s=1$ means no inactivation, while $s=0$ means fully inactivated. So that covers question 3. This is in contrast to the activation process, in which $r=1$ is full activation.

Question 4 might be a bit misleading as there may be a hidden assumption that you are starting from being at potential $u_0$ for a long-enough time and then increasing $u$ suddenly (which is how action potentials normally are generated). In that case your understanding of differential equations should make it clear that $\tau_r$ and $\tau_s$ are the time constants for the responses of the $r$ and $s$ processes to a change in $u$. The process with a shorter time constant responds more quickly, so activation precedes inactivation (on the average). The different powers associated with the $r$ and $s$ processes ($n_1,n_2$) might confuse things a bit, but try running this model and you should be convinced.

This might seem convoluted at first, but Hodgkin and Huxley worked out this model based solely on their electophysiological studies (with help from Bernard Katz). It's amazing how this model's postulates of multiple single channels and separate activation and inactivation processes have been so well verified at the molecular level over the past decades.


Does a certain tumor type being invasive mean that it is highly metastatic?

They are different things, metastasis is not the same as invasion.

Highly invasive cancer means that the tumor spreads beyond the layer of tissue in which it developed and is growing into surrounding, healthy tissues.

Highly metastatic tumors cause secondary malignant growths at a distance from a primary site of cancer.

In order for tumors to become metastatic, they must move and grow beyond their initial location and reach the circulatory system in order to travel to another location. During this phase, depending on the tumor, the malignant growth will acquire the ability to escape (tumors cannot do this at first, because it requires genetic mutations in genes responsible for cell adhesion). There are many other known factors which co-occur in cancer development - it is a rich field of research.

Here is an informative website for you to browse about the medical side of it: http://edcan.org.au/edcan-learning-resources/supporting-resources/biology-of-cancer/defining-cancer/invasion-metastasis


Defining Resistance and Sensitivity Thresholds for IC50

IC50 has an exact definition, that is half-maximal inhibitory concentration. To get this you need to make a series of dilutions of each of your drugs and test your cell lines in each concentration and assess how they grow (eg measure Optical density, colony size, cell count or whatever applies to your cells and setup). Then you can set up a inhibition curve that is a function of the concentration. This most likely looks like a sigmoid curve and the inflection point gives you the IC50 value.

An example image form the wikipedia page of IC50 (recommended to read):

So in your case, you already have IC50 values (for wild types I assume), and you need to assess your (probably mutant ?) cell lines to see if they are sensitive or resistant. Standard lab practice recommends using wild-type controls and at least 3 parallel controls. You can compare the average growth of WTs to your cell line at the IC50 concentration you already got in your dataset, and calculate standard deviation, and 95% confidence interval. With some simplification if the 95%CI of your cell line is greater than the wt 95%CI it is most likely that your cell line is resistant to that drug, on the other hand if its lower then that cell line is sensitive to the drug in question.


No, I don't think auto-regulation explain much in the population sizes of predators. Group selection may explain such auto-regulation but I don't think it is of any considerable importance for this discussion.

The short answer is, as @shigeta said

  [predators] tend to starve to death as they are too many!

To have a better understanding of what @shigeta said, you'll be interested in understanding various model of prey-predator or of consumer-resource interactions. For example the famous Lotka-Volterra equations describe the population dynamics of two co-existing species where one is the prey and the other is a predator. Let's first define some variables…

$x$ : Number of preys
$y$ : number of predators
$t$ : time
$\alpha$, $\beta$, $\xi$ and $\gamma$ are parameters describing how one species influence the population size of the other one.

The Lotka-Voltera equations are:

$$\frac{dx}{dt} = x(\alpha - \beta y)$$
$$\frac{dy}{dt} = -y(\gamma - \xi x)$$

You can show that for some parameters the matrix for these equations have a complex eigenvalue meaning that the long term behavior of this system is cyclic (periodic behavior). If you simulate such systems you'll see that the population sizes of the two species fluctuate like this:

where the blue line represents the predators and the red line represents the preys.

Representing the same data in phase space, meaning with the population size of the two species on axes $x$ and $y$ you get:

where the arrows shows the direction toward which the system moves. If the population size of the predators ($y$) reaches 0 (extinction), then $\frac{dx}{dt} = x(\alpha - \beta y)\space$ becomes $\frac{dx}{dt} = x\alpha \space$ (which general solution is $x_t = e^{\alpha t}x_0$) and therefore the populations of preys will grow exponentially. If the population size of preys ($x$) reaches 0 (extinction), then $\frac{dy}{dt} = -y(\gamma - \xi x)\space$ becomes $\frac{dy}{dt} = -y\gamma \space$, and therefore the population of predators will decrease exponentially.

Following this model, your question is actually: Why are the parameters $\alpha$, $\beta$, $\xi$ and $\gamma$ not "set" in a way that predators cause the extinction of preys (and therefore their own extinction)? One might equivalently ask the opposite question? Why don't preys evolve in order to escape predators so that the population of predators crushes?

As showed, you don't need a complex model to allow the co-existence of predators and preys. You could describe your model a bit more accurately in another post and ask why in your model the preys always get extinct. But there are tons of possibilities to render your model more realistic such as adding spatial heterogeneities (places to hide for example as suggested by @AudriusMeškauskas). One can also consider other trophic levels, stochastic effects, varying selection pressure through time (and other types of balancing selection), age, sex or health-specific mortality rate due to predation (e.g. predators may target preferentially young ones or diseased ones), several competing species, etc..

I would also like to talk about other things that might be of interest in your model (two of them need you to allow evolutionary processes in your model):

1) lineage selection: predators that eat too much end up disappearing because they caused their preys to get extinct. This hypothesis has nothing to do with some kind of auto-regulation for the good of species. Of course you'd need several species of predators and preys in your model. This kind of hypothesis are usually considered as very unlikely to have any explanatory power.

