Easy derivation of Kimura's approximation for the probability of fixation of a mutation Kimura's approximation for the probability of fixation of a mutation under selection finds recurrent use in population genetics models till date. I am trying to understand the mathematical basis of this equation but none of the textbooks or online resources that I have checked, provide an easy derivation of this approximation but rather simply cite Kimura's 1962 paper.

$$P_\text{fix} \approx \frac{1-e^{-4Nsp} }{1-e^{-4Ns}} \qquad (1)$$

So, I was reading the original paper but the provided derivation doesn't appear clear to me.

Details

Kimura starts with definition of probability of change in allele frequency as:

$$u(p,t+\delta t) = \int f(p+\delta p; \delta t)\ u(p+\delta p,t)\ d(\delta p) \qquad (2)$$

where (quoted exactly)

$u(p,t)$ is the probability that an allele will be fixed in a time interval $t$ given that it's initial frequency is $p$.
$f(p+\delta p; \delta t)$ is the probability density of the change from $p$ to $p+\delta p$

Then he uses Taylor series approximation to obtain an equation of this form:

$$\frac{\partial u(p,t)}{\partial t}= \frac{V}{2}\frac{\partial ^2u}{\partial p^2}+M\frac{\partial u}{\partial p} \qquad (3)$$

He defines $M$ and $V$ as mean and variance of change of $p$ per generation. These are formally defined as:

$$M=\lim_{\delta t \to 0} \frac{1}{\delta t}\int (\delta p).\ f(p+\delta p; \delta t).\ d(\delta p)$$

$$V=\lim_{\delta t \to 0} \frac{1}{\delta t}\int (\delta p)^2.\ f(p+\delta p; \delta t).\ d(\delta p)$$

($V$ actually should be just the second moment as per the mathematical definition and not variance)

Then he solves equation 3 at steady state with boundary conditions $u(0,t)=0$ and $u(1,t)=1$ to obtain this:

$$u(p)=\frac{\displaystyle\int_0 ^p G(x) dx}{\displaystyle\int_0 ^1 G(x) dx} \qquad (4)$$

where:

$$G(x)=\exp\left(-\int \frac{2M}{V}dx\right)$$

I understood the derivation till this point.

Then he just puts:

$$M=sx(1-x)$$
$$V=x(1-x)/2N$$

and obtains equation 1.

In short

Is there an easy derivation for equation 1?
If not, can someone explain me how M and V were approximated as above?

Presumably you've solved this, but, in case not, it's because the PDE is a Kolmogorov backward equation, so the first and second order coefficients are the mean and variance of the underlying stochastic process being modeled.

In detail, consider a stochastic differential equation (which has a solution given by an Ito diffusion process):
$$ dp_t = \mu(p_t,t) dt + \sigma(p_t,t) dW_t $$
then the following system holds (under some conditions):
$$ -\frac{\partial}{\partial t} u(p,t) = \mu(p,t)\frac{\partial}{\partial p}u(p,t) + \frac{1}{2}\sigma^2(p,t)\frac{\partial^2}{\partial p^2} u(p,t) $$
where $u$ is the density of $p$ at $t$.

Note that the drift (infinitesimal mean) $M=\mu(p,t)$ and diffusion coefficient (infinitesimal variance) $V=\sigma^2(p,t)$ are as in the paper (except for the negative sign, which I assume is ignorable since he mostly seems to care only about the case when $\partial_t u\approx 0$ anyway).
Indeed, they are equivalently written:
\begin{align}
\mu(p,t) &= \lim_{\delta t\rightarrow 0}\frac{1}{\delta t} \mathbb{E}\left[ p_{t + \delta t} - p_t \mid p_t=p \right] =: M \\
\sigma^2(p,t) &= \lim_{\delta t\rightarrow 0}\frac{1}{\delta t} \mathbb{E}\left[ (p_{t + \delta t} - p_t)^2 \mid p_t=p \right] =: V  \\
\end{align}
as Kimura writes.

Note that a useful approximation of the transition density is given by:
$$ \mathbb{P}[p_{t+\delta t} \mid p_t] \approx
\mathcal{N}(p_{t+\delta t}\mid p_t + \mu(p_t,t)\,\delta t, \sigma^2(p_t,t) \,\delta t) \tag{TD} $$

Ok, so everything above is just basic stochastic processes theory. Thus, if we have a stochastic model for the population dynamics, we can derive values for $M$ and $V$ from it (by computing its moments), and those will carry over to the backward Kolmogorov equation, upon which Kimura's work rests.

Here is where my ignorance of population dynamics shows. However, since Kimura mentions Fisher and Wright, I looked up the Wright-Fisher model.
It seems like Kimura is using the diffusion process approximation of the Wright-Fisher model.
This appears to be a well-studied and storied model that I cannot fully describe here; instead, I found the work by Tataru et al, Statistical Inference in the Wright–Fisher Model Using Allele Frequency Data to be an excellent description of it, though I do not pretend to understand much of it.

What's important, however, is that the change in genes (transition density) can be described by a binomial distribution. This can be approximated by a normal distribution:
$$ \mathbb{P}[p_{t+\delta t} \mid p_t] \approx \mathcal{N}(p_{t+\delta t}\mid p_t + a(p_t) \delta t,\, p_t (1-p_t) \delta t ) $$
using the standard approximation to the binomial.
This then gives us a forward Kolomogorov equation (not backward) written:
$$
\frac{\partial}{\partial t} u
=
- \frac{\partial}{\partial p}\left[a(p_t) u(p_t) \right]
+
\frac{1}{2}\frac{\partial^2}{\partial p^2} \left[ p_t(1-p_t)u(p_t) \right]
$$
This basically implies that $V=p(1-p)$.

( I noticed that another way to prove this is to notice that the Wright-Fisher approximate diffusion (without any selections, etc... so $a\equiv 0$) has an infinitesimal generator given by: $ \mathfrak{G} f(p) = p(1-p)\partial_{tt} f(p) / 2 $. This immediately implies $V=p(1-p)$. But may be less straightforward to understand. )

Confusingly, however, the paper has changed timescales (variables), so that $\delta t \leftarrow \Delta t / (2N)$, and then set $\delta t$ to $1$ (probably so that they would not have to write $2N$ everywhere).
If we undo this transform, we get
$$ \mathbb{P}[p_{t+\delta t} \mid p_t] \approx \mathcal{N}(p_{t+\delta t}\mid p_t + a(p_t) \delta t,\, p_t (1-p_t) \delta t/(2N) ) $$
If you compare this to our approximate transition density above (equation (TD)), you will see that this implies: $$ \sigma^2 = V = p(1-p)/[2N] $$
as desired.

Now, what is the infinitesimal mean, i.e. $a$ or $M$? Clearly this depends on the selection model, since it controls how the "environment" deterministically affects the process. Kimura describes this as a "constant selection advantage" with coefficient $s$.
The Tataru paper notes that the diffusion approximation to Wright-Fisher under genetic drift, mutation, and selection is given by:
$$
a(p) = -\nu p + \xi(1-p) + 2N\tau p(1-p)[h-(1-2h)p]
$$
If we (1) ignore mutation by setting $\nu=\xi=0$, (2) remove allelic dominance effects by setting $h=1/2$, and (3) define $s:= N\tau$, we get:
$$
a(p) = s p(1-p) =: M
$$
which of course we see by noting $M=a(p)$ matches $\mu$ in the equation (TD) above. (Note that the $2N$ transform also occurred here, but it was hidden inside $s$).

Thus, we have derived where Kimura's $M$ and $V$ come from, albeit probably not in the simplest possible manner.

All that remains is to derive the (steady-state) equation for $u$. I guess I'll do it for completeness.

Ignoring the steady-state subscripts, we get:
\begin{align}
G(x) &= \exp\left( -\int \frac{2M}{V} dx \right)
     = \exp\left( -\int 4sN dx \right)
     = \exp\left( -4sNx\right) \\[0.15cm]
u(p) &= \frac{\displaystyle \int_0^p G(x) dx}{\displaystyle \int_0^1 G(x) dx}
     = \frac{\displaystyle \frac{1}{4Ns}\left[ \exp\left( -4sNx\right) \right]_0^p}{\displaystyle \frac{1}{4Ns}\left[ \exp\left( -4sNx\right)
\right]_0^1}
     = \frac{\displaystyle -\left[ \exp\left( -4sNp\right) - 1 \right]}{\displaystyle -\left[ \exp\left( -4sN\right) -1
\right]} \\
&= \frac{1 - \exp(-4Nsp)}{1 - \exp(-4Ns)}
\end{align}
as required.

Apologies for any errors. (I am neither a population dynamics modeler nor a mathematician, so please point out any problems).


Can the sulphur content of biological material be reliably calculated from fat, fibre and protein contents (Weender Analysis)? An often-used analysis of feedstocks for lifestocks is the Weender analysis, which basically divides the volatile solid content into fats, fibre and protein, as well as N-free matter. I found no English language article on the process.

The individual values and how they are arrived at are:

Fat: anything dissolvable in organic solvents;
Fibre: anything not dissolvable in acids or bases;
Protein: N-content times 6,25 or 6,38 for plants or protein (and the N-content in protein is most of the times close to 16%);
N-free: the rest (sugars, starch, pectines, organic acids ...).

This data is often more readily available than a full chemical analysis.

I want to know the sulphur content - can I reliably say fats contain v mass-% sulphur, proteins x mass-% ... and so on?
If yes, would this be applicable to manure also?

I would say theoretically yes, if you know lots of other information, but practically no, because of all the unknowns.

According to this paper cysteine content and therefore sulfur content of proteins vary depending on what kind of organism your organic material is coming from. Animals have more, bacteria tend to have less.

The sulfur content as a fraction of protein content in a given animal or plant is constant or nearly constant across the entire animal/plant. If a sample's protein content is 50:50 corn protein:sheep protein it's possible to estimate the sulfur content of the protein fraction from the sulfur contents of the sources of the protein.

