text:
Effective Mycoplasma Elimination from Primary Human Cultures
Obviously the best way to avoid mycoplasma contamination is to avoid it in the first place.  In our case, however, it is not possible to avoid.  We are culturing viruses and tissues out of human nasal swabs, washes, and biopsies.
As you can imagine, anytime you are pulling something out of someone's nose, it is quite often full of mycoplasma.  I've reviewed current examples of mycoplasma elimination, but found the section in Current Protocols in Molecular Biology to be the most helpful.
Indeed, we found that checking with a PCR based kit for common lab mycoplasma contaminants often missed detection of mycoplasma, because it did not include "clinically relevant" strains. Kits which did include these species clearly detected mycoplasma, in addition to Hoechst stain confirming their presence.
Now the question is there a good way to remove them?  We've tried serial dilutions, elimination kits (also MycoZap), titrated doxycycline and enrofloxacin doses, and even mouse passage.
Of these mouse passage was probably the most effective, but not desirable because it might induce additional confounding factors.  The kits had fair results, but 5 passages post treatment mycoplasma were again detectable via Hoechst stain (PCR detection persisted through out).
The dox and enrofloxacin may have produced better results at higher concentrations, but cell toxicity became a serious issue.  I was wondering if anyone had examples of combination methods for cleaning primary cells and/or virus stocks (been grown on more standard immortalized cell lines) for a more complete treatment method.
Just as note on our hands, we regularly check our cell lines, and are in fact maintaining a mycoplasma free tissue culture system.  Further, as proof of concept, we were able to clean a culture that was known to be infected with M. hominis with kits.
It doesn't seem worth the effort, but is there any evidence to suggest typing down to the species would be worth it in the eradication effort?  Each donor could conceivably be providing a different contaminate.

did you try using a combination of dox/enrofloxacin? Also, did you try using an iron-free medium during enrofloxacin treatment? enrofloxacin kills by chelating iron, so the less iron in the medium the better (however the cytotoxicity may also increase)

As far as I know Doxicyclin is a bacteriostatic antibiotic not a bactericide which in your case could be the reason why some mycoplasma survived and started to grow again at the end of treatment. As a potential solution, you could try (combinations) of other bactericide antibiotics that are effective against mycoplasma.

@Thawn Dox/Enrofloxacin were used sequentially (in both orders), but we found the direct combination too toxic.  EMEM was tried, which has minimal iron.

@Thawn I believe combinations of antibiotics are used in the commercial kits.

I guess then I am out of ideas, sorry

@Thawn No worries, that's why I posted here. I'm still considering multiple antibiotics where the concentrations aren't proprietary (ie mixing ourselves)

I use plasmocin. I haven't had any mycoplasma contamination, but we use it preventatively so when we make the complete media, it has the plasmocin in it. You have to keep in mind though to make fresh media every 2 weeks or so. The prophylactic, catalog ant-mpp, will cost you around 135.00. For active contamination, the treatment kit ant-mpt is 205.00 (prices through Fisher).

@CMosychuk You don't by any chance know what antibiotics are in Plasmocin do you?  Also, prophylactic treatments are meaningless for us because we will be spiking in/starting from known contaminated samples ("wild" from donors).

@AtlLED It's a proprietary mixture, from their webpage "The first component acts on the protein synthesis machinery while the second acts on the DNA replication." So very vague.

I don't want to defer you but this is also a good paper, and the same authors did another study elsewhere where they used plasmocin very successfully.

@CMosychuk I agree it's a good paper, that's why I linked to the same paper in my question [but in Wiley instead of Springer] :)

@CMosychuk Just to fill you in, gave Plasmocin a shot, at recommended and higher doses, and did not find it better than other kits/antibiotic treatments.  Mouse passage remains the best so far, but still doesn't allow for complete removal (and adds all kinds of other confounding variables).


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Could the Warburg effect be used to starve cancer cells in situ?
What is wrong with the following chain of reasoning?

Nearly all cancer cells rely on high rates of glucose uptake (upto 200 times more than normal cells). This is known as the the Warburg effect.
There is only one cell type in the human body that is obligated to use glucose - red blood cells (erythrocytes).
Starve cancer cells of their only energy source by inducing hypoglycaemia in the patient, preserving red blood cells using, for example, erythrocytapheresis or an oxygen-carrying substitute.

I understand that to completely eliminate a tumour such a regime would need to be in place for an extended period, but would like to know why such a treatment isn't feasible in theory?

As a type 1 diabetic for 39 years I can only say that hypoglycaemia is a life threatening  state which any of us tries to avoid  as much as possible.

Any untreated episode of  hypoglycaemia can easily lead to diabetic coma and irreversible   brain damage.
When encountering low blood sugar, the organism anyway  releases sugar stored in liver just for that case, so prolonged  artificially induced  hypoglycemia would place a lot (i imagine unsustainable) stress on liver. Not sure, but I suspect it would lead to liver failure.

The human body needs energy (ATP) for even the most basic function. I guess when you decrease glucose levels (which is broken down for ATP), you will be in affect taking away the ability of the body to function normally by depriving it of energy. This is my theory so I would be happy to if someone with more information on the topic could tell me if I am right or wrong.

I think Kelvin is suggesting the use of beta oxidation to yield ATP from fats.

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Why are hybrids infertile?
Let's take a quote from Wikipedia about zebroids.

Donkeys are closely related to zebras and both animals belong to the horse family. These zebra donkey hybrids are very rare. In South Africa, they occur where zebras and donkeys are found in proximity to each other. Like mules, however, they are generally genetically unable to breed, due to an odd number of chromosomes disrupting meiosis.

First, if I understand meiosis, the resulting cells don't actually end up with half the number of chromosomes, but closer to a full set of halves of chromosomes. How is the meiotic process disrupted?
Then,

A donkey has 62 chromosomes; the zebra has between 32 and 46 chromosomes.

Apparently this difference doesn't obstruct producing (infertile) offspring. How comes the process of recombination of such vastly different number of chromosomes in gametes is viable? What happens to chromosomes that don't find their 'pair'?
And then,

Horses have 64 chromosomes, while most zebroids end up with 54 chromosomes.

54 is an even number. How comes zebroids can't just normally produce fertile offspring with other zebroids of the same number of chromosomes?

A critical step in meiosis is the formation of tetrads. In diploid organisms like donkeys, they have a paternal version and a maternal version of each chromosome. Two chromosomes 2, for instance. During prophase 1, these two "matching" or homologous chromosomes form a tetrad and cross over, swapping alelles. There is no recombination and no tetrad formation in mitosis, which is used for growth and daily living.

How is the meiotic process disrupted?

In a hybrid, there are no matching maternal and paternal chromosomes. In the zebroid testes or ovary, a lonely donkey chromosome 2 is wandering around, looking for another donkey chromosome 2, and there isn't a homologous chromosome. This mucks up Prophase 1, and the first division pulls crazy numbers of chromosomes to each daughter cell, and the gametes from this division are likely full of extra chromosomes and missing some critical ones.

Apparently this difference doesn't obstruct producing (infertile) offspring. How comes the process of recombination of such vastly
  different number of chromosomes in gametes is viable?

An embryo needs a solid set of genes to grow up, but chromosomes don't have to be homologous for mitosis, and organisms use mitosis for growth and living, not meiosis. As long as the crazy mis-matched chromosomes in a zebroid have everything needed for a healthy organism, the hybrid will be healthy. It isn't until the zebroid starts to make gametes via meiosis that a problem occurs, as the homologues ONLY form tetrads in making eggs and sperm. The inability to recombine only occurs in the making of gametes.

54 is an even number. How comes zebroids can't just normally produce
  fertile offspring with other zebroids of the same number of
  chromosomes?

Because even chromosome number doesn't help if they are not each homologous with another chromosome.

I am a little bit out of practice but i researched the genetics of the horse tribe during the 1990s at the start of the Internet.
Male hybrids of the diverse equine species are all infertile.
However, a fairly large number of fertile female mules have been observed for over a century and much genetic research has been done to them, both in the West as in China. A few fertile female hinnies have also been proven.
With zebroids only one fertile mare, a zebra-horse mix, is known and sadly her remains as that of her foal have not been subjected to serious research.
That donkeys are closer related to zebras as horses is not generally accepted.
There exist three species of zebras -and possibly one recently extinct species, the Quagga- but all three species have interbred and at least a few of the female hybrids have produced foals, in the wild even!

Please reference your response!

If you think about the complexity of successful reproduction -- sperm recognizing egg, entering egg, the intricate choreography of cell division and differentiation -- it's staggering that any two individuals of the same species ever successfully interbreed, and it's obvious that enormous selective pressure is needed to maintain this perfect compatibility.  It's much less surprising that genetic drift would randomly lose one or more of the thousands of adaptations that are needed to guarantee compatibility.
So random drift is probably the reason for most genetic incompatibility, but in cases where closely-related species come into contact (whether through sympatric speciation or through changes in the range of one or both), there's also positive selection that will actively prevent cross-species interfertility. Hybrids are almost certainly going to be less well adapted to either parental niche than are purebreds, and so investing energy into developing hybrids is likely to be much less evolutionarily successful than putting the same energy into purebreds.

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Horse female bias
I don't know if it's a more general fact, but in most stables I visited, I have observed that there were more mares than stallions or geldings. Typically in a group of ~7 horses I'd notice one or two males, the rest would be females. I think the only place where stallions outnumbered mares was a stallion stud farm, where this was a kind of focus/purpose of the establishment, and I wonder if this is just my confirmation bias or result of some artificial selection, or are horses naturally more inclined to produce female offspring.
Can someone confirm or debunk and explain this observation?

