Strange looking mushroom found in a dead tree I'm trying to identify this mushroom my sister found at her campus' lawn. It is brown, looks somewhat like a turtle or a brain and is growing inside of a dead tree trunk. Geographically the campus is in Haifa, Israel in case that helps. Any ideas?

Images:

Possible Species: California Fungi - Gymnopilus luteofolius

Source:

http://www.mykoweb.com/CAF/species/Gymnopilus_luteofolius.html

http://mushroomobserver.org/174362?q=28kZM


Recommend good conversational books to learn about cell and developmental biology or biochemisty? I'm an engineer by training and teaching myself the basics of cell and developmental biology. I'm using Scott F. Gilbert's Developmental Biology and Alberts' Essential Cell Biology right now, and they are both great resources.

Can you recommend good books on similar topics that are written in a non-textbook format?

EDIT: For example, I found books like The Greatest Show on Earth, Extended Phenotype, and Nature via Nurture really useful to learn about the basics of evolution while I was reading Watson's Molecular Biology of the Gene. Are there similarly well-written books for cell biology, developmental biology, or biochemistry? I know this sounds broad, but I'm not asking for textbook recommendations. I'd like to read something that isn't a textbook alongside my current studies.

SECOND EDIT: I'd be like to extend this question to recommendations of broad review articles in Cell and Developmental Biology too. For example 'How do cells know where they are?' is an excellent article on different strategies cells may use to assess distance in a developing embryo.

Endless Form Most Beautiful: The New Science of Evo Devo introduces the reader to several classic embryology experiments and some key principles too.

I'll edit this answer when I find more books or reading material of this nature.


Can a strand not be synthesised in 5' -> 3' direction? I've been solving some biology questions, and according to one of them ( I have the responses too) the following phrase is false:
"Both strands are always synthesised in the 5' to 3' direction."
How can this be? From what I know DNA is always read from 3' to 5', and synthesised from 5' to 3'.
When is DNA not synthesized from 5' to 3'?

It sounds like your guess was right and the exception is the lagging strand. The statement is phrased poorly, but it seems like they were going for the lagging strand. Either they meant for you to think that
-the lagging strand is growing 3' to 5'
or, more likely,
-the polymerase is only synthesizing on one (leading) strand at a time
While technically the book was correct, it was a trick question and phrased unfairly.
You are completely right that DNA is synthesized from 5' to 3'. I don't know of any exceptions to this, but hesitate to say anything overly definitive because there are so many rare exceptions in science. I can say with some confidence that DNA polymerase cannot ever synthesize in the 3' -> 5' direction. If you haven't already, I highly recommend checking out the Khan Academy article, Molecular mechanism of DNA replication! Nature Education also has a pretty good article on the topic, Major Molecular Events of DNA Replication.


Can we attack viruses by attaching proteins and such to their shells? Haven't looked into this to very long, but an initial search didn't return anything.

HIV uses certain co-receptors to enter our cells. Could we flood the blood stream with specially designed proteins that mimic cell receptors, either locking the virus out of vulnerable cells, or even causing the virus to release its genetic material (pre-mature ejc. for viruses... :|), so it can't accidentally get away and infect a cell anyway? It seems like an obvious idea, so the fact that it may not already be happening must mean there are a host of challenges to it? Basically we design a protein that acts like a strait jacket, with tags that allow our body to recognize it as a 'friendly' body. T-cells use the same receptor though, so are there ways to identify a T-cell compared to a HIV virion?

Obviously this wouldn't prevent some viruses managing to infect actual cells, but over time, if we can attack enough, say 2/3 of the virions, for each generation, we could seriously harm the population, and maybe keep it in check?

Yes, that should be possible. And it is one of the ways antibodies work. It is already used as a treatment against rabies. There you get a dose of immunoglobulins directed against the rabies virus together with the vaccine. The immunoglobulins neutralize the virus.

The same is possible when you vaccinate against the surface proteins which a virus uses to dock to a cell. With the resulting antibodies this docking is at least hindered if not completely prevented. And there are interesting results from the Ebola research which used a small molecule inhibitor to identify the protein which is important for cell binding of the virus. It is called NPC1, when the receptor on the virus surface is blocked, no docking can take place. This will be one of the approaches to make a therapeutic. See here for more details.

And it is of course possible to design more of the molecules - the problem is to find substances which do not interfere with body functions or which stimulate other pathways.


Plants that are siblings: How does this impact pollination? A friend has two cherry bushes (prunus tomentosa). Prunus tomentosa is said to be partially self-fertile, but benefits from cross-pollination.

The two cherry bushes set fruit, and so my friend saves me two seeds. I plant the seeds at my house, which is miles away from his, and now have two bushes of my own. They are in bloom.

My cherry bushes are siblings. How will this impact their ability to pollinate?

In your case, your two bushes might well be half sib (the pollen might come from different donor) and one (or maybe even both) bush might result from selfing event. In any case, if the species is able to self, I don't see why full sib mating would cause any trouble.

Many species protect themselves from selfing. These plants are said to be self-incompatible. The evolution of the genetic mechanism underlying such self-incompatibility has been highly studied and is fascinating. The wikipedia article offers a good overview.


How does fluorouracil inhibit pyrimidine synthesis? I was reading in my pharmacology textbook about fluorouracil, and my book mentioned that fluorouracil is an antimetabolite whose function is to inhibit pyrimidine synthesis. However, I was curious to know how exactly does fluorouracil accomplish this. Is this drug involved in a specific mechanism that enables it to prevent pyrimidine synthesis? If so, how?

Your cells produce thymidine by converting uridine using an enzyme called Thymidylate Synthase. 5-Fluorouracil irreversibly inhibits this enzyme.

Here is the structure of 5-Fluorouracil:

Here is the mechanism the enzyme uses to make thymidine:

It looks like the enzyme forms a bond to the uridine ring through a sulfur. 5-Fluorouracil probably inhibits the reaction at the next step because fluorine is much more electronegative than the proton it replaced. This makes it harder for the electrons on the double bond in step 2 to attack the folate. Might also inhibit step 3 because the fluorine won't want to give up its electrons to reform the double bond. Because 5-fluorouracil is stuck to the enzyme's active site, the enzyme can't perform its given duty of converting uridine to thymidine. Because you can't use uridine in your DNA, your cells don't have enough thymidine to copy the DNA and replication is slowed. If thymidine supplies are low enough, cells might die.


Organisms that use more than the 20+2 commonly occurring amino acids? I know scientists have created synthetic bacteria, with a genetic code containing 6 letters instead of 4, with the aim of creating more complex proteins (using amino acids outside of the standard 20+2, the +2 being selanocysteine and pyrrolysine).

Are there any examples of already-known organisms that use more than the 20+2 commonly occurring amino acids, when translating from RNA codons to amino acid sequences using the ribosome? Post-translational modifications would not count, as the ribosome would not be directly responsible for the incorporation of the amino acid into the protein through that method.

Source: 22 proteinogenic amino acids
https://en.wikipedia.org/wiki/Proteinogenic_amino_acid

At this point in time it appears that no known organisms use amino acids beyond the standard repertoire of the 20+2 common amino acids (the +2 being selanocysteine and pyrrolysine) to construct proteins directly from the ribosome. The extensive review provided by Ann Rev Genetics 2017 mentions only selanocysteine and pyrrolysine, and no other amino acids beyond that.


Over what range do Thyroid Stimulating Hormone (TSH) levels tend to fluctuate over the course of a day? I understand that the majority of doctors consider the TSH test "the gold standard" for measuring thyroid function (examples stating that literally are here and here).

The reference range for what is a healthy TSH is under debate, but no matter what range is considered there tends to be a proportionately large difference between the top and bottom (eg 0.3 to 3.0 μIU/mL).

However I've never seen anything indicating over what range TSH varies over the course of a day, or a few days. It's also unclear as to how much error there tends to be with a test result, even if tested from the same sample.

How much fluctuation in a TSH result is normal over the course of a day?

I wrote an answer about the circadian rhythm of TSH for a question over on Health.SE: How does one's TSH tend to vary over the course of a day. That answer also has some information on how this is different in people with hyperthyroidism and hypothyroidism.

The paper Physiological variations in thyroid hormones: physiological and pathophysiological considerations has a section on circadian and seasonal rhythm and a table with some values. Quoting from the paper:

  There is a clear circadian variation in circulating TSH concentrations in animals and in humans. TSH concentrations are low during the daytime, increase in the evening, and peak shortly before sleep.  Amounts decline slowly during sleep. Table 3 summarizes studies of mean, nadir, and peak serum

This is table 3:

A chart is available in figure 2 of the paper Modulation of Endocrine Function by Sleep-Wake Homeostasis and Circadian Rhythmicity

As for test error, it's important to note that TSH itself is not used for or at least should not be used for diagnosis of Hypothyroidism and hyperthyroidism. T3 and especially T4 need to be measured.


Meaning of "standard reactions" in a DNA extraction procedure description From a DNA extraction procedure description (an in-house pharma document I'm translating into Russian):

Preparation of Standards

All the standard reactions should be prepared at least in duplicates. The template DNA stock (CHO cell genomic DNA) should not be less than 100 ng/μL concentration.

Does "standard reactions" here mean what it says, or does it really mean "reactions with standards" (reactions using standard samples)?
P.S. It is part of a qPCR procedure to quantify host cell DNA content. The company is producing a monoclonal antibody drug and is describing its internal procedures. As a translator I cannot disclose the document in full.

Is this PCR or just DNA extraction and if so, what is the source material?
If it is just DNA extraction, maybe the standard reaction means to extract some given standard sample DNA (CHO cell genomic DNA?) to optimise the yield and reaction conditions.
You would ideally use these same reaction conditions to extract other genomic DNAs.
Also, is the CHO genomic DNA included in the kit?


