Why does deamination in the lagging strand lead to an increase in the relative number of guanine and thymine to cytosine and adenine? My question arose from this article on Wikipedia on the GC-skew in bacterial genomes: https://en.wikipedia.org/wiki/GC_skew
As far as I understood, the lagging strand (the template strand), during replication, is more often single stranded than the leading strand (template), so it is more prone to mutations like deamination. Therefore, on the lagging strand (here, the one being the template to the strand formed by Okazaki fragments), one should expect more Ts and fewer Cs. Conversely, the strand synthesised by Okazaki fragments should have an enrichment of As and a depletion of Gs. Why does the Wikipedia article mention a increase in the ratio of (G+T)/(C+A)? I would expect an increase in the ratio of (T)/(C). I don't get the link with Gs and As.

I think you are referring to this sentence from your wikipedia link:

  There is a richness of guanine over cytosine and thymine over adenine in the leading strand and vice versa for the lagging strand.

Note that this is not saying (G+T) > (C+A). It is instead saying (G)>(C) and (T)>(A). I think that those are not the same as saying (G+T) > (C+A), though that is also true, probably?

So I think that there is no contradiction there- it is just, as you say, more Ts (relative to expected equality with As) and fewer Cs (relative to expected equality with Gs).

If I understand correctly, the importance of the GC skew, and the reason that it is interesting, is that it acts as a deviation from Chargaff's parity rules (as explained in the article). These rules state that for any given hunk of DNA, (G)~(C) and (T)~(A), which is the expected composition for (for example) double-stranded DNA, because of base-pairing.

Edit: for more information on Chargaff's rules, see this other question which I have (through pure coincidence) answered here. Note that the second parity "rule" is a statistical phenomenon, rather than a logically required rule. The wikipedia page on Chargaff's rules is also informative.

Hope that helps.


Possible Genotypes -- Linked Genes Please consider the following question:

Genes A and B are located on the same chromosome. If two individuals with the genotype shown below are crossed, which genotype could be formed?

Let / represent the line in the image above.
a.)AB/ab
b.)Ab/Ab
c.)aB/aB
d.)AB/Ab

Why can't the answer to this question be all of the choices? I thought that recombination was completely randomized through the process of crossing over. Why is only one of the choices correct and how would I go about doing this on my own?

One of the alleles for each gene must come from the parent on the right hand side. That parent only has a and b alleles (is homozygous) - meiotic crossing over will not change this. So the offspring must inherit at least one a and one b. That should point you to the answer.


How to gather life-history traits data on bird species? I am looking for a data base of life history traits for bird species. Is there such data base? If not, what are your advice on how to collect those data? Is there a handy website that list characteristics of birds around the world? I started to gather those data using wikipedia (and the references anytime wikipedia actually have reference) but it is very cumbersome.

The kind of data I am looking for are:

Reproductive age
Average lifespan
Average gestation time
Number of eggs per reproduction event

Have you tried the Ecological Data Wiki? It's an NSF-funded repository of links to ecological databases and datasets:

Main page:
http://ecologicaldata.org

Find data tab, refined to Taxon=Birds, Ecological level=Species:
https://ecologicaldata.org/search?f[0]=taxonomy_vocabulary_2%253Aname%3ABirds&f[1]=taxonomy_vocabulary_6%253Aname%3ASpecies

The 'Avian body size and life history' dataset on the above page looks relevant to your search.


Is it true that some organs "sleep" at certain times of the day and does this affect dieting or when to take medicine? Is it true that some organs (such as the liver or the intestines) are less active at certain times of the day, regardless of whether the individual is awake or not?

No, this is not true. The only "organ" (if you will) that needs to "sleep" is the brain, and even then it is still quite active. The body needs to rest.

When blood is needed in a prioritized manner in certain areas of the body - for example, during a marathon the muscles and the heart need more oxygen - mechanisms are in place to shunt some of the blood away from organs that need it less (the stomach and intestines, for example) to those that need it more. But that doesn't mean the GI tract is sleeping, or even resting.