2) Life-dinner principle. While the wolf runs for its dinner, the rabbit runs for its life. Therefore, there is higher selection pressure on the rabbits which yield the rabbits to run in average slightly faster than wolves. This evolutionary process protects the rabbits from extinction.

3) You may consider..

more than one species of preys or predators
environmental heterogeneity
partial overlapping of distribution ranges between predators and preys
When one species is absent, the model behave just like an exponential model. You might want to make a model of logistic growth for each species by including $K_x$ and $K_y$ the carrying capacity for each species.
Adding a predator (or parasite) to the predator species of interest

... and you might get very different results.


What's the longest intron in the human genome?

Short answers: Based on gencode the longest intron for protein coding transcripts, is the intron 3-4 of the transcript ENST00000465127.1 with 1,240,120 bases.

Long answer:

Download the main annotation gff3 file from gencode first. Then run this script:

zcat gencode.v29.annotation.gff3.gz \
| awk -v FS="\t" -v OFS="\t" '$3=="exon" && $9 ~ /transcript_type=protein_coding/ {match($0, "transcript_id=([^;]+)", transcript); print $0,transcript[1]}' \
| sort -k1,1V -k10,10 -k4,4g -k5,5g \
| awk -v FS="\t" -v OFS="\t" 'BEGIN {transcript=""; max_d=0; max_line=""} \
 $10 != transcript  {transcript = $10; end = $5; next;} \
 {d=$4-end-1; end=$5; if(d>max_d) {max_d=d; max_line=$0"\t"d;}} \
 END {print max_line}'

It will:

decompress the file
filter only lines that describes exons of protein coding transcripts
append the transcript id at the end of the line
sort by chromosome, transcript and position
calculate the distance between two exons of the same transcript
save the current maximum distance and the annotation line that belongs to it
print out the line of the exon which follows the longest intron. The last column contains the distance.

The output will be:

chrX    HAVANA  exon    38666121    38666309    .   +   .   ID=exon:ENST00000465127.1:4;Parent=ENST00000465127.1;gene_id=ENSG00000250349.3;transcript_id=ENST00000465127.1;gene_type=protein_coding;gene_name=AF241726.2;transcript_type=protein_coding;transcript_name=AF241726.2-201;exon_number=4;exon_id=ENSE00003644052.1;level=2;protein_id=ENSP00000417050.1;transcript_support_level=5;tag=basic,appris_principal_1,readthrough_transcript;havana_gene=OTTHUMG00000161009.1;havana_transcript=OTTHUMT00000363378.1  ENST00000465127.1   1240120

fin swimmer

EDIT:

gencode also includes transcripts that are only predicted, but never seen, or those where it is not sure that they exists. So you maybe want to just include transcripts having a certain transcript support level. For this you have to change the first awk line. E.g. to include only trasncripts with TSL1 change it to:

awk -v FS="\t" -v OFS="\t" '$3=="exon" && $9 ~ /transcript_type=protein_coding/ && $9 ~ /transcript_support_level=1/ {match($0, "transcript_id=([^;]+)", transcript); print $0,transcript[1]}'

You can also define a range, e.g 1-3:

awk -v FS="\t" -v OFS="\t" '$3=="exon" && $9 ~ /transcript_type=protein_coding/ && $9 ~ /transcript_support_level=[1-3]/ {match($0, "transcript_id=([^;]+)", transcript); print $0,transcript[1]}'

In both cases the result will change to intron 4-5 of ENST00000402655.6 with 1096453 bases.


What is the name of the appendage that mantis shrimp use to rub their eyes?

Mantis shrimp use their first maxillipeds for grooming (maxilliped=modified appendage), which is specialized for this purpose. Details and a picture of the organ can be found in the link. The second maxilliped is their famous specialized organ for striking or spearing prey with enormous force. More about their raptoral appendage, with links to further references. Mantis shrimp also have very complex eyes, described as "...the most complicated visual system of any animal on Earth". Their eyes can see a range from ultraviolet to infra-red and has specialized regions for observing motion, form, depth, and color. More on Mantis shrimp eyes can be found in Marshall & Oberwinkler (1999).


Is there any program or website that allows me to create phylogenetic trees without having to insert an already created dataset?

Have you ever tried using Mesquite?

I used it when I taught intro bio lab course as a TA.
Basically you try to come up with yes or no questions, and based on how you answer these questions it will generate a tree similar to your second link.

Can be a little bit complicated, but I am sure there are many tutorials for it on its website.


Number and function of voltage-independent ion channels

Those would be referred to simply as 'ion channels'. Voltage-dependent channels should get special mention.

This link from the Blue Brain Project should prove useful:

  http://channelpedia.epfl.ch/ionchannels/

A query for 'voltage-dependent' gives me results from 58
channels. According to the original
reference
there should be at least 187 channels in this database (out of the
estimated ~500 ion channels expressed in the brain). That should
give you at least the order of magnitude.

An additional resource can be: https://icg.neurotheory.ox.ac.uk/.
'Non-voltage gated' channels are used to depolarize and
hyperpolarize the membrane. That is as much as specific the answer can be when talking about all of the ion channels. Their specific compartment localization makes a big difference, though. Those that are located in synapses have different functions compared to those that are non-synaptic, for example.

No, voltage-dependent channels are not
solely found in the axon. That is what makes dendrites active
compartments as well. See this very good
review
and my
answer
to
How does a neuron change as you learn?.
In brief, voltage-gated channels facilitate the integration of
inputs on the dendrites independently from the integration at the
level of the soma. This is thought to extent the information
processing capacity of the
neuron.

PS: A lot of your questions suggest that you would be interested in the NEURON simulation environment, as previously suggested to you by @bryan-krause. Using this tool could save you some trouble of trying to re-invent the wheel, as you can start off by using a pre-existing model from ModelDB.