Did you do any research on this, and can you share what you found? I only know from a couple of horse trainers I know that stallions are harder to control. Hence the prevalence of geldings.

It's often the case, as mentioned, that stallions are harder to control, even dangerous - and so people elect not to keep as many (or have them gelded).
For breeding purposes you don't need as many stallions as you do mares, since one is easily able to service several mares.

@JoeHealey: I'm specifically seeking male:female disparity; I know the number of ungelded stallions is low, but I'm finding male population in general to be small.

@anongoodnurse: female: male populations: Zabierzow stable: about ~8f:3m. "Pegasus" riding club stable: ~7f:2m. Private stable in Kokotow: 3f:0m. Rudawka Rymanowska: 4f:1m. Wernejowka: ~12f:4m. Stable south or Tarnow: 8f:2m. "Natural Horsemanship" stable in Krakow: 6f:1m. Private stable near Lomza: 4f:1m. State stallion stud in Klikowa was a big stable with ~120 horses had about 60% male population but that's a rough estimate, I didn't count. That's different breeds, different purposes, I'm bundling all stallions+geldings as 'male'

The ratio of males to females in stabled horses is irrelevant. You need to look at birth records.

@anongoodnurse: I don't have access to these. This is why I'm asking this question - I know my observations are encumbered with a slew of errors, therefore they can't be treated as anything but a suspicious artifact of data that may or may not be based on an actual trend. And I'm looking for some evidence that either supports or debunks this suspicion. (if I had access to raw data like this I wouldn't need to ask!)

I just googled and found two papers immediately with the answer. You can, too. Try Google Scholar "birth sex ratios in horses".

@anongoodnurse: Just personal experience, but among the people I ride with, it seems like about 3:1 geldings:mares.  Likewise with "horse for sale" ads.  So there must be some human selection going on somewhere between the 50:50 birth sex ratio and the trail.

@jamesqf: "for sale" ads will have an extreme bias of desirability. Example: "Why are all real estate / rent a room ads total crap?" - because the good ones are taken hours after they are published, and the crap ones remain available for years. This effect is accumulating over time (number of crap ads grows constantly, as new ones appear and old don't vanish) so the bias reaches extreme proportions. I don't know about "people you're riding with" though.

@SF.: But I would expect desirability to correlate to price, not numbers.  E.g. if geldings and mares are produced in similar numbers, but geldings are 3x as desirable as a mare of similar age, breed, and training, the mare should be about 1/3 the price of the gelding, no?

@jamesqf: That's what perfect Smith's capitalism would suggest. The reality is the seller dictates the price, and a large part of sellers are quite stubborn and not quite realizing inferiority of their offer and the state of the market. The offers from sellers who are well aware of the market vanish quickly, while bad offers stay up indefinitely long. An analysis of ads as they are listed, over a long time, discarding re-listing would provide a more neutral image. A snapshot of ads from any given moment is severely biased. Even then, breeders will tend to keep the mares and sell geldings.

My guess is the same reason you see a 30 chickens in farmer brown's backyard coop, but only one rooster.
Because, like joe Healy said, stallions and roosters both have testosterone, and that gives them a short fuse.
Also, because a stallion can mate a horse any day of the week, but mares can only have sex when they're not already pregnant or infertile.
Consider the reverse situation. If there are a ton of stallions and one mare, and the mare gets impregnated by one of the stallions, all the other stallions have to wait for the baby horse to be delivered before they can mate again. With one stallion and a bunch of mates, though, you can get as much baby horses as there are mares!

This would be a possible reason if horses were more popular as meat stock; but especially with the new restrictions on transport horse meat industry is a small margin (and I haven't heard of euthanizing male foals - meanwhile on egg farms, newly hatched chickens are sorted and all males are ground up into feeder, almost immediately after hatching.) So while the economy could suggest this bias, that doesn't explain where all these unwanted geldings go.


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System diagrams showing biofeedback processes or interactions (- and + symbols) in ecosystems
Overview:
I apologise profusely for asking a silly question, but I am feeling confused and I am seeking more clarity on this subject.
Could anyone, please help me understand why the interaction between climate change, habitat loss, and monotonous diets with the energetic suppression of the bee itself (shown in diagram 1) has a positive (+) symbol?
Would these factors not have a negative effect (-) on the bees energetic suppression?
Would you only add + or - symbols to define interactions when:-

Two biotic factors (i.e. foxes predating on rabbits);
One biotic and one abiotic factor (i.e. the effects of elevated temperatures on bee population abundance);

Would you not add + or - symbols for two abiotic factors i.e. elevated temperature produces extreme weather events and negatively affects a species habitat as these interactions don't involve living organisms (as shown in diagram 2)?
If anyone can help me understand better, I would be deeply appreciative!
Diagram 1
Diagram 1 provides an overview of how parasites, pathogens and pesticides as well as other stressors interact to affect honey-bee health, eventually causing CCD in honey-bees.
How do insecticides and pathogens act synergistically to affect honey-bee health?
There are several hypotheses that have been proposed to explain the effects of pesticides (insecticides and fungicides) and pathogens on bee mortality and CCD including:
Exposure to insecticides suppresses the immune response of bees, promoting pathogenic parasitic (Nosema) infection and as a consequence increased viral loads. The mechanism by which immune suppression occurs has recently been elucidated (see Optional material).
Nosema infection induces energetic stress in honey-bees that results in higher food intake by infected individuals. This directly results in an increase in insecticide oral exposure, increased toxicity and higher mortality in infected individuals.
Bees are commonly exposed to plant compounds that can be toxic (called phytochemicals, e.g. nicotine) and use enzymes (P450 enzymes) to detoxify these compounds and also insecticides. When exposed to fungicides, these enzymes are inhibited, increasing the toxicity of insecticides to bees and also making them more susceptible to Nosema infection.
Honey-bees have a form of social immunity (which is found in many social insects) that consists of cooperation between individual group members to prevent disease contamination. This collective immune defence includes hygienic behaviour where diseased individuals in the brood are removed from the hive, as well as the secretion of antiseptics to sterilise brood food. Exposure of bees to a combination of pathogens (Nosema) and neonicotinoids compromises their ability to sterilise larval food by decreasing the activity of an enzyme known as glucose oxidase. This enzyme catalysis the oxidation of beta-D-glucose to D-gluconic acid and hydrogen peroxide, the latter having antiseptic properties. A decrease in hydrogen peroxide allows the pathogens to spread within colonies.

Diagram 2
Currently, the main stressors on bee populations are considered to be agrochemicals, parasites and poor (monotonous) diets. The interactions between these stressors can also increase the stress on bee populations. However, the Goulson paper also briefly discusses how climate change may become an increasing stress on bee populations in the future, and mentions a couple of impacts of climate change that may negatively affect bee populations. In Session 3.3.11, you also learned how ecosystem function can be impacted by climate change through different processes.
Drawing on the information of climate change in bee populations and the knowledge of ecosystem processes, draw a systems diagram that illustrates the potential impact of climate change on bee populations. This diagram should include impacts of climate change that can affect bee populations directly as well as indirectly (i.e. via impacts on other bee stressors). Your diagram should, therefore, show the cascading effects of climate change, acting both directly and through specific stressors, on bee populations.

Can you link the original source of this diagram? +/-  is usually used to denote positive/negative interactions, respectively. Some people also use arrowhead for positive interaction and a "tee" (⊣) end for a negative interaction. So there is no universal standard. In your case it looks weird. For instance, P450 system should reduce toxicity but in the diagram the interaction is depicted with a +.

Hi WYSIWYG. The first diagram was taken from an example supplied from the Open University. I supplied the relevant information that accompanied the diagram. The second diagram, I drew myself to answer the question. I placed as much information as possible so my question might make more sense now. I hope this is what you need to provide insight. Many thanks for your help!

The original diagram is inconsistent with the use of symbols. As I mentioned, P450 should have a negative effect on toxicity but it is indicated by a +. The interaction between deformed wing virus and mortality (which is a positively correlated interaction) is also denoted by a +. Now, again, immune response also has a + interaction on the virus. Overall, this diagram is inconsistent with the usage of the symbols and is hence wrong.

I'm voting to close this question as off-topic because it is based on a non-standard and incorrect premise that is not even a part of a published text.

Hi WYSIWYG. In this systems diagram (diagram 1: which is definitely a published text with the OU and designed by a doctor in his field), the description says "+" and "-" signs are read as "positive" and "negative" in terms of the recipient box in the diagram. There are some boxes with a negative statement/factor, e.g., "Deforming wing virus", it has "+" on the arrow from "Immune response". That means that immune response allows deforming wing virus to develop. So "+" here confirms the strength of the negative statement in the box.

This question may help many people like me who are suffering from confusion over the theory of "+" and "-" symbols in systems diagrams. I really do appreciate your help and advice. Many thanks

I understand. But the most common usage is that + denotes a positive effect (causal) on the on the node where the edge is directed to (conversely for -).  However, I cannot be sure about this diagram because it doesn't really rationalize these interactions. For example, I would expect immune response to suppress the virus and P450 system to reduce toxicity. Both these interactions should have a - but they have a +. The premise is, hence, unclear. By published text, I mean something published in a book or a peer-reviewed article (at least a place that has figure legends).

