text:
Why has evolution made neurons use spiking?
I'm going to be forward and say that I'm not a biologist.  I don't claim to fully understand the functionality of a neuron from an electrical/chemical perspective... I'm curiously gazing from the outside in as a computer programmer.
Now, the big new thing these days with artificial neural networks is simulating them with biological precision- that is, with spiking, actually placing the network in Euclidean space, and all sorts of other goodies (Check out the Human Brain Project; it's really cool.  Our own brains are computable, given a powerful enough computer.  Probably has a lot of fascinating and horrifying philosophical implications too.), but this isn't the only model that people have commonly used for neural networks.
In the past, multilayer perceptrons were the big thing; I'm not going to go deep into explaining them, but essentially, each neuron simply performs a mathematical function on its inputs and then outputs it.  There's no spiking in the sense similar to our neurons.  A neuron could be blasting its output at nearly 100% indefinitely and there isn't any objective difference between that and it being at 0% or anywhere else.
Now, I sort of went off on a tangent, but what I'm trying to understand is- why this behavior is not observed in the system of computation that evolution has produced over the many, many years?  Why do we use spiking neurons instead?  We have instead developed a strange system where spikes travel down chains of neurons in patterns we don't really understand yet to produce the behavior necessary for the creature to survive.  My question is, why has this been favored over something similar to multilayer perceptrons?  Is it more energy efficient to use a spiking model?  The brain does use a disproportionate amount of energy.  I wonder if it is more effective as a model for computation.

Just to be sure what you mean, you ask why the neurons get activated only in a "on or off" method and why there are no intermediate states?

Sorry, no; I'm asking why neurons that spike is what is consistently used by every creature with a brain; spiking at specific times rather than, for example, conveying 50% "signal" for a sustained period of time or any other pattern of behavior.  So, not necessarily binary as you describe, but spiking itself.  This is hard to describe without explaining multilayer perceptrons in detail...

Especially since you have a quantitative background already, the book Spikes addresses a lot of these issues from an Information Theoretic point of view.

You should read up on how neurons work. You admitted that you don’t know this, yet it directly answers your question.

One of the many advantage of an all-or-none system is that resources can be conserved for timing events that require synchronized collaboration between many cells (like locomotion).  Binary behavior may also partially be a side effect of speed and efficient long-distance information transfer (which is one of the great advantages of neurons as cells in the first place).
It should be noted, however, that neurons aren't universally binary.  It is more accurate to say that neurons have binary properties that allow neurons to behave in a binary manner (but not all neurons do).  On top of that, many continuous (i.e. non-binary) processes underlie the neuron's excitability threshold.

One thing I think you're missing: a perceptron model and a spiking model aren't mutually exclusive. In fact, in perceptrons, people usually use a sigmoid function. That's not a coincidence: it kind of simulates a spike. If you were to model an organism such as C. elegans, whose neurons aren't spiking (but use "graded action potentials"), you would use another type of activation function (by the way, if you like the Human Brain Project, you should also check the Openworm project, which is much more realistic and already gives some interesting outcomes).
So, back to the point. When you try to model learning and generation of memory, you often end up dealing with plasticity. Just a quick explanation: a neuron does fire an Action Potential (AP) when its transmembrane potential gets above a limit. The potential itself changes due to the inputs from other neurons. But all the inputs don't have the same weight. Let's say you look at neuron A, which gets synapses from neurons B, C, D... If B fires at the same time as C, it can be enough to create an AP from A. But, even in combination with B or C, D doesn't elicit enough transmembrane potential variation to create an AP; it has to fire exactly at the same time as E, F, G and H to get enough "firepower" to activate A.
However, if each time D fires, A gets an AP (because of the simultaneous activation of other incoming neurons), then you could say D is "useful" for A, and (depending on the type of neuron and other parameters) the weight of D can increase, so that, after a while, D is able to elicit an AP in A just by firing at the same time as any other incoming neuron. That's a plasticity phenomenon. Note that here, it is a really simple hypothetical example. And also that the idea that learning results from plasticity is more and more being challenged.
So, if you want to understand how learning and memory happen, you might be interested in the weights of the connections only, and how they evolve depending on the network's activity. In that case, a perceptron model is a good way to go.

Now, the big new thing these days with artificial neural networks is simulating them with biological precision- that is, with spiking

So, as you already mention in your question, this is a matter of choosing a model that you're going to use (as a researcher trying to understand the brain). This is not trying to give an accurate depiction of how the brain really is. If you're interested in the weights of the connections, you can use a neural networks approach (and this is not outdated), and you don't care about a single ion channel on the membrane of a neuron. But alternatively, you can also dedicate your life to understanding the structure of a single ion channel, and how simple details can determine the whole working of the brain.
Projects such as the Human Brain Project are trying to create a "biologically exact" model of the brain. So, it involves modeling the spikes, as well as the network organization, as well as single ion channels. So, in that case they don't need to use paradigms such as artificial neural networks, since the network's behavior should be a result of the single neurons' modeling: that would be an emergent response. However, they don't have anymore the simplifications brought by other models, so, they need huge computational power, and they also face new problems: for example they have to give values to all the individual parameters of their models, some of which are currently unknown, and could even not exist at all.
So, I think this gives answers your question, in the sense that it is not the brain which evolved to pick a model, but the scientists who try to modelize one particular aspect of the brain.
EDIT: I see in fact this question is quite old; sorry it was a suggestion on the right which brought me here. But I think my answer can still bring an aspect which wasn't already mentioned in the other answers. So I hope you will see it.

The use of spikes is a mixture of their computational advantages and the limitations of the biological substrate in which they are implemented:

They can travel long distances at high speeds because they can profit from the saltatory conduction which is much faster than diffusion and it can be maintained over long distances. A digital signal is less prone to noise which is also a reason why our computers operate digitally.
Spikes can be modulated using chemical synapses which are a) directional b) can amplify the signal c) can modulate the signal d) can even inhibit the signal. Analog voltages have to be transmitted through electrical junctions that do not allow modulation.
Frequency coding (signal lies in interspike interval) has a wider dynamic range than voltage coding.
The spike generation can serve as non-linearity in the transfer function
The spike can be used to code temporal signals more efficiently by reducing the redundancy (only signal change).

Btw. do not expect too much from the human brain project - it is a lot of talk and no results.

It would be great if you added some references to your response!

My answer is a mixture of textbook knowledge and common sense. There are very few papers broad enough to substantiate one of the reasons.

Neurons are binary, in that they either "fire" or "don't fire".  There is no intermediate, because neuronal firing (biochemically) is an avalanche process.  Excitatory triggers raise the membrane potential and inhibitory triggers lower it.  Once the "threshold voltage" is reached, the neuron lights up and fires.  This is how "decisions" are made by neurons: inputs from many inhibitory and excitatory neurons are summed, and if the sum is above the threshold voltage, the neuron in question fires.  To use computational language, a neuron is a logic AND gate with hundreds of inputs.
Read the wikipedia article on "action potentials" for a more in depth discussion.

But my question is, what exactly is the evolutionary fitness benefit of using this mechanism over some other mechanism more similar to "nondiscrete logic gates" such as what is seen in multilayer perceptrons? Is it simply because spiking was the easiest method to develop, and as brains became more complex, they continued to be dependent on this architecture?  I mostly understand how spiking neurons work, at least from a mathematical perspective, not an electrochemical one (I need to read up on that...), but I don't think that's within the scope of my question. Sorry for not being clear.


text:
Why did scientists think humans had 100,000 genes (before the Human Genome Project)?
One of the major results of the Human Genome Project (HGP) was that humans have far fewer separate genes than previously thought. From a 2004 article about the HGP:

Francis S. Collins, director of the National Human Genome Research Institute (NHGRI), said, "Only a decade ago, most scientists thought humans had about 100,000 genes. When we analyzed the working draft of the human genome sequence three years ago, we estimated there were about 30,000 to 35,000 genes, which surprised many. This new analysis reduces that number even further [to 20,000-25,000] and provides us with the clearest picture yet of our genome."

What was the old estimate of 100,000 based on? I assume that in 1994 no one had sequenced the entire proteome...
As Remi.b points out, the 100,000 gene estimate may be based on the one gene–one enzyme hypothesis. If that's true, did people before the human genome project think that there were 100,000 distinct enzyme activities? If so, what experiments/data were used to establish that number?

Importance and amount of non-coding DNA was underestimated.

There's actually no need to speculate on the answer to this question since scientists have published their estimates and methodology, as is their way. The following paper is a good review:
Fields C, Adams MD, White O, Venter JC. 1994. How many genes in the human genome? Nature Genetics 7:345-346.
Below are some truncated excerpts from the paper but, if possible, I recommend reading the whole thing and the references therein.

In genomic sequencing pilot projects... we found... an average of about one gene in 23.4 kb... Extrapolated for the whole genome, we would predict about 129,000 genes; however, the regions that we sequenced were chosen for high-GC content and hence gene richness. At most, half of the genome, in the GC rich... bands, is likely to have high gene density; if the rest has half of the density we observed, the human genome might contain 97,000 genes. But the gene-poor fraction of the genome probably has much less than half the density of the gene-rich fraction... If we assume that the genome comprises a gene-rich half with [23.4 kb per gene] and a gene-poor half with a tenth that density, we obtain an estimate of about 71,000 genes...

Making estimations based on average gene size has been discussed in another answer. The varying estimates result from the different assumptions made: there were a lot of unknowns at this time. You can read about the correlation between GC-content and gene density in this answer.