The issue here can be explained by colligative properties:

In short, colligative properties are properties that depend upon the concentration of the molecules, but not upon the type of those molecules. And osmotic pressure is a colligative property.

That being said, have in mind that 10 molecules of a huge polysaccharide in a given volume or 10 molecules of a small monosaccharide in the same given volume will have the same osmotic pressure.

So, let's imagine that the cell needs a given amount of glucose. Let's say 1000 molecules, just for an oversimplified example. The osmotic pressure can be calculated by (assuming Van't Hoff factor is 1):

  π = MRT

Where M is the molar concentration, R is the ideal gas constant and T is the absolute temperature.

Thus, the osmotic pressure created by those 1000 molecules can be calculated taking into account their molar concentration.

However, If we simply polymerize those 1000 glucose molecules into a huge polysaccharide (be it starch, glycogen or cellulose, it doesn't matter)...

  1000 molecules of glucose → 1 molecule of starch

... the molar concentration will be 1000 times smaller, and the osmotic pressure will drop 1000 times.

As the actual numbers are way higher than this simplified example, we can clearly understand that cells avoid storing glucose to prevent a deadly osmotic pressure, in which the cell bursts due to the amount of water that moves in (specially animal cells, which have no walls that could prevent the membrane from rupturing).


Why is the GenBank entry for the genomes of RNA viruses like coronavirus written as DNA?

It would appear that the current policy at GenBank is to represent all genomic sequences as DNA, even though this is not made explicit in any of the easily retrievable documentation on their website or in their publications. The way in which one determines the nature of the genome is from the first (LOCUS) line in the report, which, in the case of SARS-CoV-2 is:

LOCUS       NC_045512              29903 bp ss-RNA     linear   VRL 30-MAR-2020

The ss-RNA indicates a single-stranded RNA genome.

The only indication I could find that this might be GenBank policy was a reference to the fact that a separate RNA division of GenBank used to exist, but was discontinued in 1999:

  The RNA division of GenBank was removed in release 113.0 (August 1999). Sequences that were previously in the RNA division have been moved to the appropriate organismal division.

The genomes of certain RNA phages were originally sequenced as RNA (as were bacterial rRNAs and 18S rRNA) — that of phage MS2 was the first in 1976. The GenBank entries for these are currently in DNA format. Thus V00642.1 contains references to the 1976 MS2 paper by Fiers et al., but is in DNA format. As GenBank was not established until 1982 it is not clear whether the original entry in GenBank was in RNA or DNA format.

UPDATE 15.04.2020

I wrote to GenBank to ask them whether there was a specific policy and if so when it had been introduced. I had a reply from someone on their help desk to the effect that GenBank policy was that of International Nucleotide Sequence Database Collaboration, the shared rules of which includes table "7.4.1 Nucleotide base codes (IUPAC)". This states that "t" can represent thymine in DNA and  uracil in RNA. However my respondent could not find out whether this representation was used in GenBank for RNA viruses from the beginning or introduced later. As originally GenBank releases were on CD-ROM this is difficult to check.


Bilirubin metabolism and UGT1A1 inhibition in human vs. monkey?

UGT1A1 is common among most mammals that produce bilirubin (rats too). I haven't seen any evidence to suggest an alternate pathway in mammals of bilirubin metabolism. Look into gilbert's syndrome patients (they have a SNP which is a mutuation that causes UGT1A1 to be less efficient and thus a rise in plasma unconjugated bilirubin).

Also, bilirubin analysis from plasma/tissues is fairly straight forward with HPLC or LCMS assays. UGT1A1 inhibition in vitro however, is still a challenge. I think the trick will be to use CRISPR to knock it out.


Reason for Migrating Birds:

Migrating for a Meal

Food scarcity: If birds will stay and eat all the bees they find, there will be no bees to feed on for the next generation. So, they fly away to take advantage of these places where there is abundance of food. And when the insects or replenish they come back.

Migrating for Family

Flocks of bird can be easy prey as they are small and are in a huge number. Also for the young ones who can't fly it is very dangerous. (Though they abandon the young one as soon they can fly)

Migrating for better Climate:

Some of the species fly away to sustain the temperature and come back when it is suitable for them. So, Northern birds migrate to tropical places for the winter and come back to north for the summer to lay eggs.

There are too many other theories for bird migration. It had been confirmed that migration is often due to genetics than any other reason.

Further Reading: THEORIES OF BIRD MIGRATION

Sources:

[1] http://birding.about.com/od/birdbehavior/a/Why-Birds-Migrate.htm

[2] http://en.wikipedia.org/wiki/Bird_migration


What happens to dextrorotatory amino acids in humans?

For the most part they are not used.  there are amino acid racemases, which interconvert L- and D- forms of some specific amino acids, which may be used in some particular biosynthetic or metabolic pathways.

In particular I'm thinking of firefly luciferase which uses D-Cysteine as a re-dox reagent to regenerate the luciferin substrate that the light - generating enzyme consumes.  see slide 4 here.

Nonribosomal peptide synthases often use D amino acids too.

I don't believe that d- amino acids are really incorporated into proteins though.


Dexamethasone can be used to regulate PGC-1β
http://www.ncbi.nlm.nih.gov/pubmed/20647557


How are behaviors genetically coded in animals?

I will focus my answer on the evolution of orb web spiders (Fig. 1), because arachnids, like insects, are relatively non-complex creatures with an obvious systematic behavior (the weaving of highly symmetric, repeating structures). Hence I reasoned this would be a good approach of investigating the answer to this question.

The orb-weavers (Orbiculariae, an informal name for the taxon containing the superfamilies Deinopoidea and Araneoidea) are among the most diverse lineages of spiders (Hormiga & Griswold, 2014).

Fig. 1. Orb web. Source: Encyclopedia Britannica

Bond et al. (2014) report that according to various estimations the date of the origin of the orb web was somewhere around the Lower Jurassic 201 million years ago and 52 million years ago, and the authors support the view of a monophyletic origin of the orb web.