In addition the sulfur content can be affected by fermentation and sulfur/sulfate-reducing bacteria, many but not all of which are anaerobes. So figures for whatever the source of the feedstock is (as far as cysteine content) can be affected by whatever happens to it afterwards. Chicken waste with a high sulfur content allowed to ferment may give off fairly large amounts of hydrogen sulfide, lowing the sulfur content without necessarily affecting the bulk protein content. The reverse might also happen, where protein is digested and sulfur is released as sulfates but kept in the mixture.

Even if you know for certain the sources of all of the protein(and that no further biological activity has changed them), the unknown amount of organic acids is going to throw off your sulfur estimates if there are sulfur-containing organic acids in the N-free fraction.


How is the gradient in density gradient centrifugation made? From the literature I read, I came to understand the following. Please correct me if wrong.

In CsCl gradient centrifugation, the gradient is achieved by centrifuging the CsCl solution at high rpm, which causes the heavy Cs ions to distribute unevenly throughout the solution, due to sheer centrifugal force.
In Sucrose gradient, solutions of decreasing concentrations of sucrose are layered one over the other slowly.

Why can't we achieve the sucrose gradient by simply centrifuging it like in the CsCl gradient?

Sucrose gradient separations are an example of a rate-zonal centrifugation technique (fair technical document on centirfugation separations). The idea is you layer lighter sucrose solutions on top of one another, for example, 35% at the bottom of the tube (the most dense zone) and 15% at the top band (the least dense zone). There might be 5 or 6 layers. You then layer the sample, which may contain cell lysate, at the very top.

This method is done on a centrifuge, and the idea is that in order to penetrate down to a more dense layer, the component of the lysate must exceed the density of the preceeding sucrose layer. Quite literally the dense components are pushing down through the sucrose layers. The method is suitable for organelles and proteins. The layering must be done deliberately and carefully, however, because the sucrose layers and sample will easily mix if they're disturbed. The same concept applies to ficol density gradient separations: If there's no density of solution below the sample for it to pass through, there's no separation.

On the other hand, the CsCl method is an example of an isopycnic centrifugation suitable for the separation of nucleic acids, see the above mentioned reference. This is because the DNA migrates to where the denisty of the DNA equals the density of the gradient, referred to as the neutral buoyancy or isopycnic point.


Could we slow down life? As far as I know there is no absolute time measurement (one that would measure time in a non-environment-dependent fashion) in any life form. Only such time measurements exist, which are dependent upon cyclic changes like the strength of the sunlight, heat, amount of water, etc. (In case this assumption of mine is false, than please answer it in mind of life forms which in deed have only relative)

Theoretically, if we could change everything a given life form is dependent upon, could we increase it's entire lifespan?

For example if we were to put a plant inside a manually controllable environment and slow down the day/night cycle to it's third, maybe even decrease air pressure and the strength of gravity(thus eliminating any problem that might arise from the actual transport of water and the solution of organic matters produced in photosynthesizing cells), change (even though I'm not sure if this could be physically possible at all, but my whole question is theoretical, thus it should not cause a problem) the adhesion/cohesion as well (so the speed of transports in the cells would change accordingly) and change other, (for me) unthinkable aspects that, for example, regulates the time between cell divisions, would it live for longer?

Even if we could not change every aspect that regulates a given life form's working, changing many could we (although this time not linearly) increase it's actual lifespan?

Short answer: Changing something (instead of everything) yes.

There are several studies on the impact of environmental factors on life span.
Of course it depends from organism to organism. Diet restriction for example has been shown to extend life span of worms and mice. Temperature is also working well, at least with microorganisms, the metabolism of E.coli for example (that usually grows at 37 Celsius) can be greatly slowed down at lower temperatures. Also, there are enzymes involved in the "ageing pathway" some of these enzymes can be promoted or repressed by small molecules, like resveratrol does, and as result you get some increment in life span. The opposite is also true, you can accelerate ageing with external factors. For plants works as well, changing the environment you can definitely have an effect on the life span and the overall metabolism rate, indeed tricking the light cycle is a very well known method to induce flowering for example.

Here some interesting links if you wanna dig more into this topic.
http://web.mit.edu/biology/guarente/
http://genetics.med.harvard.edu/sinclair/
http://www.ted.com/talks/cynthia_kenyon_experiments_that_hint_of_longer_lives?language=en
http://jxb.oxfordjournals.org/content/58/12/3099.full


What did cyanobacteria gain from photosynthesis? As far as I understand, cyanobacteria began performing photosynthesis long before plant cells as we know them arrived on the scene. But cyanobacteria do not seem to use polysaccharides in the same way as plant cells do (building materials, for example). So what evolutionary benefit did cyanobacteria gain from photosynthesis, that could have made them continue to produce oxygen generation after generation?

But cyanobacteria do not seem to use polysaccharides in the same way as plant cells do (building materials, for example)

The Calvin-Benson cycle produces glucose which is the starting
material for a lot of biosynthetic pathways including that of the
nucleotides (ribose from the pentose-phosphate pathway). Glycolytic intermediates are also involved in the synthesis of amino
acid, isoprenoids etc. Essentially all the carbon that cyanobacteria
contains comes from fixation of carbon (from CO2) via
photosynthesis.
Cyanobacteria also store starch as an energy reserve (Suzuki et
al., 2013). They also produce other polysaccharides (Phillipis
and Vincenzini, 1998). Moreover, the peptidoglycan cell wall has a "glycan" part which is a glucose derivative (a polysaccharide).
Moreover, cyanobacteria cannot produce ATP via the photosynthetic
electron transport chain in the dark and ATP in dark conditions
is generated (though significantly less) via substrate level phosphorylation in glycolysis.


Assembly of polypeptides expressed from different chromosomes to make a fully functional protein In case of human beings, the whole genome is divided in 23 pairs of chromosomes in somatic cells. It has been observed that in case of many proteins (e.g. immunoglobulins, hemoglobin) different polypeptides are assembled together which are expressed from different chromosomes. My question is- what guides them to assemble them for the making of a fully functional protein??

They have complimentary interfaces on their surfaces and it is energetically favourable for them to stay together once they have found each other by Brownian motion. Often they are highly unstable alone. So it is simply thermodynamics and a decrease in Gibbs free energy.

This happens across life. Enzyme complexes in bacteria are on the same operon, especially in thermophiles, because they come together at a faster speed. In Eukaryotes it makes no difference as the mRNA needs to be processed etc.


How do stem cells produce 200 billion new blood cells every day? Does 1 stem cell division result in production of just 1 blood cell or many? It is given on wikipedia that everyday 200 billion new blood cells are produced in the bone marrow. I want to understand how this is achieved. Does one hematopoietic stem cell's single division result in just one blood cell produced, or many so as to meet the demands of trillions of cells? For example, does single myeloid progenitor (refer to the image) produced from one stem cell further divides into several other identical progenitors before further differentiating into specialized cell, so multiple blood cells are created with the involvement of just one stem cell so that work load is shifted from stem cells towards the progenitors? Also, does the fact that RBC lacks nucleus help in producing trillions of them, making the entire process cost effective.

The hematopoietic stem cells are quite rare, and each progenitor cell produced by a stem cell gives rise to a large number of red blood cells (and other blood cell types).

I'm not sure if the precise number of offspring for the earliest progenitor cells is known in vivo, but recent cell culture models indicate that early progenitors can give rise to as many as 70,000 mature red cells. We also know that the erythrocyte burst-forming cell, a later stage cell in the erythroid lineage, gives rise to around 5,000 mature erythrocytes.

Red blood cells eject their nucleus in the final stages of maturation, and cannot divide thereafter. The ejected nucleus is consumed by a bone marrow macrophage, so it cannot be "reused" in any way. Hence, loss of the nucleus is probably not an advantage in terms of "production economy" --- a nucleus must be generated for each single red cell anyway.


Why is Lamarck's evolutionary theory considered incorrect? Many sources postulate without much argumentation that Lamarck's evolutionary theory is "obviously incorrect". A classic example provided in such sources is the one with giraffes, in which Lamarck argued that the necks of these animals became long as a result of continually stretching to reach high foliage. This mechanism for adaptive change in response to the environment is considered wrong.

Now the counterargument to Lamarck's ideas comprises modern findings in genetics. So it is stated that organisms pass down traits through predetermined genetic information, not based on environmental adaptations during their lifetime. Furthermore, genetic research shows that living organisms cannot alter their genetic material as needed.

Doesn't the latter contradict the evolutionary theory itself, which states that nature (i.e. environment) shapes species through natural selection in the environmental milieu? If it is not the environmental/natural factor that is responsible for genetic evolution, then what is?
If genetic material were to be unchangeable, then how would adaptation to the environment and consequent evolutionary change would ever be possible? The genotype (following this logic) would remain the same.
And lastly, maybe giraffes do not develop long necks at such a rapid pace (as Lamarck probably thought) by simply stretching their necks in one generation; however, what, if not neck stretching, is responsible for their consistently longer necks throughout thousands or millions of years? Hadn't the environment gradually shaped their genotype? Similarly, if I run more often, maybe my children will not become more fit, but isn't it just a matter of time (thousands of years) that if all of us consistently run a lot, then our offspring will eventually become more fit? Wouldn't our voluntary efforts eventually enhance our genetics? I do not see any problem with Lamarck's logic at all, except probably the time frame.

In science theories are tested by experiment evidence and, as scientific knowledge progresses, how they can be accommodated in the framework of knowledge. In brief, there is no evidence to support Lamarck’s idea (Lysenko is notorious for his falsification of such evidence during the Soviet era to fit with Stalin’s philosophy), and the subsequent discovery of the molecular basis of heredity provided a requirement for the theory to show how changes in somatic cells could affect the DNA of the germ cells. No such evidence has emerged.

It is true that there are epigenetic effects, but none so far support Lamarckian postulates of the giraffe neck variety.

