text:
Why are primary sensory cells considered neurons but muscle cells are not?
The rough picture of cells involved in neural processing looks like this:

I wonder, why primary sensory (receptor) cells (like rods, cones, hair cells, Merkel cells) are consensually considered neurons (accidently having no synaptic input, but synaptic output), while muscle cells are not (even though they have synaptic input, but accidently no synaptic output).
The last cells that are unequivocally neurons are the motor neurons. Curiously, the first cells that are unequivocally neurons are mostly called "ganglion cells", not "ganglion neurons".
Is it all just a manner of speaking/matter of parlance and words? I believe not.

I received an interesting answer from Quora:

"Muscle cells are derived from mesoderm, sensory cells (like all
  neurons) are derived from ectoderm."

But is this all that is to say? What’s the “deeper” source of this asymmetry? In the beginning there was only one type of cell, which was both sensory and muscle?

The quote from Quora has the correct answer; the distinction between neuronal and non-neuronal is an embryological rather than functional definition. Along those same lines, some of the other receptors you listed are considered sensory epithelia rather than neuronal. The retina is of neuronal origin, but hair cells in the cochlea are often not considered to be neuronal, despite the diagram you provided, nor are Merkel cells. The distinctions between epithelial and nervous tissue are often more difficult to identify because both derive from the same preceding embryonic tissues, whereas muscle is entirely separate.
In the end, these distinctions don't matter much unless you are interested in development or trivia (though I don't mean to discount the importance of development there, I just recommend that you don't worry too much about the distinctions if you are thinking only about final functions).

But muscle cells cannot be entirely separate, because they react on synaptic input: at least in this respect they behave like neurons.

What do you mean by "trivia"?

They are from a completely separate embryonic tissue: mesoderm, not ectoderm. That means that if you draw a diagram of cell fates, muscles and neurons are not on the same "family tree." You'd have to go back to embryonic stem cells to find cells that could eventually become either mesoderm or ectoderm. "Reacts on synaptic input" is not a defining characteristic of neurons, they can be excited by other sources as well, and lots of other non-neuronal cells also show some excitable properties; immune cells, for example, and many others.

"Trivia" refers to typically obscure facts that are most useful as facts rather than for some other purpose.

There seems to be a parallel to species which can have accidental phenotypical resemblances which are not backed genetically, right? (Does this observation count as "trivia"?)

I'm not after "trivia" but want to understand terminology. I always believed the term "neuron" is somehow defined functionally (phenomenologically), but now I've learned it's  defined genetically (developmentally).

Interesting thought, though the very important difference between convergent evolution, which is what you seem to be describing, is that cells with different fates do have access to the same genome and so can evolve together. I.e., a mesoderm-derived tissue and ectoderm-derived tissue can express some of the very same proteins; this of course isn't possible in convergent evolution.

Let us continue this discussion in chat.


text:
Why are many skin depigmenting agents toxic to the body?
Coming from a developing country, I know many people who regularly use these creams to lighten their skin and some of them have developed liver and kidney problems as a result.
I know that many contain hydroquinone which is said to cause cancer and mercury which can cause neurological issues. Are these the two main problematic skin lightening ingredients or are there any else?

Not sure why your question got downvoted. I am upvoting.

I am not downvoting, but it seems to me that the question is really not about biology - perhaps more suitable for the medical sciences. It could be biology, if it were, e.g., about the mechanisms of damange caused by these cremes, etc.

In the late 19th and early 20th centuries, cosmopolitan women in the US and Europe would eat 'arsenic complexion wafers' because the arsenic made skin look pale and caused an attractive "rounding up" (generalized edema) of the skin. These "arsenic eaters" were partly why it was one of the first agents identified as a carcinogen. Accidental overdoses were also common, which made it difficult to prove murder by arsenic poisoning. But, to my knowledge, this kind of arsenic eating went out of vogue some time in the early 20th century.

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Intuitive explanation for Kin- and Group- selection
It is known from theoretician in the field of kin selection that kin selection (inclusive fitness theory) and group selection are actually two sides of the same coin. In other words, these two concepts are actually only one single process.
Questions

Is group selection equivalent to kin selection for any evolutionary game or exclusively for the prisoner's dilemna?
Can you please provide an intuitive explanation of why kin- and group-selection are the same thing?

I found this http://www.pnas.org/content/104/16/6736.full , Am I in the right direction?

Yes it seems to be in the right direction, given the title at least. This paper however failed (at first look) to give me a intuition of why these two concepts are the same process. Thanks @DevashishDas

First of all, there is a very heated debate about this in the field of social evolution at present, and you aren't likely to get a conclusive answer. One theorist may give you one answer, but another will vehemently disagree. I'll start by logically answering your questions in reverse order!
Question 2: Can you please provide an intuitive explanation of why kin- and group-selection are the same thing?
The first formal 'proof' that they are the same came from a paper by Queller in 1992. Let me give you the gist of what he found without being ultra rigorous. I won't focus on relatedness, since that was answered nicely by @falsum, "Price's equation tells us that this happens when the genetic variance between-groups is higher than the genetic variance within-groups. This is equivalent to saying that altruists tend to interact with other altruists and, accordingly, that the coefficient of relatedness increases."
Queller explored the change in mean additive genetic (breeding) value for a trait over a single generation using Price's equation
$
\Delta \bar{G} = \mathrm{Cov}(W,G)
$
where $G$ is the additive genetic value, and $W$ is relative fitness. Now, the key question is, how should we define fitness? If we treat it as a random variable, we can do a linear regression of fitness on a set of explanatory variables. What should we choose for the explanatory variables? One natural choice might be to predict the fitness of a focal individual based on the phenotype of the focal individual itself, and the phenotype of the individuals the individual interacts with socially:
$
W_{i} = w_{0} + \beta_{direct} P_{i} + \beta_{social} P_{j}
$
where $w_{0}$ is baseline fitness, $P_{i}$ is the phenotype of the focal individual, and $P_{j}$ is the phenotype of the focal individual's social partner. However, another way to predict the fitness of a focal individual might be to focus on the average phenotype of individuals in the focal individual's group, and the deviation of the focal individual's phenotype from the mean phenotype of individuals in the group
$
W_{i} = w_{0} + \beta_{deviation} (\bar{P}_{group} - P_{i}) + \beta_{group} \bar{P}_{group}
$
These regression equations for fitness hold exactly (only for a single generation), independent of the 'true' form of the fitness functions (they can be as non-linear as you like). What these allow us to do, however, is partition fitness effects into those that are due to benefits of a behaviour, those that are due to costs of a behaviour (and unmodelled residuals or 'noise').
From a kin-selection perspective, it turns out that the benefit in Hamilton's rule is
$
\beta_{social} = B
$
and the cost in Hamilton's rule is
$
\beta_{direct} = C
$
From a group-selection perspective, it turns out that the benefits and costs in Hamilton's rule are distributed over deivation and group effects
$
\beta_{deviation} = C - B
$
and
$
\beta_{group} = C + (N-1)B
$
Thus, we find that it possible to write fitness equivalently in two different ways that relate the change in frequency of individuals to benefits and costs associated with a social phenotype.
Question 1 Is group selection equivalent to kin selection for any evolutionary game or exclusively for the prisoner's dilemna?
They are always equivalent no matter what game is being played or how the population is structured if you are happy use the regression methods described to define fitness, benefits, costs, and relatedness. Many people have issues with the regression methods though. See Allen et al for a discussion of this. Others argue that kin-selection holds in every case and that the problems with kin-selection stem from a misunderstanding of the methodology. See Gardner et al for a discussion of this.
Many biologists find the debates in social evolution about kin/multi-level selection unhelpful (at best), and choose either framework to work in based on the question at hand. A few are very partisan on either side. There is a great deal of misrepresentation floating around about both, so read papers making strong claims with skepticism and caution.

Nice answer. I realize that the answer is under debate and is not as easy as I hoped it to be. At first sight, I don't feel like your explanation of Queller's article is a proof that Kin and group selection are the same things, but I'll have a look at the paper as well as to the other papers you linked. Thank you! +1

@Remi.b you raise a good point - it isn't really a proof in the mathematical sense, hence my use of apostophes! However, it is the paper most commonly cited as showing the equivalence of the two approaches. Let me know if you have further questions regarding the paper and I will update my answer accordingly. Thanks

Are kin selection and group selection the same thing?  Yes and no.
Yes:  These days people tend to use the "direct fitness approach" (Taylor and Frank JTB 1996).  It turns out that this is based on EXACTLY the same equation as is contextual analysis, which is the currently favored approach for measuring multilevel selection in natural populations (Goodnight 2013 Evolution 67:1539).  So, yes, at this level they are the same thing.
No:  Although they use the same equation kin selection theory solves for the optimum, whereas MLS theory solves for the strength of selection at the state of the current population.  In other words, they are asking two different questions.  kin selection is asking "where are we going" MLS is asking "how are we getting there"
No (2):  Kin selection is oriented towards genes.  MLS is oriented towards phenotypes.  This may seem like a trivial difference, but in structured populations the relationship between genotype and phenotype can become impossibly complex.

Welcome to Bio and thanks for your great answer. The citations are much appreciated. +1 To improve your formatting - could you add links to your citations? I added one link and that should get you going to add the Taylor&Frank citation. You can use the chain symbol, or manually key in the weblink.