Despite this excellent opportunity to elucidate the (monophyletic) genetic underpinnings of the evolution, and hence the passing down of orb weaving, are largely unknown. Not only is the evolution of orb weaving unknown, even the evolution of the intricate physical properties of the silk are largely unknown. For example, how the sticky glue araneoid orb web evolved is a mystery. This complex multicharacter trait is hypothesized as a key innovation to explain the increase in diversity of araneoidea. For example, araneoid orbs contain viscous adhesives and they have low UV-reflectance and are hence less visible to insects. There exists one proposed evolutionary pathway from the dry, cribellate orb web of deinopoids to the sticky glue orb web of araneoids, but that's about it (Hormiga & Griswold, 2014).

In all, physical properties of silks contained in webs are hardly explained in evolutionary terms, let alone the behavioral profiles necessary to make a web out of it.

References
- Bond et al., Curr Biol (2014); 24(15): 1765–71
- Hormiga & Griswold, Annu Rev Entomol (2014); 59:487–512


How to categorize non-coding sequences in the genome?

Here's my likely over-simplified and incomplete categorization I could come up with. Please, for those more informed, please edit!

Noncoding sequences can be broken up into the following categories:

1. RNA genes (genes that produce tRNAs, rRNAs, miRNAs, etc.)
2. Introns and untranslated regions (UTRs)
3. Cis- and trans-regulatory elements
    3.1. Cis-regulatory elements (CREs)
        3.1.1. Promoters
        3.1.2. Enhancers
        3.1.3. Silencers
    3.2. Trans-regulatory elements (genes for transcription factors)
4. Repetitive elements (repeated sequences, repeats)
    4.1. Long terminal repeats (LTRs)
    4.2. Variable number tandem repeats (VNTRs, tandem repeats)
        4.2.1. Satellites
        4.2.2. Minisatellites
        4.2.3. Microsatellites (short tandem repeats, simple sequence repeats)
    4.3 Transposable elements (TEs, transposons)
        4.3.1. Retrotransposons (Class I TEs)
            4.3.1.1. Short interspersed nuclear elements (SINEs)
            4.3.1.2. Long interspersed nuclear elements (LINEs)
        4.3.2. DNA transposons (Class II TEs)
    4.4. Telomeres
5. Pseudogenes


The reason why researchers usually use cell lines from "blast cells"?

Some of the reasons why immature blast cells are studied:

They have self renewal ability
Can be differentiated to different types of cells
Serve as model for studying development: this is quite pertinent to the gene expression studies. Because it is important to know what changes take place during differentiation and simply looking at the differentiated state won't explain that process.


What is it that specifically kills ALS patients?

TL;DR Ventilation assistance does help but there are other factors.

ALS is a degeneration of the motor neurons, replacing them with hard material (i.e. sclerosis). The neurons innervating the muscles involved in respiration deteriorate and so it becomes difficult to breath, swallow, cough, etc. There's a compounding effect: we know that blood O2 and CO2 levels are balanced by respiration, O2 in/CO2 out, and we know that swallowing/coughing clear secretions from the respiratory tract. Basically you end up with trouble oxygenating your blood and riding it of CO2, hence CO2 poisoning. This can result in what is known as type 2 respiratory failure, characterized by low oxygen and high carbon dioxide levels (hypoxemia and hypercapnia). Low secretion clearance in the lungs can also result in aspiration pneumonia in addition to poor gas exchange.

Ventilation methods such as BCV are capable of mechanically assisting breathing, and maintaining ventilation-perfusion state in a way that prevents respiratory failure, but unfortunately doesn't slow the progression of the disease. The problem then is that in, for example, particularly severe cases, the quality of life has to be taken into account because tongue movement, gross movement in general due to motor neuron degeneration just fails. This can lead to what is essentially locked-in syndrome (LIS), and clinical outcomes can widely vary, though in this article they report the more pleasant outcomes of patients experiencing LIS. I think that for these cases due to poor communicaton patient-reported outcomes are fairly underreported, but family and caregivers at this point have to take into serious consideration options such as euthanasia. Cutting off ventilation can result in type 1 or 2 respiratory failure.

Now, I lack actual data for this statement but I should also point out that not all patients receive the same care or opt for assisted ventilation, in which case late-stage ALS would invariably lead to total neurodegeneration to breathing muscle innervation, where assisted ventilation becomes a requirement for survival.


How do other primates treat their fingernails?

Both. Many primates bite their nails. Some do not. It's not species specific.

Chimpanzees, for example, usually bite their nails; some prefer to leave them alone. They do grow continuously, and nails not kept trimmed probably break off (looking at the thickness of those nails, I get the impression that this might be rather painful if they break too short.)

The caption for the picture below (taken from a chimpanzee sanctuary) states

  Annie likes to pick Missy’s nose. Missy tolerates it but doesn’t seem to enjoy it. I don’t blame her – Annie doesn’t bite her nails down like the other chimps do.

They bite their toenails as well.


How is the flicker fusion rate interpreted as an on-off rate?

Short answer
A light flashing at 50 Hz means there are 50 flashes of light per second. The unit hertz means cycles per second. 1 cycle is the time it takes for the light to be ON and OFF. 50 Hz cycle time means a cycle time of 20 ms. If light ON and OFF times are equal, then the ON and OFF times are both 10 ms each.

Background
The unit hertz (Hz) is a frequency unit in the International System of Units (SI) and is defined as

  one cycle per second

A textbook example where the unit Hz finds its use is a sine wave (Fig. 1). 1 cycle per second would be 1 Hz. A cycle, as can be seen in the figure, is a complete wave consisting of a positive phase followed by a negative phase.

In the right panel of figure 1, a square wave is shown. Again, a cycle time is the time it takes for one complete square wave to develop, i.e. a positive phase and a negative one.