Is territoriality only the domain of the male of a species? Is territoriality (recognizing a geographic location, and challenging another member of the same species and gender) only in the domain of the male of a species? Are there any species out there where the female of the species identifies, defines, and protects her territory? What about any species where territoriality is gender neutral (tic humans excluded!)?

I thought of bees, but that is a community affair - even if it is a matriarchial society.

Absolutely not.  In many spiders and insects, the female holds the territory.  Many spiders for instance have much larger females than males - in these species the female can be > 10x bigger than the male and the male may not even eat during its lifetime.

Ordinary garden orb weaving spiders are this sort of animal.  Only the females even spin webs.

Another example is the Australian red back spider.

Quite often males are the 'disposable sex'.


Which bacteria have the highest mutation rate? From my reading on M. tuberculosis, I know that this organism has a pretty high mutation rate due to uncorrected sloppy replication, which leads to a high rate of development of spontaneous resistance to treatment.

Are there species that can beat M. tuberculosis in this degree of mutation, due to polymerase sloppiness or other mechanism?

You seem to be assuming that mutation rates are somehow constant over evolutionary time. They are not. Mutation rates will change according to the stresses a species is subjected to. If you take a bacterial population and place it in a stressful environment (high/low temperatures, oxidative stress, lack of nourishment or whatever) you are likely to see an increase in the mutation rate.

That said, this review and references therein might interest you:

E. Denamur and I. Matic,Evolution of mutation rates in bacteria,Molecular Microbiology 60(4), pages 820–827, May 2006


Why don't vegetables taste good despite being healthful? This is more a question about the evolution of taste than about the chemical composition of vegetables. Why don't vegetables taste good despite being healthful?

We are evolved to survive starvation, and live to be perhaps 35. So fatty foods with lots of calories taste good to us. Our genes (and preferences) lag thousands of years behind our present environment.


Which process is needed to complete male reproductive development? In order to properly complete male reproductive development:

A. primordial germ cells must begin Meiosis I in utero.
B. Sertoli cells must produce testosterone.
C. Dihydrotestosterone must masculinize Wolffian duct derivatives
D. the paramesonephric ducts must degenerate
E. the metanephros must form the genital epithelium

My attempt:  I think the answer is C because testosterone turns into DHT which then masculinzing the wolffian duct.  Other people I am studying with claim the answer is D (which is true) except that I dont think the loss of the paramesonephric duct is needed to complete male repro development.

Regarding option C:

Although it is correct that testosterone is converted into DHT, it is the former, not the latter, which is responsible for differentiation of the mesonephric (a.k.a. Wolffian) ducts:

  Between 8 and 12 weeks, the initial secretion of testosterone stimulates mesonephric ducts to transform into a system of organs—the epididymis, vas deferens, and seminal vesicle—that connect the testes with the urethra.*

DHT (dihydrotestosterone) is produced in the Leydig cells by the 5α-Reductase enzyme. It is required for induction of the external male genitalia (urethra, penis, and scrotum) and prostate from the embryonic ureteral groove, and for testicular descent into scrotum.

Regarding option D:

Sertoli cells secrete Anti Müllerian Hormone (AMH), which causes degeneration of the müllerian (a.k.a. paramesonephric) ducts between weeks 8 and 10.  It is normal to speak about degeneration of the müllerian ducts as a defining aspect of male embryology, and thus I believe answer D is correct. Your point is taken, however:

  Nevertheless, small müllerian duct remnants can be detected in the adult male, including a small cap of tissue associated with the testis, called the appendix testis, and an expansion of the prostatic urethra, called the prostatic utricle.*

*Larsen's Human Embryology , Fourth Edition. Chapter 15: Development of the Urogenital System. pp 479-541.
Copyright © 2009 by Churchill Livingstone, an imprint of Elsevier Inc.


Can we graft a tree with cells from the seed directly? Are there any limitation to the grafting so we could only graft a branch to another tree? Could we just use cell from the seed, or just a sprout, to graft onto the tree of the same species?

Tree grafting involves a rootstalk and a scion (part to be grafted onto the rootstalk). Here is a reference on the basics of grafting. Usually, this is done to combine the advantage of a strong or mature root system (from the rootstalk) with desired fruits or buds (produced by the scion). In this case, the roots of the scion plant might be sensitive, disease-prone, or just take a long time to develop (read more here). It is also possible to graft a scion onto the same variety of cultivar. Grafting refers to the joining of vascular cambium of a scion and a rootstock in such a way that they can share resources and continue to grow together (see previous links). As such, adding cells from a seed to a larger plant would not be grafting, because there wouldn't be that pre-established vascular tissue to connect with the vascular cambium of the rootstock. Additionally, a single sprout would not have a good chance of binding well to the vascular system of the rootstock (see link 2).

If you are interested in reading more about different forms of (non-seed) propagation, this resource describes the why/when/and how of grafting and layering (another technique that might help you, if you're trying to propagate only one tree variety as indicated in your question), and there are many great resources on youtube about the basics.


Where does all the carbon go? I found it hard to get numbers on global primary production, but apparently it's somewhere around 50-60 $Gt / year$ for carbon content in the terrestrial production, and a similar figure for the oceans. Global oil and coal extraction together are around 10 $Gt/year$, only a part of which is pure carbon (iirc, around 80%). Since carbon in the air actually increases, does that mean that most or all of the carbon fixed in Net Primary Productivity go back into the atmosphere through consumption and destruction of the biomass?

I was assuming that the biosphere in total was fixing more carbon that it emitted, and that the surplus was what turned into oil and coal. But with these figures it looks a lot more like a full circle that nets to zero input / output. Which is true, or is that unknown?

Good question! The way that the carbon budget stays balanced was a bit of a mystery until recently. The amount of carbon in the atmosphere (and the rate at which it was increasing) was lower than models suggested that it should be, given rates of photosynthesis, respiration, and other carbon releasing processes. Somewhere, there was a "missing carbon sink" that wasn't being accounted for. After many years of debating whether the sink was terrestrial or aquatic, it turned out to be a little of both (which is the conclusion to just about every debate in ecology), although somewhat skewed towards the terrestrial (I think). Forests were sequestering more carbon than had been thought, and so were oceans to some extent (although ocean sequestration relied on a chemical reaction that is in danger of reversing if the water warms up too much). For a thorough account, see this article.

But it sounded like you weren't wondering what the missing sink was so much as how there continues to be enough carbon in the air for plants to photosynthesize (if I'm interpreting your question correctly). Carbon gets back into the air for the most part because of cellular respiration, which all organisms, including plants, do. Net primary productivity takes plant cellular respiration into account, but there are still tons of living things on earth eating plants (and thus taking in their carbon) and then breathing it back out after they break down the carbon-containing molecules for energy. There is also natural combustion and volcanoes and such putting carbon back into the air.

EDIT (to address the new part of your question): Oil and coal were produced when large quantities of carbon were buried deep under ground (and thus subjected to lots of pressure) for thousands of years. This is suspected to have happened during the aptly name Carboniferous era, which featured giant plants and, scientists hypothesize, no decomposers to break them down. Since we have much decomposers now, and much smaller plants, oil and carbon are not currently being produced. This is why we call them non-renewable resources. By extracting them from the ground and burning them, we are taken carbon that had effectively been removed from the pool of actively cycling carbon and adding it back in.

The carbon cycle doesn't really have input or output (except the occasional carbon exchange with outer space), because it is defined as already including standing sinks like oil and coal. When we take carbon out of a sink, like oil or coal, it needs to go somewhere else. Terrestrial and aquatic sinks can store some of the additional carbon that we're adding to the not-underground part of the cycle. However, they can't store all of it, so some is staying in the atmosphere instead.


Is a nucleus more dense than the rest of the cell? On the one hand, it seems like there's a higher density of 'stuff' in a nucleus, so it should be more dense. On the other hand, if it was more dense, I'd imagine that nuclei would 'settle' to the bottom of a cell, which would be rather surprising.

Update:
http://www.eng.umd.edu/~nsw/ench485/lab10.htm reports nucleic density of 1.4 g/cm3.

https://books.google.com/books?id=t4ZQRWvr510C&pg=PA85&lpg=PA85&dq=cytoplasm%20density%20g%2Fcm3&source=bl&ots=rEkcu0HzZE&sig=Bs4MVad_pbS7vWY2pRv6ALWf-bc&hl=en&sa=X&ved=0ahUKEwj2rLXVnLbJAhVSlogKHUjAAS8Q6AEIMTAD#v=onepage&q=cytoplasm%20density%20g%2Fcm3&f=false
reports a density of 1.03 - 1.1 g/cm3 for cytoplasm, so less dense.

do nuclei sink then? or is ICF too viscous / there too much disturbance through movement for nucleus to sink?

When you hang up your shirt does it fall to the bottom of your closet?

The nucleus is a dense fibrillar network of DNA, RNA, and proteins, yes, but the subject you should take a look at is the cytoskeleton of the cell. It will will answer all your unanswered questions on the intracellular network that stabilizes, supports, and aligns the organelles within the cell.


Is there any research on wolf and bobcat interactions? I'm studying interactions between North American predators, specifically wolves, coyotes and bobcats.

There's evidence from data I've collected from game cameras that suggests a significant interaction between wolves and bobcats. However, I haven't found published literature or reports on interactions between these two carnivores besides a paper by Stenlund (1955) that suggests bobcats may benefit from scavenging wolf kills.

Does anyone know of any publications in peer-reviewed literature, government reports or even reliable observations from biologists in the field on interactions between wolves and bobcats?

Thank you!