There is no organ that "sleeps". The brain as a whole is highly active during sleep, even secreting certain hormones predominantly during that time.


Quantitative Trait Locus process? I do not seem to understand the concept of Quantitative Trait Loci(QTL's), can anyone explain it to me in detail? Reading the wikipedia article helped somewhat, but I do not understand it well.

What I understand:

-quantitative traits = a number(quantity) of phenotypes(traits) that vary

-To "make" a QTL, one must "map" the phenotypical traits to the genes that they are believed to propagate.

-This is done by AFLP-PCR, or Amplified fragment length polymorphism, according to wikipedia at least. This process cuts the genomic DNA into pieces and then amplifies the target region with primers via PCR. Then it is visualized using some type of gel technique?

-I am not sure how this gel data then allows the mapping of phenotypes to genes. I know gel electrophoresis can help to identify the length of a gene or region of a gene if using a ladder to compare.

When we say that something is a genetic marker we mean that we can establish its linkage to a chromosome AND that we have some way of discerning, or detecting, how the marker has segregated after meiotic recombination (this definition is only valid for diploid species that undergo sexual reproduction).

So, for example, in the fruit fly, Drosphila melanogaster, the white gene is linked to the X chromosome. Wild-type flies have red eyes, a mutation in the white gene yields flies with white eyes.  You can use genetic crosses (matings) to (a) show that the white gene is linked to only one of the four fly chromosomes, and (b) if you have other genetic markers on that chromosome you might be able to construct a genetic map showing the order and distance of the linked genes.

You can also use a DNA fragment from a chromosome as a genetic marker--if you have an assay that lets you track the fragment after meiotic recombination.  A Southern blot, using a labeled probe is one way to accomplish this (e.g. a Restriction Fragment Length Polymorphism or RFLP).  PCR that lets you detect a RFLP or a SNP serves the same function, and is much quicker than a Southern Blot (and requires less DNA starting material).

A QTL is just one of these "molecular" genetic markers that shows linkage to the trait you are assaying. So, for example, if you had RFLPs or SNPs sprinkled all along the human chromosomes, and if you had a way to detect the segregation of all those markers after meiotic recombination, then you could use DNA from a family with lots of kids of varying heights, where one parent was tall, and the other parent was short, to see if any of your genetic markers were segregating, or linked to the phenotype of height in the children (this example assumes that height in humans is a quantitative trait).


How is the Energy Systems Language used in ecology? Is the Energy Systems Language useful in systems ecology? It is developed by Howard T. Odum, "the father of system ecology", to make analogies between ecological systems and electronic circuits. But how far this analogy remains correct? What important results have been found from it? Is it widely used by ecologists in other subfields? If yes, then why isn't it applied to other fields?

Related: What are the branches of system ecology?

Energy systems language is definitely still used in systems ecology, ecosystems ecology, and ecological engineering. It can be used in other aspects of ecology depending on the approach of the ecologist, some ecologists appreciate it more than others. For a broad snapshot of how science is currently building upon Odum's 1983 Intro to Systems Ecology, check out a Google Scholar search for the latest papers citing that text by Odum.

In my experience with it, energy systems language and energy flow diagrams introduced by Odum are used as helpful conceptual tools: understanding and organizing complex systems qualitatively and then potentially quantitatively; putting useful constraints and guidelines on brainstorming the effects of adjustments to complex systems; etc. This can be done in a teaching context, helping students understand systems and bridging the fields of engineering and ecology. It can also be done in a high-level professional context, sorting out the intricacies and potential solutions of very complex environmental problems.

I find that energy systems language is used most wherever the study of complexity and ecology intersect. This is especially true in the study of ecological complex adaptive systems and in studying agro-ecology. Even in those cases it depends on the ecologist in question: those with more of an engineering background tend to appreciate Odum's framework, whereas those without an engineering background may rely on other frameworks.


Can nature create codes and specified complexity? Is there an example of nature creating codes or specified complexity? It is said by Creationists that codes can only come from minds, and since DNA has a code, it was created by a mind.