Fig. 1. Sine wave (left) and square wave (right) sources: Simon Fraser University, Integrated Publishing

Now, in your example, you can depict the lights ON as the positive block in the square wave in Fig. 1 above, and the lights OFF as the negative phase. Together (light ON + OFF phases) they form one cycle. So a light pulsing at 50 Hz means it flashes ON and OFF 50 times.

The cycle time then is 1 second/50 = 20 ms. If we assume that the ON and OFF times are equal, which they obviously do not have to be, then light ON is 10 ms, lights OFF is 10 ms.

On the wikipedia page on the hertz unit, a flashing light example is actually illustrated and explained.


Yes, photosynthesis provides nearly all of the ocean's biomass and energy. The best is to study oceanic food chains and webs.

It looks like there aren't enough plants in the ocean because they don't form huge reserves of foliage and branches... 10% of land plant mass dies and is replaced every year, and 100% of oceanic plants die and are replaced every week:
"Biologists set out to estimate the total biomass of phytoplankton and calculated that less than one billion tonnes of the single-celled microorganisms were alive in the ocean at any one time. There were 45 billion tonnes of new phytoplankton each year, 45 times more than their own mass at any given time. The phytoplankton would therefore have had to reproduce themselves entirely, on average, 45 times a year, or roughly once a week. In contrast, the world's land plants have a total biomass of 500 billion tonnes, much of it wood. The same calculations showed that the world's land plants reproduce themselves entirely once every ten years.
https://www.nature.com/articles/483S17a
Every step of the marine energy pyramid has about 10% efficiency, so the shrimps and zooplankton take about 10% of the phytoplankton energy, then small fish 1%, and big fish 0.1% of the plant energy.
For every 500,000 units of radiant energy that hit the ocean surface, 2% is converted to phytoplankton at the base of the trophic pyramid, and the 500,000 units are necessary for every 10 units that a tuna consumes. https://oceantracks.org/sites/oceansofdata.org/files/energytransfer.png
https://en.wikipedia.org/wiki/Marine_food_web
In biology, we typically study coccolithophores and phytoplankton to learn the marine food web.
Areal map of sea chlorophyll in Mg/m2.


Nice photo - Yes, I would agree with you the bands on the tail and the wings suggest that it is a Honey Buzzard Pernis apivorus

http://www.birdwatch.co.uk/categories/articleitem.asp?item=995
http://www.gobirding.eu/Photos/HoneyBuzzard.php

Photo by Dominic Mitchell http://www.birdingetc.com


What would be the molar concentration of human DNA in a normal human cell?

I will assume that be "molar concentration of DNA", you mean "molar concentration of nucleotides".

Cell size

Assuming a cell with a radius of 0.05 nm (nanometer). Assuming a perfect sphere, the volume of such sphere is $5.24\cdot 10^{-4} nm^3 = 5.24\cdot 10^{-11} L$.

Of course, cell volume varies a lot among different cell types. The final result will drastically depend upon the cell volume considered (Thanks to @Roland fo highlighting that in the comment).

Number of nucleotides

In the nucleus

The haploid genome size is about $3.2\cdot 10^9$ nucleotides. The whole genome is therefore $6.4\cdot 10^9$ nucleotides. As there are two DNA strands at each haploid genome, we have to further multiply by two to get $1.28\cdot 10^{10}$ nucleotides. Note that this number could be lower at other moment of the life cycle. What I am computing here is the maximal number of nucleotides per nucleus

In the mitochondria

There are about 1500 mitochondria per cell. Each mitochondria contains about 16,000 nucleotides resulting in a total of $2.4\cdot 10^7$ nucleotidesin mitochondria per cell.

mitochondria + nucleus

$2.4\cdot 10^7 + 1.28\cdot 10^{10} ≈ 1.28\cdot 10^{10}$. That is mtDNA is negligible.

In the mols

The Avogadro number is about $6 \cdot 10 ^{23}$. Hence, there are $\frac{1.28\cdot 10^{10}}{6\cdot 10^{23}} = 3.2\cdot 10^{-14}$ mols of nucleotides per cell.

Molarity

Molarity is defined is defined as the number of mols per liter. It is therefore

$$\frac{3.2\cdot 10^{-14}}{5.24\cdot 10^{-11}} ≈ 0.0006M$$


The overall body shape of the second dragonfly looks like a Libellula (Skimmer), due to its broad flat abdomen. It also seems to have distinct bands on all four wings, dark patches where the wings connect and a light blue abdomen, which would indicate e.g. a male Plathemis/Libellula lydia (Common whitetail). The picture is blurry though, and there might be other similar species that it can be mistaken for (the wiki page mentions a couple of similar species).

(picture from wikipedia)


To me that looks like a "hover fly" — specifically the Pellucid Fly (Volucella pellucens).

Compare with:

This species is present throughout Europe according to the wikipedia article (first link).


What is the smallest touch sensation that a human can feel?

Short answer
The detection threshold of static indentation stimuli on the palm of the hand is approximately 10 to 40 micrometers, dependent on the exact location under investigation.

Background
The sensitivity of a sensory system can be expressed as the detection threshold. This threshold is in psychophysics generally defined as that stimulus level where, on average, 50% of the stimuli are felt.

There are several types of touch receptors that can be investigated, each with specific types of stimuli that are associated with them (Fig. 1)

  Fig. 1. Skin receptors and their response properties (Delmas et al. (2011)

The question specifically targets detection thresholds of indentation stimuli. These stimuli are typically expressed as distance units (meters) or pressure units (Newtons). The skin indentation under constant force is dependent on the area of contact, skin type and other parameters of lesser importance. Hence, indentation units using distance metrics may be the most appropriate way of expressing static detection thresholds. Johansson & Valbo (1970) investigated static detection thresholds on the palm of the hand (Fig. 1) and found average thresholds of 10 to 40 micrometers indentation. This variation was due to geographical differences; towards the fingers sensitivity is higher and thresholds are hence lower (Fig. 2).