In Sweden, no clear evidence have been found for interactions between wolf and European bobcat (Lynx lynx), see Wikenros et al, 2010. There, they have studied effects of wolfs on the survival of Lynx kittens, spatial movement of Lynx and Lynx' homerange size. The study finds no significant difference in any factor with/without wolves. They also speculate that the reason for a lack of interactions is that the two species use quite different main food resources (at least in Scadinavia); Lynx being specialized on Roe deer and wolves having wider preferences, but mostly Moose.

You should also look at the report Meta-analysis  on  the  effect  of  competition  between  lynx  and wolf on their diets (Lelieveld, 2013 which might provide more information. From reading the abstract, the report indicates no effects on wolves but that Lynxs might change their foraging behavior.


Why do we have two kidneys but one liver? Both the liver and the kidneys are involved in "cleaning" our blood. But why we have one liver and two kidneys? I can just as well imagine that we have two livers and one kidney. Is this just a coincidence?

Your organs can be grouped into two categories.
The digestive tract organs, which are singular.
These form from the endoderm.
The liver and pancreases are all direct outgrowths from the digestive tract which is again singular.
Everything else which comes in pairs.
These form from the mesoderm or ectoderm.
Even the brain and heart are paired organs, the heart starts as two organs and fused during embryonic development. The whole circulatory system starts perfectly paired, (embryonically and evolutionarily) but then specific parts close off to create the complex circulatory system of "higher" animals so we can have a high pressure section and low pressure section.
The spine is also sort of singular but is basically the the line splitting the left from right half of the body. Everything that branches off the spine does so in pairs.
The real questions is why you have two lungs and one spleen. They are the only organs that breaks the rules. The lungs are an outgrowth of the digestive tract and the endoderm, but is paired. The spleen is part of the circulatory system, but singular. Now the most primitive lungs are singular, it split later likely as animals got larger and needed more lung capacity and also needed a tight connection with circulatory system (paired). The spleen is the weird one, and likely harkens all the way back to before vertebrates existed, it predates the closed circulatory system, and develops on the midline of the body just like the spine and digestive organs. Just like how the spine is the midline dividing the nervous system into pairs the spleen divides the circulatory system into pairs.
https://www.researchgate.net/figure/Embryology-of-the-gallbladder-A-In-the-early-embryo-liver-specification-occurs-in-the_fig1_303959093
https://embryology.med.unsw.edu.au/embryology/index.php?title=Cardiovascular_System_-_Spleen_Development
https://embryology.med.unsw.edu.au/embryology/index.php/Gastrointestinal_Tract_Development#Germ_Layer_Contributions


What labs/companies provide comparative genomic analysis? I’m looking for labs/companies that provide comparative genomic analysis between two different species, AND do so for the “paying” public.  I am looking to test a hypothesis regarding genetic expression and would appreciate any direction.  Thanks!

Earlham Institute have the expertise for this work.  They primarily work in public-funded research but they carried out private-sector work in the past.


What are the main differences between lab-grown tissues and natural tissues from living animals? What are the main differences between lab-grown tissues and natural tissues from living animals?

Using a biologist's classic "structure (anatomy) and function (physiology)" idea, I thought about the followings:

Structure:

It might be difficult to recreate the composition of different tissues / cells in living things precisely with artificial methods. This may lead to bad results when the tissue is used for tests of medicines and cosmetics.

Function:

Cells might not function and produce as expected (or is harder to make them function) in artificial compositions, as cells need strictly regulated environments to function correctly.

Also, I think using artificial tissues for drug tests helps researchers to avoid ethical issues which may arise if they use living animals instead. I think the cost is generally higher to make artificial tissues than to gather test animals; however I am not sure if this applies to every case - maybe some tissues are relatively cheap to grow in labs.

What are some other differences? The answer can be about the advantages and the disadvantages of making / using lab-grown tissues and using living animals as well.

Immortalized cell lines are often cancerous, which has large effects on their gene expression profiles. They also adapt to cell culture conditions after several hundred passages in culture. I've cultured both HepG2 cells and primary mouse hepatocytes. The HepG2 are a hepatocellular carcinoma line and grow fine in culture, while the primary hepatocytes don't really grow in culture and only live for a few days after you extract them from a mouse. We were doing gene transfer assays with both sets of cells. The quickly dividing HepG2 cells were easy to transform using PEI, and while primary hepatocytes could be transformed with PEI, they worked better with calcium phosphate, probably because it was less toxic. I noticed that HepG2 cells were smaller than primary hepatocytes, and the primaries were far more fragile, we could run HepG2 through a peristaltic pump with no problems, but had to handle all the primary cells by hand.

As far as cost goes, tissue culture is almost always cheaper. You can make a bottle of media for a few dollars worth of material and use it for several passages. The cost of running an incubator and biosafety cabinet are also minimal. So unless you're doing something unusual, cell culture is affordable. However, it's an incomplete model at best. Trying to create whole tissues in the lab counts as unusual.

Doing work with animals is far more accurate than cell culture, but much more expensive. Our mice are cheap, but still cost about a dollar each, and about 20 cents a day for housing. A typical biodistribution experiment will use 3 mice per time point and 8 - 10 time points, so 24 to 30 mice assuming no mistakes during dosing. On top of that, doing animal work has a lot more bureaucracy involved, with animal use protocols and approvals and inspections.

The question about whether to use animal models vs cell culture comes down to what you're trying to accomplish. If you're studying gene regulation or protein interactions, cell culture usually works better because it's more amenable to gene transfer and allows you to use techniques like fluorescent fusion proteins and bioconjugation. If you're studying drug metabolism and toxicity you should use whole animals because the interactions between metabolites and specific tissues are hard to reproduce in cell culture. In some far off future we may be able to totally replace animal models with cell culture or even computer models, but we're not even close.


How can I get the yield of my purified immunoglobulins? In order to get the yield of IgY through a set of purification steps, what method can I use?

Thanks in advice!

You can use western blotting with a anti IgY secondary antibody conjungated to  a HRP (or other reporter), this method will primarily help with troubleshooting your purification and locating IgY in the different fractions and flowthroughs.

If you want to quantify, consider using an ELISA kit. Here is one such kit (https://www.antibodies-online.com/kit/1563240/IgY+ELISA+Kit/)


Storage of energy in muscles vs fat depos How does the body control, where consumed energy (fat, glucose) is stored? And what is its strategy?

More specific:
1) How does the body control storing glucose in muscles and not as (subcutan) fat? (E.g. exercise lets more GLUT4 translocate in muscle cells; because of this, do muscles in total simply have more transporters to suck in glucose than all fat cells, or is there another mechanism? How does the liver refills its storage)

2) Same question as 1) but regarding FFA and not glucose.

3) What is the overall strategy for energy storing? (Can it be stated as simple as "Fill the liver and muscles until they reach full capacity and then store extra energy as fat?")

The  regulation of metabolites and signals in general (glucose (Glc) or FFA in this case) and their selective uptake by cells, depends on the number (from few hundreds to many thousands) of receptors expressed and displayed to the surrounding environment, the type of receptors (what they can bind, and do as a result) and their properties.

The regulation of glucose storage and use in muscle cells is determined largely by the dynamic energy state of the cell, which is can be described (to first approx at least) as the cellular equilibrium between ATP<-->ADP<-->AMP. The key here is to be aware that nothing is happening in isolation and the whole metabolism of the cell is one giant equilibrium. You make a change at one place that will result in proportional changes elsewhere to compensate.

If you start exercising, that is using ATP rapidsly, suddenly AMP spikes, the increase in [AMP] results in the upregulation of key enzymes in Glycolytic and TCA pathways, which causes and increase in ATP producting. How much is made? Whenever possible always just enough enough to meet the demand (assuming cell has resources to supply it and manufacture it).
Absolutely, in the same way, the energy state of the cell, and the availability of Glc in and outside the cell (which it knows, because increase in blood Glc causes insulin to be release, muscle cell can bind to insulin, which results in activation of more GLUT4 to be expressed and expressed) directs how Glc is used, stored as glycogen, converted to FFA etc. When cell energy state is low, or exercising, signaling from muscle cells to liver (via lactate in blood/ Cori cycle) tell liver to release stored Glc into the blood.  l
If Glc in high in blood, insulin released from the pancreas, which tells the liver, to start sucking up Glc from the blood, and the way it does that is a lot better than muscle. Why? because of the binding properties of the different isozyme receptors expressed (same function but slightly different physical properties).
FFA metabolism is a same general concept, but different key enzyme players. The increase in metabolite availability causes or is a signal, which results in the appropriate response to balance level out the steady state dynamic equilibrium.

The strategy is basically, if available in excess and can be stored it's stored until the limit of cell is reached, which is determined by the thermodynamic equilibrium of metabolite inside and outside the cell.

Nelson, D.L.,Cox, M.M.(2013) Lehninger Principles of Biochemistry, 6th Ed.

In response to your comment questions,
In general, like I mentioned, the strategy for coordinated fat uptake holds.

Majority of insulin-stimulated glucose uptake occurs in skeletal muscle, with about 10% of insulin-stimulated glucose uptake occurring in adipose tissue. However When it comes to FFA, these are largely delivered, absorbed and stored (as triglycerides) from bloodstream into adipose tissues. This process is definitely actively controlled and regulated, as you do not want fat globules floating around in your bloodstream. When energy state of the organism as whole is low, adipose tissue can sense and release FFA that can be take up by other cells (muscle, liver) and energy in molecules extracted during FFA catabolism pathways. How exactly adipocytes translate changes in glucose uptake or storage into signals that affect global energy balance is not completely understood it seems.

For a good place for much more detail, check out

Leto D., Saltiel A.R (2012) Muscle and adipose tissue in energy homeostasis, Nature reviews of Molecules Cell Biology, 13, 383-396
http://www.nature.com/nrm/journal/v13/n6/box/nrm3351_BX1.html


Does evolution go only in one direction? In this video Richard Dawkins talk about intermediate fossils. He describes how the whales were evolved from a common ancestor who lived on land.