Finally, a word of advice. Don’t waste your breath on this list with arguments of the sort “Doesn't the latter contradict the evolutionary theory itself”. This is not a philosophical debating society, but community that is concerned with the factual basis of science. If you have never studied molecular sciences, stay away — or ask questions in the course of genuine attempts to acquire knowledge in this area.


What is the translation termination efficiency in mammalian cells? When I express proteins in bacteria I put at least two stop codons at the end of the gene to increase the termination efficiency. Is this the case in eukaryotic cells too? If I put a single stop codon is there a risk for the ribosomal complex to readthrough and continue translation?

There is a paper for transfection of mammalian cells here which has a bit of comparison of stop codon and protein yield.

Otherwise this article suggests having a 'rare' codon after the stop to prevent readthrough. But yes, I'm not sure why one wouldn't simply put two stops after each other.


Is there any species (subespecies?) whose DNA or RNA contains only in stem-loop structures? I am a computer-scientist working in nucleic acids from the perspective of Formal Language Theory. I am dealing with several papers that analyze the Stem-Loop structure of DNA.

I would like some solid reference that confirms (or negates):

1) The existence of these structures

2) The existence of a (species, subespecies, ...) whose DNA or RNA has been confirmed to have strings which are a pure stem-loop structure.

The existence and importance of stem loop structures is well documented.

I'm not sure what the second question means exactly. If you mean 'an organism that has at least one stem loop, try HIV. The MS2 system is also a stem loop binding protein that's used as a tool a lot in biology, I suggest googling it.


Why is beta-mercaptoethanol often added to cell culture media Many protocols suggest that beta-mercaptoethanol is necessary for growing cells. It is a reducing agent but what does it mechanistically do for your cells. When would one not add it.

2-me is a reducing agent necessary to be added to help keep free radical oxygen from affecting mouse cells.  It is generally not necessary for human cells.

Also from S Bannai and Ishii et al., 2-mercaptoethanol improves tumor cell uptake of cystine by creating a reducing environment.


How does a cell sense its size? Cells come in all sorts of sizes. How do they regulate their cell size to the point where similar cell types have a fairly mono-disperse size distribution?

Reasked from http://www.quora.com/How-Does-a-Cell-Know-Its-Size

This is a question that has been the focus of study for the last century (e.g., Amodel of cell size regulation - Ycas et al, J. Theoret. Biol. (1965) 9, 444-470). Cell size regulation may be in part determined by ribosomal activity (through mTor regulation) and is a critical checkpoint in cell division.

How the cell senses its size, however, is not understood. In 2009, two reports suggested that protein gradients could be responsible for the sensing of cell size. You can read a commentary about those articles in Cell size control: governed by a spatial gradient. - Almeyda and Tyers, Dev. Cell. (2009) 17(1), 3-4:

  The phenomenon of cell size homeostasis, whereby cells coordinate
  growth and division to maintain a uniform cell size, has been an
  outstanding issue in cell biology for many decades. Two recent studies
  in Nature in fission yeast demonstrate that a gradient of the polarity
  factor Pom1 is a sensor of cell length that determines the onset of
  Cdc2 activation and mitosis.

These articles demonstrate one way in which cells may sense their size, but most probably several other mechanisms are also in place.


Do human anal glands have any function? Anal glands in all kinds of animals play a range of diverse functions - providing fat for preening feathers or waterproofing fur, generating distinct scent, even repelling predators.

None of the resources I found about human anal glands ever mention their function. There's info or infections, location, treatment of problems related to them, but never a word about a role they might serve - if any. Are they vestigial organs without any role or do they have some evolutionary purpose?

They are vital if not incredibly useful in many other mammals but not humans. People have tried to look for a role and seen that they activate in times of stress or anxiety. However this probably only serves to heighten both stress and anxiety as you now also don't smell that great either. There is no identifiable role in humans apart from causing a variety of disorders such as an abcess.

When physically examining a patient with a very painful condition, doing something that greatly provokes symptoms will often cause the anal glands to secrete. As a doctor, I interpret it as a clear warning that the patient is in distress, and that I need to exercise greater caution. I don't know of any science supporting this, but the function seems self-evident.


Do adult humans exchange beneficial bacteria? If so, how? Recently there has been increasing interest and research into symbiotic bacteria present in humans and human gut. I'm aware of two new discoveries:

humans are surrounded by a unique cloud of bacteria, similar to fingerprint, useful in forensics
human brain and cognition could be influenced by gut bacteria that produces up to 90% of serotonin and dopamine in the human body (not sure how, vagus nerve is implicated)

I've also heard that mothers pass a lot of beneficial bacteria to children during childbirth to jump start intestinal flora. Apparently this happens as the baby travels through the birth canal.

This makes me ask - do adult humans share/ pass along symbiotic or beneficial bacteria or other micro organisms?

I'm aware of viruses and diseases that can be passed via blood or be transmitted sexually, but does anything beneficial get transmitted this way?

Nonpathological bacteria Staphylococcus epidermidis on the human skin are part of normal bacterial flora that help to protect against pathogenic bacteria, such as Staphylococcus aureus. You can get "infected" by Staphylococcus epidermidis the same way as by other skin bacteria and you get a potential benefit from them.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2807625/
You can get beneficial effects from pathological bacteria passed from someone, if you you develop antibodies to them, so you can become more or less resistant to them. For example, if you are from US or Europe and travel to South Asia, you at high risk to get food poisoning from either bacteria or viruses, bot local people are at much lower risk, because they have "shared" microbes one from another.
You can get beneficial effects from probiotic supplements (live nonpathological bacteria), which prevent overgrowth of pathological bacteria in your large intestine. The bacteria in probiotics are similar than those in the human gut.


Limits of phase-locking In Rieke et al, Spikes, p. 31, I found this instructive picture showing and explaining phase-locking.

I take it for granted, that phase-locking is possible only upto a maximal stimulus frequency (well below the maximal firing rate of the neuron). Beyond this, phase-locking must break down: the neuron cannot follow the stimulus anymore.

My questions are:

Does this maximal frequency count as a characteristic of a neuron (type of neuron), and what is its name?
Are there typical values of this frequency, e.g. as a fraction of the maximal firing rate of the neuron?
Does the breakdown of phase-locking typically occur suddenly (as a phase transition) or gradually?
What happens beyond? Does the neuron follow a virtual stimulus of 1/n of the original frequency (which would be in phase with the original stimulus)? Or does chaotic firing take over?

Phase-locking can occur at very high frequencies at the early stages of a sensory system. In addition to the answer provided in your previous question, let me offer an illustrative example from single neurons in the somatosensory system.

Deschênes, Timofeeva and Lavallée (2003), have stimulated the whiskers of an anesthetized rat with a sinusoidal stimulus while recording extracellularly in the main feed-forward brainstem nucleus of this system (Pr5). Practically all neurons responded with at least one action potential to all cycles of a stimulus up to a stimulation frequency of $300Hz$. Neurons always fired an action potential at first cycle up to the maximum frequency tested, $400Hz$, and ~$30\%$ fewer spikes upon subsequent stimulation (questions 2 and 3). The extend of coupling to the stimulation frequency depended on the amplitude, velocity, and direction of the stimulation.

 Encoding of high-frequency whisker deflections by primary afferent axons
(Figure 6 from Deschênes, Timofeeva and Lavallée (2003))

Given the physical limitations of the stimulation device (piezoelectric element) it was not possible to directly stimulate at higher frequencies. However, the authors took advantage of the harmonic oscillations of the stimulator to look at the phase-coupling of spiking to higher frequencies of stimulations. Remarkably, units that were sensitive to small displacements of the whisker were able to follow the harmonic oscillations of the stimulator up to at least $1kHz$ (see Figure above).

  What happens beyond? Does the neuron follow a virtual stimulus of 1/n of the original frequency (which would be in phase with the original stimulus)? Or does chaotic firing take over?

It is important to note that $1kHz$ is very close to the refractory period of most neurons, thus I would predict that this could be the only limiting factor in these cells. Of course, different types of cells will show different responses to high-frequency stimulation based on their intrinsic properties, the degree of short-term synaptic plasticity of the activated synapses, and/or the filtering of the incoming inputs by upstream circuits.

  Does this maximal frequency count as a characteristic of a neuron (type of neuron), and what is its name?

I am not aware of something like this. Stimulation at such high frequencies is not typically used to classify neurons as it is rather non-physiological for neurons beyond those receiving direct inputs from the sensory receptors. Most often researchers apply a DC pulse and observe the degree of spiking adaptation.

Reference
Deschênes, M., Timofeeva, E., & Lavallée, P. (2003). The relay of high-frequency sensory signals in the Whisker-to-barreloid pathway. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 23(17), 6778–87. https://doi.org/23/17/6778


Textbook Recommendations Covering The Adaptive Immune System I will soon begin work on a project about immunology. I would like to read more about the main mechanisms of the immune system.

What books or articles could you recommend to me? In particular I am interested in the adaptive immune system.

I would appreciate if some of the recommendations are at an undergraduate level as I do not have a background in biology.

Thanks a lot.

There are two books that I see as "standards" in the field:

Cellular and Molecular Immunology, 8th Ed. by Abbas et al.
Immunobiology previously by Charlie Janeway, who unfortunately has passed, so it is now authored by Kenneth Murphy. Many people just refer to it as "Janeway".

These texts can be used both at an undergraduate and graduate level (I used them in both), and are updated every few years. The latest edition of Immunobiology is from 2011, while CMI is from 2014, so you may want to start with that one first.

An earlier version of Immunobiology is also available for free on NCBI's Bookshelf, but unfortunately you can't read directly through it, you'll need to search for what you're interested in.


Introgression vs admixture What is the difference between genetic introgression and genetic admixture ?
Are these terms interchangeable ?
Do they apply to different population scopes or something ?
The usage seems kind of vague to me.
Looking for some clarification.

The difference is referring to the directionality of gene flow.