Regarding the equivalence of MLS and kin selection, here is how I see the equivalence between these two approaches to selection.
MLS says that cooperation is favored when the response to
between-group selection outweighs within-group selection. Price's
equation tells us that this happens when the genetic variance
between-groups is higher than the genetic variance within-groups.
This is equivalent to saying that altruists tend to interact
with other altruists and, accordingly, that the coefficient of
relatedness increases.

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text:
"Secondary refractory period" of class 1 neurons?
In Izhikevich's Dynamical Systems in Neuroscience, p. 199, one learns about a class of neurons that can be forced to fire with maximal frequency:

Injecting a constant current of appropriate size (here: 320pA) a neuron of this class can be forced to fire with maximal frequency. The frequency is determined by the refractory period of the ion channels producing the action potential.
What the picture does not show is what happens beyond the half-of-a-second that is recorded and displayed: Will the neuron continue to fire with maximal frequency forever, as long as the input current is applied? I guess eventually it gets exhausted and must pause or lower its frequency.
My questions:

General: For which injected currents can the neuron continue to fire with the corresponding frequency forever? Beyond which current $I_{critical}$ does it have to make a pause or lower the frequency?
Qualitative: Beyond $I_{critical}$, does it make a pause or does it lower its frequency? Or both?
Quantitative: If it makes a pause, when and how long is it? (This could be called its "secondary refractory period".) If it lowers its frequency, when does it start (to lower its frequency) and by which rate?

Your picture shows tonic firing, not bursting; tonic firing is sort of the opposite of bursting. Try a Google Images search for "neuron burst firing" or something similar.

Burst firing is due to intrinsic properties of some neurons that cause them to fire >1 action potentials in a group. Maximal tonic firing is just the maximum firing rate of a cell given a constant depolarizing current.

Bursting!=maximally firing - those are not the same things. Have you tried a google image search for "neuron burst firing" to see how that is clearly different from the activity you show here?

Let us continue this discussion in chat.

I removed any mentions of "bursting".

@BryanKrause: I asked a more mathematically oriented question at Mathematics SE. If you want to take a look...

I think you've already picked up on this from your realization that E is not a depleting resource but rather one that is regenerated: as long as that regeneration rate is greater than the loss rate, there will be no change in the capability to fire. Hopefully that helps answer some of your questions here.

@BryanKrause: It does (help).

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What are the response frequencies of sensory neurons?
Both visual and auditory stimuli are sent to the brain via ganglion cells (retinal resp. spiral). Both are the first cells along their resp. pathways that produce action potentials.
My question concerns typical frequencies of action potentials sent along the axons of the visual vs. auditory ganglion cells as a reaction to a "typical stimulus", i.e. a medium long, medium strong signal of some fixed frequency (e.g. light: red, sound: 440Hz) against a white resp. silent background.
Are these frequencies of comparable range, or does one type of ganglion cell (retinal vs. spiral) fire with a significantly higher or lower rate than the other?
(The question would not make sense, if the physical frequencies of light and sound - which trigger the receptor cells - would be coded by frequencies of action potentials. But I assume that this is not the case, is it?)

The auditory brainstem shows "phase-locking" typically up to 1-3Khz at most; 3000Hz is an incredibly high firing rate for a single neuron, but this phase-locking is achieved not by individual cells firing in-phase with an auditory stimulus, but rather with a population of cells that tend to fire in-phase, such that if you average across the population you get a phase-locked population volley.
In some cases, in some animals, this phase locking can even get to the higher frequencies (see here for example).
However, this phase locking seems primarily important for sound localization via interaural time differences. Frequency itself is encoded by which population of hair cells is activated, according to the properties of the basilar membrane. Firing rates of individual spiral ganglion cells are only faster than 100 Hz at very high stimulus intensities.
Similar to the spiral ganglion cells, retinal ganglion cells primary encode intensity information in their firing rates.
However, in both cases, it's important to recognize how crucial adaptation is in sensory systems. RGCs in particular fire primarily to transients, so it is typical to use light flashes, drifting gratings, or other dynamic stimuli. The response to a "medium long, medium strong signal of some fixed (wavelength)" is going to be brief, followed by silence, not a constant response like you imply.

You write "phase-locking" in quotation marks, as if you were not quite sure what it means, resp. you don't want to commit to a specific definition. Can you give me a source where phase-locking is explained in detail which I can rely on?

The quotes were not to indicate I don't know what it means, but to indicate it is a specific term rather than a descriptive one. https://www.google.com/search?q=phase+locking+in+the+auditory+system gives many sources.

Please don't feel offended, I know that you know what you are talking about. Alas, your hint how to google doesn't help me a lot (short-termed). There's too much to go through (and too specific: "auditory system"). Do you have a more general and concise reference?

I don't have a specific reference in my pocket, which is why I am suggesting you look at some relevant search terms and try to find the answer on your own.

Just for the record: I already gained some knowledge about "phase-locking" (its meaning, its origins, its contexts, its mechanisms,..) but am still missing an overarching overview.


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Typical firing patterns of (some) motor neurons
Since it is not so easy to imagine and visualize the behaviour of the muscles in the fingers, hand, and arm of a violinist performing a fast and accentuated trill vs. a slow and soft vibrato, I try to imagine and visualize how corresponding motor neurons do fire.
I assume there are some motor neurons that are immediately responsible for the speed and amplitude of the movement of a finger performing a fast trill, and — partly the same, partly others — that are immediately responsible for the speed and amplitude of the movement of the same finger performing a slow vibrato.
The behaviour of these motor neurons — during the trill or the vibrato — can be recorded as corresponding sequences/trains/bursts/chats of spikes.
I wonder:

How may these spiking and/or bursting patterns look like?

Maybe like this?

fast and accentuated (= large amplitude) TRILL, each group corresponding to one movement (up-down) of the finger

slow and soft (= small amplitude) VIBRATO, each group corresponding to one movement (to-fro) of the finger

I can't answer the whole question, but what I do know:
An actionpotential can't have a small amplitude, the amplitude is always the same. If a neuron gets excited enough there will be an action potential, otherwise there won't.
"When the depolarization reaches about -55 mV a neuron will fire an action potential. This is the threshold. If the neuron does not reach this critical threshold level, then no action potential will fire. Also, when the threshold level is reached, an action potential of a fixed sized will always fire...for any given neuron, the size of the action potential is always the same. "
quoted from https://faculty.washington.edu/chudler/ap.html
Neurons have a maximum firing rate, a point where they cannot fire faster. (http://blog.eyewire.org/why-do-neurons-fire-at-a-maximum-of-about-200-hz/)
So to move faster your body can't fire faster. But it can fire more: more neurons fire if you want to move faster, and less neurons fire if you want to move slower: https://blogs.scientificamerican.com/scicurious-brain/when-you-run-fast-your-brain-runs-faster/
I do think your first row of spikes is kinda correct. Neurons fire as long as you move your hand/finger(s) in a certain direction, and as soon as these neurons stop firing and their complementairy neurons start firing you move your hand to the opposite direction.

"An actionpotential can't have a small amplitude, the amplitude is always the same" - this is not true in a series of action potentials. Many cells show amplitude reductions in their action potentials when firing several spikes in close proximity. Though I'm not sure why you brought this up since it has little to do with the question.

@BryanKrause do you think that there is going to be a somewhat continuous but erratic like graph at each spike points in the picture OP mentioned? something like seismograph?

@gfdsal Not sure what you are referring to or asking.

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Why is there smooth muscle in our bronchioles?
Having muscle tissue in our bronchioles that can constrict seems like a poor choice for tissue. Why would our airway want to ever close up? Wouldn't it be more beneficial for our bronchioles to just remain open?

There are at least two things to consider.
First, ability to limit airflow is a defense mechanism for animal. Imagine getting into area of some sort of toxic evaporation, e.g. CO2 cloud near volcano , then it makes sense to decrease delivery of toxin via lungs to minimum. As I understand, that is what an allergic asthma attack. (Sorry for not providing good enough source of that)
Secondly, you are incorrect in assuming that normal state is "dilated". Dilation of branchioles is sympathetic ("fight-and-fly") response of the nervous system to something like danger, that requires short-term boost in energy production. That is, by default, your airflow is limited. Probably, to limit amount of energy you effectively burn via oxygenation. But most importantly, you leave yourself a reserve in terms of oxygen supply for critical moments.
Some more information you might find here.

Our airways close up to make them more efficient. The respiratory tree from the mouth to the terminal bronchioles can't absorb oxygen, so each time you breath all the air moved into and out of the trachae, bronchi and terminal bronchioles is extra unnecessary work for the muscles. We can make it more efficient by shrinking the size of those tubes down by contracting the smooth muscle.
When you exercise these small tubes won't be large enough to get enough air in and out, so the smooth muscle relaxes to allow more air to move into the lung faster. It's a bit less efficient, but necessary when you need a lot of oxygen.


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Ideal lab glassware cleaner for molecular biologist
I'm using Alconox powder detergent to clean my laboratory glassware, the powder is a pain to store near the sink and to use. Would like to make a   concentrated liquid to use in its stead.
I have tried a few things, obviously just water and mixing, even up to overnight. It really doesn't go into solution very well, even at seemingly low concentrations. Ive got it up to about 20% w/v but I've tried heat, glycerin, adjusting pH to ~8.8, adding EDTA. I have not added these things in any scientific way, just kind of pouring glycerol or EDTA in.
Wondering if anyone in here has experience with detergents like this , or has done something like this before. Thanks in advance!

for usual cleaning a regular liquid detergent should be sufficient. I subsequently wash it thoroughly with milliQ water. Unless you are doing some sensitive experiments such as mass spectrometry, the minute amounts of residual detergent should not be a problem. For what purpose do you use Alconox ?