So, I also asked a fellow colleague and he explained it like this: a systems diagram is not constrained to involving a biotic interaction. For example, the well know positive feedback loop involving sea ice and warming, whereby warming leads to reduction in sea ice, which increases surface albedo (water is darker than ice), further warming the surface and more melting. This could be represented as [warming] -- (-) -- [sea ice] -- (-) -- [albedo] -- (+) -- [warming].

Are you thinking along the same lines?

Let us continue this discussion in chat.

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What does the difference between GPP - NPP represent?
Overview
Researchers have been studying a temperate grassland ecosystem with the view to understand it's current status with regard to carbon inputs and outputs and to identify whether the grassland is currently a sink or source of carbon.
The aim of this self-study is to become more familiar with different energy fluxes within the terrestrial carbon ecosystems.
The idea is to calculate energy fluxes in various scenarios, and I am feeling a little confused about the scientific theory of gross primary productivity (GPP), net primary productivity (NPP), net ecosystem production (NEP), net ecosystem exchange (NEE), RT = Total respiration, RA = animal respiration, and RS = soil respiration,
If anyone can help, I would be deeply appreciative.
The relationship between production fluxes and other fluxes summarised by these key equations:

NPP = GPP - (plant respiration)
NEE = NPP - (heterotrophic respiration)
NEP = NEE - (carbon losses from leaching, disturbance, and other inputs, and outputs)

Table of flux values

Questions:
What does the difference between GPP - NPP represent? In other words, what is GPP-NPP equal to (see table for values)? Is the value of 800 equal to respiration?
GPP - NPP = R
1400 - 600 = 800
I understand that GPP is the overall rate of biomass production by producers at the rate at which solar energy is captured as carbohydrate molecules during photosynthesis and NPP (energy captured per unit error per unit time). Producers such as plants use this energy respiration and growth.
I also understand NPP is the rate of energy lost to metabolism or maintenance or the energy stored as biomass by plants or primary producers in the ecosystem.
I am making the assumption that this value of 800 is associated with the respiration of plants; although, I am still confused with what this value represents. From my way of thinking, the difference between GPP-NPP equals the extent of CO2 lost from plant respiration within the ecosystem. Since the value of GPP (1400) is larger than the value for NPP (600), more energy has been used during the intake of carbon during photosynthesis rather than plants using glucose or the energy stored in the system to photosynthesise for the emission of CO2.
Therefore, this value represents the amount of CO2 that is lost from plant respiration or from metabolic activity.
Am I on the right track? Do I have the right ideas about energy fluxes?
Diagram

I'm going to answer your question, but could you say where your table of values comes from?

Hey Oosaka, the table values in the table come from a study on a temperate grassland ecosystem. The values are part of an assignment question without a reference, so I am not sure if they come from an authentic study. I think the idea is to manipulate values to obtain an understanding in the theory, which I am finding very confusing the more I read. Many thanks for your help, I am extremely grateful.

I'm mostly confused by how the respiration rates don't add up, but I'm going to guess it's because Rs (soil respiration) includes respiration from the roots of plants, meaning it includes some plant respiration.

Me too! That's the next question. What is included in total respiration (RT). I’ve been sitting here for 2 hours trying to figure this out

Total respiration is all of the respiration happening in the ecosystem; it can be broken down in many different ways. For example your schematic breaks it down into "heterotrophic respiration" and "plant respiration" (i.e. plant respiration, and respiration by all other organisms in the ecosystem). Your table of values doesn't so much break it down as give some different subsets; soil respiration is respiration by everything underground, which includes both plants and heterotrophs. Hence why you can't add Rs+Ra to plant respiration to get total respiration because you double-count plant roots.

I am glad you said that because I was thinking along the same lines; RS + RA = 950; RP = 800. However, this still does not make any sense because the values put into different combinations do not seem to RT=1300

I don't think you're supposed to combine them to get total respiration. Did the assignment suggest you were? I'm guessing those values are given because they're those that were measured; some quantities are easier to measure than others. Soil respiration for example can be measured by finding how much CO2 comes out of a patch of soil; simple enough.

It just says: The researchers have meaured the total ecosystem respiration as RT=1300 g/C/m-2/yr-1. What is included is included in this total respiration? Then researchers have calculated soil respiration as 900 g/C/m-2/yr-1, and animal respiration above groundas RA = 50g/C/m-2/yr-1. The values for plant shoots (R,P-S), plant roots (R, P-R), and microbial activity (RM). I am just making a power point diagram now to add boxes and values to get the + and - parts right

What does the difference between GPP - NPP represent?

The answer is in your overview: it's plant respiration.

Is the value of 800 equal to respiration?

That is the unavoidable consequence of the equation you presented and the numbers you plug in, yes.

I understand that GPP is the overall rate of biomass production by producers at the rate at which solar energy is captured as carbohydrate molecules during photosynthesis and NPP (energy captured per unit error per unit time).

That sentence was fine until "and NPP". What is it doing there? Did you misquote something or are you thinking NPP is a process like photosynthesis?

Producers such as plants use this energy respiration and growth.

Yes.

I also understand NPP is the rate of energy lost to metabolism or maintenance or the energy stored as biomass by plants or primary producers in the ecosystem.

No. NPP is the energy stored as biomass by plants or primary producers in the ecosystem (or rather the rate at which such energy is stored, if it's productivity you're talking about and it's in gC/m-2/yr-1). Respiration is the energy lost to metabolism or maintenance. GPP is what that sentence seems to be describing, as it's the total carbon captured by plants, i.e. whether it's used to build up the plant's biomass or used by the plant in its metabolism (i.e. respired).

I am making the assumption that this value of 800 is associated with the respiration of plants;

Correct.

although, I am still confused with what this value represents.

Maybe you should study the metabolic processes of plants and life in general to better understand this. All life consists of chemical reactions that build up structures; in order to build them up you need energy (because of the second law of thermodynamics), and all living things create that energy by breaking down complex molecules into simpler ones. (as such it would be more accurate to say that all life consists of chemical reactions that build up and break down various structures). You might be wondering "but what about the difference between autotrophs and heterotrophs I heard about"; the difference between those is where they get the complex molecules from in the first place. Autotrophs use a different source of energy to build them up while heterotrophs get them from their environment. As such, you can think of every living thing as being made of two kind of molecules: those that actually form their structure (in humans, the molecules that make up cell membranes, bones, muscles, etc) and those that are stored in order to be broken down to power the whole system (in humans that's fat, glycogen, glucose, etc). Of course a molecule can do both; if you're starving your body may start to break down structural molecules for power. There are many different ways of breaking down those big molecules for power; the most efficient one, that starts with a big chain of carbon atoms and cuts it down into individual CO2 molecules using O2 molecules, is called aerobic respiration (i.e. respiration that uses oxygen).
Because those complex molecules are required to power all life, autotrophs (the organisms that actually make them) are very important, and the processes they use to make them are very important too. The process that makes almost all of the molecules that power almost all life on earth is photosynthesis, which uses the energy from the sun to power a reaction that converts CO2 from the atmosphere into big carbon-based molecules we'll call carbohydrates. This is called "fixing carbon", since the carbon atom is the most important one; measuring how much photosynthesis is happening is another way of measuring how many carbon atoms move from being part of a CO2 molecule to being part of a plant.
GPP is the measure of that exact process: how many grams of carbon per square meter per second (or year in your graph) move from being in a CO2 molecule to being part of a plant via the process of photosynthesis.
You might think this is also a measure of how much the plant grows, but it ignores the fact that as I said, all living things contain molecules that make them up and molecules they break down for power; obviously the plant's growth will involve the former, and the latter just goes back to being CO2 through the process of respiration. Another way of putting it is that in the flow of carbon, there is a huge flow of CO2 to plant molecules via photosynthesis (GPP), but a huge proportion of that flow goes right back the other way (the plant molecules are broken back down into CO2 via the plant's respiration); the remaining flow that's actually still in plant molecules at any given time is NPP.
NPP is the proxy for plant growth; it's basically how much of the carbon the plant fixed via photosynthesis it actually contains in its body, while R (respiration) is how much of the carbon it fixed it broke back down for energy, releasing it as CO2 into the atmosphere. Hence, GPP = NPP + R, and GPP - NPP = R.

From my way of thinking, the difference between GPP-NPP equals the extent of CO2 lost from plant respiration within the ecosystem.

Indeed.

Since the value of GPP (1400) is larger than the value for NPP (600), more energy has been used during the intake of carbon during photosynthesis rather than plants using glucose or the energy stored in the system to photosynthesise for the emission of CO2.

I don't understand this sentence at all. The fact that GPP is larger than NPP shows that plants don't keep all of the carbon they convert from the atmosphere to make up their physical structure; they also use a lot of this carbon to power their metabolism through the process of respiration, which results in said carbon turning back into CO2 and being released back into the atmosphere (as opposed to staying in the form of wood, leaves, starch, etc).

Therefore, this value represents the amount of CO2 that is lost from plant respiration or from metabolic activity.

That is correct, with the two caveats that it's the amount of carbon fixed in organic molecules that is lost by being released into the atmosphere under the form of CO2, and that there is no "or" - all plant metabolism is fuelled by respiration, and the only metabolic activity that releases CO2 is respiration.

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text:
Simulating Migration in SimBit Software Program
How is migration incorporated into the SimBit program of Remi Matthey-Doret and Michael Whitlock?
Is an island model or stepping stone model assumed?