We talk about genetic introgression when there is some gene flow from one population (say, population A) to the other (say, population B). We would say that population A introgresses into population B. If gene flow goes both ways, then the two populations A and B are admixing.

From Wikipedia > introgression

  Introgression, also known as introgressive hybridization, in genetics is the movement of a gene (gene flow) from one species into the gene pool of another by the repeated backcrossing of an interspecific hybrid with one of its parent species.

From Wikipedia > Admixture

  Genetic admixture occurs when two or more previously isolated and genetically differentiated populations begin interbreeding.


How to Determine the Most Conserved Sequence from BLAST Result? I used BLAST to find the best aligned DNA sequence to my query sequence.  The BLAST result shows a number of sequences that it calculated to be the best aligned sequences to the query sequence that I give.  Each of those aligned sequences have an e-value score representing the best hit by chance.

My question is how can I determine if the query sequence is highly conserved with the aligned sequence, or not?  Can I determine that by saying the sequence with best hit e-value (lower e-value) is the one that is highly conserved? or are there other concepts that I need to consider in this case?

I appreciate your help!

The "best" is the one with the highest ID score, lowest e-value and greatest length. There is no magic bullet, all of these are important. You should always look for the most similar sequence that matches as much as your query as possible with the lowest e-value possible.

Furthermore, it will depend on what you are looking at. If, for example, you are blasting a protein with a specific domain and are looking to find its homolog in another species, it is often more important that the domain be conserved than that the whole sequence be very identical. If you find a sequence with high identity but where the domain is not conserved, that is probably not what you are looking for.


Radiation and "Safe Dose" I've read about safe doses of radiation, briefly. Why is there such a thing a safe dose? Can't any radiation give you cancer? Why is it more and more dangerous to take in larger and larger doses? Just because the body can't recover as easily? Is there something fundamentally more damaging about larger doses of radiation, as opposed to smaller more frequent doses? I suppose this could go to a health forum, but I don't know of one, and I came about this question via thinking about Physics.

Great question. It is unfortunate that more people don't understand what "radiation" is and how it affects biology.
Firstly, you must distinguish between ionizing radiation and non-ionizing radiation.
Wikipedia describes the former rather well:

Ionizing (or ionising) radiation is radiation composed of particles that individually carry enough kinetic energy to liberate an electron from an atom or molecule, ionizing it. ...
When ionizing radiation is emitted by or absorbed by an atom, it can liberate an atomic particle (typically an electron, proton, or neutron, but sometimes an entire nucleus) from the atom. Such an event can alter chemical bonds and produce ions, usually in ion-pairs, that are especially chemically reactive.

X-rays, alpha, beta, and gamma radiation are all ionizing forms of radiation. Non-ionizing radiation, as you might guess, is radiation of a sort which lacks enough energy to to strip an electron from an atom. All light is actually "radiation."
The reason why ionizing radiation can be so damaging is because the molecular underpinnings of our cells depend on many very specific chemical bonds. Changing those bonds can damage or even break anything from proteins to strands of DNA itself. Our bodies naturally try to repair damage, but the amount of damage is a crucial factor in how successful our bodies can be. More radiation means more damage; also greater amounts of radiation increases the chances of serious genetic damage to one or more cells. Damaging a cell's rulebook in just the right ways can disable its repair processes and even its fail-safes that keep it from becoming a hazard to the rest of the body (such as growing uncontrollably into a cancerous tumor).
A more in depth explanation of radiation effects can be found at http://www.epa.gov/radiation/understand/health_effects.html#q1

In general, the amount and duration of radiation exposure affects the severity or type of health effect. There are two broad categories of health effects: stochastic and non-stochastic.
Stochastic Health Effects
Stochastic effects are associated with long-term, low-level (chronic) exposure to radiation. ("Stochastic" refers to the likelihood that something will happen.) Increased levels of exposure make these health effects more likely to occur, but do not influence the type or severity of the effect.
Cancer is considered by most people the primary health effect from radiation exposure. Simply put, cancer is the uncontrolled growth of cells. Ordinarily, natural processes control the rate at which cells grow and replace themselves. They also control the body's processes for repairing or replacing damaged tissue. Damage occurring at the cellular or molecular level, can disrupt the control processes, permitting the uncontrolled growth of cells cancer This is why ionizing radiation's ability to break chemical bonds in atoms and molecules makes it such a potent carcinogen.
Other stochastic effects also occur. Radiation can cause changes in DNA, the "blueprints" that ensure cell repair and replacement produces a perfect copy of the original cell. Changes in DNA are called mutations.
Sometimes the body fails to repair these mutations or even creates mutations during repair. The mutations can be teratogenic or genetic. Teratogenic mutations are caused by exposure of the fetus in the uterus and affect only the individual who was exposed. Genetic mutations are passed on to offspring.
Non-Stochastic Health Effects
Non-stochastic effects appear in cases of exposure to high levels of radiation, and become more severe as the exposure increases. Short-term, high-level exposure is referred to as 'acute' exposure.
Many non-cancerous health effects of radiation are non-stochastic. Unlike cancer, health effects from 'acute' exposure to radiation usually appear quickly. Acute health effects include burns and radiation sickness. Radiation sickness is also called 'radiation poisoning.' It can cause premature aging or even death. If the dose is fatal, death usually occurs within two months. The symptoms of radiation sickness include: nausea, weakness, hair loss, skin burns or diminished organ function.

Another thing to understand about why the dose matters is that we are surrounded by ionizing radiation. We always have been. Our bodies have evolved needing to successfully deal with ionizing radiation. You can find a very informative and fun visualization of this fact at https://xkcd.com/radiation/.


Selective Androgen Receptor Agonist I'm looking for an inducer that strongly activates the androgen receptor, but not the glucocorticoid receptor that is not DEA regulated. I know that SARMS (Selective Androgen Receptor Modulators) are something of a hot topic in some corners of some fields, but I'm having some trouble in my laboratory finding one. My research necessitates the independent induction of AR (Androgen Receptor). Does anyone know how to go about this search or know of some chemical?

Take a look at the paper by Ojasoo et al. [1] It reports the relative binding affinity for a wide variety of steroids for the AR, PR and GR.

Nandrolone (19-nor testosterone) does not have any affinity for the GR, but is a potent activator of the AR. However, it does have some affinity for the progesterone receptor (does this matter for your research?). Additionally, nandrolone bound to the AR also binds estrogen response elements (does this matter for your research?).

5a-DHT also is a potent AR activator with no affinity for the GR. Do note that a metabolite of 5a-DHT has significant affinity for the estrogen receptor, might that matter for your research.

You might also use a strong GR antagonist in conjunction with an androgen which also has some affinity for the GR. Then you can simply use methyltrienolone, as commonly used as AR ligand.

On a sidenote: do note that androgens usually only lead to significant dissociation of a GR ligand for the GR at very high concentrations. Therefore, androgens most likely act as GR antagonist by heterodimer formation of the AR with GR [2], as well as through squelching [3].

References

Ojasoo, T., et al. "Towards the mapping of the progesterone and androgen receptors." Journal of steroid biochemistry 27.1 (1987): 255-269.
Chen, Sei-yu, et al. "Androgen and glucocorticoid receptor heterodimer formation A possible mechanism for mutual inhibition of transcriptional activity." Journal of Biological Chemistry 272.22 (1997): 14087-14092.
Zhao, Jingbo, et al. "Oxandrolone blocks glucocorticoid signaling in an androgen receptor-dependent manner." Steroids 69.5 (2004): 357-366.


How to calculate relatedness in haplodiploid organisms (mainly full sisters and full brothers)? I have tried to calculate the relatedness for haplodiploid organisms, but cannot understand the calculations behind full sister and full brother.

(taken from Wikipedia: haplodiploidy
I have managed to solve the other cases. Since female will have ZZ:

female will share 1/2 of her genes with daughter
female will share 1/2 of her genes with son
female will share 1/2 of her genes with mother
female will share 1/2 of her genes with father

Since male will have only Z:

male will share 1 (100%) of his genes with his daughter
male won't share anything with his son (unfertilized eggs leads to male in haplodiploidy system)
male will share 1(100%) of his genes with his mother
male won't share anything with his father (unfertilized eggs leads to male in haplodiploidy system)

Even though I figured out how others work, I wasn't able to calculate r for fullsister fullbrother? How can it be 3/4,1/4,1/2,1/2?

It is useful to think in terms of the proportion of genetic material that an individual gets from each parent and the amount of this genetic material that will be shared with another individual (on average), and then sum this together i.e. as:

$ relatedness = prop_{mother} * shared_{mother} + prop_{father} * shared_{father} $

A female will share 50% of the genetic material from the mother's side and 100% if the material from the father's side with her full sisters: $0.5*0.5 + 0.5*1 = 0.75$

A female will share 50% of the genetic material from the mother's side and 0% if the material from the father's side with her brother: $0.5*0.5 + 0.5*0 = 0.25$

A male will share 50% of the genetic material from the mother's side with his sister (and doesn't get any from the father's side).

A male will share 50% of the genetic material from the mother's side with his brother (and doesn't get any from the father's side).

Remember also that this only holds if the female has only mated once.


Trying to extract DNA with an incomplete kit I'm wanting to run a DNA extraction on soil and root samples using a 96-well plate kit.

My problem is that someone has gone through the kit and taken the Sealing Tape. This tape it used to seal the plates so they can be vortexed and centrifuged.
I have considered using a similar tape from our qPCR reactions, but this would be a very expensive last resort. I've tried filling a plate with water and covering it with strips of label tape, but the tape doesn't provide a good enough seal for the vortex.

I'm wondering if anyone has run into a similar situation, and if they were able to find a solution. This particular kit is this PowerSoil kit from MoBio, which no longer exists after being bought out by Qiagen. I called Qiagen and they do not sell the Sealing Tape individually.