Estimates giving much lower gene content are, however, easy to come by... Wagner and colleagues note that only about 12% of a typical mammalian genome... is transcribed. Using an average gene size of 18 kb obtained from a list of characterized genes... they estimate a total of 20,000 genes. By assuming that 2,500 housekeeping genes (from estimates of Escherichia coli) constitute 18% of the total number of genes the same authors obtained an even smaller figure of some 14,000 human genes.

I don't have access to this reference so it's hard too delve to deeply into their methodology, but the number of genes expressed really depends on the cell-type. Thymic medullary cells, for example, express 85% of the coding genome. RNAseq has also suggested that over 90% of the genome is transcribed, though this controversial. All that said, their first estimate was rather spot-on.

Measurement of RNA reassociation kinetics suggest that approximately 10,000 distinct genes are expressed in a typical mammalian cell, from which Lewin estimates a total gene number of 20,000 to 40,000.

This is C0t analysis with RNA instead of DNA (called R0t). You can read more about this here. This estimate, too, has proved reasonably accurate.

Using restriction analysis with the methylation-sensitive enzyme HpaII, Antequerra and Bird estimated that the human genome contains 45,000 CpG islands. They also report that about 56% of sequenced genes contain CpG islands, and hence estimate a total gene number of about 80,000... This number, however, may be an overestimate, as even "complete" gene sequences rarely include extensive 5' or 3' flanking sequence and hence may muss associated CpG islands.

We now know that there about 30,000 CpG islands in the genome, of which about 9,000 are intragenic and that  72% of genes have CpG islands. This would revise their estimate to 30,000 genes.

We have used a collection of 3,483 nonredundant coding sequences as an effective genome against which to compare a collection of human ESTs... If [this] set of complete cDNA... is representative of human genes in general, the fraction of known cDNAs matched by randomly-selected ESTs should equal the fraction of novel sequences matched by randomly-selected ESTs. Our human EST sequencing project has so far identified ESTs matching 1,877 of the 3,483 unique coding regions (54%). We can, therefore, estimate that the novel ESTs that we have sequenced represent about 54% of previously-unknown human genes... To estimate how many genes these novel ESTs identify, we... [clustered] the ESTs. This step reduced 65,297 ESTs to 40,077 clusters... indicating that the novel EST set was 40% redundant. We can then calculate an expected number of human genes as: 40,077 / 0.54 + 3,483 = 77,700 genes. This calculation is an over-estimate, since the clustering procedure cannot identify ESTs from the same transcript unless they overlap. If the true average redundancy is 50%, we predict about 64,000 genes; if the true average redundancy is 60%, we predict 52,000 genes.

Although they accounted for alternative splicing, assumptions were made about how representative their collection of coding sequences was. It seems that one of the problems at this time was that many of the overestimates made, using different methods, more or less agree with each other. Unfortunately, many of the assumptions did not stand up.

Human genome is 3.2Gbp (giga=billions of basepairs). If you assume there are 100k genes, this yields around 32kbp (kilo=thousands base pairs) per gene.
Before human genome project, let's say before 1990, people were isolating a lot of genes from human-derived tissues. You can use google scholar to find relevant papers. From quick search, you can see that range is pretty large:

"186,000 base-pair (bp) human factor VIII gene"
"human TF gene spans 12.4 kbp"

So you can see how one might guess "average" size of the gene to be somewhere around 30kbp, if you haven't yet found too many genes. Whereas after genome sequencing, we know that genes are 100-10000 bp long. I think the issue was that there was no enough statistics yet to judge appropriately "average gene size". And as it turns out, the distribution is very funky. You need to isolate a lot of genes to reconstruct that distribution.
By the 1990 phenomenon of the alternative splicing was already known. What perhaps wasn't quite obvious, is how many genes there are, what their real sizes, and how many genes are overlapping

I think it was rather thinking that more of DNA is coding than in reality, but that's another possibility.

From Pertea et Salzberg, Genome Biol, 2010: An estimate of 100,000 genes appeared in the 1990 joint National Institutes of Health (NIH)/Department of Energy (DOE) report on the Human Genome Project; this was apparently based on a very rough (and incorrect) calculation that typical human genes are 30,000 bases long, and that genes cover the entire 3-gigabase genome.

@tsttst thanks! i think it also interesting that today we can easily peek into the past literature (thanks google scholar :)

I don't know much about the evolution of thoughts on the subject but I would suppose that the estimate of 100,000 genes is probably caused by the one gene - one enzyme/protein ideas

The one gene–one enzyme hypothesis is the idea that genes act through the production of enzymes, with each gene responsible for producing a single enzyme that in turn affects a single step in a metabolic pathway.

The idea that genes are affecting cell functions via the protein that they code for is not so outdated. However, the idea that a gene codes for a single unique protein is a little outdated.
In reality a single gene can code for several different proteins via a mechanism called alternative splicing.

Alternative splicing, or differential splicing, is a regulated process during gene expression that results in a single gene coding for multiple proteins. In this process, particular exons of a gene may be included within or excluded from the final, processed messenger RNA (mRNA) produced from that gene. Consequently, the proteins translated from alternatively spliced mRNAs will contain differences in their amino acid sequence and, often, in their biological functions [..]. Notably, alternative splicing allows the human genome to direct the synthesis of many more proteins than would be expected from its 20,000 protein-coding genes.

The original estimate of the number of genes was hence probably alined with the observed number of proteins by assuming that there is a one-to-one function from gene to protein (as by the one gene - one protein hypothesis).

Makes sense. What I want to know though is where that particular number came from. In quantitative terms, what experiments or line of reasoning lead to 100,000? It's (basically) within an order of magnitude of the correct answer, so I'm assuming (maybe incorrectly) that the estimate was originally based on some solid numbers from somewhere. Like, was there a reason to think there were 100,000 distinct enzyme activities?

The original estimate was probably based upon the number of proteins. I'll clarify that in my answer

Thanks for the update. There's still a missing piece, though. How did they establish 100,000 as the likely count of distinct proteins? Was there an experiment that existed in the pre-omics era that was able to resolve the presence of thousands (or 10's of thousands) of proteins in a single sample? Or was it merely an informed guess based on decades of various observations?

I don't know... :D I would assume whoever found identified a protein sequence would upload it in a shared data base and comparison among protein sequences (some ancestral equivalent to BLAST) allowed one to estimate the number of proteins found in humans.

source: Stack Exchange

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

sort: votes


text:
Finding population dynamics models for multi-generational species evolution with delays
I am working on a problem from economics, to understand how populations evolve within business organizations. I have found that some of the population dynamics literature is very relevant to my own work, but I am not very familiar with the vast biology and ecology literatures on this topic. I have been reading Murray's Mathematical Biology volume 1 bible, and it is really great--but it only gives a taste of the models out there.
I was hoping someone could help point me towards articles or models that deal with multi-generational evolution of a population with time delays. I have having a little trouble deciphering some of the biology or ecology terminology, so I am not sure if my search terms are correct--I have not been able to find anything like this, though I am certain it exists.
I am looking at a very simple model, something on the order of a system of differential equations with delays. So a simplified examples is below with just one species that matures over time. So for the same species, there are 3 populations, A, B, C, which represent stages in its lifecycle. I can refer to these in the model as $N_A, N_B, N_C$. Then there is a birth and death rate for each population, as well as a migration rate between the different populations--as individuals mature. I am thinking of the populations in continuous time, though of course suggestions for articles about discrete dynamics for such systems would also be valuable.
So the continuous system would be as below:
$$
\frac{dN_A}{dt} = \beta N_C - \gamma_A N_A - \nu_A N_A(t - \tau_A) \\
\frac{dN_B}{dt} = \nu_A N_A(t - \tau_A) - \gamma_B N_B - \nu_B N_B(t - \tau_B) \\
\frac{dN_C}{dt} = \nu_B N_B(t - \tau_B) - \gamma_C N_C \\
$$
So constants $\beta, \gamma, \nu, \tau$ represent constants for the birth rate, death rate, migration-maturation rate, and time lag. In my actual model I have 2 species that compete, but this model above gives a flavor of the dynamics. I would like to include some carrying capacity constraints too, but that can come later.
Like I said, it is easy enough to express this model in a discrete form as well. But I was hoping someone might be able to reference articles or something that shows someone analyzing discete multi-generational models--so that I can see a flavor of how they deal with the nondimensionalization, bifurcations, chaotic dynamics, etc.
I was hoping to see if other biologists or ecologists had studied this kind of continuous system, and how they had analyzed it. It seems like the first thing to do is nondimensionalize it, as I have way too many parameters. But I am not sure of a good scheme or equilibrium condition to use to do that.

"evolutionary game theory" plus "delay differential equation" (or "discrete lag") would be a good search terms to start with

e.g. https://scholar.google.ca/scholar?hl=en&as_sdt=0%2C31&q=%22evolutionary+game+theory%22+%22delay+differential+equation%22 .  Can you say more specifically what kind of  qualitative conclusions you want to draw (invasibility, evolutionary stable states, etc.) ?

@BenBolker Okay, I can start to search under these headings. I am seeing a lot of gene expression articles come up--can you tell me if gene expression models would apply here? Thanks for suggestions, at least I know I am starting to look in the correct places.

@BenBolker good question about what I am looking for. So I would like to understand if there are changes in the qualitative behavior of this system as I vary the parameters or move in and out of different parameter regimes. But right now there are like 9 parameters in the model. So I don't believe that I can explore the dynamics or find bifurcations without non-dimensionalizing the system or such to reduce the number of parameters. But yes, I would like to see if there are fixed points in the model, find bifurcations, any potential for chaos, etc.

Did you leave out a delay term in the final equation, i.e. should the first term on the RHS be $\nu_B N_B(t-\tau_B)$ ?