In SimBit, no specific model is assumed as the user can very much chose for himself/herself what migration scenario to be simulated. The migration scenario can be set with the option --m (or equivalently with --DispMat).
There are four ways (what is called "Mode" in the manual) to indicate migration.
 A, LSS, OnePatch, Island and LinearNormal.

A

This mode stands for "All". This mode allows the user to enter a matrix with the migration rate between any two patches. Any migration scenario including the ones achieved with the below modes can be achieved with A. The interest of the other modes is to simplify the user's life esp. when dealing with many patches. I rather recommend using the other modes when these apply.

LSS

This mode stands for "Linear Stepping Stones". It allows the user to set linear stepping stones with any number of stones and potential asymmetry.

OnePatch

This is the default. It means no migration scenario apply as there is only one patch (or one deme if you prefer)

Island

This is a classical island model. The user must input the probability of not migrating.

LinearNormal

This is just like a linear stepping stone model except that the dispersal kernel follows a normal distribution.

Note also that the migration scenario (as well as the number of patches) is a time specific argument and therefore it can vary through time at will. You can have a look at the manual for more information.
Note that I have been working quite a lot on new versions that I have not put online yet, mainly because I was too lazy to rewrite the manual! The last version online is 3.69 but I am currently working on version 3.139! I will upload a more recent version in the coming week(s). Most notable improvement with the new version include:

Easier user interface; no need for all these quotation marks. Migration scenario will be given by the user as forward migration while it currently is backward migration which is a little less intuitive for the user.
More freedom on demography.
Possibility to simulate several species interaction with ecological relationships. One can simulate a whole ecosystem!

Thanks Remi for such a detailed response! Is there any justification for a user to have to input the probability of not migrating in the island model?

Would you prefer inputting the probability of migrating? It was just to avoid the possibility where the sum of the probability to migrate over all patches is greater than 1 (which would have thrown an error message). It felt more intuitive to me to go this way but it might not be for others. It was also quite intuitive to me to input backward migration rate but I already got a few people telling me forward migration rates are more intuitve so, I've changed it in more recent (not online yet) versions.

I'm actually interested in how SimBit accounts for migration "under the hood". While there are many simulation tools already existing, I find that for my particular question, a new tool is needed.

At every generation, SimBit loops through each patch, ask how many individuals this patch will contain (this number can be affected by a fair number of different options) and then for each offspring look for where the father come from and where the mother come from. Select the mother and the father and then make them reproduce to get the offspring. I am not sure that really answered your question. If you have more specific questions about how SimBit works under the hood, I'll be happy to answer.

Note that it is not so unusual to not have a simulation platform that does exactly everything we want for a particular project. But many of the things you might want to implement might be done quickly by the author and they / I may accept to help. If it is a big change, then the author of the simulation platform may expect authorship on the paper resulting from the project. Note that for the moment, you have not specifically indicated anything that either SimBit, Nemo or Slim could not do.


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How can someone share 50% of their DNA with their parents yet all humans share 99.9%?
I have heard that humans share 99.9% of their DNA with other humans. I have also heard that a child shares 50% of their DNA with their parents. How do I resolve this apparent contradiction? It has been really bothering me.

There was another recent question about this that gave an answer that is equivalent to the answer for your question, asking about Neanderthal DNA in Homo sapiens sapiens, see here: https://biology.stackexchange.com/questions/61147/neanderthal-human-genetics The short version is: they are talking about slightly different things.

See here: https://biology.stackexchange.com/questions/41974/why-are-the-genomes-of-humans-99-5-the-same as well

Why was the question down voted? I think it is a common misunderstanding. We have already seen such misunderstanding several time on Biology.SE but it is the first time that the origin of the misunderstanding was so clearly phrased. I think it is a good question.

It will be clear with a simple analogy.
You are 50% related to any one of your parent
Let's say you don't have any biology books. You have two friends, Alice and Bob. They each give you a copy of the book Campbell Biology. You now have two Campbell Biology. You have received 50% of your Campbell biology books from Alice and 50% from Bob.
Similarly, you inherit 50% of your DNA from your mother and 50% from your father. You are related at 50% to any one of them.
Two randomly sampled individual are 99.9% identical
Now consider the list of all the copies of Campbell Biology in the world. As there exist different editions, all Campbell Biology won't exactly be the same.
Let's say you randomly sample two Campbell Biology from around the world and you align them letter by letter. What is the expected fraction of the letters that will be identical? If, for example you find the two sentences

Selection is a fitness variance associated to a genetic variance among individuals in a population.

and

Zelection is a fitnezz varianze azzociated to a genetic varianze among individualz in a populazion.

There are exactly 9 mismatches out of 99 characters, that is a 90% similitude.
Similarly, if you randomly sample two humans, align their DNA (a DNA sequence look like ATTTCGCTGTCGAATCGATCGGTA), you'll find that the fraction of mismatch is lower than 0.1%. Therefore, we all share 99.9% of our DNA.
Of course, alining DNA sequences (or normal sentences) is not quite that easy as some sequences (or sentence) can have more nucleotides (or letters) than others but I won't go into the details here.
How do these two measures relate?
Let's say that instead of giving you a book, Alice actually produced a copy of its book and gave it to you. The book you have received from Alice (that is 50% of all your Campbell Biology books) is 100% identical to Alice's book in term of mismatch.
Similarly, the 50% of your DNA that you inherit from your father (or mother) is 100% identical to the copy of the genome found in your mother.
Note however that

Your mother probably made between 10 and 100 mutations when copying her DNA so that the DNA you received from your mother is not exactly 100% identical to your mother's DNA

Similarly Alice could have miscopied her Campbell Biology book that she passes to you

Your mother actually recombined her two haplotypes

Similarly Alice actually had two different editions of Campbell Biology and she mixed them up a little bit before copying the resulting book!

Good answer. +1 But could you add a link between the two? E.g. that the 50% you have from your parents are 100% identical to the respective parent in terms of 'shared mismatches' (disregarding the 60-100 de novo mutations) and that because of this the expected identity between parents and offspring is way higher than between randomly sampled individuals.

@AlexDeLarge Very good point, thank you. I tried to add that. Feel free to rephrase it if you think you can make it easier to understand.


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What are null cells?
My histology textbook says the following:

The pluripotential hemopoietic stemm cell (which resembles a lymphocyte) is a member of the null cell population of lymphocytes.

It then goes on to add:
There are three types of lymphocytes:

T cells
B cells
Null cells:

Pluripotential hemopoietic stem cells
Natural Killer cells

However, this is the only text I find that classifies Pluripotential hemopoietic stem cells as null cells.
My question is: what are null cells? (and is this textbook accurate)
Thank you,
jsx

When quoting, can you please make sure to quote exactly what is being said? It is part of academic ethics to not misquote (but typos happen, don't worry about it). I doubt your textbook says stemm cell and by this small typo of yours , it makes me wonder whether (which resembles a lymphocyte) was added by you or was in the original quotation. You also want to cite which textbook it is (and eventually add a link to it). Are the bullets a direct quote as well? If yes, you should start the lines with > to highlight that it is a quote.

This seems like either a very basic, very old, or very misleading textbook. The obvious meaning of "null cell" here is one that lacks both B cell receptors and T cell receptors. Such "null cells" would cover vastly more territory than the two sets mentioned, which is why I question the accuracy of the book.

Did you have a look at wikipedia > null cell?

@iayork I understand that that is the definition of a null cell. I'm using Gartner's 'Colour Atlas and Text of Histology'. So, null cells are simply cells that lack the receptors that T and B cells possess, and as such include the natural killer cells, killer cells, PHSCs and other cells too?

@jxs As far as I know it's obsolete terminology. It probably included all those, but just by accident, just as "horseless carriage" would include a Tesla and a Ferrari as well as a Model T.

@iayork That was my impression. Thanks for clarifying!

This is easily found with a simple google search. "What are null cells"  https://en.wikipedia.org/wiki/Null_cell

@akaDrHouse as the term is virtually obsolete (and was previously used to refer to what we now call natural killer cells and killer cells), I was simply confused as to whether PHSCs truly belong to this classification. The text is misleading in that the pluripotential stem cell which gives rise to all the blood cells is then found again as a subtype of a subgroup it itself generated. I wouldn't come to ask a question before a google search.

Null cells are a "slang term" for cells of the lymphocyte progeny that are not APC (antigen presenting cells) and lack T and B cell receptors - therefore they are the natural killer (NK) cells.


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Access and decay time of long-term memory
As for STM (short-term memory), access time is 70 ms and decay time is 200 ms.
Is there any calculated time for LTM (long-term memory) also?
And does LTM really decay with the passage of time?


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Identify Huge Mystery Beetle
It is around 2 inches long, one inch wide, fat, hazel coloured with black specks and has short antennae. It's got a lot of cobwebs on its legs, since I found it in a corner in my house. It was found in North America.

Welcome to Biology.SE. Where in North America did you find it? Not the same species live in the Yukon than in the Yucatan. I edited the post to capitalize the first letters of your sentences and add the picture direclty in the post (which you can do with one of the icon above the content of the post when you click on 'edit').

It's a female Eastern Hercules beetle (Dynastes tityus). Unlike the male, the female doesn't have 'horns' as it doesn't need to fight in order to find a mating partner. It's the largest and heaviest beetle in North America. It belongs in Scarabaeidae or scarab beetles.