Thank you

It is the vortex step that is causing you the trouble. But you can vortex manually, by pipetting up and down a few times in the well. Then seal the top of the plate with parafilm for centrifugation.


explanation of conjugation experiment in bacteria In this paper (On the expression of a structural gene) I am confused about what is being plotted in Figure 6. The x-axis contains the fraction of radioactive day and y-axis contains the enzyme formation (I assume this is just the concentration of enzyme like Figure 5?). Different point shapes are different times after mating. If so, why are there multiple points of each time? Why are there multiple measurements for say 48 mins past mating? Is the correct interpretation that for each condition, they are showing the fraction of radiolabeled material for each number of day post thaw, like figure 5? It is unclear.

There are three variables being shown here:

The amount of time samples were stored in the freezer to allow $^{32}$P decay (x-axis),
The length of time samples were allowed to grow in a nonradioactive environment before being frozen (points with different symbols), and
The rate of $\beta$-galactosidase production when cells were thawed and grown in the presence of the transcriptional inducer IPTG (y-axis).

Let's break those down a bit more:

The x-axis measures "fraction of $^{32}$P decay." In terms of the experimental protocol, this translates to "length of time left in the freezer." For the $^{32}$P-decay experiments, samples are stored in the freezer for various lengths of time, where metabolic processes, like DNA replication or enzyme production, are shut down or drastically slowed. Although metabolism has been shut down, $^{32}$P can still beta-decay into $^{31}$S (according to Wikipedia), which promotes the breakdown of any DNA containing (formerly) radioactive isotopes. The implication is that the longer you leave samples in the freezer, the more $^{32}$P will decay and the more formerly radioactive DNA will be degraded. So you can read the trend in the x-axis either as "increasing fraction of $^{32}$P decay", "increasing amount of time left in freezer," or "decreasing fraction of intact $z^+$ DNA." The different points corresponding to, say, "48 minutes past mating" correspond to samples that were frozen down 48 minutes past mating but kept in the freezer for different lengths of time.

The y-axis measures "percent survival of enzyme-forming capacity." I think that it's safe to interpret this as the rate of $\beta$-galactosidase production relative to the rate with no $^{32}$P-decay. In other words, if you plot $\beta$-galactosidase production over time (as in Figure 5) for different conditions, you'll measure a slope, the rate of enzyme production. If you normalize all these rates from different conditions by the rate when there was no radioactivity (and thus no degradation of $z^+$ DNA in the freezer), you'll get the values plotted on the y-axis.

The final interpretation of this plot is that the $\beta$-galactosidase production rate is sensitive to the loss of the introduced $z^+$ DNA when the loss occurs soon after mating, but if cells are allowed to grow enough to divide once or twice before DNA degradation, then the loss of the original conjugated $z^+$ DNA does not affect the $\beta$-galactosidase production rate. This data is consistent with the idea that the DNA form of a gene is constantly required for sustained $\beta$-galactosidase production, and after several rounds of division the $z^+$ DNA has been replicated and is no longer radioactive and prone to degradation.

There's a lot of data in this plot (ah, the elegance of 1960s molecular biology!) so it can take a while to digest. Hope this helps!


Whose horns are these? Can anyone please help me identify these horns?
I guess they belonged to either European or Asian species. I don't know anything about their history -- they are my grandparents' who are from Moscow, Russia. The horns are at least 20 years old, I guess even more.

The length from the tip of each horn to its base (straight line) is about 35 cm = 13.7 inches.

A friend of mine suggested those might be a cow's horns, but I'm not sure.

Maybe someone can recommend a resource where it's possible to find it out?

Thanks in advance!

A single picture of aged and polished horns is not going to be that easy to give a confidently correct answer, but I can at least say that they appear to me to be horns of a yak or cattle steer.

See for example, the horns of a female domesticated yak (Bos grunniens):

  the horns sweep out from the sides of the head, and then curve forward; they typically range from 48 to 99 cm (19 to 39 in) in length. The horns of females are smaller, only 27 to 64 cm (11 to 25 in) in length, and have a more upright shape.

Wild Yak (Bos mutus) have similar, though smaller and sometimes curvier horns:

  The Wild Yak’s horns are grey to black and curve outwards, forwards, back, and slightly inward. The horns of males are larger than those of females. The horns of male Wild yaks average 42.6 centimetres (16.8 inches) in length, from tip-to-tip, and 35.2 centimetres (13.9 inches) in circumference. The horns of females average 32.2 centimetres (12.7 inches) from tip-to-tip and 19.5 centimetres (7.68 inches) in circumference. [Source].

 Yak (Bos mutus) in the Lüneburg Heath wildlife park, Germany. [Source].

Both Yak species are present in Asia:

  Though there are over 12 million domestic yaks in the Central Asian highlands, fewer than 10,000 Wild yaks survive.

However, as I said, these could just as likely be the horns of any of a number of variety of steer cattle. (There are over 800 varieties!).

See for example, this Corriente cow in California:

If you Google "steer horns" you get dozens of shapes and varieties of mounted horns. You could comb through looking for similar specimens...

If I had to guess, since your grandparents are from Russia where yak are present, it seems to me it would be more interesting/impressive to mount yak horns vs just steer cattle horns. But that's just a personal guess.

Here's a picture showing a variety of species and their horns:


Animal swimming styles Can a frog swim on its back, or it can use only the classic frog style? Does any (not specifically marine) animal exist with many different swimming styles, just like humans?

Don't know whether it was ever investigated, but I'm pretty sure, that frogs can in principle swim upside down (e.g. when escaping from predator), but they normally do not do it.
One example of alternative swimming styles in animals is provided by some munnopsidid isopod crustaceans. Several genera are pelagic and have very elongated forelegs equipped with rows of setae and paddle-like hindlegs (see Marshall & Diebel 1995 and Giver 1998):

the forelegs are used to "walk" in the water column
the same legs can be stretched out for passive parachuting
the hindlegs are used for backward swimming

Below is a photo of a parachuting specimen. And here is a very nice video of a backward swimming specimen.

Foto by Karen Osborn from here


Is the strength of presynaptic stimulus on the postsynaptic neuron affected more by the dendrites, or the cell body? Is the strength of presynaptic stimulus on the postsynaptic neuron affected more by the properties of the dendrites & axon terminals, or the cell body & axon?

Two years ago I asked a question here and learned that neurons do have a bias, and that various factors affect that bias, and now I'm trying to learn more specifically, how much of the strength of the the strength of a synapse, i.e. the size of the effect of a presynaptic stimulus on the postsynaptic neuron is dependent on the properties of each individual axon terminal / dendrite terminal, vs how much is dependent on the properties of the axon or cell body.

And do the properties of each individual axon terminal / dendrite ending vary diversely, even among dendrites of the same neuron, or do individual dendrites and axon terminals of the same neurons typically have similar properties?

It depends on the type of axon, of course.
In Axosomatic synapses the properties of dendrite is not important, but for Axodendritic synapses, the properties of post syn. dendrite is more important as the amplification and other processing happen there. However, Soma is much nearer to Axon hillock, so changes by Soma wouldn't be declined as the impulses won't. It may be depended on neuron properties itself and so that's wrong to generalise!


How to restrict a BLAST search to include only a few protein sequences? I am performing a BLAST search and I need to filter the output to a certain family of proteins.

Specifically, I need to get matches within the CYP152 family of the Cytochrome P450 proteins. I can parse the results to just P450 proteins fairly easily as the "hit def" field in the output will contain "Cytochrome P450" somewhere in there if it is in fact a P450 protein sequence, but the same is not true for CYP152.

Is there some way to do this? I am using ExPASy BLAST:

http://web.expasy.org/blast/

Thanks in advance

The gene identifiers that you get in BLAST output depends on the identifiers used during the BLAST database construction. So you may not find the exact identifier you are looking for.

For restricting the BLAST results to a smaller set of proteins/nucleic acid you can choose either of the two:

Make a database with the fasta sequences of the proteins/nucleic acids that you want. You have to install BLAST and run it from the commandline. Use makeblastdb to make the database. Now you align your queries against this database. Note that now your e-values would not be the same as what you would obtain from a search against the bigger database.
As you said in the question, you can parse the results later. You would have to know what identifiers were used in the blast database that you searched against. ExPASy-BLAST would most probably use the UniProt IDs (there is an ID convertor in the UniProt website to map UniProt IDs to other IDs). If you use NCBI-BLAST (nr database) then you would need to know the GenBank IDs.


How are Antibody and T-cell receptor different? Since the helper T-cell bonds with specific antigen presented by dendritic cell and that same helper T-cell also activates B-cell by bonding with antigen presented by B-cell, it made me think that the antigens presented by both of them should be same.
If that specificity is true, then what's the difference between an antibody and T-cell receptor? Because both T-cell receptor and Antibody are specific to an antigen.

B cell receptors (BCR) work on a similar paradigm, but a different mechanism than the TCR completely, structure aside. When a B cell encounters an antigen that binds to it's BCR, the receptor-antigen complex is internalized where it's processed onto class-II MHC.

Source: UP Manila Lecture, Marilen Parungao (link)

The peptides have to be processed to ~22-residue fragments to load on MHC-II, and the whole antigen is thus fragmented on the extrinsic processing pathway. To be specific, one antigen becomes many different peptides on many different MHC-II on the same B cell. You can imagine this produces some diversity among peptides suitable that at least one Th cell with similar specificity might bind. Class switching and somatic hypermutation that the B cells undergo help them attain greater utility and specificity, and the membrane-bound BCR is alternatively processed so that the c-terminus domain traffics antibodies to be secreted (2).

On the other hand, the TCR is specific with a little wiggle room to a particular MHC-I or II loaded peptide, with the exception of yδ T cells which have been shown to be largely MHC-independent (3). The same situation follows, however, that your antigen presenting cell or APC has many different peptides of the same antigen loaded on MHC, just as cells expressing MHC-I might, which means that the T cells are also not recognizing the same exact peptide sequence. Since they originate from the same antigen, however, your cytotoxic and helper T cells can be specific for the same "target."