Is this true? If false, can you explain how unguided, unintelligent nature could create coded specified information? Just as a note to show where I'm coming from, I am an Evolutionist.

But I have to admit, this appears to be a very, very strong argument in favor of special creation by an intelligence.

If you think of laws of nature as being codes, then we've found many instances of such codes that are in some sense not man-made. Of course, you could say that they are of "divine creation" or some such thing, but then you'll probably be ready to label everything seemingly intelligent, interesting or found as such. So let's not go there! These laws can be seen as codes, albeit not intentionally created ones.


Signaling pathways with Receptor Tyrosine Kinase clarification This question is regarding signaling pathways with RTKs, Receptor Tyrosine Kinase.  The picture posted has one thing wrong with it and I am supposed to figure out what that is.

What I have come up with so far is that the Ras protein shown that goes on to activate RAF, has GDP attached to it.  This cannot be true from what I understand, as Ras must have GTP bound to it in order for Ras to stay in it's closed conformation.  The closed conformation is what activates the rest of the signals.

I am wondering if I am correct in my assumption, or if there is something else in the diagram I am missing.

The active form of Ras should definitely be bound to GTP, to activate Raf. I don't see anything else wrong with the diagram. For reference, here is another useful diagram the pathway from Nature.


How do castes evolve in eusocial species? I have been reading about eusocial species and have some questions. What is the basis for caste differentiation? Is it entirely genetic? Does the queen have the code for soldiers, workers etc in her own genes, so only a mutation to the queen results in change of the whole colony's castes? As a related question, how do different castes evolve?

It seems to me that if only one individual in a colony is reproducing, the rate of evolution must necessarily be much slower because any genetic change in the queen would affect all castes, which is likely to have some negative effects.

Does a fertile queen have the code for soldiers/workers in its own genes, so only a mutation to the queen results in change of the whole coloniy's castes?

Yes.

  But how that would protect them from just going extinct due to one unfavorable mutation to a queen?

One unfavorable mutation to a queen could make that queen and her lineage "extinct". It wouldn't necessarily have any effect on the rest of the population (i.e. other queens and their lineages).  There's no difference here between eusocial and other species.


Introducing novel genes to an adult multicellular organism? Is it possible to introduce a novel gene into the genome of every cell (or at least most cells) of an adult multicellular organism? How would this be done?

Thanks,

CDB

Short answer, in theory yes, in reality, at the current state of the art, no.

I guess you are speaking about higher organisms (such as mice or humans), if you are referring to small non-complex multicellular organisms then the answer is yes as far as you can transfect every cells with the system described hereafter.

The most promising technique is using the CRISPR/Cas9 system which has already been used to create all types of modified organisms but starting from the embryo. Here a good Nature reference http://www.nature.com/nbt/journal/v32/n4/full/nbt.2842.html.

There are no ways, at the moment, to transfect an entire organism with the full system. The most recent attempts I heard off is to target specific cells using an encapsulation techniques that only merges with the membrane of specific cells therefore delivering the CRISPR/Cas9 system in a targeted manner. This was in an attempt to knock-out a defined genomic regions in those specific cells, not to introduce a new gene.

I believe in the near future that this will be possible. Not necessarily every cells of an organism but at least some specific cell types.


What kind of bug is this? I found it on my Cleome plant in Atlanta Georgia This bug was on my cleome plant in Atlanta Georgia

This is an instar (nymph) of a shield or stink  bug (order Hemiptera, family [Pentatomidae])(https://en.wikipedia.org/wiki/Pentatomidae).

Specifically, it appears quite similar to a later juvenile stage (~4th instar) of Chinavia hilaris, the Green Stink Bug.

 4th instar; Source: Oregon Dept of Agriculture

 4th instar; Source: UF IFAS; Credit: Herb Pilcher, USDA-ARS, Bugwood.org.