Source from BugGuide.com


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Wallace effect on human populations
Could sympatric speciation occur in humans, for instance, separating blue collar workers from white collar workers?

No, it won't happen
Even geographical isolation failed to allow speciation in humans (see Does it make sense to classify all humans in a single species?). So if there is some small reproductive isolation between working groups, it is definitely not going to be strong enough to cause speciation.
In an imaginary world
If there was strong reproductive isolation
In an imaginary world where there is a more important reproductive isolation that lasts for long enough between blue and white collar workers, sympatric speciation might be possible. Such reproductive isolation could be caused by very important social stigmas.
Wallace effect
Wallace effect is a type of speciation that is mediated by a reproductive isolation that has been selected for as fitness of offspring is higher when mating is assortative.
There is probably relatively little genetic predisposition to being blue collar or white collar worker. But in an imaginary world where there is very strong genetic predisposition to being a good blue collar or white collar worker, and where worker quality will affect income which will affect fitness, then yes, wallace effect could be a thing.
But really, we do not live in one of this imaginary world. In our world, speciation won't occur between blue collar and white collar workers.


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Why is the chemical of an hexose not sufficient to ID the molecule?
Here is the homework question I have to answer to

The Chemical formula C6H12O6 can mean any number of different hexose sugars that share the same chemical formula. To tell the hexoses apart, you would need to see their structural formula. This is because they are ___________ of each other

My thoughts are ...

Welcome to Biology.SE. Your question is off-topic because 1) It is more of a chemistry question than a biology question and 2) homework questions are off-topic unless you've shown your attempt at solving it.

The format of your question is not great. Just to show you what a good format is, I edited your post. Of course, as it is a homework question, I can't fill up for you what your thoughts on the question is. Good luck


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Is existence of different alleles for a gene a result of mutation?
I would like to understand evolution. Here are a few questions

Why are there different alleles for a  gene?
Is the different alleles of a gene are mutated versions of a gene?
Why selection pressure favoured the existence of more than one alleles?

technically all existing genes and alleles are the result of mutations.

I would like to understand evolution

The best way to do so is to start an introductory course on evolutionary biology. Consider having a look at evo101 for example.

Why are there different alleles for a gene?

Because mutations bring variation. Note that not all genes are polymorphic. In other words, there might be genes for which, all individuals in the population share the same allele.
The existence of this genetic variance is absolutely central to the whole concept of evolution. In short, mutations increase genetic variance while natural selection and genetic drift reduce it.

Is the different alleles of a gene are mutated versions of a gene?

Yes

Why selection pressure favoured the existence of more than one alleles?

In the exception of cases of balancing selection (e.g. heterozygote advantage, negative frequency-dependent selection), selection does not yield to the existence of genetic variance, rather the opposite in fact. Selection "selects" for a given allele, which in turn reduces the genetic variance. Mutations bring new genetic variance in populations and selection reduces it. Genetic drift (another "driver of evolution") also reduces genetic variance.

drift within subpopulations could theoretically maintain variation in the population as a whole, I think, by randomly fixing different alleles ... (don't have a reference)

1. Why are there different alleles for a gene?

Variants of a gene are called alleles. In other words, an allele is one of the possible forms of a gene. If there is genetic variation in a population (and in all species, there is variation), then there are multiple alleles.
Evolution reflects changes in DNA sequences and allele frequencies within a species over time. These changes may be due to mutations, which introduce new alleles into a population. New alleles can also, for instance, be introduced in a population by gene flow, when two populations that carry unique alleles breed together.
2. Are different alleles of a gene mutated versions of that gene?

Yes.
3. Why does selection pressure favored the existence of more than one allele?

This question is unclear and/or poorly-stated. Are you asking why there is variation in a population? The answer is that, in spite of the fact that DNA replication (copying) is high-fidelity and quite accurate, it is imperfect. This means that over a long lineage of inheritance, new alleles will emerge, and their frequency in the population will change. Also, DNA accumulates damage and mutations occur due to chemical factors. DNA is not a perfectly stable molecule. Mechanisms of repair of DNA exist but are imperfect. It is impossible for a species to maintain perfect genetic identity, especially over prolonged periods of time, when mutations accumulate.
If you are asking why variation (or more strictly, diversity) is favorable for a species, here is an excerpt from the Wikipedia entry on genetic diversity, which explains it concisely:

Genetic diversity serves as a way for populations to adapt to changing
  environments. With more variation, it is more likely that some
  individuals in a population will possess variations of alleles that
  are suited for the environment. Those individuals are more likely to
  survive to produce offspring bearing that allele.

There is also an important difference between diversity and variability to consider. Diversity is the total number of genetic characteristics in a population. Genetic variability is the generation or presence of genetic differences between individuals of a population. Variability is introduced through migration events, homologous recombination during meiosis, and other things.
Addendum/protip: asking why is often less fruitful than asking how. The how is mutation. The why makes me want to include thermodynamics and statistical mechanics as an explanation, which is definitely an unnecessarily complicated explanation.

The terms genetic diversity (aka. expected heterozygosity) and genetic variance are well defined. I had never seen the terms genetic variantion and genetic variability being stricktly defined. I have always used the term genetic variation is a loosely defined way. The definitions you're providing seem a bit unclear to me. Would you mind indicating your sources?

The vision that genetic variation is favorable to a species is neglecting the genetic load (typically mutation load and drift load) it is causing.

Sorry, I meant diversity and variability. Thanks for the fix.

Oh... so the definition should rather be Genetic diversity is the fraction of individuals that are heterozygote in the population as expected by Hardy Weinberg rule or If 'p' is the frequency of one allele at a biallelic locus, then the genetic diversity is defined as 2p(1-p). When you say "variabiility", did you mean "variance" or does the term "genetic variability" has a commonly accepted definition I am unaware of?

See here and here and here for commonly accepted definitions of genetic variability. I disagree with your proposal to edit the definition; it does not clarify anything, or help pedagogically. Diversity is a simple concept in ecology and genetics and does not require rigorous analytical definitions unless the term is defined so in a rigorous, analytical context. As you can see, the question is far from advanced.

And variance is a purely statistical concept and has no meaning with regard to biology per se. Unfortunately it is often loosely and inappropriately used as a synonym for variation.

1.why there are different alleles for a gene?

alleles of a gene are mutated versions of a gene

why selection pressure favoured the existence of more than one
  alleles?

Reason
1) No reason. Allele is both not 'selected for' nor 'selected against'. The allele do not cause detriment or advantage. It is a neutral mutation. An example is ear lob, connected or not connected. It does not affect hearing. It builds up in a population by chance due to the founder effect.
2) It has an advantage but only in heterozygous form.
Example Sickle cell anemia. In homozygous form this allele is quite lethal. However in heterozygous form (ie pair with the normal allele), it confers resistance to the malaria parasite. So selection does favour the sickle cell anemia allele but only to a point. Once the allele becomes too common in the population it is selected against, as the probability of homozygous individuals of sickle cell allele occurring becomes more common.
3) Because allele is selected for in some parts of organism's ecological range.
Example human skin colour. Human range from poles of the earth to the equator. And thus are exposed to varying light intensity. At the equator, dark, almost black pigmentation is favoured as it protects against sun damage (ie sun burn) and skin cancer. Up in the temperate region, fair almost white skin is an advantage as it allows better absorption of the limited UV light for vitamin D and folic acid production, which is required for bone and limb development. Different light intensity selects for different levels of skin pigmentation. So we have several skin alleles.

Does this mean that existence of unaffected alleles are example of silent mutation?

There is no such thing as an 'unaffected' allele. Alleles are simply variants of genes. Some alleles are favorable, some are unfavorable, some are (un)favorable only in the presence of other alleles. Some have no effect on survival and reproduction, so there exist no active selective pressures. Silent mutations create new alleles which are functionally identical - i.e. the resultant protein is the same. However, even silent mutations may affect the way the DNA or mRNA behaves, or how it is regulated (e.g. how RNA interference may work through microRNAs) and thus its abundance in the cell.

In general, a gene can have more than two alleles (except genes of the Y chromosome) at a specific locus of the chromosome. Alleles are different forms of the single gene, which takes the same place on homologous chromosomes and exhibit variations in the production of the gene product. This means, at the functional level, alleles of the same gene will interconnect in several ways, which result in variations in the type of dominance and occurrence of different phenotypic effects with different allelic combinations. Read about alleles on the biology site.

Please take the [tour] and then consult the [help] pages for additional advice on [Answer] and [ask] effectively on this site and then delete or [edit] your posts accordingly. In particular, this is poorly written, misleading, and doesn't appear to add anything to the previous answers.


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Coalescence time: Is it different for haploids and diploids in population genetics?
I'm trying to model Cyanobacteria cells divergence in 2 populations with mutation rate $-\mu$ and I need to verify my model with a valid theory. I don't have much biology background and all the theories I can find are either valid for haploid population or mating populations. All I need to know is a theory for coalescence time/divergence for a haploid population without mating. So far I have this:
$$T = 2\times N \times d\\
D = 2 \times \mu \times T$$
in which N is the number of diploid individuals, d is the number of subpopulations (in this case 2), T is the coalescence time and D is divergence. Can I apply this to my case even though it's defined for diploid population?
Thanks

Welcome to Biology.SE. The question is a little unclear to me for the moment. $D$ is divergence. Can you tell us more about what you call divergence? You write $T=2 \cdot Ku \cdot T$, therefore, $\frac{1}{2}=Ku$. This is probably not what you wanted to write. $T$ the coalescent time is in unit of generations I suppose. You might want to let us know where this equation comes from. It is always good to follow conventions; the mutation rate is usually $\mu$, not Ku.