Obsessive compulsive disorder mostly. I like having clean glassware. I pulled up msds on alconox and liquinox. They are very different chemicals. Would have been  nice to be able to make a viscous concentrated liquinox out of all the powder alconox we have though.

Have you considered bleach to clean things, and then a general purpose liquid detergent to help carry away the residue if any remains? A nice bleach soak will destroy everything biological.

SparKleen because the name is awesome

If you're just using it for hand washing, you probably don't need an ultra-concentrated solution. A very small amount of detergent goes a long way. (Think about it - what would you do with an ultra-concentrated solution? Probably squirt a small amount on the glassware and then add water.)
What I've seen done is to make your "dilute" solution of Alconox, put it in a squirt bottle, and then use a healthy squirt of it when washing. In fact, you probably find you don't even need a healthy squirt of it. A small amount of even the dilute solution is fine.
The approaches I've seen have been rather cavalier - basically just throwing a bunch of the powder in the squirt bottle and filling it with water. You quickly get a sense of how much will dissolve, and if you use a squirt bottle with a drop tube that doesn't go all the way to the bottom, you don't need to worry if not all of it dissolves.
Alternatively, as you you seem the fastidious type, you can fill another (non-squirt) bottle partway with Alconox, the rest of the way with water and let that sit (perhaps with occasional mixing). This should give you a saturated Alconox solution, which you can use to fill the squirt bottle, being careful not to transfer residual powder from the bottle. Then you can add more Alconox powder and water to the stock bottle, let dissolve, and you should be ready for the next time.
Again, this may only give you a 10-20% solution of Alconox in water, but that should be more than sufficiently concentrated for most usages.

In our labs we just use something like palmolive or bleach followed by a 95% EtOH rinse and finally rinsing with DI water.

this has nothing to due with my question


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What does these $\Delta \Delta G$ numbers signify?
I was reading a paper and came across this table showing  $ \Delta \Delta G$ numbers of different nucleotide sequences in DNA/RNA. I know that $\Delta G$ is free energy and $ \Delta \Delta G$ is relative free energy with respect to L. variegatus 5 S RNA gene. What does this numbers signify actually?

Can you please link the paper. It is important to have the context of the measurement.

These are likely binding energies relative to a known interaction. Without seeing the paper, it's impossible to say for sure.

Very basically, what they've done in the paper was incubate different DNA sequences with histone octamers which were then separated on a gel. From this they could measure the ratio between nucleosomal and free DNA, which represents an equilibrium constant. This can then be expressed as a change in free energy ($\Delta G=-RTln(K)$), which is what the table shows. A lower (more negative) free energy change corresponds to a higher equilibrium constant which represents a greater proportion of DNA bound to histones. In other words, the free energy change quantifies the propensity of these different DNA sequences to form nucleosomes.


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What is the non-local impact of a large-scale destruction of coral reefs?
I'm reading today that the "bleaching" of coral reefs is progressing with unanticipated speed. Bleaching is occurring at a scale which was previously believed to be decades in the future.
Bleached corals, according to the article in the New York Times1, have shed their symbiotic algae in order to protect themselves from toxins the algae produce when the water temperature rises above a certain threshold. If cooler temperatures are not restored quickly, the corals will die. This has happened with many affected reefs in the past and seems likely with the latest wave of bleaching too, given the apparent long-term increase in global water temperatures.
Even if the article does not say so directly it appears to me a distinct possibility that most coral reefs will simply disappear from the planet within just a few decades.
What impact will the disappearing of the coral reefs have on the oceans and the life in them, and ultimately on the whole planet? We know that reefs are the core of a complex and rich ecosystem. Which plants and animals outside the immediately affected areas depend on it? Are there migratory species who lose the reefs as a seasonal base? Which food chains will be interrupted, or over-provided? Do coral reefs have other functions like filtering, causing or reducing turbulence, influencing currents? Have there been studies or simulations which try to assess the global consequences?

[Edit]: I know the question is very broad. I'd be happy about answers to just one aspect. Specifically I'm interested in effects which are not obvious and/or have not been broadly publicized. Links to scientific publications would be awesome. Maybe somebody is working in the field, or knows somebody?
For example, affecting migratory species (fish or birds) may have unexpected consequences in otherwise unrelated parts of the world, similar to the effect of disappearing wetlands on migratory birds.
I also think it's possible that non-reef fish which are important food sources directly or indirectly depend on coral reefs. Are there specific examples?
I'm less interested in answers concerning species just living around the reef because cause and effect (though significant) are obvious.

1Which refers to the original study in this nature article.

Disclaimer: the question is too broad and speculative to some extent, and thus less likely to get complete answer (at least in a single post).
As is already known, thousands (or millions) of species are dependent on coral reefs for their survival and are likely to get extinct (unless they adapt) because of destruction of coral reefs. A short list includes fishes, turtles, sharks, eels, crabs, shrimps, urchins, sponges, algae, and so on1. Now, telling which of them will be immediately affected will be largely speculation, yet the first 5 species to lose the race, as per a speculation, are2:

Butterfly Fish: Species of fish that are obligate corallivores, such as many butterfly fishes (Chaetodontidae), feed exclusively on coral polyps. Obviously, survival would not look good for these fish if their food source disappeared.  Like parrotfish, butterflyfish assist in keeping algae from smothering corals.

Spiny Lobsters: Spiny lobsters rely on coral reefs for protection, especially during their vulnerable molting episodes. These lobsters have a significant role in maintaining a balanced ecosystem. Like sea otters, spiny lobsters are predators of sea urchins, which feed on kelp forests and can destroy them if populations are not predator-controlled. Kelp is important for many reasons, including those listed on OGP’s kelp recipes.

Whales (and dolphins): Many scientific studies have substantiated that dolphins and whales have extreme emotional intelligence. Sadly, their intellect won’t be enough to help these animals if coral reefs are destroyed. Dolphins and most whales (cetaceans) are predatory animals and they are all carnivorous. It’s a fish eat fish world and many fish survive by consuming fish that rely on coral reef habitats. Every species lost will affect another…and another…and many others. So, these come under the indirect adverse effects of coral depletion, since they themselves don't rely on corals.

Whale Sharks: Whale sharks are an important indicator of marine productivity and have a role in the conservation of other marine organisms. Although there has not been adequate research to understand why whale sharks are dependent on coral reefs, it has been shown that decreases in whale shark populations in the late 1980’s and early 1990’s may have been linked to increased rates of coral destruction.

Hawksbill Sea Turtles: Although all sea turtles would be affected by the loss of corals, Hawksbill sea turtles have become critically endangered species and are highly dependent on coral reefs for their food sources, mainly consisting of sponges. Sea turtles play significant roles in their ecosystems in that they help with nutrient cycling from ocean to land, maintaining healthy sea grass beds, and balancing food webs.

Coral reef fishes are in general rather sedentary and territoriality and home range behavior patterns are highly developed. However, many species migrate, often relatively long distances compared to body size and often with spectacular precision. Migrations in reef fishes may be associated with3:

Life history: movements of
planktonic larval stages to reefs or movements of juveniles from nursery areas to reefs
Seasons: precisely timed spawning aggregations drawing fishes to particular locations from wide areas of the reef
Diel patterns: movements to and from feeding or resting areas associated with dawn and dusk.

Another species, which is to be affected, though not immediately, by destruction of corals, is humans(!). As per experts4, coral depletion will bring threat to not only income and economic stability, but also to survival by causing depletion of food and increased risk of coastal disasters, which are likely to kill millions.
Coral reefs also serve many purposes, a few of which are5:

protect coastlines from the damaging effects of wave action and tropical storms
provide habitats and shelter for many marine organisms
are the source of nitrogen and other essential nutrients for marine food chains
assist in carbon and nitrogen fixing
help with nutrient recycling.

Finally, I suppose most of the species will be affected because of depletion of source of food rather than more-than-sufficient amount of something. Also, I was unable to find any simulations of the global consequences of destruction of coral reefs. I hope this much would help!
EDIT: I did some online research, and found lack of research about this topic. But I found a paper regarding relationship between seagrass, mangroves and coral reefs6 regarding life cycle of migratory fishes. From the same:

Coral reefs showed the highest mean fish species richness and were dominated by adult fish, while juvenile fish characterized seagrass beds and mangrove sites...Our results suggest that connectivity of seagrass, mangrove, and coral reef sites at a species and site levels, should be taken into consideration when implementing policy and conservation practices.

It becomes evident from this research that depletion of coral reefs would certainly affect life cycles of these fishes, which include Scarus iseri (Striped Parrotfish) and Lutjanus apodus (Schoolmaster Snapper), which will, in turn, affect biodiversity in seagrass beds and mangroves, leading to further break in food chain in more diverse areas.
Some researches have also suggested that seabirds might also be on a high rank in this list. Seabirds survive by eating fishes, and studies have shown that increasing water temperatures halt their growth while higher sea levels cause them problems in nesting. Now, it has also been suggested that due to coral bleaching, many fish species which live on corals might either die or move to other places, because of which seabirds could also face survival crisis7.
Another point regarding your demand I'm interested in effects which are not obvious and/or have not been broadly publicized, specifically not been broadly publicized, is that it has now become difficult to find such papers. This topic (climate change) has become so much publicized now that all related papers get attention immediately. So, it is quite difficult to find environmental research papers about climate change which hasn't gained much attention. Yet, I'll keeping on adding more research papers as I find some more.