Do DNA repressors exist? I know about enhancers and the mechanism that lead them to increase the gene expression of their targets but I was wondering if similarly DNA repressors exist. I know about protein repressors but I am looking for some kind of anti(or reversed)-enhancer equivalent in the genome which would act like an enhancer but reduce gene expression.

I am aware of repressed/poised enhancers which would kind of be that process, even if not really a pure repression of the target gene but rather a "non-overexpression". I also know about insulators but again this would be a different mechanism.

Some intergenic region binding protein repressors which by a folding mechanism similar to enhancers would repress a gene perhaps?

Yes, these sequences exist and they are called "silencers" (surprising, right?). There are different mechanisms by which this silencing of genes can happen.

In the "classical" way the silencer is bound by a  transcription factor which either passively suppress the gene by hindering the binding of specific transcription factors or by actively preventing the assembly of the general transcription factors. See the figure from paper 1:

Additionally there are non-classical negative regulatory element (NRE), which are usually elements upstream of the promoter which inhibit the binding regulatory proteins. NRE can also be enhancers depending on the proteins bound on them. Some NRE can induce a bend of the DNA, inhibiting the access to enhancer or promoter elements.

References:

Transcriptional control and the role of silencers in transcriptional
regulation in eukaryotes.
Transcriptional Regulatory Elements in the Human Genome


What are the white eggs growing from a black stem on the Napa cabbage plant? Are they fungal or eggs?
Can't find anything like this in the internet

I'm no expert in slime molds, but your specimens do in fact look a lot like the sporangia, or spore-producing reproductive structures (i.e., fruiting bodies), of a Mycetozoan slime mold in the class Myxogastria.  Physarum and Comatricha are possible candidate genera based on structure of their sporangia that I can find easily online.

See below for an example image:

 Sporangia of Physarum pusillum. Source: Wikimedia

You can see many photos of the white sporangia of Physarum pusillum here and a close-up of the white sporangium of Physarum pseudocolumellatum here

I don't think I can provide a more specific answer given the quality of your photo or my general lack of deeper knowledge of these organisms. Regardless, no quick search for slime-mold infections in plants produced any information regarding slime mold species that regularly grow above-ground on cabbage.

However, I did find this site which suggests that decreasing the moisture or increasing the salinity of the slime mold's environment will cause it to move along...


Why do food items expire? Why do food items and medicines expire after sometime?

Expiring food items is obvious: for fresh food, it's almost always due to bacterial or fungal decay (spoilage).

Oxidization and sunlight doesn't really ruin food; it might alter the taste or decrease its appeal, although it remains edible even by first-world standards. However, it might destroy vitamins and other nutrients.

Medicines are a different matter. Some chemical compounds are slightly unstable, with half-lives measured in years. Instability can cause the compounds to randomly break into subcompounds that are ineffective for treating the intended condition at best and poisonous at worst. Unlike food, it's imperative to keep medicines away from sunlight (and oxidation in some cases) as both of these can further promote instability and breakage into subcompounds.

On the comment that Charles posted about medicine shelf life being much longer than advertised: The reason why they underestimate the shelf life is that it's better to have people throwing out potentially OK medicine than to have it sitting on a shelf for 20 years, going bad and sickening people, and prompting numerous lawsuits against the manufacturer. This type of deliberate underestimation in the name of safety is common in engineering - usually with weight limits advertised below the real weight limit. For example, an elevator able to haul 6000 pounds might be advertised as only 2000 because the manufacturer knows there's always going to be a few idiots who disregard the posted limit.


What sauropod genera had a double row of chevron bones in the tail? I know that Diplodocus is marked by the feature of having a double row of or "double-beamed" chevron bones in the tail, but how diagnostic is this feature if several other sauropods have it? I mean, if there are other genera with the feature, which ones are they, so I know not to confuse them with Diplodocus?

It's fairly common. It is believed to have evolved two times independently, basically in both groups presumed to use their tails as weapons. Both Diplodocoidea and Euhelopodidae have species with forked chevrons and evolved it independently. It is fairly diagnostic since it is not seen outside these groups.

Source

Independent half chevrons (two rows of individual elongated splints of bone, one right, one left) pop up in many groups but double beamed chevrons are something different: the ends of the chevrons are joined, creating a single solid structure. In some parts of the tail they are separated into right and left halves (these are not very unique), but the joined ones are taxonomically significant. These are not chevrons dwindling into nothing along the tail but changing structure in a specific structurally significant way.


What do the signs +/+ +/- mean in this image? I don't understand what this graph is supposed to explain, especially what the signs +/+ or -/- mean. I just know it characterises some rats.

Diploid organisms, such as we are, generally have two copies of each gene (excepting the sex chromosomes in males).  In order to display the genotype for each, geneticists use shorthand.  When referring to a gene, + means the wildtype, which is the "normal" version, and - means that the gene is missing, often a knockout.  So a typical individual's genome for a particular gene will be +/+, a hemizygote (one copy deleted) is +/-, and a complete knockout is -/-.

The image is talking about peroxisomes, claiming that they are required for life, specifically the gene Pex5.  You can look at the graph at see that compared to the wildtype (open circles), the -/- individuals (open squares) don't make it to a normal birth weight; their lifespan is less than three days.  The +/- hemizygotes (filled circles) match the +/+ wildtype, indicating that this locus displays haplosufficiency, which means that only one functioning copy is needed for a typical phenotype.


Explain allelic complementation at molecular level I know that Allelic complementation is a phenomenon where two recessive loss-of-function allele generate a functional gene product by compensating each others' defect. But I don't get how do they compensate each others' defect.

As Matej said, mutations in two different genes (or even a gene and a non-coding regulatory element) can lead to complementation and as they pointed out, the complementation test is designed to check if mutations are on separate genes or not.

Molecular mechanisms that may lead to complementation (apart from Matej's example):

The double mutation in two enzymes in a pathway basically that does not alter the metabolic flux. Lets assume the pathway is linear i.e. no branches. The net flux would be limited by the slowest enzyme in the pathway. If a mutation increases activity of the rate limiting enzyme-1 while another mutation reduces the activity of enzyme-2 then the net flux may not change.
If a regulatory gene acts on another gene by some kind of molecular recognition (protein-DNA, protein-RNA, RNA-DNA, RNA-RNA, protein-protein), then the effect of a mutation in the recognition domain in the regulator can be complemented by another mutation in the target such that the binding affinity is preserved. For e.g. a miRNA regulating a mRNA. If a mutation in miRNA, A→G, is compensated by a mutation, U→C, in the target, then there may be no change in the molecular phenotype. This logic also applies for two gene products that function as a heterodimer.

However, different mutations in the same gene may also lead to complementation like effect. I cannot cite an example but there is a theoretical possibility. Therefore this answer is speculative. There can be two cases here:

Two different mutations in the same allele
Two different mutations in two different alleles

For case-1 you can imagine a situation in which the activity of the gene product depends on intramolecular interactions (secondary and tertiary structures). Two mutations can preserve the intramolecular interaction in a manner similar to the case of intermolecular interactions discussed above.

For case-2 you can imagine a gene-product that functions as a homodimer. If the two mutations preserve the intermolecular interactions in the dimer then you would see complementation. Case-2 can also be observed in a situation when the mutations occur in two different functional domains of two different alleles such that finally the dimer is still capable of carrying out its activity.


Significance of basal lamina for outer layers of epithelium In stratified (not pseudostratified) epithelia such as the epidermis, what purpose does the basal lamina serve for the outer layer cells which do not even stand in contact with it?

Also, how do these cells attach, all just through tight junctions to lower layers?

The basal lamina, a specialised type of extracellular matrix (ECM) that differs between cell types, acts as a base for stratified epithelia cells to layer on top of and therefore has a supportive role as well as providing a base for attachment (for the layer of cells immediately on top of it) 1. Layers of epithelial cells on top of further strata of similar cells with no differing tissue composition underneath would have no support and cell types would be able to mix freely within tissues. The different cell types within tissues are kept seperate by this ECM to maintain areas for specific functions. The importance of this can be seen through studying epithelial cancers, where the epithelial cell layer breaks through the entire basement membrance and invades the surrounding tissue. This can cause massive disruption of tissue function.

Yes, it seems that the layers of cells are attached by various junctional complexes, the desmosomes (maculae adherentes) being the most abundant for stratified epithelia.

Referenes

1 http://www.histology.leeds.ac.uk/tissue_types/connective/con_basal_lam.php


To diagnose osteomyelitis of vertebral column in chronic kidney failure Assume you suspect amyloidosis because of the history of the patient: problem with vertebral column and "purulent" (serous, fibrous, or hemorrhagic) inflammation when patient very young.
Now, the patient has a chronic renal failure.

Is there any other method to diagnose the fracture of some bone than röntgen?
Assume you do not know where the fracture is exactly.

Osteomyelitis can be diagnosed with the following imaging techniques [1]:

first of all: radiography to view the anatomy of the bone
the sonography can be used to diagnose fluid collections, periosteal involvement. It is also the most useful procedure for kidney assessment [2].
CT is also useful to detect early osseous erosion, but is less sensitive when it comes to bone infection
MRI is the most sensitive and specific for osteomyelitis
Nuclear imaging can be used to identify multifocal osseous involvement.

References:

Carlos Pineda et al., Radiographic Imaging in Osteomyelitis: The Role of Plain Radiography, Computed Tomography, Ultrasonography, Magnetic Resonance Imaging, and Scintigraphy
American College of Radiology, Renal failure


CO2 availability to phytoplankton in oceans and climate change impacts I learned through research that increasing levels of atmospheric CO2 was increasing the acidity level of ocean waters. I then was looking into how this was affecting the phytoplankton and read that warmer water was actually decreasing the amount of CO2 in the water for the phytoplankton to use.