@BenBolker yes, you are correct. Thanks for pointing that out. I will update the OP.

You use the word "evolution" here to mean "change in populations over time", as is typical in (say) astrophysics (or, I guess, economics?). My initial comments and suggestions were based on a misreading of "evolution" having its biological meaning, i.e. change of genetic make-up of a population over time.
With that out of the way: delay differential equations are slightly less common than ordinary DEs in population biology, but still very common. I wouldn't say there are any special mathematical tricks that aren't also used by mathematical analysts or people studying dynamics in other fields ...

What you have appears to be a linear DDE, which is considerably simpler than the typical biological DDE, which will almost always have at least one bilinear term representing a species interaction (and often a stronger nonlinearity representing something like a saturating predator-prey functional response, see e.g. Chattopadhyay et al ref below). To be honest, I don't think that this system is going to admit any very bizarre behaviour, because of that linearity. The model is simple enough that it's possible that someone has actually analyzed this case ...
Fixed point analyses for DDEs are actually relatively simple, because when the system is at the fixed point the time delays are irrelevant (e.g. $N_A^* = N_A(t) = N_A(t-\tau_A)$) so your equilibrium equations simplify to (stars suppressed for laziness)

$$
\begin{split}
0 & = \beta N_C - (\gamma_A + \nu_A) N_A \\
0 & = \nu_A N_A - (\gamma_B + \nu_B) N_B \\
0 & = \nu_B N_B - \gamma_C N_C
\end{split}
$$
which is a pretty simple linear system, general solution shouldn't be bad at all. Stability of DDEs is harder, you'd have to look in a book ...

For bifurcation analyses, I think that standard numerical bifurcation analysis methods (continuation methods) can in principle handle DDEs as well, although to be honest I haven't checked them out -- some very basic notes here

You're right that a 9-dimensional parameter space is going to be hard to handle; in general you will only be able to get rid of two parameters by non-dimensionalizing (one for space/density and one for time). For the rest, you'll have to use the standard kinds of simplifications, e.g.

plausible symmetries/special cases

search the whole space over ranges that are physically/economically/biologically plausible

Here are a couple of fairly haphazardly chosen biological dynamics papers with DDEs:
Bonsall, M. B., S. M. Sait, and R. S. Hails. “Invasion and Dynamics of Covert Infection Strategies in Structured Insect-Pathogen Populations.” Journal of Animal Ecology 74, no. 3 (2005): 464–74.
Chattopadhyay, J., R. R. Sarkar, and A. el Abdllaoui. “A Delay Differential Equation Model on Harmful Algal Blooms in the Presence of Toxic Substances.” Mathematical Medicine and Biology: A Journal of the IMA 19, no. 2 (June 2002): 137–61. https://doi.org/10.1093/imammb/19.2.137.

Yes, thank you so much for the information it was very helpful. You certainly validated some of my intuitions, and also directed me towards the right places to look. I will read through the references you suggested. I will also think a bit more about the bilinear terms you mentioned. That is something that is still an open question and not yet resolved--but I understand what you mean, because this bilinear term occurs in the Lokta-Volterra equation or SIR models.

Also, the nondimensionalization information was really helpful. Many books emphasize those methods, but they seem to only work for small systems with a handful of parameters. I have had trouble finding any good references or treatments of how to reduce the dimension of the parameters. Like you said, I have seen cases where people use some conservation law or equilibrium condition for this, but have never found a good resource explaining the intuitions and ideas. I will check with a dynamicist friend, but if you have a suggestion, please pass it along.

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
How can tooth enamel feel pain when drilled by a dentist?
Enamel has no nerves, so theoretically a tooth should not feel any pain when a dentist bores into it. Yet still we use anesthetics...
Maybe it’s dentin's fault? It apparently has some tubes filled with liquid and the tooth's nerves can feel this liquid's movement. So, maybe, the closer the dentist gets to dentin (especially if he/she touches it), the more pain is felt? By the way of vibrations from the boring tool?

I was wondering about this from the opposite direction, actually. I always refuse local anesthetic for simple dental work, because I never feel anything and I hate having my mouth numb. So I'm curious if other people's teeth are somehow more sensitive than mine!

@Glenn Willen Definitely! How a local anaesthesia is going to work, how much time it will take to numb the area does depend on an individual. There are people who are resistant to a type of local anaesthesia, eg lignocaine. In those, other types are used. Whereas many people are allergic to one of the components of local anaesthesia. Also if there is lot of inflammation then it takes time plus more dosage of local anaesthesia is required to come into action. So it depends on lots of factors.

Whether an individual wants to have an anaesthetic injection or he/may will resist that pain, purely depends on him/her.

Where I am, the dentists use a range from "No need for anesthesia here" over "Probably no need for anesthesia" to "This will hurt - would you like to get anesthesia" and "You need anesthesia". Sometimes also: "if the nerve is not yet dead, you'll need anesthesia"

@cbeleites unhappy with SX but I am sure, that must be working fine. They must be accurate in their probabilities!

@Ojasvi: they don't need to be - they express uncertainty in the intermediate variations ;-). Their repertoire also has suggestions like "would you like to try without".

@cbeleitesunhappywithSX I had a root canal near a live nerve last year taking long enough to require multiple sets of anaesthetics. They were not an option and it still hurt a lot more than the anaesthetics could hide. I'd been wondering why that was, but the answer explains it quite well. If your gum is sub-par (like mine), there's dentin exposed that really doesn't like the procedure.

@cbeleitesunhappywithSX Agreed! That maybe because of toxic effects of local anaesthesia and some patients are scared of the 'injection'!

@Mast if it was a root canal then,  below the dentin, pulp must have been exposed and severely infected. Pulp has neurovascular supply. Now generally giving local anaesthesia, the pain doesn't happen, but in your case infection would be so much that the effect of anaesthesia would be very less or your tooth might be less sensitive.

Yes you are right. It is the dentin.
Enamel has got no nerves.
So when the dentist use his/her instrument, initially there is no pain (if the enamel is intact).
The next layer is dentin. As you said, dentin has got dentinal tubules containing dentinal fluid.
Whenever there is any stimulus which has not yet reached the pulp, but may have reached the dentin, eg of stimulus in the form of pressure, temperature change, sweet food, sour food etc, cause the fluid to move or get displaced. The displacement of dentinal fluid stimulates nerve endings in the pulp and hence generates a pain/ sensitivity response to your brain.

Reference

Reference

There are three main theories of dentine hypersensitivity:
Direct Innervation (DI) Theory
Odontoblast Receptor (OR) Theory
Fluid Movement/Hydrodynamic Theory

The Hydrodynamic or Fluid Movement theory is one of the main theories in dentistry to explain the mechanism by which a tooth perceives the sensation of pain. It is currently the most widely accepted theory used to explain tooth sensitivity.

Reference
Now the drilling causes heat generation, even though little, due to the coolant, plus pressure and hence causes the fluid to displace and hence pain. This pain usually ends after the treatment.
Hence anaesthesia is required.
Hope I have answered your question :)

Very nice images too, I wouldn't be able to find them myself!

The heat generated by grinding is critical. Being very old ,my first ( painful) dental experiences were with the old belt drive drills = hot=painful . When the water turbine drills came along it was a whole new world ; Novocaine shots were unnecessary for drilling..

@blacksmith37 Oh! I didn't know about the belt drive drills. With their low speeds and without water coolant they must be very painful plus dangerous for the tooth also! In the current scenario yes, the increased speed handpieces with a water coolant are used, but if the tooth is very infected then they do cause quite significant pain as the dentin thickness in those cases is very less. And hence anaesthesia is required. Interestingly, these days lasers too are used which are not at all traumatic for the patient as well as the tooth.

I was always told that tooth structure was like a stack of straws on their side. Now, finally, I'm getting the picture. Great answer, thanks!

@uhoh well, enamel does have a kinda stack of straws like structure. It has got enamel rods which are arranged vertically and obliquely!

More generally, the same can be said of sensory nerves anywhere in the body. You don't have to physically touch a living nerve cell in order to stimulate it: Touching/stressing/heating/cooling nearby tissue causes the tissue to release chemical signals that stimulate nerve endings which can be, in some cases, up to several mm away from the site.


text:
Are there free electrons in muscles, hair, or skin?
Electric shock occurs upon contact of a (human) body part with any source of electricity that causes a sufficient current through the skin, muscles, or hair.
In metals many electrons are said to be delocalised (i.e, electrons are not bound to any of the atoms). Around one electron per atom is said to be unbound to atom in mettalic lattice. So there will be motion of electrons due to thermal energy, however due to their random motion, there won't be such a significant current produced. When electric field is applied across them there will be motion of electrons in a particular direction, causing significant amount of current. In case of insulators, due to bound electrons we can't expect current through them.
When our body becomes a part of electric circuit, current is said to pass through our skin, muscles, or hair. Does it imply that there are free electrons in skin, hair, or in muscles (like those in metals)?

conduction rarely involves free electrons usually - charge can move through a chain of capture/ionization events.

Our body contains a lot of salt and which act as electroyte,so when we comes in contact of electric source then the electrolyte helps in the conduction of electric current through out body,

Free electron is just one type of charge-carrier. Once can also have other types: such as anions and cations to carry charge from one place to another. Therefore causing a current to flow.
Even if skin and hair lack "free electrons" like metal and semiconductor, we definitely have a great amount of anions and cations in our body. One example would be -- as krushna mentioned in his answer -- is $Na^+$ and $Cl^-$ ions from common salt in our body.
Now, not all (type) potential can cause a great deal of current to flow in our body. The 12V lead-acid DC battery definitely does not cause any perceptible current to flow through our body; no matter how many free ions we have on our skin. On the other hand, 440V AC live wire will cause a great harm if we touch it even with dry hands.
If I were to model my skin, I would go for a RC circuit with high R and high C attached in series. Put the appropriate values and simulate this circuit using a circuit simulator after applying test voltage across it. The Human Body Model for capacitance, as defined by the Electrostatic Discharge Association (ESDA) is a 100pF capacitor in series with a 1.5kΩ resistor. Since we have a capacitor in series, it prohibits DC current to flow through it. This is one reason why X Volt of AC is much more harmful than X volt of DC voltage (AC passes through capacitor easily).