Interesting information in your edit. That's part of what I was looking for. My vague suspicion was that there is such interaction with other parts of the sea, and with food-chains extending elsewhere.

@PeterA.Schneider thanks, I am continuously looking for more such papers, but haven't found any more yet, as I told in answer why. I'll add more when I find more :)


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How are the axons in the white matter bundled?
I wonder if the following question concerning the axons in the white matter does make sense.
It is common knowledge that white matter is "composed mainly of bundles of myelinated axons" resp. is "mainly made up of myelinated axons, also called tracts" (= "bundle of nerve fibers (axons)".
For the following to make sense the following assumptions are made:

All axons making up a bundle are of approximately same length $L$. (Of course not all axons have the same length!)
Axons run in parallel as long as possible.
Axons normally don't branch inside the white matter. (Or is this a totally unrealistic assumption?)

Assume that it is a sensible definition of a bundle to be the set of axons running densely packed inside a virtual tube of length $l = \alpha L$, $\alpha < 1$. By this definition sub-bundles of a bundle would also be bundles, so we are interested only in maximal bundles.
It's important to notice that the definition of bundles depends on the parameter $\alpha$: smaller $\alpha$ means the axons must run in parallel only for a shorter distance (compared to their common length $L$), larger $\alpha$ means the axons must run in parallel for a longer distance. Smaller $\alpha$ means less but larger bundles, larger $\alpha$ means more, but smaller bundles.
Note that each bundle (independent of $\alpha$) defines two specific cortical regions (the two regions of the source and target neurons of the axons). For the smallest bundles (largest $\alpha$) their diameters correspond well to the size of the source and target regions (see picture below).
See here different partitions into bundles with $\alpha \approx 0.5$ (grey, 1 bundle), $\alpha \approx 0.75$ (blue, 2 bundles) and $\alpha \approx 1$ (red, 4 bundles):

For the sake of simplicity, assume that - for a given $\alpha$ - bundles don't overlap, i.e. each axon is contained in only one bundle. (This might be an oversimplification.)
My question is:

Can a rough estimate be given, how many bundles with a given number $N$ of neurons are there in the white matter (for a given $\alpha$)? (Choose the organism you like or know of.)

I suppose the largest bundle to be found in the brain is the corpus callosum which - as a tube - has a rather short length (small $\alpha$). It contains (in the macaca nemestrina brain) about $10^8$ axons.
For the sake of simplicity, let's consider only powers of ten. Then for a small $\alpha$ which would allow the corpus callosum to be a bundle, we will get as a "null hypothesis" (with purely hypothetical numbers of bundles!):

But maybe the distribution is somehow bimodal, e.g. like this:

Is a reliable answer to this question out of reach today? Don't we know enough about the long-range structure of the white matter? Or did just nobody take the effort to make this statistics? Or can I find it somewhere?


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The human brain in numbers II-A: synaptic connections per structural type
The overall number of synapses in the human brain is roughly 1,000 trillion, i.e. 10,000 synapses per neuron.
I assume that each structural type of neuron (basket, pyramidal, ...) has a somehow characteristic average number of in-coming and out-going synaptic connections, i.e. not every type of neuron will have the same roughly 10,000 in-coming and 10,000 out-going synaptic connections.

I am looking for a thorough overview of these characteristic numbers
  by structural type.

Ideally as a link to an existing resource, but if someone with a good overview over the literature would take the effort  to compile such an overview (like user another 'Homo sapien' thankfully did in his answer to The human brain in numbers I: neurons), it would be even greater.
What's for sure: such an overview will be a) not complete and b) only estimates, so don't worry about that.

Please don't keep reposting effectively the same question. This is almost identical to the one that was closed last.

Also, very importantly, we actually gave you some great resources on the last question you asked to follow up on, and you seemed entirely disinterested and expecting people here to do your work for you. I found that really rude actually. It's like you are going to StackOverflow and asking somebody to write you a sorting algorithm.

I don't understand your discontent.. I just ask for specific references which seems perfectly ok at other SE sites. "BRAIN project's web site" is not a specific reference in my humble opinion.

Concerning your first comment: I was explicitely advised to split up my question  (to make it less broad) and followed this advise. What's wrong with that?

Your new question is absolutely not any less broad than the last one, it just reflects a basic misunderstanding of neuron morphology. If you plan to make 2 more versions of this question, still asking the same question in different angles, those will also be too broad.

To give you an idea of what is acceptable, see something like https://biology.stackexchange.com/questions/57495/cortical-projections-from-layers-2-3-back-to-4/57519#57519 This is asking about just a single pathway, between two layers of cortex, only talking about excitatory cells, and I wrote a multiple page answer.

Would you please be so kind to correct my basic misunderstanding. Aren't synapses something one can count? And why shouldn't there be a difference in number between different morphological types of neurons? I just have no clue where my basic misunderstanding lies.

Let us continue this discussion in chat.


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Development of the commissural fibers
Having a look at the corpus callosum one sees that the axons passing the corpus callosum (the  commissural fibers) connect mirror-symmetric counter regions of the cortex (roughly).

Is there a model that explains how the growing axons find their way to their far-away targets in this highly structured way?
(One possibility would be that the counter regions originally were close to each other but separated during brain development.)


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Longest pathway from sensory to motor neurons

How long is the longest pathway a neural signal can take starting from a
  sensory neuron and ending at a motor neuron (without loops)?

[This is a purely theoretical question concerning only the synaptic connectivity of neurons, not the propagation of real signals.]
The number I am looking for must be greater than 8 because there are 6 layers in the cerebral cortex and 2 layers of motoneurons: upper motor neurons and lower motor neurons.
The question then reads:

How long is the longest pathway from a sensory neuron to layer I of
  the cerebral cortex?
How long is the longest pathway from layer VI of the cerebral cortex
  to an upper motor neuron (which may lie in the brain stem)?
How long can be "detours" inside the system of layers of the cerebral
  cortex?

I assume that the cortex is not strictly layered, so that a neuron in layer $i$ normally projects on a neuron in layer $i+1$ directly, but possibly also via $N$ interneurons. Theoretically, the upper bound for $N$ is just the number of neurons in the brain, but in practice will be much smaller. To get a sensible number, it may be necessary to neglect detours that are too long because they are too sporadic and/or useless. (But how to define and quantify sporadicity and usefullness in this context?)
If my question doesn't make sense in the way I ask it here: How could I improve it to make it sensible?
[Of course, I am not looking for exact numbers but only for estimates, resp. estimates of lower and upper bounds.]

Theoretically the number could be huge, if you include both the sensory and motor cortices, plus cerebellum, memory areas for specific activities. But much of the processing will occur in separate areas. Would you count every synapse involved? Or every neuron? Given that a neuron can have 1000's of synapses, not all of which will be involved in the specific action. It might help to think of a specific pathway/action, e.g. reflex response to pain, sight, sound. Or a non-reflex, e.g. a chosen response to a given stimulus.


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What is dendritic processing good for?
Can - on a very coarse level - the functional role of dendritic processing as a whole (at least one of possibly many functional roles it might play) be described as rewarding spatially coherent synaptic inputs when creating action potentials?
This would make sense, when the mapping of neurons to synapses was neighbourhood preserving (in the sense of topology): the efferent synapses of two neurons in close vicinity (in other parts of the brain) sit in close vicinity on the dendritic trees of their common target neurons.
Then it would be desirable, that when a group of $n$ neurons in close vicinity to each other (e.g. a nucleus) projects to a subtree of the dendritic tree (i.e. synapses in close vicinity) and those neurons are firing at the same time, this should evoke an action potential in the target neuron, but not, if $n$ synapses spread randomly over the dendritic tree are active at the same time. To achieve this could be the (or one) function of dendritic processing.
As I said: this is a very coarse level of description, and to be taken with some grains of salt.
(The whole question would break down if there is no such neighbourhood preserving mapping from neurons to synapses.)
References
I've read the following articles (but maybe not carefully enough to be able to pin them down to the short form above):

Buonomoano/Maass, State-dependent computations: spatiotemporal processing in cortical networks
Stuart/Spruston, Dendritic integration: 60 years of progress
Poirazi et al, Pyramidal Neuron as Two-Layer Neural Network (this article comes closest to the spirit of my question)
Magee, Dendritic integration of excitatory synaptic input

In lieu of a keyword for future google searches, the phenomenon you are describing is referred to as "synaptic clustering". I would rephrase the title of your question to reflect the main text into something like: "Is synaptic clustering reinforced by dendritic spikes?" Dendritic spikes can in principle have other functions as well, so your title suggests a more broad issue than what you are actually asking.

As you point out, a separate issue is if there is synaptic clustering, which is one of the 7 questions you asked here. Perhaps you should ask this in a separate question.

Is dendritic processing only done by dendritic spikes? In Rall's model there is dendritic processing but no dendritic spikes, isn't it? And I consciously didn't mention dendritic spikes.

It depends on your definition: dendritic spikes, NMDA spikes, NMDA plateau potentials. 'Dendritic processing' is better, my point wasn't to remove that part.