Alain Hogue also captured a wonderful picture of this species at bugguide.net

From UF IFAS:

  Distribution
  The green stink bug occurs in most of eastern North America, from Quebec and New England west through southern Canada and the northern U.S. to the Pacific Coast, and southwest from Florida though California. This is the most commonly encountered stink bug species in North America.


How to build S16 rRNAs phylogenetic trees? Here is what I know so far:

Multiple copies are present in prokaryotes, but according to a paper by Lukjancenko el al.[1] it is enough to select the most similar ones.
There are stem regions in the S16 which I suspect are much less variable than the loops.

And here are my questions:

About 1., it is this the usual procedure?

About 2., do people use the loops or the stems to construct a phylogenetic tree? It they use stems, do they consider the coupling between stem bases? And, if they do so, where can I get a "map" of this coupling (preferably in a parseable form, not an image)?

In the other hand, is it correct to use a simpler nucleotide substitution model? which one?

Thanks in advance for any hints.

Lukjancenko O, Wassenaar TM, Ussery DW. 2010. Comparison of 61 Sequenced Escherichia coli Genomes. Microbial Ecology, 60:708-720, doi:10.1007/s00248-010-9717-3.

OK, first of all do not use CLustalX to align the sequences. It is a very old algorithm and is outperformed by just about any modern aligner (including its descendent, clustal omega, and t-coffee, mafft, kalign etc etc).

As for 1) that depends on what you want to do, there is no perfect solution. What question are you trying to answer? If you want a tree with species-level resolution, use all sequences. If you don't need that high a resolution, choose a representative sequence from each order or genus or whatever. In all cases, remember to use a significantly different sequence as an outlier.

As a general rule, I would use the entire sequence to build a tree, both the stems and the loops. To include structural information in your alignment you may want to check out r-coffee from the t-coffee suite.

Finally, you don't really want to use a nucleotide substitution model. Most such models assume that the sequence codes for protein and are not suitable for non coding RNAs.


Won't natural selection miss the overall minimum of a function? Suppose we have a population of about 1000 with an environment that does not change a lot (e.g. the atmosphere is more or less the same) and we leave this population to evolve. From what I understand some genes will be lost because they won't be suited enough to continue in the next generation. Also the generations can only reproduce within their generation (e.g. a male can only have reproduce with people from his generation).

My question is: won't some genes, combined with others have a better chance to make the individual "better" but they for example make it less fit right now. Or will the mutation of the genes actually promise that eventually we will hit that minimum of the function.

I want to point out that I'm a computer scientist and my biology is awful. I'm looking at this from evolutionary algorithms point of view in particular genetic algorithms.

What I think the question is "Why wouldn't an organism be more efficient from selection than the environment demands?"  Please let me know if I'm hitting the mark here.

A scenario suggested by the question is this: If there is selection pressure on say an animal to resist a disease, and then it evolves two resistance systems to the disease.  This could be an immune response and a taste for a certain plant containing compounds with remedial properties.  If either system is adequate to resist the disease, hasn't selection gone above and beyond what is needed?

If so, the answer is either not recently, or all the time.

First, for 'not really' you have to consider the importance of competition.  Reading about the Red Queen's Hypothesis is helpful here.. First there is a natural random mutation rate, which can cause one of the system to become less effective.  Or if the disease is also changing to increase its efficiency, then either one or both of the defense mechanisms in this case would fail from time to time.  Then having two systems would not be too much. There is a clear benefit to having redundant systems of resistance here. For both these broad categories of reasons, you have much redundancy and interdependence among the genes which create traits in living things (phenotypes).  Because of the background mutation rate, traits which are not needed disappear relatively quickly; because of competition, those which convey an advantage, even if a small one, have a strong likelihood to stay in the gene pool.

now: "all the time" There are cases where traits are exhibited that are not efficient though. Evolution works over generations, typically selection influences traits on the order of 10s of generations and the population can show traits which are not helpful if the environment or the competition has changed drastically.