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


text:
Error in textbook
This must be an error

Just as proteins have a polarity defined by the amino and carboxyl
  termini, oligosaccharides have a polarity defined by their reducing
  and nonreducing ends. The carbohydrate unit at the reducing end has a
  free anomeric carbon atom that has reducing activity because it can
  form the open-chain form, as discussed earlier (p. 325). By
  convention, this end of the oligosaccharide is still called the
  nonreducing end even when it is bound to another molecule such as a
  protein and thus no longer has reducing properties.

In my opinion, this sentence only makes sense if it would read reducing. Can anyone confirm?

Just a quick comment. You would have better to write a title that is a bit more explicit than "Error in textbook". I could not have any idea of whether I'd have the skills to answer your question. (Hopefully you use one tag that is slightly more informative).

I agree that, purely from the point of view of the language used in the last sentence, it must mean 'reducing'.

In case of oligosaccharides, only the final ends matter and not the ends of the individual monosaccharides involved in glycosidic linkage. As a consequence, most oligosaccharides have only one reducing and one non-reducing ends.

I am not sure what the textbook means by the last line but the following two possibilities seem to be consistent with the understanding of oligosaccharide structure.

There is no mistake and the end being talked about is the left end of the monosaccharide on left in the diagram . This monosaccharide has both its ends non-reducing, but only one of these ends will be called the non-reducing end even though the other end(formerly reducing end) is now involved in glycosidic linkage and is hence non-reducing. The it in the last line in this case refers to the entire monosaccharide (and not the end of concern), which has lost its reducing ability.
There is a mistake. And the end being talked about is the right end of the monosaccharide on the left. It is usually not referred to as the reducing end (which is the rightmost end), but for the case of explaining the polarity of the monosaccharide, it can be referred to as the reducing end.

+1 for admirable attempt to give the author the benefit of the doubt. I agree with OP it's just a slip, your (2).

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


text:
How to find a PDB structure of a protease with a peptide like inhibitor
I wonder how, for purposes of just illustrating some concepts like substrate alignment in the catalytic triad active site, I can quickly and efficiently find a protease structure (irrelevant of what organism exactly) that has a peptide like inhibitor on the PDB database.
Hints appreciated.

I'm not a Google search expert but "pdb structure protease peptide inhibitor -non-peptide" seems to provide some useful hits

Here's one: http://www.rcsb.org/pdb/explore/explore.do?structureId=2IPH. It was crystallized with the inhibitor bound to the protein.

How did you find it?

I searched within the pdb for protease inhibitor. Since the inhibitors themselves are not proteins, most of the search results are proteases with inhibitors.

go to http://www.uniprot.org and enter the protease accession number you are interested in and look at the 3D structure databases section to see if its PDB file is deciphered or not. I came across this database (http://www.cf.ac.uk/biosi/staffinfo/ehrmann/tools/proteases/allproteases.html) just by typing into google "protease PDB databases"


text:
Phosphorylation alters structure: Specific Example?
Why does phosphorylation activate an enzyme?
A common answer to this question is that the introduced phosphate group "alters the structure" of the enzyme.
Can somebody point out a specific example of such an alteration? I.e. like a "before/after" illustration?
Or is the whole mechanism of activation still largely uncharacterized?

check the conformational change in $G \alpha$ subunit of GPCR

@WYSIWYG has answered the question neatly. Many mechanisms of activation are mostly uncharacterized. It's easy to see why: You need to do X-ray crystallography or extremely clever FRET experiments to see exactly how the structure changes shape. X-ray crystallography is really hard for proteins, especially big, asymmetric enzymes that bind things and don't like to form crystals. So that's why we don't have data on lots more enzymes.

I have no problem believing that's it's challenging to get the structures. But I am surprised that there is only so little data- many times I find the statement that xyz-pathway is among the most thoroughly characterized cell biology aspects. So I would have thought getting detailed mechanistic insight would have been among the highest priorities.


text:
How can I tell if regulation is at the transcriptional or translational level?
I was reading a paper, http://www.pnas.org/content/109/8/E471.short, where the authors claim that (e475)

Translation of the TfR (Transferrin Receptor) is regulated through sequences in the 3′ and 5′
  UTRs (untranslated regions) of its mRNA. To study the regulation of TfR levels independently
  of translational control, we measured the levels of TfR-EGFP expressed
  from a plasmid lacking the regulatory sequences present in the
  endogenous TfR mRNA (Fig. 6A).
  Endogenous and GFP-tagged expressed TfR levels were reduced equally in
  response to Rabenosyn-5 depletion, ruling out an effect of this
  protein on transcriptional or translational control of TfR expression.

I believe I understand that the effect can not be post-translational, since TfR-GFP has no region for translational regulation and so it cannot be regulated by any translationally active regulating factor.
My question is: Wouldn't the statement above, that Rabenosyn-5 has no effect on protein transcription, require a proof that mRNA levels are not affected by this protein?

Fig. 6A, http://www.pnas.org/content/109/8/E471.figures-only

From the Methods section:

Human TfR in plasmid cDNA was a gift from Tim McGraw (Weill-Cornell
  Medical College, New York, NY). Human TfR cDNA was subcloned in frame
  with EGFP in the Clontech pEGFP-N1 vector at the XhoI and BamHI
  restriction sites.

This TfR-GFP fusion protein does not have the endogenous TfR promoter. So it is not likely to be regulated by any TF that would directly regulate TfR transcription.
So they conclude that Rabenosyn-5 does not specifically regulate TfR expression.

I see. I guess if the cDNA plasmid would have had any regulatory sequence, they would have stated it.

cDNA can have 5' and 3' UTRs but that is not the case here..


text:
When was the germ theory officially accepted?
Is there a specific year that marks the official acceptance of the germ theory? In the references, it is always quoted the work of Pasteur, Koch, and Lister, but their work is spread over decades and does not mean that their papers were accepted by the medical community.
Is there a watershed year instead? Perhaps a peculiar conference where the doctors wrote black on white that the new theory had become the new paradigm? Or a paper that encountered the agreement of all the peers?

Perhaps Max Plank's famous quote, "A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.”  applies here?

Since this appears to be a historical question rather than one asking about biology as defined in the [help], I think this would be more appropriate on [HSM.SE]. If that is what you want, please delete your question from this site before reposting there because cross-posting is impolite. Alternatively, you can request migration of this question by flagging your post for moderator intervention and explaining what you want and why.

The decades long process apparently is still under way.

As in most sciences, there’s not really such a thing as “officially accepted” in Biology because there is no office and no officials who have that authority.  Rather, the usual term that I have heard is “generally accepted”.

I’m voting to close this question because it is asking for an opinion (judging whether something is "official accepted") as well as being off-topic for this site.

I don't think this is off-topic here as it asks for the process of the acception of a scientific idea.

@Chris — Even with that generous interpretation this still seems opinion based to me, as well as being focused on history rather than anything biological.

It was only after Darwin's 1859 theory of evolution sparked an interest in chemical evolution as an explanation for life that the germ theory was accepted. ref As a comparison theory, it took until the 1950's for a majority of scientist to agree that evolution was caused only by natural selection, a 90 year gap until consensus.
Pasteur published his germ theory in 1861. It took about twenty years for the most eminent international scientists (i.e. Tyndall) to give conferences on it in the UK and for Koch to evidence it in Germany. From the 1880s, Germ Theory was hugely influential, affecting nearly every aspect of medicine including public health, surgery, hospitals, training and treatments. In the 1950's we can find books like i.e. "Pasteur, plagiarist, imposter: The Germ Theory Exploded"
In France, Louis Pasteur was elected a free associate of the French academy of medicine in 1873, won the Copley medal of the Royal Society of London in 1874, medal of Grand-Officier de la Légion d'Honneur in 1878, and designed a vaccine for sheep in 1881.
Following France, Germany was one of the first to officially recognize germ theory:
On 24 March 1882 at the Berlin Institute for Physiology, Koch announced the discovery of the tuberculosis pathogen – with his lecture on the “Aetiology of Tuberculosis” he became world famous overnight. In the course of the 19th century, Roughly a seventh of the population of the German Reich died of the disease. Now Koch demonstrated that tuberculosis was triggered by the tubercle bacillus.
In 1885, Friedrich-Wilhelms-University in Berlin founded the Hygiene Institute, which was managed by Robert Koch.
In 1891, Koch agreed to direct the newly-founded Royal Prussian Institute for Infectious Diseases. ref
He was awarded the Nobel Prize in Medicine in 1905.
A famous epidemiologist, William Farr, working on cholera, published "a monograph" which showed that mortality was extremely high for people who drew their water from the Old Ford Reservoir in East London. Farr's work was then considered conclusive... Physicians were publicly denouncing germ theory in major newspapers 20 years later:
Irish physicist and science popularizer John Tyndall was superintendent at the Royal Institution in London and had published scientific works on heat, light and sound, as well as popular books. His ideas on germs became controversial in January 1870 after giving a lecture to the Royal Institution entitled Dust and disease. Here, he first reported experiments demonstrating that dust was mainly composed of organic matter, something he had not anticipated. He then demonstrated, using his light scattering assay, that air filtered by cotton wool was ‘optically empty’, meaning that the path of the beam appeared black, which he also saw with air that had been left to settle for long periods of time in a sealed glass chamber. Finally, he reported that his own breath, especially the end of his expiration, was remarkably free of floating matter. He had recently been taken by Lister’s methods for antiseptic surgery, which argued that infection spreads through the air due to the microbes contained within it and viewed his data as entirely consistent with these. Hence, Tyndall extrapolated his conclusions to make a strong statement in favor of the germ theory of disease; an idea which had not yet found acceptance with the majority of the English medical profession. Following Tyndall’s 1870 lecture, there was much criticism of his conclusions from prominent physicians who believed that ideas relating to the nature of disease ‘pertain most to the biologist and the physician’ and that a physicist should know better the facts of epidemic disease before delving into the living world. The pathologist Charlton Bastian confronted Tyndall directly in a series of letters to The Times, playing down the evidence supporting germ theory and discussing his own experiments which disagreed with previous work by Pasteur, Lister and the like. Bastian then published a 1,115-page book in 1872 describing many experiments that he argued proved that microbial growth could occur in conditions in which Pasteur argued...ref

A majority of scientists accepted evolution via natural selection as long ago as the 1950s? Damn, seems like the rest of American society is lagging very far behind. Did most of those scientists also believe in Human evolution?