Fig. 2. Detection thresholds of static stimuli on the palm of the hand. source:  Johansson & Valbo (1970)

References
- Delmas et al. Nature Rev Neurosci (2011); 12: 139-53


Fate of antigen-presenting cells after antigen presentation to Helper T cells

In Janeway it's being said without further explanation (I will edit and add reference)   that B-cells and macrophages "become targets" of cytotoxic T-cells after these killer cells have become activated, have been primed. However, this answers your question only if presentation on MHCII (not I) implies that presentation on MHCI has occurred as well. Only then APCs that present to T helper will be targeted by cytotoxic T cells and die. According to my basic knowledge that is the case with Dendritic cells and Macrophages. According to the explanatory text of your question Macrophages only receive signals to kill pathogens they include. Your inference may be correct that they cannot become target of T-cytotoxic cells. It is beyond your question to ask if Macrophages always do get that signal. If no that puts them into the same category as Dendritic APCs.
Understanding that there exists a signal to survive (destruction of phagocytoses pathogens) leads the way to understanding that there is cell death without killing by Tc cells:  cells that present antigens to T helper cells will "apoptotically" die because they have been infected, if not killed by immune cells to prevent the production and shedding of virions. According to my basic knowledge it is coherent  that a cell presenting is a cell sentenced to apoptosis. Killing by T cells is killing at an earlier stage only.
However, I found no reference that explicitly confirms that - maybe with very few exceptions -  that B cells do present on MHCI, to the contrary I found reference - I promise to add this one link quite soon - that explicitly states that B cells only endocytose debris, "protein" (in the sense of protein vaccination) i.e. isolated antigen, not whole virous. Thus, any antigen they present would be on MHC II, not I, as the antigen is not "endogeneous", not produced by the B cell as it has not become infected. This inference from basic textbook knowledge is supported, in this context, by the fact that B cells, just as T cells - which they are thought of as of one and the same category, the Lymphcytes - should not be killed after presentation (whereas T8, cytotoxic T cells do not present at all) as parallel to killing of T cells they still have their job to do: presentation activates the B cells which start to divide and produce antibody.
Consequently, it matters if a B cell not presenting on MHCI, thus not being killed by Tc cells, is sentenced to apoptosis and cell death because it has engulfed the antigen. In my opinion B cells - and I will add any reference to the contrary - not even need, as Macrophages, a signal to survive, as they cope with antigen alone and there is no interference with their regular by viral infection. (However cells hosting active HIV produce protein for viral ends for a long time before they finally die, but this is different.)
To sum up:

Dendritic cells present on MHCII and I, hence killed  by Tc cells or dying by apoptosis

Macrophages present on MHII and I, but according to textbook different to Dendritic cells have the option or are programmed to receive a survival signal to consume the pathogens they harbour

B cells present on MHCII and, according to my understanding: maybe, on MHCI, which would put them too in category 1: same fate as Dendritic cells, however basic knowledge coherently applied speaks in favor for B cells regularly not presenting on MHCI, not getting killed by Tc cells and not undergoing apoptosis as not "infected" by whole virus

So my own personal answer on B cells based on basic textbook knowledge and lack of explicit statements to the contrary might be very different from the answer regarding dendritic cells (and macrophages, see explanatory text of your question).
The B cells do not die after presenting to T Helper cells.


How to calculate biological half life of a drug that is dose dependent?

I plotted your data using MATLAB and it looks like this:

Most degradation or clearance processes happen via first order kinetics. However when the clearance/degradation machinery gets saturated then degradation proceeds via a zeroth order kinetics i.e. it becomes independent of the concentration of the drug. The half life in first order reactions is independent of the initial state. Only in the case of the zeroth order reactions, the half life is directly proportional to initial state (See here). In this graph there is a huge gap in half life, between the first and the second points. We need more data points to say anything conclusively about this observation. However, I am assuming that the clearance reaction is already in the zeroth order kinetics as half life increases with concentration (though at different rates). For a rough calculation let us consider second point onwards. By calculating the slope between second and third points you can obtain the half life corresponding to dosage of 2mg/kg.

$$Slope=\frac{t_3 -t_2}{x_3-x_2}\\ t_4=x_2+slope\times2=17.15\ days$$

There can be other complicated explanations too, such as the drug inhibiting its own clearance etc. But for assuming such models we would require more information.


Could a fetus properly develop in micro/zero-gravity?

There is some evidence that fetal development under zero gravity conditions might be problematic.

  Wakayama S, Kawahara Y, Li C, Yamagata K, Yuge L, et al. (2009) Detrimental Effects of Microgravity on Mouse Preimplantation Development In Vitro. PLoS ONE 4(8): e6753. doi:10.1371/journal.pone.0006753

The paper is here.

These authors studied aspects of reproduction of mice in a clinostat under 10-3G. They found that fertilisation was unaffected, but that preimplantation development of the embryo was affected by the mG environment.

The introduction to the paper gives a good brief review of this area of research. Perhaps of particular relevance is this section:

  In the STS-80 space shuttle mission, mouse 2-cell embryos were collected on the ground, launched into space and cultured for four days in µG. The control embryos on Earth developed to normal blastocysts, but in the space flight group, none of the embryos showed any sign of development, and all degenerated (Schenker & Forkheim, 1998) . A more reliable experiment was done on the Cosmos 1129 mission in 1979, when mature male and female rats were sent into orbit and then allowed to intermingle in a common breeding chamber (Serova & Denisova, 1982). However, none of the females gave birth, although postflight examinations revealed that ovulation had occurred. Two of the females were reported to have achieved pregnancy, but the embryos appear to have been resorbed.

Schenker E, Forkheim K (1998) Mammalian mice embryo early development in weightlessness environment on STS 80 space flight. Israel Aerospace Medicine Institute Report.

Serova LV, Denisova LA (1982) The effect of weightlessness on the reproductive function of mammals. Physiologist 25: S9–12.