But his theory sounds more like a devolution. I thought evolution was coming from the sea to the land and then to the skies. Are there more examples where species evolve back and forth between the ocean and land or skies?

Does evolution go only in one direction?

No, it does not. There is no such thing as a goal of evolution at making things fly or at making things intelligent or whatever. There is no intrinsic directionality.

In short the reasons are 1) Evolution is much more than just natural selection 2) natural selection does not systematically select for a specific trait. Some traits are advantageous under some ocnditions, some other traits are advantageous under other conditions.

I won't talk more about the mechanisms of evolution here as it would take a whole book. Instead I will recommend a source of information for you to further your knowledge.

Source of information

As your question is very introductory and shows misunderstanding, you might want to have a look at an intro course to evolutionary biology such as for example Evo101 by UC Berkeley.

Devolution

Note that this term is rarely used in the scientific literature (actually I think I have never encountered it in the scientific literature). From wikipedia:

  In modern biology the term [devolution] is meaningless: evolutionary science deals with selection or adaptation that results in populations of organisms genetically different from their ancestral forms, where evolution has no intrinsic directionality. The discipline makes no general distinction between changes leading to populations of forms less complex or more complex than their ancestors, and in such terms the concept of a primitive species cannot be defined.

The question is a bit unclear

There is a slight issue with the question as it is not that easy to categorize all animals as living strictly in the water, on land or in the sky. Note that almost no living things live strictly in the sky without ever touching the ground. Here are a few examples of limit cases

oysters
dragonflies (larvea live in the water, adult fly)
flying squirrels
Most ducks (spend much time on water but nest on land)

As a consequence the question is very undefined

Examples of potential interest to you

The question is undefined but here are a few examples of cases anyway that will interest you. For simplicity, I will reduce my list to animals only and will not consider similar cases in procaryotes, and non-animal eukaryotes such as plants and fungi.

Whose ancestors were terrestrial

Cetacea (blue whale, orca, dolphins, ...)
Sirenia (dugong and manatees)
Pinnipeds (sea lions, sea leopards, ...)
Monotremes
Marine iguana (and other swimming lizards)
Beavers
Otters

Whose ancestors were flying

Penguins
ratites (Ostrich, emu, kiwi, ...)
dodo
Many birds are able to stay for relatively long period under water when hunting for fish.
Ants and other non-flying hymenopterans.
Ground beetles and other non-flying beetles
water beetle

Again, categorization is not that easy. I just want to highlight the water beetle as example where the ancestor was marine, then terrestrial, then flying, then non-flying and finally living in the water again.

Related post

How many times did terrestrial life emerge from the ocean?


What is the structural/chemical difference leading to different shapes in facial/head/pubic hair? Different hairs have different shapes, thicknesses, properties.

What exactly is different between these types of hairs? a previous post implies that the root of the differing length (pun intended) is the rate at which the skin sheds the hair. However, this doesn't explain the different degrees of curliness, thicknesses, and textures.

hair shape texture is really easy. the biggest effect is the shape, size, and angle of the hair follicle. Hair with different cross sections have different properties. If hair is stiffer in one plane than other it will tend to bend along the weaker plane. Larger follicles will of course produce thicker, thus stiffer hair and the angle of the follicle can influence the hairs cross sections as mush as the follicles cross sectional shape can. Note the chart below is simplified triangular and kidney shaped cross sections also exist.


What is the functional and structural distinction between core (H2A, H2B, H3,H4) and linker(H1/H5) histones? Many explanations of histone biochemistry isn't quite elucidating for the undergraduate student. How does histone structure (dimers, octomers) relate to their specific functions as core or linker histones? Also, how do both linker and core histones interact within nucleosomes to ultimately regulate gene expression?

The core histones are H2A, H2B, H3, and H4, and the linker histones are H1 and H5. The structure of the nucleosome is well explained in wikipedia:

  Two of each of the core histones assemble to form one octameric nucleosome core particle, and 147 base pairs of DNA wrap around this core particle 1.65 times in a left-handed super-helical turn. The linker histone H1 binds the nucleosome and the entry and exit sites of the DNA, thus locking the DNA into place and allowing the formation of higher order structure. The most basic such formation is the 10 nm fiber or beads on a string conformation. This involves the wrapping of DNA around nucleosomes with approximately 50 base pairs of DNA separating each pair of nucleosomes (also referred to as linker DNA). The assembled histones and DNA is called chromatin. Higher-order structures include the 30 nm fiber (forming an irregular zigzag) and 100 nm fiber, these being the structures found in normal cells. During mitosis and meiosis, the condensed chromosomes are assembled through interactions between nucleosomes and other regulatory proteins.

The core histones have a positive net charge, which facilitates the interaction with the negatively charged phosphate groups of DNA.

Apart from defining the nucleosome structure, the function of the core histones is regulatory: it can switch on/off gene expression by histone modifications like acetylation or methylation.

The gene expression is ON when there is:

• DNA demethylation

• histones acetylation
• H3K4 methylation
• H3K36 methylation

• H3K9 demethylation
• H3K27 demethylation

The gene expression is OFF when there is:

• DNA methylation
• histones deacetylation
• H3K4 demethylation
• H3K36 demethylation
• H3K9 methylation
• H3K27 methylation

// H3K4 demethylation is abbreviation for: Lysine 4 of H3 is demethylated.

Berger SL. 2002. Histone modifications in transcriptional regulation. Current Opinion in Genetics & Development 12: 142–148 is also a nice review.


How should species density be calculated for a clumped distribution? Lets imagine 5 plots of different size are sampled for a target species:

plot#  count  area(m^2)  plot_density
  1      1       5         0.2
  2      3       2         1.5
  3      0      10         0.0
  4      5       1         5.0
  5      2       6         0.33

What is this species' density?  I see two ways to calculate the density that give completely different values.

The first way averages the density at each plot:

$$
\frac{\Sigma(\frac{count_i}{area_i})}{5} = 1.41/m^2
$$

This seems OK but it doesn't do much to control for changing plot sizes.  For example if plot 3 above was 10 times larger the density would still be the same.  In a situation where plot sizes are determined by the environment (say, under natural cover objects) this seems less than ideal.

The second way totals the counts and the search area and divides them:
$$
\frac{\Sigma(count_i)}{\Sigma(area_i)}=0.46/m^2
$$

I prefer the second method because it seems to describe the animals' density more accurately. And I would be happy to use the second method, but my issue is that I am unsure how to calculate a summary statistic for method two since a mean was never calculated.  Method one gives a standard deviation of 2.1. What is the standard deviation for method two??

One possible solution I've come up with involves breaking the larger plots into 1m^2 plots and dividing the number of animals across those smaller plots.  So now I have 24 1m^2 plots with the following "counts":

plot#  count  area(m^2)  plot_density
1-5      0.2       1         0.2
6-7      1.5       1         1.5
8-17     0.0       1         0.0
18       5.0       1         5.0
19-24    0.33      1         0.33

Now, using the first equation above I get:
$$
\frac{\Sigma(\frac{count_i}{area_i})}{24} = 0.73/m^2
$$
With a standard deviation of 1.26.

Is this a reasonable approach?  Is there an established solution to this problem?

To me, there are two issues that are mixed up here (if I understand you correctly). First, do you want to estimate the mean and variance for a statistical population (i.e. to characterize a larger population by independent samples), or do you want to calculate the actual density for a particular area, where you have counted all occurences in that entire area (but maybe divided the area into subareas out of convenience when counting)? This is not clear from your question.

In the second case, your second option of pooling counts and areas is suitable. However, then you have only calculated the actual density in that particular area (ignoring issues of detectability of the organism when counting), and you cannot draw any inferences about a larger statistical population.

If your aim is to draw inferences for a larger statistical population, a start is to calculate the mean and standard deviation (sd) of your sample. In that case, I assume that your samples are chosen randomly and independently from a larger statistical population. Your first option is then the right approach. However, since your samples have different sizes, you might want to attach more weight to larger samples, since they can be assumed to better describe the population average than smaller samples. This is called a weighted mean.

Generally, the weighted arithmetic mean is defined as:

$$\bar{x}_w = \dfrac{\sum_{i=1}^nw_ix_i}{\sum_{i=1}^nw_i}$$

where $w_i$ are the weights for each sample ($x_i$) and $n$ are the number of samples. Using this formula, you will arrive at exactly the same value for the weighted mean as you calculated in your second attempt (0.458), if you use the areas as weights.

The weighted standard deviation is a bit more problematic, in the sense that there doesn't exist one single standard way of calculating this. However, a commonly used formula is:

$$ \sigma_{w_1} = \sqrt{ \frac{ \sum_{i=1}^n w_i (x_i - \bar{x}_w)^2 }{ \frac{(M-1)}{M} \sum_{i=1}^n w_i } },$$

where $M$ are the number of nonzero weights. Other definitions of the weighted standard deviation can be found at Wikipedia: weighted sample variance.

Another version is defined as (called "reliability weights" on the wiki page):

$$ \sigma_{w_2} = \sqrt{ \frac{ \sum_{i=1}^n w_i (x_i - \bar{x}_w)^2 }{  \sum_{i=1}^n w_i - \frac{\sum_{i=1}^n w_i^2}{\sum_{i=1}^n w_i}}},$$

If I haven't made a mistake, these will give the standard deviations 1.15 and 1.22, using your data.