87% of Americans with a science degree believe in the theory. https://en.wikipedia.org/wiki/Level_of_support_for_evolution#United_States

@TKoL I think you'll find that the general population lags behind scientific consensus in just about every field, and particularly in America where we seem to have a prevailing sense that the lay person is just as qualified to decide the truth of a topic as an expert is

@TKoL By the time Darwin published the Origin, evolution in the broad sense of "change over time" and life unfolding in a gradual process was already broadly accepted by scientists, the public, and influential clergymen who saw divine intervention in evolution as a crude view of God. By 1874 Tyndall could declare almost unchallenged that creation was a natural processes in which God played no scientifically detectable role. But prominent scientists had serious reservations about natural selection as the mechanism which persisted until the Modern Synthesis in the 20th century.


text:
Has the human 7-day week had any impact on the evolution of species?
Most (if not all) industrialized countries follow a 7-day work week now, such that we are bound to follow a certain weekly trend in matters such as pollution generation, where to go (e.g. stay in town during weekdays, more likely to go to parks during weekends), what to do (mow the lawn on weekends).
Is there any evidence of any species evolving to adapt to the pace of the human 7-day week lifestyle?

I think I could sketch out some of the premises for how it could happen, but unfortunately I'm unaware of any specific research (which is how you phrased the question) so I don't dare give that answer since it's not in your question

I have no clear evidence of a specific species, but two pieces of the puzzle:

species can adapt to living in cities: birds living in cities are bolder (i.e. allow humans to come closer before flying away), and apparently even some genetic changes can be detected.
it may be possible for species to evolve a circaseptan rhythm: beach beetles may have a seven-day rhythm to accommodate for different heights of the tides (spring/neap tide). (PubMed / PDF)

Two comments: (1) The question asks about adaptation in an evolutionary perspective (which should be said is but a fraction of what is evolution (genetic drift etc. should not be forgotten!)). This implies that the there must have been a shift in the gene pool of the species that adapted.

(2) Experiments on bacteria and yeast show that you can have a clonal population adapt to a severe selection in less than 200 generations. In fact some do in as little as 30. Taken with that the 7-day work week should only have been a relevant strong selection criteria (if it at all is) for about 100 years. That means that the species most probably has to have a short generation time with a maximum less than 3 years. But that said, I agree with you that there could be either a cognitive entity that adapts or a circadian. But to prove that it is the work week and not urban society at large!

And a suggestion: The bacteria "flora" in the stomach could in some humans have adapted to the tides of carbon-source/toxin shifts with the host behaving on the work-days and getting drunk etc on the weekends.

@deinonychusaur: I would also expect epigenetics to possibly play a role there...

@nico: I agree that epigenetics could lead to quick adaptation. But rather than causing the species to evolve, I'd say it's a way for the genotype to resist environmental change so that the genotype doesn't need to change (hence no evolution). Further, I find it hard to see how epigenetics could cause rewiring of the signaling network of a cell so that a new 7-day circadian rhythm would appear. It could, maybe, silence cautiousness or so in the first scenario (but once again, this would not be explicitly a 7-day week adaptation, nor an evolutionary process).

@deinonychusaur: actually my previous comment was referring to the first point in the answer, and frankly I am not sure why I addressed it to you (it was early this morning... :P)!

@deinonychusaur: good point about the gut flora. Would be interesting to see how some of the flora adapts to a 7-day cycle of alcohol ingestion. Don't we all drink ourselves silly on Friday nights? (Half joking)


text:
Do mosquitoes need to pump blood out of the host?
Many species of mosquitoes have bloodsucking females.
When they bite a host, do they need to pump? Or does the sheer blood pressure combined with capillary action suffice to make the blood rush into the mosquito's stomach?
If the blood pressure is enough, can the mosquito "explode" or simply die from having too much blood rushing in?

Take a look at Ramasubramanian et al.[1] The authors have some great SEM images of the proboscis and fascicle tip and derive a mathematical model for the "bite" itself.
They mention that there are two types of feeding: pool feeding, where the mosquito creates a hemmorrhage and feeds slowly via capillary action, and capillary feeding, wherein the mosquito taps into a capillary and the feeding process is much faster, as the blood is driven under pressure.
Another source to check, one that the authors above cite, is by Kashin[2]. This only links to the abstract, however.  You'd have to hit the stacks to get the article, I'd imagine.

Edit
This source[3] indicates that:

"The blood is imbibed by action of the cibarial and pharyngeal pump muscles via the food canal formed by the labrum and hypopharynx. Blood then passes into the midgut."

For @Luke, and others:

All images are from Ramasubramanian et al.[1]

Ramasubramanian MK, Barham OM, Swaminathan V. 2008. Mechanics of a mosquito bite with applications to microneedle design. Bioinspiration & Biomimetics, 3: 046001, doi:10.1088/1748-3182/3/4/046001.
Kashin P. 1966. Electronic recording of the mosquito bite. Journal of Insect Physiology, 12(3): 281-286, doi:10.1016/0022-1910(66)90143-0.
Robert V, Brey PT. 1998. Biting physiology of Anopholes affecting Plasmodium transmission. Research and Reviews in Parasitology, 58 (3-4): 203-208.

Nice answer. Any chance you could include the SEM image for those you do not have access to that journal? Sounds worth seeing! Thanks

BTW, it appears that mosquitoes really can drink themselves to death, if you somehow prevent them from knowing they are full (e.g. by crushing their ventral nerve cord): https://entomologytoday.org/2020/03/19/when-a-mosquito-cant-stop-drinking-blood-the-result-isnt-pretty/


text:
Why do some hard-shelled seeds need hot water in order to germinate, when planted artificially?
Some plant species, such as Mimosa pudica, have hard-shelled seeds. I have tried to plant them from seeds a few times in my life, in Hong Kong.
I know that what follows is just an anecdote, but I hope it illustrates my point.
The first time I tried to plant M. pudica, I simply dumped the seeds in moist soil, in summer. They didn't germinate. So my grandma told me that I have to soak the seeds in boiling hot water. I didn't believe her at first, but my first batch didn't germinate so I thought I might as well try. So I put a second batch of seeds in a glass and dumped scorching hot water on them. Lo and behold, a few days later, this second batch started germinating.
Why is it that when planting these plants artificially, we have to use hot water? How would they manage to grow in the wild? (I do see them in the wild.) The soil temperature in Hong Kong is nowhere past 40 degrees Celsius, even in summer, according to the estimates in this article: https://doi.org/10.1016/j.enbuild.2011.09.026
What am I missing?


text:
If a "parasite" is growing on a dead part of the host or on a completely dead host, does it still count as a parasite?
What I mean is the case where the parasite does not contribute to the death of the host (tissue). Instead, the parasite may start growing after the host (tissue) is already dead.
I came up with this question when I was following the link in this answer and read the PDF about a fungus on a fungus on a fungus on a tree. In the PDF, the author first says that the Hypomyces was growing on the dead bracket mushroom, and then says again that Rhinotrichella globulifera was in turn growing on dead portions of the Hypomyces.
I'm not a mushroom expert and the author also does not mention whether the parasites caused the death of their respective hosts.
So suppose the parasites only started growing after the death of the host, do they really still count as parasites? My understanding is that a parasite should seek a host that is not dead and try to leech nutrients without killing the host too quickly (at least until the parasite is ready for the next stage of its lifecycle and no longer needs the host).

Related: Is spiders building webs on plants an example of mutualism or commensalism?

In parasitic interactions, one species (the parasite) benefits from the interaction while the other (the host) is harmed. So, whether an interaction is parasitic or not really depends on whether on the host species is harmed by the interaction. If something only grows on the dead remains of another organism, but causes no harm to living organisms in terms of growth and reproduction, it would not be a parasite, and might actually be beneficial to that organisms in some ways.
However, dead cells or tissues are not always useless to a still living organism. For example, the vast majority of cells in a mature tree are dead, but are still important for the survival of that tree by providing structure and water/nutrient transport. A hypothetical parasite could feed only on the dead cells inside of a tree and still cause harm to that tree by weakening it's trunk or impairing it's ability to transport water and other nutrients.
Not knowing much about fungal physiology myself, I can't say whether these "hyperparasites" are truly parasitic or not. But it is at least feasible for a parasite to only grow on the dead portions of a host and still cause it harm.


text:
Why are dolphins born tail first?
I heard most dolphins are born tail first. I have read many explanations from books and internet articles and they said that dolphins are born tail first to prevent them from drowning.
But now I don't get this explanation. There must be no air in the mother's womb, so the baby dolphin can't breathe either way. I think they get oxygen from the umbilical cord until it's disconnected.
If this is true, why would they drown if they are born head first? Why are dolphins born tail first unlike most mammals?