Action potentials are always based on coherent synaptic inputs. So dendritic processing means rewarding specific coherent subsets of synaptic inputs.
So to me it makes more sense to frame dendritic processing as allowing neurons to detect several different patterns (alternatively or at the same time).
In that case each dendritic spike encodes a different pattern. This allows a neuron to play roles (or maybe the same role) in several different contexts (or possibly in different combinations of contexts). If this is the case the neighbourhood mapping you describe doesn't have to exist (though it still might).

I added "spatially" to make clearer what I mean. BTW: How can a spike encode a pattern?

If a neuron always spikes when a certain pattern of other neuron activations occurs, this neuron's spike can be said to encode the pattern. The pattern is not encoded in the specifics of the spike, just in the fact of the spike.

So do you mean "dendritic spikes" (of which there are many) or "action potentials" (of which there is only one - in response to a certain pattern)? The latter is what I understand when a neuron spikes.

I mean that dendritic spikes encode for specific patterns, but these dendritic spikes may lead to the neuron spiking. So the neuron is able to encode many (combinations of) patterns.

This would be the second step of understanding. My question concerns a first step.


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Distribution of dendritic spike generating ion channels on the dendritic tree
Is there evidence that dendritic spike generating ion channels are distributed unevenly on the dendritic tree, with higher density near the branching points of the tree than along the unbranched segments? Or the other way around? Or is there no observed correlation between density and local geometry of the tree?
If there is, i.e. if those channels tend to condense near branching points: which mechanism drives them to do so?
[I believe it could make sense to have the spike generating channels condensed at branching points: there they could act as local logical gates, summing up the inputs of the afferent branches (see Poirazi et al, Pyramidal Neuron as Two-Layer Neural Network).]
[There is also a figure in Larkums article on A cellular mechanism for cortical
associations in which at least one electrode is placed close to a branching point. Maybe because there are extraordinarily many spike generating channels?]

Larkum is likely recording near that branch point because they are trying to record potentials in the distal dendrites, but the technical ability to patch those very distal dendrites is incredibly more difficult than patching the large apical dendrite.


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What is the number of synaptic vesicles in an axon terminal?
Is there any research as to the average number of synaptic vesicles per axon terminal in a human neuron's axon? On top of that, what percentage of those vesicles are used in a single action potential cycle i.e. membrane depolarization, hyperpolarization and return to resting potential?

Let us continue this discussion in chat.

Short answer
Several to thousands of vesicles per synapse. In the cortex, 0-1 are released per typical synapse. At neuromuscular junctions or specialized CNS synapses there can be more.
Longer answer
It doesn't make much sense to talk about an "average", since there are different types of synapses with very different properties, but we can talk about some examples from synapses used as models. In Rosenmund & Stevens, 1996 they estimate about 10 readily releasable vesicles at one synapse in hippocampus. Rizzoli & Betz, 2004 count hundreds at a frog neuromuscular junction.
In hippocampus and neocortex, for a typical synapse there are from 0-1 vesicles released per spike. Higher release probabilities to activation of single cells are achieved by making multiple synaptic contacts; in a typical cortical synapse, there's really just one active site so a max of one vesicle released (Silver et al 2003; Auger & Marty 2000 and Abbott & Regehr, 2004 also discuss this a bit).
Short-term plasticity can strongly influence release probability (Abbott and Regehr 2004 is a good reference to thinking about synaptic computations like this). Multiple action potentials occurring quickly can cause a sustained increase in calcium concentration in the presynaptic cell, increasing release probability at otherwise low-probability synapses (short-term facilitation). Alternatively, many spikes over a longer time period or at higher probability synapses can exhaust the pool (short-term depression). Rosenmund & Stevens used this to estimate the readily releasable pool size of about 10: they triggered a ton of rapid-fire spikes and counted release events, finding only 10. Rizzoli & Betz mention that 10 seconds of 30 Hz stimulation (300 pulses) depleted the available vesicle pool.

Abbott, L. F., & Regehr, W. G. (2004). Synaptic computation. Nature, 431(7010), 796-803.
Auger, C., & Marty, A. (2000). Quantal currents at single‐site central synapses. The Journal of physiology, 526(1), 3-11.
Reyes, A., Lujan, R., Rozov, A., Burnashev, N., Somogyi, P., & Sakmann, B. (1998). Target-cell-specific facilitation and depression in neocortical circuits. Nature neuroscience, 1(4), 279-285.
Rizzoli, S. O., & Betz, W. J. (2004). The structural organization of the readily releasable pool of synaptic vesicles. Science, 303(5666), 2037-2039.
Rosenmund, C., & Stevens, C. F. (1996). Definition of the readily releasable pool of vesicles at hippocampal synapses. Neuron, 16(6), 1197-1207.
Silver, R. A., Lübke, J., Sakmann, B., & Feldmeyer, D. (2003). High-probability uniquantal transmission at excitatory synapses in barrel cortex. Science, 302(5652), 1981-1984.

Sorry for not being specific. My focus is the neurons in the celebral cortex, will the answer still apply for that?

@Peter Neocortex and hippocampus are two parts of what is referred to as cerebral cortex.

Yeah, just seen that. I wrote the comment immediately after reading the first paragraph in second section :D


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Can you identify this caterpillar?
Hi on my walk home in may i found this larvae crawling on a small road between grass and fields. All the yellowish larvae i know are looking totally differend than this. The origin of this picture is in the middle of Germany. The larva is is around 5 cm long and 1 cm tall.

Thank you


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Why isn't the ovum reabsorbed into the body (like sperms) if it is not fertilised?
I have read that when sperms are not ejaculated out of the body, they are broken down and reabsorbed. Why can't the ovum be reabsorbed into the body instead of shedding it out during menstruation.

Are you referring to menstruation?

yes, why do females go through menstruation instead of just reabsorbing the ovum?

Apparently they are
Technically speaking, the ovum is not shed because it does not exist unless and until fertilization occurs.  Development leading up to the ovum otherwise stops before the final division of meiosis.
Now, you can ask why the body doesn't reabsorb oocytes, but then the answer is that it does, in a process called follicular atresia, with one or a few exceptions per  month.
Now what about that secondary oocyte that isn't fertilized?  Well, it's time to pull out a lovely article from 1917 (Harry Carleton).  Why are we so unable to match such prose today?  The paper describes a mouse study, giving quite recognizable descriptions of membrane blebbing and nuclear fragmentation characteristic of apoptosis in a decade during which it is usually said to have been forgotten.  (A 2005 work reports this for unfertilized human oocytes) Carleton likens the apoptotic changes he observed to atresia in the ovary, and says that the oocytes are reabsorbed by phagocytic cells.

Menstruation is not the shedding of the ovum, it's the shedding of the uterine lining, which prepares for fertilization even if fertilization does not occur. If there is no implantation of a fertilized ovum, the prepared lining is shed. That an ovum might be included in this shedding event is completely inconsequential (a human ovum is about 0.1 mm in diameter).
In most species, the lining (endometrium) is absorbed. The evolutionary reasons why some animals do not absorb the lining are unclear. Some speculation in the Wikipedia article includes that building a thicker lining ahead of possible fertilization is the actual beneficial trait being selected, and that the inability to reabsorb the endometrium is merely a consequence of not being able to reabsorb the thicker lining quickly. Evolution isn't perfect, and just because something could happen differently doesn't mean it will, especially if there isn't strong selective pressure for or against it. There is also previous Q&A on this site about the evolution of menstruation.


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Do the light and dark reactions happen at the thylakoid?
My professor said that light reactions and dark reactions happen at the thylakoid. He did not choose the answer that "both reactions happen at chloroplast".
Isn't the thylakoid a part of the chloroplast?

The light dependent reactions involve an electron transport chain with enzymes embedded in the thylakoid membrane. NADPH and ATP are generated in the stroma and water is oxidized in the thylakoid lumen.
I'm not sure if it's correct to say that the light independent reactions occur "at the thylakoid" since, in plants, they could really occur anywhere in the stroma (not necessarily "at" the thylakoid, and certainly not within it). It's possible that the enzymes involved in the light independent reactions are somehow localised at the the stromal side of the thylakoid membrane for efficient use of substrates, but I have no idea if that is actually the case and I don't have time to review any literature.
In plants, all of these processes occur in the chloroplast. However, photosynthetic bacteria don't have chloroplasts so you can't categorically say that the photosynthetic reactions occur only within chloroplasts. Cyanobacteria do have thylakoids where the light dependent reactions occur.


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What's happening in the "C" and "T" stripes of a covid test kit?
I have a COVID home test kit which produces C and T (control and test) stripes when the solution is applied to the strip. Something similar happens in pregnancy test kits.
I understand the purpose of a control and test response, but what is the difference in mechanism?
In a negative test, why will C show up even though T does not?
In a failed test, why will C not show up even though T does?
Also, these stripes are somewhat distant from the spot at which the solution is applied. Why the separation?

@tyersome Laboratory techniques in biology are on-topic here, and though I think there is an argument that it would fit more securely at Medical Sciences it's reasonable to have it here as well.

@BryanKrause — OK, I'll withdraw my close vote.

This is a lateral flow assay.