As for the biological interpretation, all calculations of average densities indicate a clumped distribution, since the coefficient of variation (CV) is larger than 1 (CV = sd/mean), but more so if you use a weighted mean. The CV using the arithmetric mean is 1.5, while it is 2.5-2.65 for the weighted mean, which is reasonable since you are giving more weight to the large area sample with a zero count. However, I should also note that you should be cautious about using a weighted mean when you have a strongly clumped distribution, since your run the risk that e.g. a large sample plot land on an area with no occurences, which might bias your density estimate. Generally, when you have a clumped distribution, you need to sample more intensively to get a good estimate of average density, and many small samples is often better than a few large ones.


Application of molecular dynamics simulation (or alternatives) for the full pathways of protein interactions? If one would like to control the biological system (e.g. to treat disease or aging/senescence) then one should introduce the drugs in the system, that initiate complex protein interaction pathways that (in final) generate the necessary proteins that induce the necessary biological mechanisms without adverse side effects from additional outcomes.

I am reading https://www.ncbi.nlm.nih.gov/pubmed/29139324 article about application of molecular dynamics simulation to the drug discovery but this article is essentially about simulation of one - target protein/molecule. My question is this: are there methods (molecular dynamics or others) that simulates the processes in the extended protein pathways that are induced by the one or more initiating molecules/drugs? It should be quite complex simulation, but nevertheless nothing less is expected from computational drug discovery and engineering.

I am quite confused. Maybe there is no need for such simulation at all? The databases of protein interactions just provide the more or less complete picture what protein interactions leads to what new proteins or structural updates of participating proteins and there is nothing to simulate - there is just empirical or predicted knowledge about what molecules leads to what other molecules/structures and one just extract the relevant pathway (starting from the drug coming close to the initial proteins) from the database and that is it?

MD is used for studying structural changes. Simulation of biochemical pathways involves the kinetics (you are not really looking at how enzyme structure changes when it binds to substrate). You can certainly pool all the modelling strategies together to make a hybrid multiscale model.

There are many people who work on such kind of modelling approaches. You can search multiscale modelling and check out recent reviews. This is a somewhat recent (2016) review on multiscale modelling of pharmacokinetics-pharmacodynamics.


Would exposure to a strong magnetic field have deleterious effect on the human? This article states of a 25T magnet, ".. If are ever caught in one of these devices, let’s just say you probably won’t live to tell the tale."

Is the above statement in order? What effect would exposure to such a high magnetic field have on the exposed human?

First thing comes to my mind is the Hall effect. Any directional ion or electron flow will experience a force (depending on the orientation with respect to the magnetic field). So if the magnet is very strong, I think some of the ion channels for action potential generation and propagation would malfunction. (11 mT stopped action potentials for a while but recovered in mouse as reported here.)

However, according to these papers static magnetic fields has little biological effect, and it seems to be reversible. But, this reports genotoxicity in 4.5T (in vitro), and developmental disorders in 1.5T (in vivo, mouse). Contradictory reports indicate we do not know the full answer yet as usual.


Alleles always occur on the same locus My book says "alleles of the same gene always occur at the same locus, but on a pair of homologous chromosome"
Please how is it possible since genes of particular character always occur on the same chromosome? When we know that alleles combine to form a gene, how comes then do they occur on homologous chromosome?

Homologous chromosomes refers to the "twin" chromosomes in your cells (one from your mother and one from your father). Each somatic cell has at least one nucleus containing all your chromosomes*2. Having two sets of all chromosomes is referred to as diploid. On a chromosome, genes are encoded through different nucleotide sequences in DNA, and thus have a specific spot on the chromosome. The spot where a gene is located is called the locus, and because you have two chromosomes in your cells that contain the same genes (though maybe different alleles!), the genes will be in the same locus (same spot) on the both of the chromosomes.

The alleles do not actually combine to form a gene. An allele is simply one variant of a gene, and there can be many alleles for one gene. If a person has the same allele on both chromosomes they are considered homozygous for that allele and if they have different alleles they are considered heterozygous for it. Genes can also be considered either dominant or recessive. If they are dominant, they can be expressed with just one copy in the cell, but if they are recessive the same allele must be present on each chromosome. The alleles do not "combine to form a gene", they are simply separate copies of the same gene with possible variations on them. Homologous chromosome simply means the other copy (e.g. a gene on the 3rd chromosome of your mother will be present on the 3rd chromosome from your father although it could be a different allele due to a variation in nucleotide sequence). I hope this helps, although I would suggest doing some more research on your own, perhaps through https://www.khanacademy.org/science/biology. Their videos tend to be very descriptive.


Is there a PSI-BLAST for nucleotide sequences? I understand that one can translate a nucleotide sequence and run PSI-BLAST on the protein (proteins if you take the 6 reading frames), but I'm looking for distant homology for bacterial small RNAs (typically 50-200 nucleotides long and noncoding).

If there is no such resource, what are the main obstacles to this implementation?

If you have a non-coding gene sequence (e.g. regulatory sequence) this answer should hold your solution:

Background theory

Firstly you must realize that PSI-BLAST is built for detecting "romote homologues",  (i.e. those that have a very "distant evolutionary relationship" to your query) - from a database of sequences. It is therefore known to be a "sensitive" analysis which can recruit distantly related matches but has a small chance of recruiting some false matches - "rogue homologues".
Secondly PSI-BLAST is known as a "profile method" that is it uses multiple sequences that are cumulatively recruited with each "psi-blast iteration", to build an empirical profile of amino acid residues along the positions of your query. This is in the same family of analyses as "Hidden markov models" (HMMs) in that HMMs use multiple sequences to build an empirical profile that is able to recruit distant homologous, except the "profile" includes probabilistic pathways to all the recruited sequences.

My Answer

I suggest you use a software package called HMMER. Indeed this method shares critical theoretical similarity to PSI-BLAST as well as functionality in your case (searching for remote nucleotide sequence matches in a database vs. a nucleotide query), it also does not assume your sequence is protein-coding - here is the wiki description:

  HMMER is a free and commonly used software package for sequence
  analysis written by Sean Eddy.1 Its general usage is to identify
  homologous protein or nucleotide sequences. It does this by comparing
  a profile-HMM to either a single sequence or a database of sequences.

Other possible answers

If you are afraid of using HMMER, then here is a list of all alignment software tools represented in a table that allows you to focus on only those that use nucleotide sequence as input:

http://en.wikipedia.org/wiki/List_of_sequence_alignment_software


Distance to source populations and/or environmental gradients? How do you disentangle the effects of distance to source populations and environmental gradients when the environmental gradient occurs in the same direction as the source population?

For example, if I have a regression model with species richness as the independent variable and distance to source population and pH as significant dependent variables, how do I know which is the driving factor?

First of all, species richness is most likely the dependent variable (the one you want to explain), while distance to the source pool and pH are the independent variables (the ones you assume to have an effect on species richness).
The problem you are describing is termed multicollinearity and a common problem in regression analyses. The good news is that it does not affect the prediction of your model as a whole. The bad news is that the estimates for you individual predictors become increasingly erratic the higher their correlation is. You can estimate the severeness of multicollinearity in your model with the Variance inflation factor (VIF). Wikipedia explains nicely how to interpret the VIF:

The square root of the variance inflation factor tells you how much larger the standard error is, compared with what it would be if that variable were uncorrelated with the other predictor variables in the model.
Example
If the variance inflation factor of a predictor variable were 5.27 (√5.27 = 2.3) this means that the standard error for the coefficient of that predictor variable is 2.3 times as large as it would be if that predictor variable were uncorrelated with the other predictor variables.

You should calculate the VIF for your model. Many sources state that a VIF < 10 is still acceptable, but since there is no rule of thumb, I suggest to do some further reading on the topic if you get some critical value (just google "dealing with collinearity" and you'll find tons of material).


Is it possible to use virus for genetic modification of embryos during the fetus stage I know this probably sounds rather hypothetical and not very feasible but I would very like an answer telling why it is possible or not possible and why.

With the advancement of crispr and other dna technologies, I have been wondering whether it is possible for us to use a virus, which would easily pass through the gaps of the placenta and infect the developing fetus to alter its genomic materials for the creation of designer babies. Since Zika can alter the genome of a baby to cause deformities, is something opposite also possible, like using a similar virus to develop favourable genes in a fetus.

Also, what are the chances of the virus actually purposely acting under orders of genetic engineering to intentionally deliver the genes through transduction?

Putting all ethical and moral issues aside is such a principle feasible and if no, why not?

Zika doesn't alter the host genome at all.

  Since Zika can alter the genome of a baby to cause deformities

Zika virus is incapable of altering the host genome. The exact mechanism by which Zika virus infection causes microcephaly is still unclear, but basically it depletes the neural stem cells at a point when the brain is developing, meaning brain development is more limited (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516183/).

Some viruses do alter the host genome and are candidate technologies for gene therapy

Retroviruses are RNA viruses that copy their genetic material into DNA which is then integrated into a host genome. As such, and as you note, this can potentially be exploited to deliver new genetic material into an organism's genome. I'm not sure what you meant by transduction above, but the term is 'retroviral vector-mediated gene delivery'.

Retroviruses have been trialled for gene therapy for decades. See this sad report on a 2002 trial that apparently succeeded but had unexpected side effects here. However, CRISPR is probably an easier method to use.

You wouldn't attempt to modify a fetus

Some terminology here: a fetus is what an embryo grows into; it's getting bigger and more complex. And as illustrated by Zika-related microcephaly, any messing about with fetal development can have nasty side effects. There are also more cells to modify and you're likely to end up with a 'mosaic' where some cells are genetically different to others within the same organism. If you're attempting to modify the somatic cells of one organ, you might as well do it after the baby is born; if you're attempting to modify every cell in a human (including germ line cells) you'd do it to an egg or embryo.

This study describes a successful attempt to modify embryos using CRISPR. They saw only 1 mosaic in 58 embryos.