In what sense is the "histone code" a code?

As already mentioned by Stefan, the "histone" code is not really a universal code; it is restricted to eukaryotic systems and even then, not unambiguous. It is, at present related to two kinds of histone modifications and their correlation with the transcriptional activity at that locus:

Methylation
Acetylation

Other modifications such as phosphorylation and ubiquitylation also exist but they are not generally considered in this "code".

Acetylation masks the histone's positive charge and thereby loosens the chromatin (DNA wrapped around the histones), which in turn facilitates transcription. Therefore acetylation is almost always activating in the sense of gene expression.

Methylation, on the other hand has different effects. Methylation on Histone-3 at 4th lysine (H3K4) and H3K36 causes activation whereas H3K9 and H3K27 are repressive. There is also some difference between different extent of methylation i.e monomethylation, dimethylation and trimethylation. The effect of methylation is executed by proteins that have domains such as chromodomain and PHD fingers, that identify these modified histones. Sometimes the histone can carry both kinds of methylation marks H3K4me3 and H3K27me3 and these are called bivalents.

Other histones of the octameric nucleosome can also be modified. However as with other kinds of modifications that I mentioned previously they do not seem to be very common and are therefore not generally considered in this "code".


If we change our breathing rate so easily, then what is the point of measuring it?

We may be able to voluntarily control our respiratory rate, but only for a short while. The buildup or depletion of CO2 will force us, against our will, to either speed up our respiration or slow it down. So, you can't effectively "fake" your respiratory rate for long, nor, to an astute observer, convincingly.

Normal breathing patterns are "normal" for a reason. Aberrations from normal do occur in pathological states, but they are compensatory mechanisms driven by a number of factors your blood chemistries: the pH and the intimately linked CO2/H-CO3 concentrations, etc. You can pretend any way you like: you won't be able to keep it up comfortably. Also, normal breathing isn't simply "breathe in/breathe out". It is a more complex thing characterized by an inspiratory phase of certain duration, an expiratory phase of a different relative duration, and a normal, periodic deeper breath (close to a sigh), all done physiologically and unconsciously.

With certain illnesses come certain breathing patterns. You might be able to fool one person's quick assessment, but not more than that. If the faked breathing pattern (largely unsustainable anyway) doesn't appear physiological, you will be quickly found out.

For that reason, a sustained increased or decrease in respiratory rate is indicative of particular pathologies, whereas a normal respiratory rate is a helpful sign.


How to search for a protein sequence of an specific taxa in NCBI?

Try going to http://www.ncbi.nlm.nih.gov/protein and searching for ' (hemoglobin beta[Protein Name] OR (hemoglobin[All Fields] AND beta[All Fields])) AND "Fish"[ORGN] '.

Search filters can be changed with the panel to the left of the search results.


What is the probability that there are no substitution between an ancestor and a modern individual?

Expected number of substitutions

Let's assume a haploid mutation rate of $\mu$ and a population of constant size $N$. For simplicity, I will assume an absence of selection because otherwise, we would need to talk about what kind of selection you want to talk about.

There are therefore $2 N \mu t$ mutations since this last ancestor. A fraction of $\frac{1}{2N}$ of them drifted to fixation. Therefore, there were $\frac{2 N \mu t}{2N} = \mu t$ mutations fixed.

If we consequence a sequence with a mutation rate of $\mu = 10^{-4}$ (about 10 kilo bases if is a human genome) and $t = 10^5$ generations. Then, there were 10 substitutions (on average).

Of course, the assumption of no selection is likely wrong and violation of this assumption could well drastically affect this estimate. However, you would need to perfectly describe what type of selection pressures (and what selection coefficient and dominance coefficient) you want to consider for more calculations.

Probability of k substitutions

As events of substitutions are independent, the probability that exactly $k$ substitutions happen given the expected number of substitutions ($\mu t$) is given by the Poisson distribution with rate $\mu t$. The probability $P(k | \mu, t)$ of $k$ substitutions had happen in $t$ generations, given the mutation rate $\mu$ is therefore

$$P(k | \mu, t) = \frac{\left(\mu t\right)^k e^{-\left(\mu t\right)} }{k!}$$

Per consequence, the probability of no mutation is obtained by replacing $k$ to zero

$$P(k=0 | \mu, t) = e^{-\left(\mu t\right)}$$

Again, if $\mu = 10^{-4}$ (about 10 kilo bases if is a human genome), then the probability that zero mutation had happen in $t = 10^5$ generations is $e^{-10^{-4} 10^5} ≈ 5\cdot 10^{-5}$


Why does the side of the mebrane of a neuron with a high concentration of positive ions become electrically negative?

Q. “Wouldn’t it make more sense to then say that the high concentration site (more positive ions) has more positive voltage?”

A. No.

I know little about electrophysiology, but looking at the graphics and explanation that precede the screen from which the sentence you quote is taken the statement is justified. Where are you going wrong?

Your argument implies that the side with the high concentration should be positive because it has more positive (K+) ions. But it has (initially) an equal number of negative ions (Cl–) so it is initially electrically neutral. (The graphic indicates this by having no charge on the K — not a very satisfactory idea in my opinion.)

The important thing to note is the premise — that the membrane is permeable to K+ ions, but not to Cl– ions. So, even though there is a concentration gradient, only the K+ ions can move across the membrane (the graphic shows those moving with a +ve charge). As they do so each leaves its ‘partner’ Cl– ion behind, so a negative charge builds up at the high concentration side. At equilibrium there may be a high concentration of K+ ions at the ‘left’ of the diagram, but there is an even higher concentration of Cl– ions!

Good luck with your further reading of these pages! It looks as if it gets much more complex as it goes on.


Many karyotype images, especially those used for introductory educational purposes, have been edited to show a regular human 2n. For example this image has been edited to depict a regular 2n (46) compliment of human chromosomes.