Searching on youtube you find videos of head-first births of whales. So, even though tail-first seems to be more common, head-first does work. Since tail-first is otherwise uncommon in mammals, there is probably an evolutionary advantage to it. Maybe drowning becomes a problem in the head-first situation if the process is taking too long.

Cetaceans are born tail first for two reasons:

Avoid drowning
Being in the same direction of the mother.

As you already wrote, they developed this particular way of giving birth to avoid drowning of the calve. As they are mammals like us, they need to breathe immediately after the birth (think about us: we come out in a world of air and we are forced to breathe immediately). So, many complications can occur while giving birth and it may take long time. If calves go out by head first, they can drown easily. On the other hand, as soon as they are totally out, they swim and are forced to go to the surface by the mother in order to have their first breath.
The second reason is that, in this way, they are already in the same direction of the mother, so they can easily swim and recognise her. Imagine being born in a dark pool, where you can't see nothing. A bit confusing right? But they just come out and see the mother, that can guide them easily where to swim, instead of turning back, spend energy and precious time. They have to be quick, because they need to breathe and also need to swim away! A calf is an easy prey!
This adaption came with the evolution of the cetaceans. In fact, some of the ancestors were supposed to give birth head-first just as land mammals. But, as other adaptations to the environment, also this trait was found to be evolutionary successful. Also other mammals were found to give birth in this way (e.g. Manatees and reported cases for pinnipeds. For more references see: Gol’din, P. E. (2011). Case of Cephalic Presentation of a Foetus in a Harbour Porpoise Phocoena phocoena (Cetacea, Phocoenidae), with Notes on Other Aquatic Mammals. Вестник зоологии, (45,№ 5), 473-477. DOI:10.2478/v10058-011-0030-5).
You can find the info I shared here with references: North American Nature (2020). Why Are Whales Mammals? [https://northamericannature.com/why-are-whales-mammals/][1]

Thanks!! I have read in a book that Manatees give birth head or tail first in about fifty-fifty. And hippo, giving birth in the water, seems mostly head-first. I think the head-first would more efficiently resolve breathe issue when they faced problems at umbilical cord during the birth because mothers could lie with her belly up at the surface. But the tail-first has no chance. So I doubted it might not be the breathe problem. The direction problem gave me insight. Now I got interested in birth of Dugongs. They lives far out at ocean, where the direction might be more important in birth.

Of course, cetaceans are really different, as they usually live out in the ocean, so harder in any case than being around the coast:) We also have to consider that we still have a lack of knowledge regarding cetaceans, as they are quite hard to study. But, from what I know and I found, experts agree with those two reasons. Always good to have some discussion too:) And nice to see some interest in a "wired" creature like Dugong!


text:
How can I set micropipette volume with decimal separators, for example 109.5 in p200?

And how can I set:

150.1 in p200
871.3 in p1000
840.5 in p1000?

What is the brand and model of your pipet?

It doesn't make sense to set volumes of decimals of microliters with a P200, since usually the accuracy of these pipettes is in the order of a few microliters. So even if you set your pipette at 190 uL, you may still dispense a volume ranging from 187 to 193 uL. For a P1000, due to higher volumes the inaccuracy is of course higher, usually +/- 10 uL or more. Some brands are more accurate than others, like Eppendorf or Finnpipette. I suggest you check the specs of your pipettes and see how much accuracy you can achieve.

I think a fair take-home message here is you shouldn't, and with many brands you often can't. Rainin and Eppendorf P1000's measure to four places from 0100 to 1000, for example. The best bet is to use two different pipets, and use them in that fashion throughout the process to maintain consistency.

I would always use multiple pipetters in cases like that, but really the answer is that no pipetter is going to give you 4 sig figs of accuracy.  It's just not possible with the technology.

For p200 the smallest unit is the tenth of a ul (0.1) but the intervals go by 0.2 so to set 109.5 (assuming your pipette can be adjusted between intervals) you need to set the pipette so the pointer is equidistant from the 109.4 and 109.6 mark. For the p1000 the smallest unit is 1 ul so you cannot set it to 871.3.

Some brands of pipeters "click" at each allowed increment, so you won't be able to go between them.

Interesting. Thanks for sharing I didn't know that.


text:
Woodland Skipper in PA. Did these get blown here?
I have seen many woodland skippers this year in Eastern PA. Never seen them before. When I did a photo search I found their usual range is Western US. Have they recently migrated here or did they just get blown off course?
This one was in my garden. It's about one inch long.

What makes you think this is a Woodland Skipper specifically and not a related species? Here's a dichotomous key someone posted for your state on another site: https://bugguide.net/node/view/542311

You may well be right. These do look similar. When I searched for skippers in PA I didn't find any, only one list that mentioned skippers in the title, but when listing butterflies and skippers of PA did not include any. https://greennature.com/pennsylvania-butterflies/ Still, I wonder why these little crtitters have become so numerous this year. They have a very distinctive wing arrangement when resting with one set of wings flat and the other at a 45 degree angle. I've never seen them before.


text:
What's the terminology for the deformity between the halluces and index toes caused by sandals?
In Japan, especially in the past, people tended to wear wooden sandals or the like, which separated their halluces from their index toes.

As a result, there is often a gap between their halluces and index toes.

What's the terminology for this kind of deformity? I searched a potential terminology, Bunion or hallux valgus, but all pictures I got were the ones where there was no gap between the hallux and the index toe. Besides, it's said that 'proposed factors include wearing overly tight shoes, high-heeled shoes, family history, and rheumatoid arthritis', where geta is not mentioned at all.
I searched hallux varus, the pictures and the description I got is nearer to what I expected, yet when I search 'hallux varus' + 'japan' or 'japanese', the results I got were all about hallux valgus. To add yet another twist, in the abstract of  The etiology of hallux valgus in Japan, the author even says:

Until recent years, hallux valgus did not exist in Japan.

I'm confused. What's the correct terminology?

@JiminyCricket. This article says the gap is a healthy deformity, not a pathological one.

Yeah. I deleted my comment because I'm unsure of my ground in arguing that or any position. I have feet a bit like that anyhow, so you can put it down to personal bias. You might still have more luck on medical I suspect.

Fwiw, my toes are separated like that and I have never worn that style of shoe in my life. The article you link is not loading for me...

@BryanKrause It can load easily for me. Its name is 'A comparative study of foot morphology between Filipino and Japanese women, with reference to the significance of a deformity like hallux valgus as a normal variation'.

there is some evidence this is actually the normal condition or at least far more common, and people without it have a deformity caused by wearing closed shoes. It is very common in hominid and fossil human footprints.

This is sometimes called a "sandal gap." See here.

Gap between the 1st and 2nd toes (sandal gap).

The above NIH link lists 58 known conditions with this feature+.
...
Other sources emphasize "halux varus" as a common cause for a larger interphalangeal gap between the first two toes. (Though, note, such a gap is unlikely to appear as in the OP's photo, which is lacking characteristic "bent" toes commonly seen in such deformations of the big toe and surrounding anatomy).

Hallux varus is a deformity of the great toe that is characterized by adduction of the hallux and medial subluxation of the first MTP joint.  [Source]1

See linked source for photos. See here2 for more technical information.

Interesting, radiopaedia suggests that these two terms (sandal gap and hallux varus) are linked.

A sandal gap deformity, also known as hallux varus, is an imaging observation in antenatal ultrasound (typically second trimester) where there is an expanded first interspace, i.e. the gap between the great toe of the foot from the rest of the toes (likened to the gap caused by a sandal).

I think the intent to synonymize these terms is inappropriate based on the other more detailed and peer-reviewed sources I linked/cited. However, I also include this quote due to their usage of the phrase "expanded first interspace" to describe this gap -- However, I cannot find much reputable relevant usage of this phrase in a quick Google or Google Scholar search, so I don't think it's the broad-usage term the OP is looking for.
Other conditions to rule out
FYI: Plantar plate disruption (or injury) is related but not what you're looking for. It's a v-like gap due to injury vs a genetic anatomical characteristic that you're describing.

Also, according to an article in the The Journal of Rheumatology3, "A gap between toes can be an indication of rheumatoid nodulosis, a relatively benign variant of rheumatic disease."

However, this condition appears between any toe due to rheumatic disease during life, and images from the linked paper rule it out as the more general genetic morphological condition the OP is asking about.

Citations:
 1. Vanore, J.V., Christensen, J.C., Kravitz, S.R., Schuberth, J.M., Thomas, J.L., Weil, L.S., Zlotoff, H.J. and Couture, S.D., 2003. Diagnosis and treatment of first metatarsophalangeal joint disorders. Section 3: Hallux varus. The journal of foot and ankle surgery, 42(3), pp.137-142.
2. Boike, A.M. and Christein, G., 1994. Hallux varus. Hallux Valgus and Forefoot Surgery, pp.307-312.
3. Prati, C., Brion, B.B., Leclerc, G. and Wendling, D., 2014. Spacing of Toes Reveals Rheumatoid Nodulosis. The Journal of Rheumatology, 41(5), pp.973-974.

 + below is an unformatted quick list of the 58 conditions (listed alphabetically) copied in case the cited link dies. One could certainly explore the literature for each of these conditions to determine if some other common phrase/term is additionally used to describe this gap across these conditions.