2 blue eyed children My husband had light brown eyes. His father had hazel and his mother, light brown as well. His younger brother however, has blue eyes. Both my children have blue eyes. Is this possible? I thought once the recessive gene was used in my first child, his dominant brown gene would take over?

There is some chance for that. Look at this chart with some probabilities:

Its from this webpage, which also gives some background information.
There is also an online calculation tool available, which takes the grandparents into account as well. It can be found here.


House sparrow or something else? These little flying fellows seemed to have made themselves a small nest in the crevices of the roof of my house (I live in Poland). I don't know if this makes a difference, but I do live near a small park.

I'm wondering if this is a house sparrow or something else.

On one of the photos I could swear these look like common sparrows, but on another (the one where the wings are apart) it seems like a different species. It also seemed larger than a sparrow, but is hard to tell due to the distance at which I've seen the birds and the photos were taken. One other notable feature was that they seemed very loud...

Any help?

Based on your image I would rather identify it as an Eurasian Tree Sparrow. They are pretty similar to the House Sparrow, but have a distinct black spot below their eyes which is missing for the House Sparrows (image from the Wikipedia):


Compatibility between spytag/spycatcher versions Background
SpyTag and SpyCatcher are peptides which can associate via a spontaneous amide bond. Because of this, they can be fused to proteins of interest as tags to cause the proteins to bind. There are (currently) three versions of the spytag-spycatcher system, where each system contains a different version of spytag and catcher (i.e. system 1 contains spytag1 and spycatcher1, system2 contains spytag2 and spycatcher2, and system3 contains spytag3 and spycatcher3).
Question
My question is, how compatible are the spytag and spycatchers between different versions? So, for example, would spytag1 bind with spycatcher2?
Prior Research
I found the following statement from this paper:

The new variants showed backwards compatibility, reacting efficiently with parental versions (SpyTag002 with SpyCatcher: 1.0±0.06×104  m−1 s−1; SpyTag with SpyCatcher002: 5.5±0.03×103  m −1 s−1;..

But these numbers don't mean a lot to me. Would you expect to see near complete binding as with spytag2-spycatcher2?

I am currently working with these tags, so going off experience here. The binding efficiency is not 100% even when the correct versions are mixed e.g. version 2 spytag and version 2 spycatcher. I believe this is the original paper (https://pubs.acs.org/doi/pdf/10.1021/ja910795a) it reported 60 % binding efficiency after an hour (I presume for version 1) and this paper (https://www.pnas.org/content/pnas/109/12/E690.full.pdf) revealed 40 % binding efficiency in the first minute (unclear whether version 1 or 2 but I presume 1).  In terms of between versions I can only speak to a mistake I made once by accidentally mixing version 1 spycatcher with version 2 spytag. There was visible binding that could been seen by SDS-PAGE however, it was very faint and required significant amounts of protein to view (~10 mg/ml). I never did any sort of quantitative measurements on binding efficiency as it was a mistake. But from subsequent correctly run experiments, I could see it was significantly lower so I would always use version 2 with 2 and version 3 with 3 etc as they were designed to have maximum binding efficiencies.


How do absorbance readings indicate diauxic growth of bacteria? How is growth rate an indicator of carbohydrate use? How do absorbance readings from a spectrophotometer show growth of bacteria if the absorbance readings are taken several times over the course of a day [and plotted on a graph]? If the second absorbance reading of a sample with bacteria and particular substrate is lower than its initial absorbance reading is there less bacteria present or more? Do the presence of substrates affect the absorbance reading in other words do carbohydrates have any turbidity?

context if needed - experiment determines if a species tested in the presence of a carbohydrate and combination of carbohydrates is able to use each as its sole carbon& energy source and if any are used preferentially to others

How is growth rate an indicator of carbohydrate use?

Carbohydrates are the carbon and energy source available for the microbes, and their assimilation is correlated to biomass production. The relationship between microbial growth rates in an aqueous environment and the concentration of a limiting nutrient or carbon source is given by the Monod equation 1 :
$$
\mu = \mu_{max} \frac{[S]}{K_M + [S]}
$$
This relation is linear when the substrate concentration is lower than the constant $K_M$, this is when it is not saturating.
Now, when the culture is in the log phase of growth biomass ($X$) can be described with the simple growth equation:
$$
\frac{dX}{dt} = \mu X
$$
where $\mu$ is the growth rate of the culture, and it can be interpreted as the biomass production speed. So the fastest carbohydrate use is, the highest the growth rate will be.

How do absorbance readings from a spectrophotometer show growth of bacteria if the absorbance readings are taken several times over the course of a day [and plotted on a graph]?

This is explained by the Beer-Lambert law. Simply put: the absorbance ($A$) of an entity is proportional to its concentration ($c$) - all else being equal.
$$
A = \varepsilon \ell c
$$
where $\varepsilon$ is the molar attenuation coefficient or absorptivity of the attenuating species, $\ell$ is the optical path length in cm, and $c$ is the concentration of the attenuating species.
So, the changes in the concentration of your bacterial population (its growth) are reflected in an increase in its absorbance.
If you find a lower absorbance value at time $t+1$ in comparison to time $t$, yes it could mean that the bacterial cell number at time $t+1$ is lower than at time $t$. However, we know neither the protocol you have been following, nor the values you have been obtaining, so there could be other explanations, such as error of measurement.

Do the presence of substrates affect the absorbance reading in other words do carbohydrates have any turbidity?

Yes, molecules in the medium can impact your Optical Density (OD) values. However, that would only be true if these molecules absorb light at the wavelength you are using to measure the absorbance (eg $600$ nm). This is why you should always blank with some filtered medium (or at least with some fresh medium).


Why outgroups are supposed to be ancestral? I was reading about phylogenies and found many literatures said that outgroups contain the ancestrial trait, but why is that? Aren't they also experiencing the same amount of evolutionary time and (presumably) changing their characters as well?

Yes, sister taxa are, by definition, of equal age. So they evolve independently after the two groups diverge. You are asking about character polarity, and you are exactly right. That is why multiple outgroups are better for inferring which state is ancestral. If an outgroup is state A, and a second more distant outgroup (an out-outgroup) is also state A, and the ingroup has state B, then you can be fairly confident that state A is ancestral. If you look at dozens of characters this way, you will find that any of the three taxa in any combination may have the ancestral state. Imagine the distant outgroup (the out-outgroup) is state A, and both the close outgroup and the ingroup are state B. Then again, you could not know which state is ancestral, and would need a further outgroup. These are inferences, not measurements.


Is the immune system suppressed during sleep? Of course we have heard that during stress our immune system is suppressed. This leads me to wonder, does the immune system get suppressed when we are sleeping and why? During both these conditions the body might need to save energy for other biochemical processes, so I wouldn't be surprised that the immune system is suppressed while a person is asleep.

During stress glucocorticoids, which are steroid hormones produced by adrenal glands (such as cortisol and corticosterone), are released into the blood-stream. These hormones have anti-inflammatory effects. You may say that they suppress the immune system.

The circadian rhythm, which controls the sleep-wake cycle, also controls glucocorticoid secretion, thereby reinforcing the sleep-wake responses [1]. Sleep deprivation is a form of stress and it causes an elevation in the levels of glucocorticoids. The effect of sleep deprivation could be replicated by adrenectomy (surgical removal of adrenal glands); there is a transcriptional downregulation of several neuroprotective genes [2]. Via glucocorticoid mediated signalling, sleep deprivation also results in inhibition of adult neurogenesis in the hippocampus [3].

Another study says that cytokine (TNFα and IL6) secretion by immune-system cells, are inherently affected by circadian rhythm via a glucocorticoid independent mechanism [4].

Overall, sleep is inversely correlated with reduced inflammation because both the processes are under the control of the circadian clock. Sleep deprivation, as a form of stress, is also correlated with glucocorticoid secretion. It should be understood that sleep doesn't necessarily inhibit glucocorticoid secretion and it would be incorrect to say that reduced inflammatory effects are because of sleep.


What determines the strength of a tree What determines the strength of a tree. A morphologically strong looking tree found broken down at trunk. can we determine the strength/ life of a plant

I was letting my fingers surfed in the internet about this question and found this interesting article : http://www.caves.org/section/vertical/nh/55/TreeAnchors.pdf
Your  question is too broad so I guess the link gives a general answer.
hope this helps


Is there any case in which excitability increases with lowering the RMP? My professor says , at a more negative RMP, less sodium ion channels are inactivated, so if you take 2 of the exact same neuron with the same threshold potentials, and try to excite them starting from different membrane potentials, the one you try to excite from a more negative membrane potential excites faster and fires an action potential faster because more sodium ion channels are activated .

I might have missed something from what he said, and I hope I did because this makes no sense to me.
Shouldn't excitability increase with an increasing positive resting membrane potential ? The closer I am to the threshold potential the weaker the stimulus that I need to fire an action potential, isn't that right ?

The only article I found on this is https://www.sciencedirect.com/topics/neuroscience/excitability and its specific to cardiac muscle so perhaps the excitability in cardiac muscle  is an exception ?

A typical voltage-gated sodium channel has 3 gating states: open, closed, and inactivated. The third category is the important one here.

Open channels are open: they let ions through.

Closed channels are closed: they don't let ions through, however, they are available to open and their probability of opening increases as the membrane voltage rises from negative towards positive.

Inactivated channels don't pass ions, but it also cannot be opened by depolarized voltage. Instead, it needs a negative membrane voltage to pass back to the Closed state.

When a sodium channel opens, it only stays that way briefly before going to an inactivated state. Recovery from inactivated back to "closed but openable" requires the

If you depolarize a cell gradually, this will open sodium channels, but it will also increase the number of channels stuck in this "inactivated" state. That means there are fewer channels available to flip from closed to open.