[image source]
A sample is applied at one end of the strip and flows across to the other side by capillary action. It first encounters antibodies against the target antigen and which are conjugated to some reporter (in the image above, the reporter is colloidal gold). When the sample contacts these antibodies, they too will begin to move across the test strip. If the target antigen is present in the sample, in your case COVID, these antibodies will bind to it.
The sample then crosses the test line. The test line contains antibodies, immobilized to the strip, against the target antigen. If the sample contains the target antigen, it will bind to these antibodies and stop moving. Since the antigen is also bound to the reporter antibodies encountered earlier, these antibody/reporter conjugates will become concentrated at the test line and produce a visual change on the strip.
Finally the sample encounters the control line. The control line contains antibodies against the reporter antibodies. Regardless of whether the target antigen is found in the sample, the reporter antibodies will bind here and produce a visual line. The purpose of the control line is to give a visual indication that the sample has in fact travelled across the whole strip.
In a negative test, the lack of antigen means the reporter antibodies will not stop moving at the test line and will only be bound at the control line.
There are several possible reasons for a failed test (when no lines are visible or the test line is visible and the control line is not). Among these, it’s possible the sample has simply stopped migrating across the strip before reaching the test and/or control lines. Or it could be that the antibodies have degraded due to improper or prolonged storage such that they no longer bind properly.

Note that this answer is just a general description of a sandwich lateral flow assay. There can be variations of this procedure in specific tests.

There's another possible explanation for test failure - antibodies failed to bind. This could be a result of improper storage, with too hot most likely, followed by degradation of the antibodies (time).

@bob1 That’s a great point, thanks. I’ve updated the answer accordingly. Feel free to edit further.


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What is the water content of avian blood?
I'm doing a work about heavy metal contamination in seabirds. I have found that the toxic threshold for Cu in blood is 1,15 μg/g wet weight. That number refers to an average between some seabirds. I need to convert that value from wet weight to dry weight.

You're going to have to be a good deal more specific than that.  "What percent water" - does that include the water inside erythrocytes & WBCs?  And what species?  Some birds increase their hematocrit in preparation for migration, which could throw levels off; some birds live in the desert wheras others live in the water, which could effect normal "percentage of water."  Please [edit] your question to specify what research you have done so far and what information specifically you are after.  Thanks!

@Ifer May i ask you why do you even want to know this information?

Sorry, I'm new in the blog and I don't know how it works yet, but thank you for the advice :)


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Are there any organisms that do not respire?
I am not talking about not needing oxygen. I understand that there are organisms that do not need oxygen. I am asking if there are any organisms that do not respire. (Eg, only ferment) I would like to ask if there are any such organisms?
Edit:
Google searches show two contradicting points of view, as shown here and here

Homework questions are expected to show some prior research.

This is a genuine question, I am not doing any homework. I have done Google searches but nothing quite gives me the answer I am looking for (all answers relating to anaerobic respiration)

"homework" is not about the legitimacy of the question but the simplicity, simple questions are expected to show prior research to prevent mass asking of simple questions like homework questions. just edit your question to show some prior research and it will be fine. what and how did you research and what did you find and why is it not enough.

I see. Thanks for your suggestion! I have edited the post. Hopefully this is better!

Please state in (bio)chemical terms what you mean by “respire” and what you mean by “ferment”. Then everyone will be clear what you are asking. It may even enable you to find the answer yourself.

One example of a nonrespiring organism would be lactic acid bacteria
They're (nonrespiring) obligate anaerobes.
They don't respire, since they can't use an external compund (oxygen, nitrate, sulfur, iron, etc.) as a terminal electron acceptor. They instead use an internal terminal electron acceptor (e.g. Pyruvate). That requires constant regeneration of NAD. Lactic Acid Bacteria regenerate NAD by oxidizing Pyruvate to lactic acid. See this

If you are talking about organism that ferment, then you have LABs (described in markur's answer).
I am interpreting your question as you are asking an organism that is aerotolerant anaerobe (organism that cannot use oxygen but tolerate its presence). In that case, Cutibacterium acnes is an example.
Also, scientist recently discovered a multicellular animal that doesn't breath at all. A parasite named Henneguya salminicola having less than 10 cells hides in the muscles of salmon and does not breath oxygen. It is the only animal with no mitochondrial genome (a crucial portion of DNA stored in mitochondria that includes genes responsible for respiration). More about it in this article.


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How easy is it for quantum dots to enter the intracellular portions of cells?
As quantum dots have better quantum yield than organic dyes, many are being developed as a substitute for them. Nonetheless, could these substitutes be small enough to enter inside cells as current dyes do, or would they be operate outside a cell?

How big are quantum dots and what is their chemical nature? Likewise for the dyes you mention. If you want someone here to do the biology, you need to research and supply the chemistry.

@David quantum dots is a rather generic term, which describes many types of (mostly semiconductor) structures, with sizes of a few tens to hundred nanometers. The caveat is that these are usually an integral part of a much bigger semiconductor crystal. What is meant here are probably the QDs embedded in small colloidal particles - perhaps of micrometer size. Still, their chemical nature may vary wildly.

As far as I can tell, they do not (readily) cross the plasma membrane. This paper, "Cell-Penetrating Peptide-Functionized Quantum Dots for Intracellular Delivery", discusses strategies to deliver quantum dots into cells. These include:

Passive delivery done by coating the quantum dots in something that allows them to be endocytosed more readily (the authors do not seem to be optimistic about this strategy)
Mechanically faciliated delivery (e.g. microinjection)
Peptide-facilitated delivery by complexing the quantum dots with a cell-penetrating peptide

Since people are going through all this effort to get them in, it does not seem like they are cell-permeable without substantial modification.


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Simple model of dendritic processing
[I striked through some passages that might be misleading, resp. where I have been misled.]
Is the following - very simplified - model biologically (or physically) plausible, explaining how spatiotemporal processing on dendrites works, especially how dendritic spikes and short-term potentiation modification of synaptic strength might work together? (I've read the wikipedia article on NMDA receptors but found it quite hard to digest. It's not a gentle introduction into the topic.)
Let the blue dots be two ligand- and voltage-dependent ion channels (eg. NMDA-gated channels) sitting on two synapses.
Let the green dot be a voltage-dependent ion channel that generates dendritic spikes.
Let the red dots be voltage-dependent ion channels that generate action potentials.
Without the green channel (generating dendritic spikes), two simultaneously synaptic inputs with depolarization V0 which sum up at the branching point of the dendrite would not suffice to generate an action potential:

But with it, two subsequent synaptic inputs (e.g. in two synchronous spike trains) would, because the postsynaptic response is stronger due to an additional voltage signal from the dendritic spikes.

At time  t2 another synaptic input and the dendritic spikes arrive at the blue channels. The resulting depolarization  V1 >  V0 suffices to evoke an action potential.

Is this picture correct in principle? Do such processes actually occur? Or are the real processes quite different? (Of course, they will be much more complicated and superimposed with other processes.) At least some NMDA receptors (which are ligand- and voltage-gated) sit on synapses.

You need to refine your concept of a dendritic spike. Perhaps you also then need to read about spike-timing dependent plasticity, and just replace backpropagating action potentials with dendritic spikes. Also note that you started by asking about plasticity but your drawings try to explain action potential generation. I think this confusion means that you need to better understand how synapses work and how they change.

@vkehayas: I didn't want to talk and ask about long-term plasticity, but only about short-term, maybe even ultra short-term "plasticity", i.e. potentation. But maybe I am misusing the term "short-term potentation". I explicitly did not want to "model" long-term potentation, but understand the short-term influence of dendritic spikes.

What I wasn't aware of is the existence of extrasynaptic NMDA receptors. This changes my picture a lot: non-synaptic ligand-gated ion channels? (Never heard of before.)

What we usually refer to as short-term plasticity has to do with pre-synaptic mechanisms. You don't necessarily need extrasynaptic NMDARs -and people don't generally consider this as the mechanism- in order to explain dendritic spike generation and synaptic potentiation through dendritic spikes (see REFs in my first comment). Having said that, this may prove interesting to you.

FYI, I found your previous question perfectly valid and interesting (it has a lot to do with what I work on). It's just that it's demanding and will take me a while to answer.

Allow me one specific question here (or would you mind continuing on chat)? In the wikipedia article on NMDAR I read "current flow through the ion channel is voltage dependent" (to explain that NMDAR is not only ligand- but also voltage-gated). Isn't this always the case, even for leak channels?

Let us continue this discussion in chat.

To whom it may concern: Any reason for down-voting is welcome! (Maybe too many and distracting edits?) Should I know the answer by myself (by carefully reading the literature)? But the literature is vast and not always gives a clear picture. Or is it about the form of my question (too much blah-blah)? Or is the question too speculative? (I would agree: it is speculative. But at least it is somehow precise.)


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Criticism of the significance of fMRI studies
On the german Wikipedia page on fMRI I read the following

"There is some critisism of the basic assumptions and possible
  insights gained from fMRI studies which is based on a constructive
  component of the visualization of measured data, whereby it is more
  the ideas and expectations of the researchers than the actual
  processes that are visualized." (translation by me)

Can these "basic assumptions" and "expectations" be made more precise, and the way how they are reflected in the "constructive component", and how the constructive component in turn is reflected in the visualization (which doesn't necessarily show the averaged but true activities of neurons)?
Especially:

What do typical "basic assumptions" concern? The processing of single neurons, the inner connectivity and processing of groups of neurons, the collective processing of different groups of neurons, the interwovenness of neurons and blood vessels?
What is a specific typical "basic assumption" in the context of fMRI?
What is a typical "expectation of the researcher"? Finding a correlation between two regions of activity while performing a task?
What is a typical "constructive component"? A method of statistical analysis? Or something else?
How does an inappropriate "constructive component" typically lead to false fMRI visualizations?
Do fMRI researchers regularly check if a BOLD signal really (or most probably) reflects neuronal acitivity at that spot - and don't display it if not?