I am now confused as to why many sources claim that greater atmospheric CO2 is making ocean waters more acidic but at the same time CO2 is decreasing in availability to phytoplankton. How does all of this relate to climate change? Would you be able to explain to me what is happening or direct me to sources that explain it?

One of my students in my Environmental Science class asked me this question, and it seemed very much worthwhile to post the Q&A to Bio.SE!  [Note: the student never got back to me with her sources of information, so unfortunately I cannot include them here].

Short Answer
I do not believe that CO2 will become less available to phytoplankton on a global scale in the foreseeable future. Instead, I believe that rates of increasing concentrations of CO2 (and resulting chemical products) will not remain sustained at their current levels.
There are two global change phenomenon occurring that are relevant :

increasing concentrations of atmospheric CO2 and
increasing global temperature.

As CO2 concentration increases in the atmosphere, diffusion will lead to higher concentrations in ocean waters as well. The rate of diffusion is related to the relative concentrations of CO2 in both air and water.
Increasing temperatures can impact this diffusion rate as well in two ways:

Increased atmospheric temperatures result in higher surface water temperatures, which leads to less mixing of water (i.e., ocean stratification). This functionally reduces the volume into which CO2 can diffuse and therefore will slow the diffusion rate. (but not decrease the amount of dissolved CO2).

Increased temperatures often decrease solubility of gasses in water, and this decreased solubility may result in offgassing of CO2. However, decreased mixing of strata may result in less upwelling and therefore less offgassing.

As long as global ocean temperatures don't get too warm and atmospheric CO2 doesn't get too low, it seems that the above will result in a net global slowing down of CO2 absorption into oceans but not a reversal (i.e., loss of CO2). In other words, I do not believe absolute CO2 availability to phytoplankton will decrease, but that only the rate of further increased availability will decrease.

Though perhaps such decreases could be experienced as local phenomena?

 Long Answer
It's not immediately clear to me what you're referring to. I'll try to react with some things that come to mind. My response is mostly biogeochemical in nature, so I invite others to address this physiologically if appropriate.
Are you sure you didn't misread something and it's really the declining CO32- that you read about?  (see here). Assuming that's not the case, read on...
Raising Temperatures may decrease the absorption rate of CO2
Alternately, maybe you read that as oceans warm, the water can absorb less CO2 (as in the rate of absorption will decline).
This is due to warm water not mixing as well with cooler lower water, so you end up with unmixing layers (called stratification). Eventually, the surface layer saturates with CO2 and can't absorb more from the atmosphere. It's not that this top layer of water has less CO2, but rather that it gets so saturated that it can't absorb any more (causing the rate of absorption to decline).

Under less warm conditions, the "layers" of water aren't so different in temperature, and so more mixing of layers can happen. As a result, all that CO2 that is being absorbed at the surface level can be exchanged to lower layers of water where it will be stored longer. In other words, with cooler surface temperatures (and therefore greater mixing of layers), the volume of water that can hold CO2 is no longer just the surface, but a much "thicker" layer of water. Result: more CO2 absorbed.

So... raising temps can cause less mixing of water due to more stratification (layering), which results in less water in the ocean available to absorb and hold the CO2. This means that as atmospheric CO2 continues to increase, the non-mixing surface layer of ocean water (which will become saturated with CO2 at some point) won't be able to keep up with more and more and more CO2 in the air. As a result, the ocean will decline (and eventually potentially fail) in its ability  to "buffer" the ever increasing CO2 in the air. This would mean that the rate of CO2 in the air will start increasing more rapidly (since less and less of it is being absorbed by the oceans).
As for the phytoplankton (which are in this top layer of water), this stratification will not directly result in less CO2 availability to them. As atmospheric CO2 increases, so will the amount in this top layer of water. The rate of increase in CO2 concentration will just slow until a saturation point is reached, but absolute levels will not decline.

Though, note, however, that phytoplankton tend to thrive in areas of high nutrients (i.e., upwelling zones). Less mixing of waters will decrease the upwelling of nutrient-rich, cooler subsurface water. So less mixing (i.e., more stratification) likely would lead to declines in phytoplankton abundance due to decreased nutrients availability. (See here). Perhaps this decreased nutrient availability (which would include loss of carbon sources) is related to what you're referring to?

Read here for some more thoughts: https://earthobservatory.nasa.gov/features/OceanCarbon
Raising Temperatures may decrease CO2 solubility
However, given all this, the solubility of CO2 in water does decline with increasing temperature (see here for raw data). This suggests that some rise in global temps may impact CO2 concentration in ocean waters.

The cause for this decreased solubility with increased temperature is due to the imbalance of an equilibrium state of free energy required for dissolving/off-gassing gasses in/out of water. Since the dissolution of CO2 into water is an exothermic reaction (i.e., one that releases heat), any addition of heat will lead to a greater favoring of the opposite endothermic reaction (release of dissolved gasses in this case). In more chemical terms, added heat to the solution provides energy to overcome attractive forces between the CO2 and the solvent (i.e., water) molecules. The result is decreased solubility. See here and here for further explanation.
(The partial pressure of gasses are also impacted with increased temperatures, so investigating Henry's Law might also be pertinent to your research. (This link I found quickly explains a bit of the phys/chem).)

However, to further complicate all of this, not all of the CO2 absorbed by ocean water remains as carbon dioxide. Much of it undergoes reactions to become carbonic acid, carbonate, etc. (see here). This means that the equilibrium state is more complex to model or understand vs simply examining these effects on CO2 in water in a controlled lab setting.

This solubility fact, in combination with the mixing of layers above, are why polar waters tend to absorb more CO2 and equatorial waters tend to give off more CO2
From http://www.carboeurope.org/education/CS_Materials/CO2solubility.pdf:

In the world oceans, the northern Atlantic and the southern Oceans act as major sinks of CO2 because they are colder. Adding to this, cold water is denser than warm water, causing it to sink. The carbon dioxide taken up in the surface can be effectively transported to the deeper waters by convection hence the CO2 is stored in the bottom layers of the ocean.

On the other hand, warm equatorial waters tend to release CO2 into the atmosphere. In these regions upwelling of CO2-rich deep waters occurs. When the water reaches the surface, it is warmed decreasing gas solubility leading to the degassing of CO2 .

However, note that if there is greater stratification of water (i.e., less mixing) as described above, such upwelling could actually decline. This would result in trapping more CO2 in cooler waters instead of warming those wters as they rise to the surface and offgassing that CO2 back into the atmosphere. (We won't even begin to discuss how ocean currents in general will be impacted by changes in temperature...).

To add even more complication, as waters warm, ice will melt leading to (at least temporary) increased mixing of ocean waters!! (see here). If temperature decline from ice melt is great enough to combat the increased surface temperature, then perhaps for some period greater mixing and offgassing could occur.

End results
What does this all mean for climate change?
Well, as atmospheric CO2 increases, it's going to make the oceans more acidic (due to carbonic acid). In other words, if we kept temperature constant, more atmospheric CO2 means more ocean CO2. (You can see that from my previous two links -- again, here (showing ocean CO2 concentration on Y axis) and here).
However, as the earth warms up, water temps will warm up. This means less mixing and therefore less net CO2 absorption. It also means that the warmest surface waters will not hold as much CO2 due to its reduced solubility, which will result in greater "off gassing" of some CO2. The combination of these two things means essentially that the rate of CO2 increase in the atmosphere will likely accelerate.
But what is the net impact on oceans? (and therefore on plankton)
Well, in regards to the oceans themselves, there is probably some calculable "tipping point"  in which the higher concentration of CO2 in the atmosphere (and therefore greater diffusion pressure) doesn't outweigh the reduced solubility of CO2 (due to higher temps and less mixing). At whatever temperature that tipping point occurs, then I assume that the CO2 in the oceans can/will decline instead of increase. In reality, though, gasses are constantly moving back and forth between atmosphere and ocean, so it's unlikely the ocean water will get so warm that there is a net movement out of the water, but probably just a decrease in the rate of further absorption.
On a more local level, if the phytoplankton are living in an especially warm region of ocean, it is possible that off-gassing of CO2 increases in those regions due to the decreased solubility as described before. However, again, if the concentration of CO2 in the atmosphere and the temperature of the atmosphere both keep increasing, then these effects would (at least partially) negate the increased offgassing from reduced CO2 solubility. Again, modelling when this would occur is much too complicated for me to figure out for this post, but I'm sure we have experts working on calculating this "tipping point" as we speak.

Again, the bigger issue for the phytoplankton is probably the reduced upwelling of nutrient-rich water due to greater climate-mediated stratification of the ocean column.

Final Thoughts
Given all of this explanation, ice-core research by Pedro, Rasmussen and van Ommen (2012)  (summarized here) suggests that after only a few hundred years of warming, CO2 concentrations seem to decline in ice cores. This may suggest that perhaps a net release of CO2 is possible within a hundred years or so. Again, this "tipping point" is likely calculable, but it's too complicated for me to figure out for a stackexchange post ;p.
Further readings:
Basu, S. and Mackey, K.R., 2018. Phytoplankton as key mediators of the biological carbon pump: Their responses to a changing climate. Sustainability, 10(3), p.869.
Hülse et al. (2017) Understanding the causes and consequences of past marine carbon cycling variability through models. Earth-science reviews, 171, pp.349-382.


Why does replication require primers while transcription does not? In transcription, there is no need for any primer. I guess the basic mechanism of DNA polymerase & RNA polymerase is the same. So why does replication have the need for a primer?

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.


What does a read simulator do? There are various software for simulating reads for Next Generation Sequencing. Can anyone tell what exactly is done by a read simulator software

In general, a read simulator does the following:

Selects regions from a reference sequence with some sort of predefined or user-specified size distribution.
Converts the sequence from those regions into single or paired end reads with quality scores. The quality scores and read lengths are typically user defined.
Introduces errors into the aforementioned sequences, typically with more at the 3' end, which matches what comes from a sequencer.