When a cell is hyperpolarized, there are fewer channels stuck in the inactivated state, and so the threshold potential can actually become more negative: it takes a smaller voltage step to open enough channels to trigger an action potential, because there are more channels available to open. You can describe this situation as being "more excitable" despite the hyperpolarized potential.

Some cells, including cardiac cells and other types of pacemaker neurons, will actually depolarize spontaneously from this hyperpolarized potential and reach threshold all on their own without external input.


Is all of the DNA used by genes (encoding DNA)? My intuition tells me no, since only 1.5-2% consists of exons, so this statement would be wrong.
The correct statement would have to be: only about 1.5-2% of the whole DNA is used by genes (encoding DNA).

Am I correct in this assumption?

The ENCODE consortium classifies human and mouse genome elements to try to answer this very question.
Work by ENCODE suggested up to 80% of the genome is functional in some way. In other words, by that upper-bound measurement, there is apparently more to DNA's role in biology than just encoding genes — there's a lot going on under the hood.

Case for Abundant Functional Genomic Elements.
Genome-wide biochemical studies, including recent reports from ENCODE, have revealed pervasive activity over an unexpectedly large fraction of the genome, including noncoding and nonconserved regions and repeat elements (58⇓–60). Such results greatly increase upper bound estimates of candidate functional sequences (Fig. 2 and Fig. S2). Many human genomic regions previously assumed to be nonfunctional have recently been found to be teeming with biochemical activity, including portions of repeat elements, which can be bound by transcription factors and transcribed (79, 80), and are thought to sometimes be exapted into novel regulatory regions (81⇓⇓–84). Outside the 1.5% of the genome covered by protein-coding sequence, 11% of the genome is associated with motifs in transcription factor-bound regions or high-resolution DNase footprints in one or more cell types (Fig. 2), indicative of direct contact by regulatory proteins. Transcription factor occupancy and nucleosome-resolution DNase hypersensitivity maps overlap greatly and each cover approximately 15% of the genome. In aggregate, histone modifications associated with promoters or enhancers mark ∼20% of the genome, whereas a third of the genome is marked by modifications associated with transcriptional elongation. Over half of the genome has at least one repressive histone mark. In agreement with prior findings of pervasive transcription (85, 86), ENCODE maps of polyadenylated and total RNA cover in total more than 75% of the genome. [emph. added]

However, people outside the ENCODE consortium have criticised this research with very strong language, such as in this article by noted critic Dan Graur:

Actually, the ENCODE authors could have chosen any of a number of arbitrary percentages as “functional,” and … they did! In their scientific publications, ENCODE promoted the idea that 80% of the human genome was functional. The scientific commentators followed, and proclaimed that at least 80% of the genome is “active and needed” (Kolata 2012). Subsequently, one of the lead authors of ENCODE admitted that the press conference mislead people by claiming that 80% of our genome was “essential and useful.” He put that number at 40% (Gregory 2012), although another lead author reduced the fraction of the genome that is devoted to function to merely 20% (Hall 2012). Interestingly, even when a lead author of ENCODE reduced the functional genomic fraction to 20%, he continued to insist that the term “junk DNA” needs “to be totally expunged from the lexicon,” inventing a new arithmetic according to which 20% > 80%. In its synopsis of the year 2012, the journal Nature adopted the more modest estimate, and summarized the findings of ENCODE by stating that “at least 20% of the genome can influence gene expression” (Van Noorden 2012). Science stuck to its maximalist guns, and its summary of 2012 repeated the claim that the “functional portion” of the human genome equals 80% (Anonymous 2012). Unfortunately, neither 80% nor 20% are based on actual evidence.

Other critics, including Sean Eddy, focus a great deal on what is called the C-value paradox: Given a genome of a certain size, if it was all functional — or a lot of it functional, as the ENCODE consortium has claimed — then it would all be under evolutionary pressure, i.e., you'd expect certain mutation rates to occur, but they don't. Evolutionary biology would say about 5-10% is functional, I believe, based on how much selection takes place and where. So those critics would say (and have had indeed said) a lot of negative things about results reported by ENCODE.
I suspect that the true answer, if there is one, depends on what one defines as functional and as evolutionarily conserved, by what means you measure it, and what the balance is between functionality and conservation. You pick your definitions and processes, and you accept the compromises and limitations that go along with those choices.
Caveat: I am a contributing author to ENCODE papers released in Nature in 2012.


circularizing DNA molecules? I have been reading about next-generation sequencing technologies that can sequence long reads. Even though the origin of my question is sequencing technologies, the question I am asking is about the basics of DNA biochemistry.

One of the issues in manipulating DNA samples seems to be the ability to circularize long linear DNA molecules into circular DNA molecules. What are the technical challenges of circularising DNA molecules longer than 50Kbp?

For ligation of a linear molecule to occur, the two ends must come together at the active site of the DNA ligase. In a simple molecular cloning experiment the aim is usually to avoid recircularisation of a cut plasmid vector, and instead to get a new fragment of DNA inserted into the vector. However because the ends of the linearised circular plasmid are tethered together (by being ends of the same molecule) they are more likely to encounter each other than they are to encounter the end of an incoming fragment. This is usually overcome, in part, by increasing the relative concentration of the target fragment.

If you are aiming to recircularise a long fragment then the opposite effect will come into play: the ends of individual long molecules will be much more independent and so the end of another molecule will be likely to compete successfully for ligation. In theory this effect could be overcome by reducing the DNA concentration, but I assume that this is impractical for other reasons.


Can sperm cells penetrate the cell membrane of non-ovum cells? Sperm cells have tiny bags of enzymes on their tip (the acrosome) which allow them to penetrate the ovum. My question is whether or not the process that allows sperm cells to penetrate the cell membrane is specific to ova, or if it allows them to penetrate other types of cells.

In either case, a quick description of the process would be greatly appreciated!

It is very possible that the specific enzymes that penetrate the cells can be used to lyse other cells or at least manipulate the plasma membrane. That being said, I don't think that sperm cells can be used to do that because the acrosomal reactions that you discuss are only activated by the presence of the zona pelliculada of the egg cell. Since that doesn't exist in any other type of cell a sperm cell can't be used for Acrosomal enzyme delivery for somatic cells, for example.
If you want to read more, the Wikipedia page about the Acrosome reaction is pretty good.


Isoelectric point of aspartate On the 3rd diagram (from left), the one with net charge = -1 ,

Why did it lose the H to OH- from the CH2CO2H from the neutral form ?

I was told that while moving to the right , the OH- will take H+ electrons meaning it should take H+ from NH3 .. ?

Is there any reason why they took the H from CH2CO2H ?

Any help would be appreciated!

The -CO₂H groups are carboxylic acid groups, better represented as -COOH. These, being acidic, have a lower pKₐ than the -NH₃⁺ group and so as the pH increases these -COOH groups lose their protons first.

The pKₐ values for the three are shown below the arrows: as you can see the α carboxylic acid is more acidic than that in the side chain, but they are both much more acidic than the protonated amino group.


Controlling for phylogenetic signal - what is statistically appropriate? I am currently collaborating with a fellow PhD student. We are both in the same Biology department, but my collaborator is more of a natural historian, so I am handling the statistical side of things.

He has published a few papers using only Brownian models and Pagel's $\lambda$ (with a different collaborator). However there seems to be a plurality of different methods available - Grafen, Blomberg, and Martin, to name a few. OU models seem inappropriate for our data since we have relatively small phylogenies (Cooper 2016). The literature seems to indicate that Pagel's $\lambda$ is more robust than Blomberg's $\kappa$, and in general an okay method to check for phylogenetic signal. I've also found that some people throw everything and the kitchen sink at their data, and then compare log-likelihoods, AIC, BIC, with log-likelihood ratios for any nested comparisons.

My first question is then, should you have any a priori assumptions of which method will be appropriate for your data?

I am still new to PCM so I used old R scripts and an AmNat paper (from 2019) as my reference. I am also using the same phylogenies as that AmNat paper. From that paper, and from those scripts, only two models were used, a Brownian model (which is essentially $\lambda$ = 1, anyways) and an estimate of $\lambda$. They compared the two models, chose the more appropriate model by log-likelihood ratio and that was it.

My second question is, shouldn't you always compare your models to a model with fixed $\lambda$=0?

For example, I have the following output in R,

#Brownian Model

pglsModel_BM <- gls(sum_dep ~ ContGroup, correlation = corPagel(1, phy = UltTree, fixed = TRUE), data = temp, method = "ML")

#Estimated Lambda Model

pglsModel_E <- gls(sum_dep ~ ContGroup, correlation = corPagel(0.50, phy = UltTree, fixed = FALSE), data = temp, method = "ML")

###Output of comparison is...

             Model df      AIC      BIC    logLik   Test  L.Ratio p-value
pglsModel_BM     1  3 528.4584 534.5344 -261.2292
pglsModel_E      2  4 528.0454 536.1468 -260.0227 1 vs 2 2.412992  0.1203

###ANOVA output of preferred model

Denom. DF: 54
            numDF  F-value p-value
(Intercept)     1 7.039370  0.0104
ContGroup       1 6.480427  0.0138

And the Brownian model got the go ahead. It seems that Brownian is treated as a null model, but I can't wrap my head around why $\lambda =0$ isn't also a null model or the null model. Moving forward with my assumption,

###Adding in a lambda=0 model

pglsModel_0<- gls(sum_dep ~ ContGroup, correlation = corPagel(0, phy = UltTree, fixed = TRUE), data = temp, method = "ML")

###Using anova for model comparison

             Model df      AIC      BIC    logLik
pglsModel_0      1  3 524.0962 530.1723 -259.0481
pglsModel_BM     2  3 528.4584 534.5344 -261.2292

###checking out the 0 model...