Not anywhere close to an answer, but you should probably be made aware of the dead salmon fMRI experiment.

I've heard of it.

Are you familiar with the expectation of one question per question? I like fMRI analyses. This is unfortunately too broad.

What is too broad? My question or your liking of fMRI


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The concept of high genetic load of disease and non-random segregation of deleterious variants
I'm new in this field. I was reading Shakeel et al. (2018) and came across the below sentences

The rate of emergence and distribution of deleterious variants in populations is important in determining the patterns of underlying genetic load for diseases, because the increased accumulation of genetic load of diseases due to non-random segregation of deleterious variants is so detrimental that fixation or near fixation of these mutations can play a significant role in the extinction of isolate populations with small effective population size.

I'm not sure to correctly understand the concept of the sentences, especially

genetic load of disease and its accumulation due to non-random segregation of deleterious variants.

Could you please kindly explain a bit to me about it?
Many thanks in advance

Can you please edit your post to link to the original paper?

Do you understand the definitions of genetic load, fixation, effective population size, non-random segregation and deleterious variants?

Hi, I added the link of paper. Yes, I knew, but not sure about genetic load and non-segregation in these sentences and totally the main concept of the text. Thank you in advance for any explanation and clarification.

Genetic load is the difference between the fitness of an average genotype in a population and the fitness of some reference genotype. Here the reference genotype would be a hypothetical genotype free from any deleterious mutations. They don't say "non-segregating" but "non-random segregation", likely referring to loci that are in genetic linkage, hence do not segregate independently. Does it help a bit? Do you manage to pinpoint to exactly what is unclear?

Thanks. Regarding, non-random segregation, as I searched it refers to non-random segregation of sister chromatids during meiosis. That's why I confused with its concept here. Any comments, please.


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What is the process by which fluorescent proteins in two photon microscopy are optically stimulated by membrane depolarization?
How does the flow of calcium ions through neurons cause the dyes to activate? The voltage is extremely small, so the dyes have to be extremely close to the neurons, which would disrupt the cells, so is there any additional process?
Fluorescent protein dyes are used, however this article prompted me to ask this question, which might be different:
https://news.berkeley.edu/2020/03/19/high-speed-microscope-captures-fleeting-brain-signals/

What type of dye are you referring to? Can you name one?

There are entire families of fluorescent protein dyes used, so it not a clear cut answer, which is why I am asking this question.

And I'm asking to narrow it down. Are you talking about calcium dyes? Voltage-sensitive dyes? Bath-applied dyes or genetically expressed? SE Q&A should be specific questions with specific answers, not for questions that beg an entire tutorial for an answer.

@C-Consciousness Small correction in your description. Fluorescent protein or fluorescent dye. Not Fluorescent protein dye.

I agree with @BryanKrause here. The question is a bit vague and there are multiple questions in a single question. The title says about membrane depolarization based dyes. Description talks about calcium dyes. Also lack of source validating "dyes close to neurons disrupting the cells". Finally as mentioned above either fluorescent protein or fluorescent dye. Fluorescent protein dye is technically confusing.

They are using a GCaMP, specifically GCaMP6s and GCaMP6f.
This is a genetically encoded sensor. Cells expressing the gene make a protein. The protein is inside the cell, in the cytoplasm. This protein includes a domain that can bind calcium ions. There are lots of other proteins in a cell that bind calcium, too, so this is just one of them.
When bound to calcium, the protein is in a different conformation than when not bound to calcium. When a particular wavelength of light shines on the calcium-bound protein, it emits a photon of a different color. Two-photon imaging takes advantage of a summation trick in that two longer-wavelength (reddish) photons of light combine to provide enough energy to release a single shorter-wavelength photon (greenish).
No voltage or energy from the cell is needed to operate the fluorophore, except the energy used to synthesize the protein originally and the small voltage changes that cause calcium channels to open and allow calcium into the cell. It only fluoresces because of the excitation light used in the microscope.
It would be possible that if you had too much calcium binding protein around it would actually bind enough calcium to interfere with cellular signaling. However, people use these proteins quite a bit and it doesn't seem to affect function all that much. Lab mice who have these proteins expressed throughout their brains all their life aren't any behaviorally different than wild type animals.

Thank you for your answer. This is more of a question related to the fluorophore. As I understand, green fluorescent protein transform ultraviolet-blue light to green light, however I am unclear how the protein do the non-linear photon conversion of infrared light. Could you expand on that? Thank you

@C-Consciousness The wikipedia page I linked on two photon microscopy explains it fairly well. It's not really anything special about biology, it's a chemistry/physics thing about excited states. Two long wavelength photons can have just as much energy as one shorter wavelength photon so it excites the molecule to the same state from which it then emits a photon is the short story.


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higher-order evolutionary adaptations
Most adaptations are what I'd call first-order. Cats evolve better eyesight; redwoods evolve to grow taller; male cardinals evolve attractive bright feathers. All of these changes were selected for because they directly make the organisms and their offspring more likely to reproduce. But it seems like there is a fundamentally different minority of adaptations, which I'll call second-order. Second-order adaptations do not make organisms and their offspring more fit directly, but rather they make an organisms' lineage more quickly responsive to selective pressures. Sexual reproduction is certainly the best example. The primary evolutionary benefit of sex doesn't come from increasing immediate fitness. In fact, I'd argue that many organisms' fitness is reduced by the fact that they cannot reproduce without a partner. Sex evolved not because it confers fitness to individuals, but because it confers adaptability to lineages: sexually reproducing lineages are quicker to evolve to fill new niches and avoid new threats. This is why I'd call sex a second-order adaptation, because it seems to be a rare adaptation which improves the process of evolution itself rather than the evolving organisms.
Is my understanding correct here, that sex is a fundamentally different kind of adaptation from, say, prehensile tails? If so, are there any other documented examples of second-order adaptations? Is there more accepted terminology than "first-order" vs "second-order" for this distinction?
Also, can anyone think of a third-order adaptation or pressure? Does that even make sense?
Possibly related: this post. But to be clear, I'm not asking about epigenetics.

When you say "fitness" do you mean individual or evolutionary?  Because in the context of evolution it usually means the latter, which is defined by reproductive fitness.  Evolutionarily, organismal aptitude is secondary to reproductive.  That's why there's a often a distinction, or even discrepancy, between natural selection and sexual selection

@Punintended Even "reproductive fitness" defined as something like "number of offspring" or even "copies of gene in the next generation" is an operational definition of the overall theoretical fitness concept. It works as a good estimator in many common situations, but it's perfectly reasonable to think about it on a longer time scale over multiple generations.

@BryanKrause Sure, but owls' better eyesight is an organismal fitness advantage that increases the probability / frequency with which that organism procreates.  On longer timescales it absolutely makes sense to consider organismal fitness, but I'd call reproductive fitness the end goal because of creatures like salmon, which die shortly after procreating

@Punintended I mean reproductive fitness. Changing my list of first-order adaptations to include a clear product of sexual selection.

I think that you are talking about evolvability (as I see it).
Sex is indeed a sort of a meta-adaptation, see e.g. The Evolution of Sex by John Maynard Smith. The math for this is rather well developed at this point.
In terms of higher order effects like e.g. sex, I think that you are really talking about ideas like evolvability, and questions about constraints on paths over high-dimensional fitness landscapes. Examples of this might include the evolution of genome structure, and the idea that certain genome architectures are more conducive to generating novel variation. A specific example (though one could argue about it) would be V(D)J recombination's role in the adaptive immune system.


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Which species is this in below picture? Can you please give me some details or reference link?

Can you name this species ? What are some characteristic ?
Size: 2.5 inches to 3 inches
Diameter: 1mm to 2 mm
Can crawl like millipede but little slow.
Always in red colour
Get fold like in one of image tile when touch.
After sometime it able to crawl again.
Has too many thin and small legs both the sides.
Mostly seen in June to August
Don't know if venomous.
Place : North India about 150km south from Himalya Mountain.

Could you please edit your question add more details, for example: how big was this animal? Where did you find it and at which season? Could you observe any details that might be hard to see on the images? etc. pp

For hints on the information we need for species-id questions you can click on the tag under your post.

It's a millipede, but which one is very difficult without locality.


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Flower Identification
I found a flower along the banks in north India ( near Ganges ) and I am not able to identify it. According to its leaves I thought it as Prosopis cineraria but I am not sure about it..
Leaf - pinnately compound
Flower- yellow coloured and
clustered
Other features include thorns on raches axile

Its Vachellia farnesiana Wikipedia link
Common name - Sweet Acacia, दुर्गंध खैर durgandh khair (In India)

Is it also called "Babool" as a common name?

Yes, "gandh babul". Common names may vary region to region also.

Can I please know, why my answer was downvoted ?

Well, I don't know...