While this image is unedited and shows the replicated homologous pairs in metaphase:


Are the cells (structures) of deep sea fish different? And can they survive in shallow water?

Cells consist mainly of water and the compressibility of water is very low. Wikipedia states:

  The low compressibility of water means that even in the deep oceans at 4 km depth, where pressures are 40 MPa, there is only a 1.8% decrease in volume.

This means that the pressure alone does not affect the size of the cells much. However, the pressure affects the cells and the metabolism of organism living in extreme depths in other ways:

Under pressure, the folding and unfolding kinetics of proteins can change drastically. Which means, that the protein structures may need adaptation to great pressures.
Under pressure the fluidity of the cell membrane lipids changes, requiring adaptations in membrane lipid composition.

In conclusion, I would say that the pressure in the deep sea does not directly affect the size of cells but rather their metabolism.

Edit: I just saw the second part in the heading of your question ("and can they survive in shallow water?"). Again, I'll quote Wikipedia:

  The extreme difference in pressure between the sea floor and the surface makes the creature's survival on the surface near impossible;

The reason for this is that pressure works thermodynamically in many ways similar to temperature: As explained above, changing the pressure significantly alters reaction kinetics and phase transitions. Thus, the whole metabolism has to adapt to increased pressure (similarly to the adaptation to increased temperature).

However, at least some deep sea creatures seem to be able to adapt to surface pressure if the transition is not too abrupt (from the same wikipedia article as above):

  A marine biologist, Jeffery Drazen, has explored a solution, a pressurized fish trap. This captures a deep-water creature, and adjusts its internal pressure slowly to surface level as the creature is brought to the surface, in the hope that the creature can adjust.


Are genes uniformly dispersed throughout the genome?

No they are not. High gene density is correlated with GC content. Most genes are found in GC rich isochores, which are not distributed uniformly (as can be seen on any karyotype). It is also shown through direct sequence analysis (gene density shown in red, GC content in green):

Venter et al. go on to say:

This inhomogeneity, the net result of millions of years of mammalian gene duplication, has been described as the “desertification” of the vertebrate genome. Why are there clustered regions of high and low gene density, and are these accidents of history or driven by selection and evolution?

Desertification refers to this 1985 article by Ohno:

The net consequence of hundreds of millions of years of continuous gene duplication is the desertification of the euchromatic region of modern vertebrates; the average distance between still functioning gene loci becoming progressively longer.

This would seem to suggest that these deserts are the result of gene duplication and subsequent degeneration into pseudogenes, though this is surely not the only cause. I'm not sure that your last question or those posed by Venter et al. have an answer as of yet. This more recent paper offers some interesting ideas (references removed for brevity):

More than 20 years ago, Ohno (1985) postulated the desertification of the euchromatic region of the higher vertebrates’ genomes owing to continuous gene duplication events followed by degeneration of newly emerged gene copies in their evolutionary history. Only with the first release of the complete sequence of the human genome in 2001 was Ohno’s prediction of the existence of such deserts confirmed. Another mechanism to generate imbalance of gene deserts between chromosomes might be large intra-chromosomal duplications, from which some mammalian-specific gene deserts appear to have evolved. However, the functional or evolutionary significance of gene deserts is not yet fully understood. At first glance, gene deserts seem to be devoid of biologic functions because of their lack of protein-coding DNA. Despite this, some gene deserts have been shown to contain important regulatory and sometimes ultraconserved regions for neighboring genes that function over large distances. In addition, the observation of stable gene deserts with homologous flanking genes (often transcription factors), which are maintained for long evolutionary durations, as well as the existence of numerous conserved nongenic sequences in mammalian genomes suggest that gene deserts are not just genomic junkyards but instead might be of functional significance. However, some gene deserts can be deleted without noticeable phenotypic effects. Thus, it remains unclear whether the uneven accumulation of gene deserts in eutherian chromosomes, which appears to be the strongest causal agent for the observed relative lack of a NG/LC relation in the genomes of placental mammals, is simply a byproduct of their genome evolution and possibly the long-term decrease in population-size, as would be suggested by the enrichment of gene deserts along the evolutionary lineage leading to mammals. Upcoming detailed reconstructions of ancestral vertebrate genomes will help clarify these points.
Alternatively, this particular architectural feature of eutherian genomes might be linked to some of their morphologic, physiologic, neurologic, and cognitive evolutionary innovations, possibly by regulating genes with essential functions in development. It has recently been hypothesized that regulatory elements in gene deserts function in the regulation of core vertebrate genes. Furthermore, approximately 20% of conserved noncoding elements are eutherian-specific. Thus, the uneven distribution of gene deserts could itself be caused by an underlying pattern of uneven distribution of some core genes in placental genomes. This hypothesis should be tested in the future.


The pectoral fins of a whale and a shark. Are they analogous organs?

They are analogous at to function. Whales derive from terresterial animals that "returned" to the sea. Evolution adapted the forearms to fins. Here's a great diagram: http://evolution.berkeley.edu/evolibrary/article/evograms_03

The bat wing and the bird wing are also analogous as to function. Their functional aspects evolved independently. But as forelimbs they are homologous sharing a common ancestor with forelimbs.http://evolution.berkeley.edu/evolibrary/article/evo_09

We haven't yet answered the question of whether the whale fin and shark fin are homologous in origin: http://evolution.berkeley.edu/evolibrary/article/similarity_hs_09


Identify this insect (looks like part of a tree)?

Alight, best guess, based on it being the most common species, a decent match in pictures, and a decent match to the description on Wikipedia is Diapheromera femorata, the common walking stick.


What is meant by genomic imprinting being reversible?

Only during the formation of egg-cells or sperm-cells one can leave the maternal or paternal imprint, respectively. So for example a man, will leave its paternal imprint on an allele (during spermatogenesis) that was given by his mother (while the allele was maternally imprinted in the rest of his body).