You should write your equations in MathJax (LaTex). Here is a quick tutorial. You might want to have a deeper look at coalescent theory. There is a wikipedia entry. Coalescent Theory: an introduction is a very good book on the subject.

Out of curiosity, what is your background (or what are you studying) if not biology?

Can you provide a background for the assumed model (the equation) and the details of the model that you want to build? Details would include assumptions, parameters to be considered etc, phenomena to be accounted for etc. If you do not include these details then your question would be too broad. There can be several ways to model a system.

Need background for those equations and need a definition for "divergence".

Coalescence time: Is it different for haploids and diploids in population genetics?

Short answer
The coalescent time is twice as high in a diploid population than in a haploid population
Long answer
Imagine you sample at random one chromosome. Then, you sample a second one and you ask the question, what is the probability that they coalesce in the previous generation. This probability is $\frac{1}{PN}$, where $N$ is the population size and $P$ is the ploidy number (1 for haploids, 2 for diploids). The probability that they do not coalesce is therefore $1-\frac{1}{PN}$. The probability that the two chromosomes coalesce $t$ generations ago is the probability that they don't coalesce for $t-1$ generations and then coalesce. That is the probability that the two chromosomes coalesce $t$ generations ago is
$$P(t) = \left(1-\frac{1}{PN}\right)^{t-1}\frac{1}{PN}$$
You might recognize here a geometric distribution, where the probability of success is $\frac{1}{PN}$. In order to simplify the math, let's assume that the population size is very large. In such case the above formulation is well approximated by
$$P(t) = \frac{1}{PN} e^{-t\frac{1}{PN}} $$
You might now recognize the exponential distribution with rate $\frac{1}{PN}$. We can now compare the mean expected time to coalescent of a diploid population and a haploid population.
The mean of the above exponential distribution is simply $PN$. Therefore, the expected time to coalescent is $2N$ in diploids and only $N$ in haploids.
Details of your post
In your post you talk about structured populations. The calculations are a little more complicated in structured populations and I don't remember on the top of my head how they work.
You talk about divergence $D$. I don't know what you mean by divergence. From coalescent results you can compute the expected heterozygosity, the number of pairwise differences, and even the whole site frequency spectrum. You can as well make those calculations for the structured coalescent model.

Yes you can, as the model speaks of allele coalescence as states the wiki article on that.

It comprises a probabilistic assessment of variation in time to common ancestry of alleles in a relatively small sample of individuals, from a much larger population.

What you are trying to check is how many generations have passed since any number of alleles were a common ancestral copy.
For that we have:
$t=\frac{4N}{n·(n-1)}$
This was developed by JFC Kingman (based on the Wright-Fisher previous model) for a constat population size of $N$ where $t$ is the coalescence time of $n$ alleles. In this equation the numerator is $4N$ because $2N$ is the total allele population size. You have to multiply it again by $2$ because the total time two alleles have been differing is twice the time it has been separating from the most common recent ancestor. Therefore for haploid populations you should use $2N$ in the numerator.
There are versions of this model that include mutation rate and non-constant $N$, but maybe it would be easier to simply look a good model to find your $N_e$ (effective size of a population, or the N that in a constant size population has the same genetic drift as your actual population) as it can be used in this formula when $N$ is not constant.

Can you please provide some equations to show how it is affected by the "only difference" you cite? You might want to add a reference for your claim as well especially if you don't provide a complete explanation. Given that $N$ is the number of individuals, I would expect the coalescence time to be $T = Nd$ for haploids (assuming $T=2Nd$ is correct for diploids).

It's T=2Nd because there are two paths by which the alleles have been changing, not because there are two copies im each genome. Anyway, let me rewrite it propperly.


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How can predicting protein folding speed up drug discovery?
I'm asking this as a layperson without much knowledge in biology, so please correct me if my understanding is wrong.
Recently DeepMind's AlphaFold managed to predict protein structure from acid amino sequence with stunning accuracy. We are being told that this could "pave ways toward advances in drug discovery."
But I fail to see how it can happen. From my understanding the combination of protein structure is infinite, and if we tweak a protein structure that works good to fight against a virus, it will either work better or worse, but the key point is we don't know until we try. So essentially this is still a hit-and-miss thing, depending purely on dumb luck.
So how can the fast, accurate prediction of protein folding help in drug discovery?

Why is this question downvoted?

Firstly protein structures are not infinite. Most proteins adopt specific structure.
Drugs carry out their function by binding to its target protein. Structure prediction helps drug discovery process in two ways -

it allows identification of pockets in target proteins (where drugs can bind) whose structures are not yet solved using experimental methods
it allows in silico experimentation i.e. you can take a large number of molecules and simulate whether they will bind to a specific location in your target

I would suggest going through these reviews to get a better overview of the importance of fast and accurate structure determination in drug discovery :

Verinde and Hol, 1994, Structure
Montfort and Workman, 2017, Essays in Biochemistry

Simulation of Ligand-Receptor binding usually involves the retrieval of X-ray crystallography of the desired protein (Receptor) from bioinformatic databases. The X-ray data of the protein contains important features of the protein, one of which is Protein conformation (Folding status). Gathering X-ray crystallography data of a specific  new polypeptide is an extremely hard, time consuming process which requires advanced techniques and accurate purification of the protein from biological systems. Therefore if an algorithm can predict the folding of a protein correctly (at least to some extents) , other algorithms can benefit from this independence from experimental results. Fast, simultaneous docking surveys (Virtual Screening) would be available with a lot more ease eventually leading to designing new medications only by computer simulations (Which requires extremely less financial budget compared to testing each candidate molecule one by one on animals)


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Evolution of autophagy and apoptosis
Autophagy and apoptosis are both necessary for survival of a multicellular organism. I have some information on evolution of autophagy and apoptosis.
However, the key difference between the pathways struck me as - autophagy is for self survival whereas apoptosis is for survival of the population. Given this, is it logical to assume that autophagy evolved earlier than apoptosis ?
Edit1:
I thought of a reason for autophagy to precede apoptosis. Since apoptosis requires mitochondria for its initiation and was acquired as endoymbiosis, I assume that:
$1)$ The primitive organisms taking up primitive mitochondria were eukaryotes.
$2)$ These were already sophisticated enough to have preliminary autophagy.

Firstly, it is important to note that, while autophagy's primary role is to 'shut down' the cell in hopes that reduced function will aid its return to homoeostasis, there is evidence of it being used as a primary method of programmed cell death in certain animals, where periods of massive cell elimination are required (e.g. insect metamorphosis). Source (Nature Review)
Your question is somewhat difficult to answer, as there is fairly limited research in the area, to my knowledge. For extrapolation purposes, it's also necessary to establish what we should consider as autophagy and apoptosis in simple organisms - how similar do the mechanisms of the processes have to be in a rudimentary form in primitive organisms to qualify as such? There is evidence of an apoptosis-like process, as well as autophagy in a range of protozoan bacteria. PLOS article (Evolution of apoptosis-like programmed cell death in unicellular protozoan parasites, Kaczanowski et. al) (Metamorphoses of Malaria: The role of autophagy in parasite differentiation, Isabelle Coppens) Additionally, based on genomic investigations, macro-/micro- autophagy are likely to be present in all non-parasitic primitive eukaryotes. (Origin and Evolution of Self-Consumption: Autophagy
Timothy Hughes and Tor Erik Rusten)
Based on some reading I did during my first degree, it is likely that both apoptosis and autophagy both developed from a precursor process that was present in prokaryotes (some bacteria, such as Myxobacteria, undergo a type of programmed cell death). An older nature article extrapolates that, based on prior research, it is likely eukaryotes came to possess the rudimentary processes of apoptosis (and likely autophagy) through the absorption of multiple strains of bacterial genomes. (Origin and evolution of eukaryotic apoptosis: the bacterial connection, E V Koonin and L Aravind)
Ultimately, I feel it's difficult to state which developed first, if, indeed, they did so separately. Assuming they originated from the same initial mechanism, which seems likely, there is a distinct possibility of them developing alongside each other. If this is the case, it's a bit of a 'chicken and egg' situation - at which point would you consider apoptosis apoptosis? There is evidence of both occurring in yeasts, as well as protozoa, so assuming these processes are indeed precursors to what we observe in multicellular eukaryotes, simultaneous development seems most likely.
Please excuse me posting the names of some articles, rather than links - I have limited use of them at the moment.

Much appreciated. I will wait for other answers and in their absence your answer deserves to be chosen as accepted.


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What is a "homologous pair" of chromosomes in cross joining or cross over?
I never understand if the chromatids attached at the centromere is a homologous pair, or the male and female chromosomes?

The term sister chromatids refers to the pair of chromosomes which have been produced from a single parent chromatid by DNA replication, while a homologous pair refers to chromosomes which are more distantly related (e.g. those inherited from one's mother and father, which I guess is what you mean by 'male and female chromosome').
So the two options you give are identical, and neither are joined together by a centromere. The sister chromatids are joined by the centromere, while the homologous chromosomes exchange material during recombination (cross over).


text:
Ion channels affected by gravity
In the literature I have found that action potentials behave differently when gravity is changed (cannot access fully).
Action potential properties are gravity dependent.
http://link.springer.com/article/10.1007/BF02870977
Are there any ion channels in the neurons that are more or less likely to be sensitive to changes in gravity?