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Visual maps of the neuronal membrane
There are lots of visual maps of the brain as a whole, especially the cortex, that show the distribution of "features" over a two-dimensional map, e.g. the Brodman areas (their morphology and their functions), or the cortical homunculi.
What I would like to literally see - on a quite different level of resolution - are density distributions of some "items" over the membrane of some prototypical neurons, especially:

excitatory synapses
inhibitory synapses (mainly on the soma?)
vesicles (not in, but near the membrane)
receptors (not only at and near synapses?)
the different ion channels
electrical synapses
sodium–potassium pumps

So my question is for instructive graphical displays of such distributions, may it be in an article, on a website, or in a book.
(I assume that none of the items above is distributed evenly. But maybe some are on a specific type of neuron?)

I can easily answer about excitatory synapses and perhaps about receptors, and some ion channels but your question is too broad and I'm afraid any answer is going to be too lengthy. Please consider breaking it up.

Why not breaking up answers? I am not especially interested in one of the "items", any single example is welcome (but of course I would appreciate as many as possible - this would allow for comparisons and detecting of correlations). At other StackExchange sites such questions are called "big list" question and are rather well-liked.

With biology, though, there is bound to be nuance involved. So, a whole discussion is required for any of the 'items'. Furthermore, when you ask about categories such as 'receptors' or 'ion channels' which each have numerous members you are expanding the scope of the question (and the answer) substantially. Lastly, you are not asking for just a list but rather the full distribution of each category on a cell, which requires more than just mentioning a single number.

In fact I am only asking for (samples of existing) "instructive graphical displays". If there aren't any: what a pity! If there is only one:I would be happy to see it. (I cannot see, which discussions are required to answer my question. Am I too naive?)

Also, this website's guidelines suggest to ask and answer specific questions (https://biology.stackexchange.com/help/how-to-answer), and for good reasons, I think. Making a thread tractable facilitates both answering and perusing.

Please tell me, what is unspecific about my question? In which respect is it vague? (I admit it is broad, because it allows for a lot of answers. This makes it a "big list" question. But what is bad about "big list" questions?)

For example, answering about excitatory synapses would require me to discuss how they were measured with sufficient detail that someone could evaluate what I would present. There are some technicalities that are important in those studies. It would also require me to mention in which cells their distribution was evaluated. Additionally, and based on the previous comments, it would be necessary to present the two contrasting theories and how they take this evidence into account. All these would be required in my view in order to give a comprehensive answer, just for this part of your question.

Let us continue this discussion in chat.


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Neuro-simulation tools
I know, this question is opinion-based, but where else should I ask it (or how could I ask it here without being flagged "opinon-based!")?
Which neuronal simulators - in the line of NEURON and GENESIS - are the "best", "mostly used", "mostly influential" today, not only in supercomputing, but also for small scale (personal) use?
The one I found best - even though designed for large scale applications - for my small scale (personal) requirements was nest, an explicit successor of NEURON. I have the impression, that nest is "better" for me than NEURON, in terms of usability, visualization, data exploration, ... but I don't know if there aren't even better neuro-simulation tools.
Would anyone mind giving his/her opinion?

I'd suggest asking questions like this that are a bit outside the normal SE rules in the Biology.SE chat. I'm most familiar with NEURON, though mostly in an educational rather than research context; I haven't used nest but if it is a successor to NEURON that's what I would recommend. One nice thing about NEURON is that there are so many examples/preexisting models to choose from, including some beautifully detailed volume reconstructions of real neurons; GENESIS may have the same, and I hope nest was able to preserve some of them.

I think it's a great question, and I hope it is not closed for being opinion-based. This is exactly the question I had years ago when starting on a project! Btw, you might also try the scientific computation SE as well.

I also think that this is a worthwhile, on-topic question, even if it is opinion-based.

I have only used NEURON and the DIY approach for small scale simulations.
You can have a look at this comparative table:

https://grey.colorado.edu/emergent/index.php/Comparison_of_Neural_Network_Simulators

which was last updated in 2014.
Except NEURON, GENESIS, and NEST, I have also heard positive comments about Brian.

Another option, maximal in terms of flexibility albeit also possibly effort, is to write your own. Of course, this presupposes some extra knowledge. However, I think coding your own gives a level of control that may be very difficult to attain with a library. Hence, it depends on the application.
(ModelDB has some examples of these.)
Of course, I don't recommend this for highly complicated cases (don't try to write your own code to solve SPDEs in volumetric meshes or something).
Using matlab or python (numpy & scipy) makes writing at least some of these models fairly straightforward, and both (especially the latter) have extraordinary scientific visualization capabilities. I have run neurosimulations like this on both my personal laptop and via hundreds of cores in clusters, so it is certainly viable in both cases.
Every model is approximation, and sometimes it is worth it to have better understanding and control of a simpler model than to have an enormously complicated one with huge numbers of different channels that may be hard to realistically choose (what is their spatial distribution and concentration? what are their parameters for the species of interest?). It depends on the phenomenon you want to simulate.

In any case, to speak of libraries, both NEST and NEURON seem like great choices. Personally, I've heard only of NEURON in any detail before.


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The evolutionary advantage of sending the most fearless to the front
Let species A be prey to another species B. Assume that predators B only attack isolated individuals A (because they are afraid of larger groups of A's). So it is good for species A to be on the way in rather dense herds, flocks, or swarms. But individuals A which find themselves isolated (which happens every now and then) are well adviced to flee when a predator is in sight. So there are two antagonstic tendencies in individuals A: to flee or not to flee when a predator comes into sight. To flee as a herd, flock, or swarm should keep it together, so it's best to flee into the same direction as one's neighbours. Nevertheless single individuals will become isolated and be easier catch for the predator, so for the species (herd, flock, or swarm) it is best not to flee at all.
This can be advanced when the individuals with the least tendency to flee (the most fearless ones) are at the outside border of the herd. So it would be an evolutionary advantage of the species when the most fearless individuals come to be at the border of the herd - by own initiative or being pushed by the others.
I wonder if there have been investigations of this: Are there species of which it is known that (and how) the most fearless individuals are at the border of their herds, flocks, or swarms?

This is how the investigation could go:

Are there the same individuals at the border of the herd more often than not?
Do they start fleeing at a smaller rate than other indivuduals which happen to be at the border?
Why are they at the border? Is it because the others tend to go away from the border, into the center of the herd?

Note that my question is not for the strongest individuals but for the most fearless. (I know the case of the muskox and its typical defense formation.) There may be species with no extraordinally strong, but with extraordinally fearless individuals - which is another case.

Species evolve, but individuals are selected. Simplifying a bit, a species evolves as relative allele frequencies change in the population. When natural selection is at play, these changes in allele frequencies come about because individuals are more effective at transmitting their genes to offspring compared to others of the species. If individuals with a particular allele P reproduce more than individuals with a different allele Q, there will be a bigger ratio of P:Q alleles in subsequent generations.

So it would be an evolutionary advantage of the species

is not the sort of question you should ask, because advantages of the species don't affect allele ratios within the species. If there is a "fearless allele" F that causes individuals with that allele to occupy the outside of the herd, and a fearful allele G that causes individuals with the allele to group up and occupy the inside of the herd, and this reduces predation of the herd equally, then the ratio of F:G stays the same. If, on the other hand, those fearless animals on the outside of the herd are at higher risk for being on the outside (remember that fighting in nature is very dangerous; you don't have to lose the fight to die, as an injury that makes you slower, more susceptible to infection, interferes with eating, etc, still prevents you from reproducing in the future, even if you kill the predator and save the day), you can expect that the ratio of F:G will decrease: there will be more G alleles in the future generations. This is the basis of selfish herd theory
Herd behavior is fairly well-studied and doesn't really result from the antagonism you mention (flee vs not flee/stay as a group) but rather from aggregating sets of individual selfish rules like trying to be at the center of a group of conspecifics.
The exception to this is group selection, which is when selection acts on whole groups. In this case, instead of thinking of selection by individuals you might think of different herds. If herds with a higher percentage of F alleles reproduce better than herds with a higher percentage of G alleles, then there could be selection in favor of F across herds. However, the features that promote selection of this type can still be thought of at an individual level and this helps to reveal the features of groups that promote selection. For example, if the F allele promotes aggression against predators specifically in adults, or perhaps specifically in adult males, and the animals live in small groups of related individuals, then individuals with the F allele may have better reproductive success due to improved survival of their offspring, and those offspring are also more likely to have the F allele than offspring of parents without F, so the frequency of F increases.
In summary, think of the advantages that a trait like this provides the individuals with the trait, rather than the group as a whole. If you're looking for species where certain individuals defend the borders of their groups, you want to look for species that live in familial groups, where related individuals benefit the most from this behavior. Examples where you might find this would be in species like elephants (smaller family groups) rather than species like wildebeest (very large groups of only distantly related individuals).

Thanks a lot for this instructive answer! But what about alleles that allow individuals to become or better: to learn to become more fearless? Not all individuals that have them will necessarily become fearless, but due to circumstances some of them do. (The predecessor species without the allel may have had only fearful individuals.)

@Hans-PeterStricker You'd think about it exactly the same way: if an allele "allowing an individual to become more fearless" causes there to be more instances of that allele in the next generation relative to other alleles, it is being selected for and you can expect its frequency to increase in the population. If that allele causes some individuals possessing it to behave in riskier ways for that individual and therefore not reproduce as well on average (for example, because they are more likely to die of injury) you'd expect it to be selected against.

The only difference is that you'd expect the rate of change in the population (in either direction) to be slower if only some individuals with the gene express the trait.