A simulator may also try to reproduce the uneven coverage that you'd see in real data. In any case, most simulators will place the original coordinates of the simulated reads in the read names so that the quality of the resulting alignments of the reads can be assessed.


Can stress-induced cortisol reduce diabetes type I? Let us consider a patient with type 1 diabetes.

I think it would be safe to say that diabetes is an autoimmune disease. Now let us suppose this patient comes under stress. As far as I know, cortisol is a stress hormone and is released when a human is under stress. Now cortisol, as far as I know, increases the amount of blood sugar and at the same time is an immunosuppressant.

I would like to know whether this stress-induced cortisol can suppress the immune system, which has gone haywire, and help against diabetes - thereby rendering a suitable amount of stress useful.

Short answer
Stress is not a good candidate to treat autoimmune disease like diabetes type I.

Background
Stress indeed weakens the immune system, and diabetes type I is indeed caused by an auto-immune response against the insulin-producing beta cells in the islets of Langerhans. However, autoimmune diseases like diabetes type I need chronic treatment (i.e., insulin treatment and dietary changes).

Even if stress would help reduce diabetes type I (admittedly, theoretically it could), chronic stress would also reduce the immune response against viral and bacterial infections, it would reduce wound healing and perhaps even increase the risk of contracting cancer (Glaser & Kiecolt-Glaser (2005), as well as increase the risk of depression and cardiovascular disease (Cohen et al., 2007).

Hence, it is safe to say that deploying chronic stress as a treatment against auto-immune disease effectively means substituting one life-threatening condition with another.

References
- Cohen et al., JAMA (2007); 298(14): 1685-7
- Glaser & Kiecolt-Glaser, Nature Rev Immunol (2005); 5: 243-51


What is a simple protocol for staining cells in suspension? I am an engineering student studying how electric fields affect cells, specifically the phenomena of electroporation in living cells.

I know that electroporation is widely used for introducing genes into cells, but running a full transfection experiment is not so feasible in the lab I am working in.

My question is:

If I want to demonstrate the principle of electroporation, i.e. that cells open up and intake substances from the environment, by using some kind of a dye which i can later visualize, what could be a good model for this type of experiment? Specifically - what dye, what cells could be used for this?

You will get a lot of false-positives using the following method, and a real transfection of a fluorescent protein is always the way to go, because then you will prove that the transfection was really successful.

That said, you could try to use DAPI, followed by fluorescence microscopy or flow cytometry. DAPI is a very bright stain for DNA and cannot pass an intact cell membrane. If the cell membrane is disrupted, e. g. by necrosis, DAPI will enter the cell. I have never seen anyone trying to prove electroporation this way, but it will be worth a try. It could be that the time is too short for DAPI to enter the cell, so you'd need to tweak your setup.

I'd try the following: Prepare a cell solution with DAPI (something like 5 µM concentration should do). Split the solution into two electroporation cuvettes, perform the electroporation with one cuvette, leave the second one untouched as a control. After the electroporation, analyse the samples MOMENTARILY (means: within a few minutes) using a flow cytometer (if possible).

As for the cells, you could use anything. I suggest eukaryotic cells because they are more easy to visualize than bacteria. However, you would need a laminar flow hood for aseptic handling of cells. In theory you could also use bacteria, which are more easy to handle, but I'm not experienced with immunofluorescence in bacteria.


Why ml scale blood retrieval is required to run a blood test? Substances found in blood are present at microscopic scale and tend to be invisible to the unaided eye. Why is a whole vial of blood (at ml scale) is required to assess the presence and concentration of substances, given that the modern equipments and testing is becoming more and more sensitive and efficient?

It might be better to consider the sampling technology, economic and logistical issues with this question as well as the tech behind the tests.

First, some tests still will want a few milliliters of blood - e.g. cell counts for specific cell types.  Then there is the need to create and stock many different kinds of sample collection devices and train the collection centers to use them effectively- the industry won't be wanting to completely retool whenever the volume requirement for the assays changes.

Mainly, since the expense of collecting and shipping a few milliliters of blood is pretty much identical to doing so with a few microliters of blood, what has happened is that the tubes are used to do multiple assays with excess included in case an assay has to be redone.

Probably all this blood currently collected is not necessary at all.   I expect that the cost of changing all the blood collection systems and protocols is not worth the cost of collecting the exact minimum amount of blood needed.  The benefit to the patient or the lab between collecting 3-5 ml of blood and collecting 50 microliters of blood to be shipped off for lab analysis is probably close to zero.

Having more blood than you need does not hurt the lab either should they need to repeat the assay or when you need to use the sample for more than one different test.


Why do the humans become sleepy after meals? I don't know about all the mankind, but I know enough people, who becomes sleepy after their meals. Also, I'm not sure, what kind of food do they consume, but I personally get sleepy almost from any food: sweet, fat, spicy, salty, liquid, tasty, not tasty, etc.

Is this phenomenon well known? And, if yes, how does it occur?

The name used frequently for this phenomenon is "Post-lunch dip".

"The post-lunch dip is a real phenomenon that can occur even when the individual has had no lunch and is unaware of the time of day. This dip has its roots in human biology, and may be linked to the size of the 12-hour harmonic in the circadian system. It is certainly exacerbated by a high-carbohydrate lunch, and may be more likely to occur in extreme morning-type individuals."

PubMed Source

Another PubMed source
"The 'post-lunch' dip is a bi-circadian phenomenon, largely unrelated to lunch, and worsened by a disturbed prior night's sleep. Despite anecdotal claims of adverse effects of larger lunches on afternoon driving ability, there is little actual driving data to support this belief."

For other reading and research...


Why don't we hear all the processes in our body and what prevents us from doing so? Why don't we hear all the processes in our body (like the rushing of our bloodcells through our arteries, the pumping of our heart, the electricity of neurotransmitters or our brain cells, etc.)

Is it only because the sound is too low for us to hear?

The issue here is with the definition of the word "hear". We do hear most of this, that is our ears actually pick up and transmit this sound. But we don't hear it in the sense that our brain ignores it and filters it out of what we are aware of. Only a very small fraction of all the sensory data that comes into the brain ever gets processed. The brain does something called sensory modulation it filters through all the sensory data and decides what is worth paying attention too.

This happens at several levels, not surprising since we see these same levels of filtering in animals, as soon as you evolve sensory organs the ability to filter sensory information is an advantage. Some happens directly at the sensory organ, many have a threshold before a signal is sent. In the brain the reticular formation is responsible for much of it. But each sensory processing area also does some of its own filtering. Basically each portion of the brain contributes in some way. Attention and how it is controlled is greatly studied as it is central to how consciousness functions. A complete rundown of all sensory processing is tricky since some senses (like smell) skip some parts of the process, and there are so many ways it can be filtered. you can find a more detailed breakdown here.


Basic question about multiplex PCR Let's say I have a DNA sequence with the following structure:

$$ 5' - N_n - S_1 - N_{1000} - S_2 - N_{1000} - S_3 - N_n - 3' $$

Here, the $N$s represent stretches of arbitrary sequence of the indicated length. $S_1$, $S_2$ and $S_3$ are all unique sequences 20 bp in length.

I design a forward primer PF that anneals to the positive strand at $S_1$. I design two reverse primers PR1 and PR2 that anneal to the negative strand at $S_2$ and $S_3$ respectively. Their annealing temperatures are reasonably close.

Clearly, PF and PR1 together will produce a 1040 bp PCR product, and PF with PR2 will produce a 2060 bp product.

Now let's say I add 0.2 μM PF (standard concentration), 0.1 μM PR1 and 0.1 μM PR2 all in the same reaction. I then run a PCR, with extension time sufficient for the 2 kb product.

I can see the following possible outcomes:

I get a 50/50 mix of 1 kb and 2 kb products.
PR1 competes aggressively and I get hardly any 2 kb product and a lot of 1 kb product.
After a few cycles, the 1 kb product that has accumulated serves as an efficient forward primer for PR2, and I end up getting mostly 2 kb product and little 1 kb product.

Which one will happen? If it depends, what are the major factors on which it depends? Primer concentrations, extension time, annealing temperature, dNTP amount, number of cycles?

This question is basically a very simplified multiplex PCR. While much has been written about practical applications of multiplex PCR, I am hoping that answers will help me understand the considerations that go into deducing optimal multiplex PCR conditions without doing any empirical calibration. This isn't to circumvent the calibration, but to understand the process conceptually.

I haven't done this exact experiment. I am just deducing from the known facts about PCR.

The product of PF and PR2 will serve as a template for both 1kb product (P1) and 2kb product (P2).

Lets assume that after 2nd cycle there is 1 copy each of P1 and P2 (Delay of 1 cycle to make a smaller length product. See here).

Lets assume that the primer binding affinity is equal (and for just preliminaries consider that only one primer can bind a template — such as in overlapping binding sites). So half of P2 will bind to PR2 and half to PR1.

Number of copies at nth cycle is:

P2(n) = 1.5 × P2(n-1)

P1(n) = 2 × P1(n-1) + 0.5 × P2(n-2)

However this is not really the scenario; the primers are not competing for the template.
Which means that both primers can bind to the substrate simultaneously.

DNA polymerases (except Klenow) will remove any DNA strand bound to the template, ahead of it. In that case P1 will not form. But when PR2 runs out, the unused PR1 can produce P1.

Now there are additional factors, such as the distance between PR1 and PR2 binding sites. If the distance between the primer binding sites is less than the smaller product then the abovementioned case occurs. If the inter-primer distance is larger than smaller product then both products will form (and just like the competing primers case, P2 will serve as a template for both products). In this case the copy numbers will be:

P2(n) = 2 × P2(n-1)

P1(n) = 2 × P1(n-1) + 1 × P2(n-2)

This means that P1 will dominate.

Other assumptions:

No limiting concentrations of dNTP, primers etc
PCR efficiencies are equal for both primers and is equal to 2 (in non-competing cases)
Primer annealing temperatures are same i.e binding affinities are same.