Please link to the article abstract about gravity.

http://link.springer.com/article/10.1007/BF02870977Action potential properties are gravity dependent

I'd say this is a fairly niche area of research so it's unlikely this has been studied beyond the citations you will find in that article.


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Insect making continuous sound in summers
In deciduous to tropical-deciduous forests and residential places with such vegetation I have found this insect to make continuous sounds all through the day (even in nights sometimes) during the summers. Average summer temperature > 30 C around noon and ~ 28-29 around nights with high humidity.
I want to know what is the reason they make this sound ?
Recently, I found several of them dead near light poles etc..., and some sit very close to windows of air-conditioned rooms. I think they want some cool environment.

These are Cicadas, of the superfamily Cicadoidea of the order Hemiptera.
These sounds are made by the males to attract mates. There are a lot of species, each making there own specific sound to attract the right mate. However, to the human ear, these sounds don't sound different. Cicadas are also able to produce other sounds, in distress or during courtship and mating, but the continuous sound during night and day is to attract mates.

As a resident of Costa Rica, especially one who's seen plenty of these, I can confirm.

Two minor points – most cicadas (there are exceptions) don't sing at night (other than dusk) unless conditions are unusually hot and humid along with an unusual light source such as a full moon.  Nighttime sounds tend to be crickets, katydids, and frogs.  Also, most species-specific cicada songs can be recognized by the human ear with a little practice/attention.

These are definitely cicadas and mating is the primary reason for their long calls. As for the reason you've found many dead or dying cicadas, their lifecycle is primarily spent underground where they feed on tree roots. They will emerge and transform into their adult form which enables them to make their calls and mate, and many species die shortly after mating, having fulfilled their evolutionary function of reproduction. Some species spend up to 10+ years underground before emerging to mate for only a short week or so.


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How does DNA organise a cell's physical position within a body?
How do cells "know" where they are supposed to be in relation to other cells, in forming the physical shape of the body?
If you have ever tried to organise or choreograph the physical positions of a group of people on a field, for example for a dance or display, or even just to stand on stage at a graduation, you will know it takes a bit of communication and working out between the people, and the choreographer (who could be considered a kind of "central intelligence")
Analogy: If you give each person on the field a simple diagram or map (DNA) which shows where the final positions should be, they may or may not be successful, but they would definitely have to talk: "You stand here; I'll be next to you; Jo you need to turn a bit; etc, etc..."
How do cells do this, given than cells could attach and clump in any direction? Especially, how do cells coordinate with other cells quite distant? (Given how small cells are in relation to the shapes & structures of the body)
For example, in my diagram here, 4 cells are forming a specific shape (let's say they're building the corner of an ear or something.) Does cell A "know" where cell C or cell D is?

Please, I'm not after specific chemicals names or in-depth mechanics of cell physiology. I'm after a layman's description or a general principle that is at work.
Bonus related concept: Why/how does the body build a bone of such exact shape the first time around (in the womb) but when you break that bone later, the body seems to just slap bone material around all over, like a bad plasterer?
I've appended a list below of articles I've read (or attempted to read) to try to answer my question.

https://www.reddit.com/r/askscience/comments/3ln201/how_do_cells_know_where_they_are_in_the_body/
http://www.newswise.com/articles/how-do-cells-know-what-to-do-and-when-to-do-it
http://www.thenakedscientists.com/HTML/interviews/interview/1000544/
https://www.sciencedaily.com/releases/2014/10/141027085032.htm
https://www.ucsf.edu/news/2014/05/124831/how-do-our-cells-know-where-they%E2%80%99re-going

I think this might help.

I found this related question after I posted. http://biology.stackexchange.com/questions/21881/how-does-an-embryo-know-where-to-grow-limbs-etc

Does that post answer your question or you have some confusion?

Still reading it ...  :)

OK, so I get that my map analogy is flawed. DNA is not a map, it's directions on "how to get there." But still in my diagram, all 4 cells have exact copies of these instructions. How does cell A get to execute different instructions to cell B or C ?

Stewart, read about Hox genes. @another'Homosapien' Quora is an abomination!! :D

Its related to transcription factors. Transcription factors are important to determine which genes are switched on in cell A and switched off in cell B. There is a complex mechanism behind why cell A has transcription factor while cell B doesn't.

@another'Homosapien' Thanks! You've given me a specific terminology which I've never heard of before, which I can look into. That's a very definite starting point!

@WYSIWYG yeah but since there were references in its answer, so I thought it would be useful ;) BTW OP wants layman answer, so hox genes would be a bit difficult to them :P


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Mouse Colony Software
I am looking for a good mouse colony census program that tracks the mice we have in the lab, breeding success of breeders, lineages, multiple user access, etc. Jackson labs has a program that does this but is is cumbersome and a bit pricey (softmouse is also an option, though a pricey one). I figured that since colony management is so pervasive there must be a good piece of software to deal with this, but have mostly been out of luck. It would also be great if it is currently supported software that isn't outdated and free or sold at a relatively cheap price. Thanks.

check this out: http://e-mouselab.com

When you mean track the mice, you mean like mice movement?

And what counts as cheap?

Here is a list of programs that could work:

E-mouse Lab: http://e-mouselab.com, seems like the best fit for your requirements.
ZooEasy: http://standalone.zooeasy.com/en/pedigree-software/breeding-mice/breeders/index.html, Cheaper, good for breeding and pedigrees.
LabGuru: http://www.labguru.com, seems very professional and great for mice specimens as well.
MouseHouseApp: http://www.mousehouseapp.com, This is an iPhone and iPad app, so this would be good in the lab, but it depends on whether or not you have an iOS device.

Also, from what I found, Jackson Labs actually offers free mouse colony management programs. It does seem like it is cumbersome, but also a great program.
I will add more when I get time.


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Studying effects of alcohol on cells
I am wondering about the logistics of a simple experiment with say 3 types of alcohols, and various concentrations of each, with a control(s)
I would like to research the effects of alcohol on mitosis. Do I need any expensive equipment or is it do-able with standard equipment? I am looking specifically at what I expect to be a negative impact on cell growth. Potential cell death and slowing of mitosis.
"We expect mitosis to slow and stop entirely or become corrupted as the amount of alcohol used increases. Of the three alcohols we will be testing, we expect some to be more damaging than others. The 3 chosen alcohols, listed in order of their hypothesised toxicity are Methanol, Ethanol and 2-methyl-2-butanol."
I am basically trying to see if tertiary alcohols are safer due to ethanols main metabolites acetaldehyde & acetic acid being more harmful/toxic? Since Tertiary alcohols for example, aren't metabolised into aldehydes. Which makes them less toxic than ethanol in theory.
Now I am unsure as to which cells to use to test upon, I'm also unsure as to the legitimacy of my hypothesis. Budget, time and a lack of knowledge is against me. I am researching as much as possible as quick as possible. Thankfully I have help available soon or I would be totally out my depth. This is me out of practice in the field trying to get a head start and help a friend out as best I can. I'll be his assistant and feel thats more than appropriate.
I realise I can't test for too many things. I need one clear goal. If we choose to test certain cells over others do you have any suggestions? and why?
The liver processes ethanol into acetaldehyde & acetic acid. So I would need these chemicals potentially in order to properly test ethanols effects on cells?
I am so out of my depth I feel right now I'm hoping for a prod in the right direction.
The 3 chosen alcohols, listed in order of their hypothesized toxicity are Methanol, Ethanol and 2-methyl-2-butanol. - concentrations of each yet to be considered.
I suffer from dyslexia and my wording is bad at the best of times. But please realize this is a very first draft / ideas on the table. Any help at all is appreciated, apologies for my lack of knowledge in the field. I'm trying to refresh my studies from 10 years ago in 2 days.

Based on T Abraham's answer, a hemocytometer would work. However, a hemocytometer requires a microscope, but if you are in a cell lab, you probably have access to a microscope. I would recommend using mammalian cells instead of bacteria, they're larger and easier to see, and more relevant to human health. If you're interested in liver toxicity in particular, try getting HepG2 cells. These are human liver cancer cells, but extra paperwork might be involved because they can potentially carry hepatitis virus.
Should also use trypan blue staining with your hemacytometer to mark live and dead cells. Dead cells can't export the dye and turn dark.
Alternatively, an MTT assay is a nice way to measure cell viability if you have access to 96 well plates and plate readers. Using a cuvette based spectrophotometer is possible with this assay if you can grow your cells in larger containers, such as 10cm dishes.
The hemacytometer assay can get you total cells and percent of cells that are alive. The MTT assay tells you number of live cells. To get percent of cells that are alive you need a control that represents 100% viable.
Neither of these can tell you much about mitosis specifically, just the number of cells and percent viability.

I didn't realize about dead cells... but would it matter? Sure, it you want a quantitative and really accurate measure on whether the cells are undergoing mitosis, then maybe it matters... But if you are just thinking about whether mitosis is occurring, I think we can still get a reasonable estimate without worrying about dead cells...

The trypan blue staining just gives you some more options for collecting data. You can still count total cells, but with the stain you can differentiate between live and dead. I find it also makes seeing the live cells easier when you have a slightly darker background.