Acromesomelic dysplasia 4; Acrootoocular syndrome; Al-Raqad syndrome; ALG12-congenital disorder of glycosylation; Arthrogryposis, distal, type 2B2; Arthrogryposis, distal, with impaired proprioception and touch; Atelosteogenesis type II; Atelosteogenesis type III; Autosomal dominant intellectual disability-craniofacial anomalies-cardiac defects syndrome; Cardiac malformation, cleft lip/palate, microcephaly, and digital anomalies; Chromosome 6q24-q25 deletion syndrome; Clark-Baraitser syndrome; Cleft palate-stapes fixation-oligodontia syndrome; CLOVES syndrome; Coffin-Siris syndrome 1; Coffin-Siris syndrome 5; Congenital disorder of glycosylation, type iit; Coxopodopatellar syndrome; Cranioectodermal dysplasia 3; Cutis laxa with severe pulmonary, gastrointestinal and urinary anomalies; Desbuquois dysplasia 1; Developmental delay with autism spectrum disorder and gait instability; Duane-radial ray syndrome; Ectodermal dysplasia with facial dysmorphism and acral, ocular, and brain anomalies; Endocrine-cerebro-osteodysplasia syndrome; Growth delay due to insulin-like growth factor I resistance; Hydrocephalus-costovertebral dysplasia-Sprengel anomaly syndrome; Intellectual developmental disorder with dysmorphic facies and behavioral abnormalities; Intellectual disability, autosomal dominant 1; Intellectual disability-facial dysmorphism syndrome due to SETD5 haploinsufficiency; Kohlschutter-Tonz syndrome-like; Larsen-like syndrome, B3GAT3 type; Lethal acantholytic epidermolysis bullosa; Lethal hemolytic anemia-genital anomalies syndrome; Linear skin defects with multiple congenital anomalies 2; Mandibuloacral dysplasia progeroid syndrome; Meier-Gorlin syndrome 6; Menke-Hennekam syndrome 1; Menke-Hennekam syndrome 2; Microcephalus cardiomyopathy syndrome; Micrognathia-recurrent infections-behavioral abnormalities-mild intellectual disability syndrome; Microphthalmia with limb anomalies; Myofibrillar myopathy 10; Neurodevelopmental disorder with dysmorphic facies and distal limb anomalies; Neurodevelopmental, jaw, eye, and digital syndrome; Nicolaides-Baraitser syndrome; Oculofaciocardiodental syndrome; Orofaciodigital syndrome 18; Orofaciodigital syndrome V; Oto-palato-digital syndrome, type I; Seckel syndrome 1; Short stature, facial dysmorphism, and skeletal anomalies with or without cardiac anomalies 1; Short stature-optic atrophy-Pelger-HuC+t anomaly syndrome; Short-rib thoracic dysplasia 16 with or without polydactyly; Specific granule deficiency 2; Toes, space between first and second; X-linked intellectual disability Cabezas type; Zechi-Ceide syndrome;

Note: I'm not an expert in this. I'm a biology professor who teaches human A&P and has the internet (e.g., Google Scholar) available to me. So take my answer as a summary of 10 minutes of compiling relevant knowledge/info. There may be a better/broader answer. (In fact, I feel like I've come across something in the past -- I'll take a look at some of my A&P textbooks when I return to my office).

So, how about your 'look' when you return to your office?

@Michael turned out none of them mentioned this explicitly. So, I'll leave my answer as is.


text:
What is the best current understanding of how yeast transformation works?
I would like to get myself up to speed with what is currently known to science about yeast transformation. Specifically, transformation of plasmids and linear DNA fragments. I am particularly interested in practical aspects, such as:

What is salmon sperm for? What if I don't use it? What if I use more?
What is the relationship between number of cells, amount of transforming DNA, and transformation efficiency?
How does choice of electroporation cuvette gap size affect efficiency?
What can I do to get more colonies after transforming my yeast?
What steps can I skip to make the transformation quicker if I am doing an easy transformation?
How do different genetic backgrounds and media affect efficiency? Are there any common ones that reduce efficiency substantially? Are there any uncommon ones that increase it substantially?
What is the effect of post-transformation "recovery" time (eg. culture 12 h without selection and then plate on selective medium)?
Is making spheroplasts worth it?
Does the same transformation protocol work for different species (S. cerevisiae, S. pombe, C. albicans, etc.)?
Is there a coherent theoretical framework for understanding what actually happens when yeast are transformed, as opposed to "you mix some chemicals with DNA and throw cells in, abra kadabra, transformants come out"? The sort of theory I'm asking about would be useful in predicting conditions for better efficiency.

I am not expecting you to comprehensively answer these questions in your answer. This would make for a very long answer, and each of these could be a question here in its own right.
Instead, I want the answer to name reference materials which can be studied to learn more about the points above. For example, recent reviews or textbooks by established experts in the fields would be quite useful.

If "what should I read to learn more about concept X" is not an appropriate question for this site, I would be happy to delete this and split it into many smaller questions.

I think the question is absolutely ok. However, collecting a number of references takes time...I can look into this tomorrow when I have a little spare time.

I have never worked much with yeast, but I can still give some answers:
Salmon sperm is used as a the so called "carrier DNA". It is thought to bind to the yeast cell wall and thus prevents that the DNA which shall be transformed does so. This raises the transformation efficiency. See here for more details: "Transformation of yeast by lithium acetate/single-stranded carrier DNA/polyethylene glycol method.".
Effect of the recovery time: In this time you typically give the cells the chance to recover from the relative rude treatment and to express the resistance gene (when used) before moving them to a selective medium. This raises the efficiency of the transformation.
Spheroblasts: As far as I remember they are terribly fragile and painful to handle. However, when you succeed making them, you have cells which take up high quantitaties of DNA which makes them pretty efficient.
I think these reviews are definitely worth looking at (they also contain a lot more references):

Genetic Transformation of Yeast
Transformation of Saccharomyces cerevisiae and other fungi - Methods
and possible underlying mechanism
Foreign gene expression in yeast: a review

If you have a chance to get hands on a copy of "Methods in Yeast Genetics: A Cold Spring Harbor Laboratory Course Manual", I think this will be very helpful.


text:
How are fingerprints formed?
I know what influences the fingerprints to be unique always. But what actually process is taken to make these as we grow?

The question you are asking is very broad. One can dedicate an entire review if not a book chapter, on this topic. Can you please narrow your question down so that it could be answered in a few sentences (2-3 para at max)?

Fingerprints are called epidermal ridges in Biology. These are formed between the 3rd and 4th month of the fetus. The epidermal layer looses its strength, becomes like a cloth from a piece-board and falls on the underlying layer of skin cells, the dermal papillae. Now, you must realize that not all tissues are as smooth as the skin for evolutionary reasons. This tissue, although a part of the skin, is irregular and some its cells get little more swelled up. After falling, the epidermis begins to harden up and thus takes its shape.
This phenomenon has probably evolved from providing friction for traction and increase in surface area for sweat to come out.
Read more at:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1013024/


text:
What is solid-phase bridge amplification?
During Illumina sequencing there is a step called bridge amplification by which DNA is amplified by isothermal enzymes. What is this stage, and how does it work?

This stage is after the adsorption of the adapter-ligated fragments on the flow cells.
The fragments are isothermally amplified to generate clusters [1]. The exact nature of the enzyme mix that catalyses this step is not disclosed by Illumina. I assume it must be similar to Helicase mediated isothermal amplification, which uses helicase and single strand-binding proteins (SSB) to denature the template. The polymerization is catalysed by a mesophilic DNA-Pol such as Klenow (E.coli).


text:
What fraction of bacterial proteins are membrane bound?
Almen et al. showed that around 27% of the human coding proteins are membrane bound.
How does this compare with bacteria such as E.coli or B.subtilis (or any other bacteria)? Is there a paper out there demonstrating this?
I assume that there would be massive variation among bacteria, so although one specific example would be enough as a ballpark, I expect that this may not represent all bacteria well.

As a proxy for asking which bacterial proteins are membrane-bound, we can ask which proteins harbor transmembrane domains. A popular tool for detecting transmembrane domains is TMHMM, which looks for transmembrane helices in protein sequences using a hidden Markov model.1
For E. coli strain K12, UniProt reports 4,391 protein sequences corresponding to unique genes. Of these, TMHMM predicts 1,072 proteins have at least 1 transmembrane helix (24.4%), and 775 proteins have 2 or more transmembrane helices (17.6%).  Expanding this analysis to the E. coli pan proteome, which contains 165,127 non-redundant protein sequences, TMHMM predicts 28,369 proteins have at least 1 transmembrane helix (17.2%), and 16,105 proteins have 2 or more transmembrane helices (9.8%).
For B. subtilis strain 168, UniProt reports 4,260 protein sequences corresponding to unique genes. Of these, TMHMM predicts 1,175 proteins have at least 1 transmembrane helix (27.6%), and 845 proteins have 2 or more transmembrane helices (19.8%). Looking at the pan proteome for this species, which contains 18,046 non-redundant protein sequences, TMHMM predicts 3,967 proteins have at least 1 transmembrane helix (22.0%), and 2,463 proteins have 2 or more transmembrane helices (13.6%).
The relative depletion of transmembrane domain-containing proteins in the pan genomes of both species relative to their reference genomes could point to a functional skew of accessory genomes towards proteins that are less likely to be membrane-associated, such as strain-specific virulence factors, antibiotic resistance genes, integrated phage proteins, and proteins involved in niche-specific metabolism.2,3 Additionally, the relative enrichment of transmembrane domain-containing proteins in B. subtilis compared to E. coli could reflect the different membrane compositions of Gram positive and Gram negative bacteria, though it's hard to say without sampling more species.
One important caveat to TMHMM -- predicted transmembrane segments in the N-terminal region are sometimes just signal peptides. Signal peptides are likely to appear as single transmembrane domains, which is why I've also reported the fraction of proteins with 2 or more transmembrane helices, above.  A more complete analysis may run the same protein sets through SignalP 4 and cross-reference the TMHMM hits to identify transmembrane domain-containing proteins that also contain signal peptides.
Note that the cited publication by Almen et al. uses TMHMM and directly references the results of the original TMHMM paper,1 which reports transmembrane protein abundance in the proteomes of several species.