Denom. DF: 54
            numDF   F-value p-value
(Intercept)     1 289.67228  <.0001
ContGroup       1   0.21234  0.6468

I realize the differences are rather small, but all methods point to $\lambda  =0$ as the 'more likely' model.

My third question is -- although, possibly answered by the second question -- do we assume that there must be some amount of phylogenetic signal due to shared history, such that it is suffiecient to only test for $\hat{\lambda}$ and compare to $\lambda = 1$?

Note: Just to be sure, I compared all Brownian models with corBrownian to their $\lambda = 1$equivalents, and got the exact same outputs.

I have a tentative answer to my question.

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

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

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

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

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

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

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

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

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

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


What is omicron squared in genetics? Saw this equation in the conext of epidemiological genetics:

I have never seen it before and I don't know what variable it represents. Help?

Please always link to where you found this equation. It might well have been an equation that is not common enough to be recognized without the original reference. By chance, it is a very easily recognizable equation, one that has nothing to do with biology.

What your showing is an equation to compute a variance. It is not omicron but sigma ($\sigma$). In statistics, $\sigma$ is the standard deviation and hence $\sigma^2$ is the variance. $\mu$ is the mean and $n$ the number of elements from which these statistics are being computed.


How to calculate biological half life of a drug that is dose dependent? I would like to calculate the half-life of a biological drug.

This biological drug is said to have a biological half-life of:

3.33 days when a patient is receiving a dose of 0.04 mg/kg of body weight,
13.4 days when the dose is 0.2 mg/kg
and 14.9 when the dose is 1 mg/kg

How could I extrapolate this information to get biological half-life estimate for 2 mg/kg of body weight?

Could I also graph this? for example in R?

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

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

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

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


why can't we digest cellulose I have a very tricky question....all the veg and fruit

cells contain cell wall and it is made of cellulose when we eat how our body digest though we

don't have the enzymes to breakdown 1-4 beta glucose sugar?

Humans, unlike cows for ex. don't have the necessary bacteria in their intestines that make the enzymes(cellulases) necessary to breakdown the 1-4 beta glucose bonds. So we cannot break down cellulose into its constituent glucose molecules as we do with starch. Therefore this is secreted as unbroken down chains of glucose called fiber.


Why do rodents' teeth continue to grow? Why do rodents' teeth continue to grow? Enamel formation and/or root formation continue throughout rodents' lives.

What's the mechanism that causes this?

The article "Dental Anatomy of Rabbits and Rodents" (Crossley et al. 2010) provides a basic review of rodent teeth, as there are over 1700 species.  But, in general, the teeth are constant replaced due to abrasion.

What the mechanism is for continual dental growth in rodents are, in part, discussed in the article "Tooth organogenesis and regeneration" (Thesleff and Tummers), where they state

  During the cap and bell stages the lateral sides of the epithelial bud start enveloping the underlying dental mesenchyme, and from this point onwards the leading edge of the epithelium is called the cervical loop. The basal epithelial cell layer of the loop bordering the dental papilla is known as the inner enamel epithelium and the part facing the dental follicle is known as the outer enamel epithelium. The core of the loop is populated by loosely arranged stellate reticulum cells and a thin layer of stratum intermedium cells facing the inner enamel epithelium This cervical loop structure is maintained in continuously growing teeth and constitutes the epithelial part of the adult stem cell niche (Harada et al., 1999; Tummers and Thesleff, 2003: Tummers and Thesleff, 2008)


When to measure resting heart rate and blood pressure for following day-to-day trend? I'd like monitor my resting heart rate and blood pressure to see how they are affected by regular cardiovascular training and other events in life.

When should I take the measurements in order to reduce day-to-day variations? In the morning or evening? Before or after eating? Right after waking up?

Of course I'll make sure to follow good routines when measuring, but the ones I've found prescribed by hospitals don't state an appropriate time of the day.

I would be very surprised if the time of day made a difference.  I've personally never heard mention of such a phenomenon in discussions with intensive care practitioners (where of course HR and BP are measured constantly).  However this is only the case during rest, this paper (on horses) suggests that there is some difference in HR and BP after exercise depending on time of day.

You are right to attempt to control it however, I suppose.  It doesn't matter when you take your measurements, as long as you take them at the same time each day.

I would say that one of your first suggestions of taking the measurements almost immediately after waking.  Of course you would have to be relaxed as you assembled your equipment then rest for a few minutes in the posture you have chosen (this will matter) before taking the measurements but otherwise I can't see you having too great a problem.


Why is glucose our primary source of energy? Is there any evolutionary reason for glucose being the "main" molecule used as a source of energy, beginning with glycolysis and subsequently cellular respiration (after being converted to two pyruvate molecules)? Or did this particular biochemical pathway arise "by fluke" early on in the history of life?

A colleague of mine told me that it was because as shown below, all of beta-D-glucose's hydroxyl substituents are all equatorially positioned (when in the correct chair conformation), which lends to its general stability.

Image taken from the Wikimedia Commons.

Could this have played a factor in the beginnings of energy metabolism, and if so, why?

I thought this was a great question.  In particular because it hints at two questions.  The first is 'why carbohydrates are used to store energy' in general. The second being 'why glucose rather than other carbohydrates?' in particular.

Glucose metabolism (and glycogen storage) is a core gene pathway - its found in bacteria archaea and eukaryotes.  So probably the most that we can readily say about question one is that as @rwst points out this pathway has proven to be useful at a critical juncture of the formulation of living things on earth.  If you look at glucose metabolism pathways, you can see that glycerate compounds and pyruvate are the actual intermediates that are used to create energy.  The first thing about these molecule worth noting is that they have a good mix of carbon and oxygen, which would make it easier to extract energy - creating CO2 from these compounds may even predates the existence of atmospheric oxygen.  So glucose and fructose (which is actually derived from glucose in the metabolic pathway) are actually storage molecules themselves, easily broken down to smaller molecules.

As to the second question: there are quite a few ways to arrange oxygen around the carbohydrate ring.  why glucose?  The advantages of glucose is probably a subtle one.  The structural properties of glycogen might be a reason that the use of glucose monomer is so important for glycogen. There is no evidence that I can find for this, so its always possible that glucose was just the first hexose carbohydrate to be biologically used.  Its sort of hard to imagine that the structure of glucose does not play some sort of role in cell structure though.


Where can I get my exomes sequenced? I am a private individual and I want my exomes sequenced, I don't know of any place that will keep my data confidential as I read reports of companies sharing this data with insurance companies. I tried looking into a number of university research centers but none of them sequence DNA of private individuals. Does anybody know of any university that does WES of individuals or a place which can keep my information confidential? Many thanks in advance for your replies!

I will be doing my own whole genome sequencing soon. The best reputable company I've found is Veritas. For 999 dollars and at a pretty decent depth (30+ reads).  They will produce a canned report of diseases and traits similar to cheaper alternatives, but also for another $100 will provide you a complete Variant File.

My understanding is that they will only use your data after anonymization for scientific endeavors, but I still need to finish reading the fine print. See here under privacy.  Good luck!

p.s. I also would skip exome and go with WGS since the price has come down. More and more diseases and traits are being linked to variation in intergenic regions of DNA.


Speciation of humans? Since we homo sapiens (and all other species) are continuously evolving, are humans undergoing speciation based on our environment? OR has there been any difference across people that could say speciation has already happened?

PS: I am asking this question because I have recently seen a documentary in which some Chinese anthropologists claim that Chinese people are a different species altogether. Ref: Incredible human journey (Section Asia)

While its possible humans could speciate into two distinct groups at some point in the future, it hasn't happened recently (100 kya) and it doesn't seem to be happening now. If it were happening we would see two or more distinct populations that didn't interbreed and over time accumulated increasing differences.

These skin colors track most of the evolution we've done in the last hundred thousand years or so, and none of the people from different parts of the globe have any difficulty reproducing with any of the others. If speciation is happening it hasn't happened yet. I doubt speciation could happen given how much gene transfer there was between populations just on foot and boat, never mind planes and trains. Although, speciation does not require physical isolation to occur. See Wallace Effect and sympatric speciation. If there's enough pressure agaist hybridization, human speciation could occur. Think blue collar versus white collar worker and the disappearing middle class.

Speciation has certainly happened with humans before. Not with 'evolutionarily modern' humans, but 'evolutionarily modern' is mostly defined as 'humans since the last speciation event', so that's tautological. But Homo erectus, the neanderthals, the denisovans, et cetera(seriously, there's a lot of Homo species) were all descendants from one common ancestor.

As for the idea that Chinese people are a different species, or descended from Homo erectus? See the geneticist from the documentary who mapped out the genetics of the Chinese population. Archeological and skeletal evidence is easily blurred, but the genetics can't be explained without an out of africa theory.


What are the variables that control/influence the color of oranges(Citrus sinensis)? I hear that Oranges cultivated in tropical areas of the world tend to be greener when ripe, is that correct?
Even the same type of Orange differs in color if cultivated in California or Florida. I hear that's because of the climate (colder nights == oranger oranges)

But, at the same time, oranges tucked in between the tree leaves tend to be greener, for they need more chlorophyll to make the most use of less access to sun rays.

Could someone correct my assumptions? Also, if possible, list the pigments and processes involved?

Naturally, ripe orange skins are usually green-yellow. In colder climates, they will turn orange over winter, since in the cold the chlorophyll will start degrading and stop masking out yellow/orange carotenoids in the skin. If the fruit is picked in fall (and not let ripen or degreened) they will have green color.

Ehtylene gas, which is a natural plant hormone, is sometimes used to give them more uniform, orange color (degreening). Also, some oranges cultivated in the US are dyed with Citrus Red 2 to make their peels orange.