Great idea! It seems that this research has been done before and your hypothesis is correct, but testing a wide variety of alcohols and testing your hypothesis for each one of them would be a great project.
Now, to do this, I would expect you would need to check the growth of the cells after certain periods of time. How would you do this? Well, the best device would be a hemocytometer. It looks like the price varies from 10.00 - 400.00 which is pretty cheap. Of course, there would be mitosis if there is growth during periods of time, which means there would be more cells after certain periods of time.
What cells to choose? I would choose something easy to work with, such as E.coli. It is safe and easy to work with. It needs minimal resources. Here's a link to a simple culture you can probably make at home:
http://makezine.com/projects/how-to-make-bacterial-brothagar/
Later, however, when you get good data, you can use mammalian and human cells to show that the research is relevant to human beings. However, mammalian cells are harder to grow because mammalian cells grow slower than bacteria and fungi and moreover contamination can cause problems. You would also need biosafety cabinets or laminar flow hoods and learn of aseptic and sterile technique.

For assaying effect of alcohol on cell growth:
    Prokaryotic Cells

Take ~5ml medium (LB for E.coli) in test-tubes/plastic tubes and add appropriate concentration of alcohol(s) in these.
Inoculate 1% bacteria from a starter culture (OD~0.6)
After different time intervals or a fixed time point take some culture, dilute and spread plate it (for each tube).
Count colonies (CFU)

    Eukaryotic Cells

You can try this in a 6-well plate.
Seed the cells such that they attain 60% confluency in 24 hours
After 24 hours: Change the medium and add the medium with appropriate concentration of alcohol
Wait for at least one division time (varies for different types of cells). 24 hours would be a fine interval for the treatment.
As mentioned in the other answer, you can use MTT assay or Trypan blue to detect viable cells (It would be difficult to differentiate inviable cells from the viable ones by just looking in the haemocytometer without staining)
You can use indirect assays similar in principle to bacterial spread plating; split the cells (if the cells are adherent) and inoculate a small percentage of them into another flask/plate. Count the number of cells in the new flask after 24hours.
You can also do flow cytometry of cells stained with propidium iodide to identify cells in different stages of mitosis.
For yeast you can simply grow them as you would grow bacteria and assess cell viability by either colony based methods or flow cytometry


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Plasmid Expression Vector
If I am going to transform a plasmid into a bacterium, which will then be fed to a C. elegans, the expression vector needs to be bacterial correct? The other option is to have a worm expression vector, but I just wanted to make sure that I was correct in my thinking. Thank you.

Are you trying to express the plasmid's contents in C. elegans, so that its cells are producing the gene product, or do you want the bacteria to produce the gene product so that C. elegans can consume the protein?

I am trying to express the plasmid's contents in c. Elegans so it's cells are producing the product

C. elegans do not express the genes of organisms they eat. To create  transgenic worms you either need to microinject purified DNA, or use biolistic transformation (using microparticles coated with DNA). Ingestion does work if you are trying to use RNAi to knockdown expression of a C. elegans gene, but in that case the double-stranded RNA is expressed from a bacterial expression vector. You can also synthesize dsRNA in vitro and soak the worms in that, which I believe also enters when the worms "eat."

You are trying to express the plasmid in C. elegans, so it should be a worm expression vector and promoter. Otherwise, it will be expressed in the host bacteria, but not in the worm, so you won't get the phenotype you're looking for.
One thing you can do is add a bacterial origin of replication to the vector (if one isn't already present) so the bacteria produce multiple copies of the plasmid after transformation. Otherwise, the amount of plasmid consumed by the worms will be rather low.

Perfect, thanks a lot for your advice, I appreciate it.


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How to attract spiders to my house?
I've been living in my apartment for a year now, and noticed something lately. During spring and summer I have quite a lot of midges indoors, and even now there are some. But there are no spiders! So my question is a bit of the opposite of what all spider/house related articles are about: how do I get more spiders to my house?
Some info about my place: I live in a 40 year old apartment in Rotterdam, Netherlands on the second floor. It's always between 17 and 20°C, and the place is poorly isolated; plenty of cracks for visitors to crawl through. Also, I usually have a window open a bit. I have quite a lot of plants, and an aquarium, so I guess air humidity is relatively high. There is a large backyard with lots of weeds and some trees, however people use it for dumping old furniture so it is not a real garden (it's most likely the source of the midges).
Can I do something to stimulate spiders to come to my house?

Spiders are naturally attracted to areas where preys are abundant, insects are primary source of spider prey. So you must create proper environment for insects in your home,  lights to attract nocturnal insects, flowers to attract butterflies and moths are attracted towards decaying vegetation(It may make your place smelly). Once insects are abundant then spiders are attracted towards these areas. Some parasitic wasps are predator to spiders like those belonging to Ichneumonidae, try to avoid these species. Also you can collect spider eggs and place it inside your home, so they will hatch out in your place.Some times you can see web full of spider nymphs, take it out and place it in your home.
Attracting spider to home can be achieved through buying some spider traps like poison free spider glue, which contains pheromones. Spiders are attracted to it and they will stick on them and eventually die, so you need to be careful and use the phenomenal effect of the spider trap to attract spiders or it will be dead and become useless.
The analysis revealed that the wasp spider pheromone is methylcitric acid trimethyl ester, a derivative of citric acid.
Some other techniques to attract spiders

An unspecified Cheiracanthium species is attracted to the smell of petroleum and has caused problems by weaving webs inside the canister vent of particular models of Mazda vehicles, resulting in blockages and build-up of pressure that could potentially cause fuel leakage from the fuel tank and an increased risk of fire. Mazda issued a voluntary recall of Mazda 6 models built between 2010-2012, to fit them with software which would alert drivers if problems were developing.
Reference

A spider that preys on the malaria-carrying mosquito is attracted to the odour of people's smelly socks, say scientists.
Reference

Eugenol baited traps we found seven individuals of two species of Thomisidae. In contrast, with the control traps, no Thomisidae were caught . Since Thomisidae were caught only in traps baitedwith eugenol, whereas no crab spiders were attracted to control traps, we postulate that eugenol served as an attractant for these spiders.
Reference

We had a tractor with a clogged fuel filter once because a spider somehow got inside it, so an attraction to gasoline might be more widespread than just that species.

These seem like great tricks. I think I'll try Eugenol first, smelly socks and petroleum don't seem like such attractive options to use indoors;) Done some research on Eugenol, Apparently there's quite a lot of it in clove buds and leaves. Will see if I can get myself a small clove tree. Also just lay some cloves around and get some oil from them.

In addition, here's more details on growing a clove tree: http://gardendrum.com/2013/11/27/how-to-grow-cloves/

in order to attract spiders in to your home

make sure there is plenty of light to attract prey for the spiders
leave a window open
put some leaves in the corner
if you find some insects or flies try to catch them then get some glue and stick them on the window seal

thanks for reading

Welcome to Biology.SE! It isn't clear that this adds anything to the existing answer.
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Enzymatic Protein Deglycosylation of TCL
I've done O- and N- linked enzymatic deglycosylation of purified and lyophilized proteins. Following some modifications to the manufacture's protocol, I had great results.
I now want a student to deglycosylate some total cell lysates (TCL) of infected cells.  Can he just Vacufuge/vac-spin Triton-X  TCL's and then proceed with the deglycosylation?  The first step is often resuspend in water.  I'm wondering if ether the salts would need be removed by dialysis, or if it doesn't matter, if he can start just from the lysate.
The antibodies we have for western detection are excellent, so that is unlikely to be a limiting factor.

I can tell you anecdotally that, at least when I did it, all was fine just using the raw lysates.  You have to use the total protein content which means, of course, your favorite protein will be in lower concentration, but c'est la vie.

@Amory Did you dehydrate at all, or did you just go with fresh lysates?

"Fresh" as in not dehydrated, yes, just straight lysates.

So we went ahead and did this.  The efficiency was much lower, which you might expect, and dehydration did aid the process.  We found that taking raw lysates and running them through the linked protocol, had about ~20% the efficiency of conducting it on purified protein.  I assume the majority of the loss was due to reagents being expended on proteins that were not of interest.


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.Net DNA/Codon Parser
Does anyone know of an .NET (C#,VB.NET, maybe F#) DNA parser?  If not I will probably make one, it doesn't seem like it would be hard.  My general idea was to generate something that would report the codon biases and go back and adjust the bias for engineering.
I realize the later part is a bit specific, but if anyone knew about an open .NET program that annualized/parsed the codon bias, I'd be interested.
Thanks.

Something like this?  I know nothing about .NET.

Maybe, I'm going to check it out.  I solid library would do the trick.

@amory Looks like it might.  I don't know how I missed that in my searching

Cool.  Posted an answer for the sake of statistics.

If you're thinking of entering bioinformatics, I strongly urge you to use a Unix/Linux platform and not .NET or other MS tools. It will make your life much much easier.

What's cool about bioinformatics is that it has both a father and a mother !

@terdon oh I agree. My real entry into programming was with R on Debian, and I am a fan of BioPup, but I don't think that's being developed anymore.  The only reason I'm interested now, is to throw something into an already existing WPF.

I don't know much (read: anything) about the .NET framework but a little search turned up Microsoft's .NET Bio:

.NET Bio (formerly Microsoft Biology Foundation [MBF]) is a language-neutral bioinformatics toolkit built as an extension to the Microsoft .NET Framework—initially aimed at the area of Genomics research. Currently, it implements a range of parsers for common bioinformatics file formats; a range of algorithms for manipulating DNA, RNA, and protein sequences; and a set of connectors to biological web services such as NCBI BLAST.

Free (as in beer) and open source!  And from this it looks pretty cool.