One of the most referenced papers regarding the percentage of membrane proteins in proteomes is from 2001 where the membrane topology prediction method TMHMM was applied on a number of proteomes from different species to estimate the membrane protein content, for example, Caenorhabditis elegans (31%), Escherichia coli (21%) and Drosophila melanogaster (20%).

Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001 Jan 19;305(3):567-80.
Kung VL, Ozer EA, Hauser AR. The accessory genome of Pseudomonas aeruginosa. Microbiol Mol Biol Rev. 2010 Dec;74(4):621-41.
Álvarez VE, Quiroga MP, Galán AV, Vilacoba E, Quiroga C, Ramírez MS, Centrón D. Crucial Role of the Accessory Genome in the Evolutionary Trajectory of Acinetobacter baumannii Global Clone 1. Front Microbiol. 2020 Mar 18;11:342.
Nielsen H, Brunak S, von Heijne G. Machine learning approaches for the prediction of signal peptides and other protein sorting signals. Protein Eng. 1999 Jan;12(1):3-9.

A very thorough answer! Thanks!

It's interesting how much discrepancy there is between modern uniprot annotation and the original TMHMM paper.

I assume you are referring to the values for E. coli, 24% vs. 21%?  I haven't read the original TMHMM paper in detail, but the difference may be due to different inputs (was a complete E. coli proteome available in 2001? Or perhaps a different strain) or differences in TMHMM parameters.

Yes I realise my comment was misleading. I am actually shocked that they are so close even 20 years apart!


text:
What is the difference between 4th generation sequencing and NGS?
The generation of sequencing technologies has come on leaps and bounds and there are stark differences between the types of technology used. There is a great Q&A here What is the difference between second and third generation sequencing?
NGS refers to high throughput massively parallel sequencing technology (See illumina's marketing page). 4th generation sequencing has been used to describe single molcule nanopore based technology (Nanopore-based Fourth-generation DNA Sequencing Technology).
So is NGS just a massively parallel version of 4th gen, or is there a more fundemental difference?

Older related question: https://biology.stackexchange.com/questions/21080/what-is-the-difference-between-second-and-third-generation-sequencing

I think that all of this is pure marketing and can be safely ignored. There is no basis for the generations of sequencing in chemistry or instrumentation, except possibly that second-generation (Illumina) was a massive parallelization of first-generation sequencing (Sanger)  through clever chemistry and instrument modifications. It is merely "I want to argue that this technology occupies a particular niche right now".
All of these terms are obviously very imprecise. I had not heard "4th-generation" before; having analyzed all of these data types, I personally would lump nanopore into the third-generation sequencing box with PacBio: they are long, relatively noisy reads that grew popular around the same time for roughly the same applications and are currently under very active research and development.
I would allow that these two technologies (PacBio and nanopore) are definitely distinct from Sanger (much older, now fairly restricted to specific applications like confirming plasmid sequences) and Illumina (older, mature tech with deep market penetration, making tons of money and trying to hold onto market share).
"NGS" itself is a rather silly term for similar reasons, as it is now clearly a technology that is fighting for relevance in certain applications where it was once dominant (e.g. genome assembly). If we must apply generational terminology to Illumina, it is clearly second-generation. This is one of Joe Felsenstein's oft-repeated rants.
Nanopore itself is as far as I know established in 1996, whereas the first publication I am aware of that forms the basis for PacBio is 2003. For comparison, the Solexa sequencing that became the basis of Illumina instruments from thence was introduced in 2005. So any meaningful chronology also does not support these generations.

Illumina sequencing is not massively parallell Sanger sequencing.  Sanger generates different length sequences each with a single labeled base.  Illumina labels all the bases, takes an image, the strips the label off, and adds the next labeled base to image.

@swbarnes2 that is a good point- I would still label the chemistry as derivative (sequencing-by-synthesis, but a lot, and with optics and fluorophores and non-terminal chain extension), but I will correct my answer.

Nanopore took longer to commercialize though (by ONT, 2015) compared to SMRT (by PacBio, 2011). This adds massive confusion to any attempt to order these two. Some authors use LRS (long-read sequencing) to group them. There was also pyrosequencing, which has now been commercially discontinued, as another "generation" perhaps.

There was a slightly more clear Q here about those (commercially speaking) pre-2010 "NGSes" https://biology.stackexchange.com/questions/7075/what-is-the-difference-between-solid-454-and-illumina-next-gen-sequencing


text:
Literature about industrial enzyme application
Can someone point me out to literature (reviews or better workbooks) describing industrial chemical reactions catalysed by enzymes?
What I'm most interested in are case studies of how the output of a reaction on a big scale is optimized with respect to enzyme activity.
Most of the literature I'm finding is about optimization of cell growth, which is not what I'm looking for.

Can you please accept answers, which answer your question satisfactory? This is a nice gesture towards the people who write the answers. You can find all your questions in your User menu.

I'll give you examples I have encountered in talks. There are too many variations and types of applications for me to break out in a comprehensive way.
Example 1: enzyme production.  this sort of application is about optimizing the amount of a desirable enzyme or protein produced from a liquid culture.  Sorry if this is too close to cell growth, but its terribly important.  Small cell cultures are used in research and the genetics of the cells (usually e coli or yeast, but also often a fungus that's been found somewhere) to improve the amount of the desired protein.  The protein may be secreted, which makes it much easier to purify.
Once a good strain is found, the culture is scaled up to thousands of liters, often many kilos of pure enzyme can be produced this way.  Examples: the enzymes that break down fabric for acid washed jeans, the proteases and cutinases which are included in laundry detergent
and insulin.
Example 2: glucose isomerase.  This enzyme is used to produce high fructose corn syrup.  The enzyme itself actually changes fructose into glucose, but by le Chatlier's principle, a glucose syrup under high pressure will be converted to fructose by the enzyme.  This enzyme, produced by an industrial process like that described in example 1 is immobilized onto a resin and packed into large industrial sized columns.  I seem to remember from the talk that they were similar in size to an oil barrel.  Large amounts of glucose rich corn syrup are passed through the column; it comes out about 40% fructose.
Another example (3)  along those lines might be how to use an enzyme with a product.  In an industrial manufacturing process like enzyme denim treatment, the fabric may be washed in a solution including the enzyme.  The process has to be careful to control the amount of enzyme the denim is exposed to and to optimize the conditions so that none of the fabric is ruined but comes out consistently.  The enzyme might be killed by a pH change or heating.  This is essentially chemical engineering and there are courses about this sort of thing at most major chemE departments now.

Your ex. 2 is in the direction of what I'm looking for. I agree cell growth optimization is important, but not relevant for my specific case- the engineering considerations are also not applicable. Do you have more examples of your example 2 type?

Ive added another example - these two examples are immobilized enzyme and heterogeneous enzyme processes that broadly cover a lot of applications.

Google's first hit for me. Also consider invertase  to make soft centered toffee candy. And citric acid and beer/wine.  Although they use microbes, enzymes do the work.

that's a good one @PlaysDice.  fermentation with organisms would include vitamin production, beer brewing, nacho cheese... a whole new frontier.

no probs. I like your answer and didn't want to add mine and dilute yours.  My Google link is a very accessible intro.  OP should now be able to find a reaction and search for literature on its optimization.


text:
Why is dsDNA nuclease activity by CRISPR/Cas9 only shown indirectly?
In Jinek et al., the authors show nuclease activity of their CRISPR/Cas9 system using the so-called Surveyor assay method. This assay recognizes small mismatches in dsDNA which are introduced by error prone non-homologuous end-joining after dsDNA breaks have been introduced by some foreign agent (such as Cas9).
The Surveyor assay then -again- breaks the DNA at those points where it recognizes mismatches. The newly broken DNA fragments can be assayed and so one can identify the position of the initial, Cas9 dependent breakage.
My question is: for direct demonstration of Cas9 activity at the programmed position, why not first silence non-homolguous end-joining repair mechanisms in the cell and then directly analyse the lysate? Is this technically not feasible? Or is it not done in order to not introduce another change in the cell for which there would be no control?

Because Double Stranded breaks will initiate apoptosis if they are not repaired, so you can't really tell if it is an effective method of gene editing if the cells die. Also you would need to see how many cells survive to determine transfection and site directed recombination efficiency. You also likely want to see how the breaks resolve in this experiment so silencing NHEJ would not give you that information.

@AMR You make good point about apoptosis. I'm not sure, but are these cells dividing after having been transfected? I dont understand what you mean by your last sentence.

Cells are dividing after transfection. A bit too long for comments but read Materials and Methods either from that paper or any of the other related papers on CRISPR Cas9. The first generation of engineered Cas9 nucleases were double strand cutters, so breaks are resolved via Non Homologous End Joining, as you pointed out. NHEJ introduces random nucleotide insertions, so to see how these were resolved. In other words you are looking at how the cell would behave if, say, you wanted to use this for site directed knock outs. You want the data that comes from the repair itself.