An example of this is the current obesity epidemic in the developing world. The sudden availability of cheap food has caused those people who have access to become obese.  This is because they have a heavily reinforced trait to save up fat because humans experience famines regularly.  Its going to take a long time before efficient energy storage in human beings is lost.  In the meantime, people are not living to reproducing age at the same level of fitness they might have.

I want to add a note on genetic algorithms.  The difference between genetic algorithms to achieve the same efficiencies as biological systems are worth thinking about:

As far as failing to find a global minimum, natural selection usually has many more samples.  A typical selection experiment with flies or worms often have thousands or tens of thousands of individuals screened.  GA models of reasonable complexity have usually a few score at most - am I right?  A bacterial or yeast experiment can have 10^9 individuals.

The genetic system of the chromosome itself is tremendously complex.  We can't even model itself and so the product of millions of generations of selection already will has a heretofore unmodelable response to selection.
The typical selection environment is equally undefinable, so a survey of living things in a complex environment has unpredictable effects.  While most of us think as laboratory experiments as being less complex, its not completely true.  I was at a talk recently that mentioned how E coli K12 seems to have diverged in various laboratory environments despite the fact that its mostly been in frozen stocks since being isolated in the mid 20th century.


Boletus or Xerocomellus? I have been trying to identify a mushroom I collected in the nearby forest, and while I think I have it identified, it seems to have a couple of names and I am confused about which grouping is correct.

According to one site, the mushroom is Boletus Chrysenteron :http://www.mycokey.org/result.shtml?genSpec=Species&ID=213&showList=DropDown&random=11837

According to Wikipedia it is Xerocomellus Chrysenteron (picture on right) https://en.wikipedia.org/wiki/Xerocomellus

But the info suggests that this Xerocomellus group is relatively new and belongs to the family Bolataceae.

Are these two different mushrooms (Xerocomellus Chrysenteron and Boletus Chrysenteron)? Or is it that the mushroom is now in a new group?

As you will see in the Species section of the Wikipedia Xerocomellus genus page you linked, Xerocomellus chrysenteron has a basionym (original name) of Boletus chrysenteron.  The Xerocomellus genus was first described in 2008.  The Wikipedia page for the X. chrysenteron species similarly gives three synonyms for X. chrysenteron in the box at the top; among them is Boletus chrysenteron Bull. (1789).  Thus the two names are referring to the same mushroom species.

One should note that Wikipedia says about this species:

  Macroscopic observation of Xerocomellus chrysenteron is not sufficient
  to determine this species with certainty, as many intermediate forms
  occur between it and other taxa

It appears from this that correct identification of a mushroom as being X. chrysenteron is not a trivial task.


Is there term for thermophilic-halophilic plants and if so, what is it? I know that in botany there is a wide classification for plants that can survive in hot deserts (semi-arid or arid) and harsh climates such as 4-season countries with a tendency to droughts each year (such as Palestine).
This classification, as I understand it, is thermophilic plants (hot loving).

But there is also the classification thermophilic plants which are also halophilic (salt loving), i.e. can survive and even flourish in extremely low and salty ground? Examples of such plants are found in the Ein Feshkha reserve near the Dead Sea in Israel and other similar Thypa and other plants reserves near the Dead Sea (current) shorelines in Israel, Palestine and Jordan.

Is there term for thermophilic-halophilic plants and if so, what is it?

Well, at least in other biological contexts, such organisms are often simply referred to as being thermohalophiles (or as being thermohalophilic).
Examples of the use of these words:
Bacteria:

Mavromatis, K., Ivanova, N., Anderson, I., Lykidis, A., Hooper, S.D., Sun, H., Kunin, V., Lapidus, A., Hugenholtz, P., Patel, B. and Kyrpides, N.C., 2009. Genome analysis of the anaerobic thermohalophilic bacterium Halothermothrix orenii. PLoS One, 4(1).

Denger, K., Warthmann, R., Ludwig, W. and Schink, B., 2002. Anaerophaga thermohalophila gen. nov., sp. nov., a moderately thermohalophilic, strictly anaerobic fermentative bacterium. International journal of systematic and evolutionary microbiology, 52(1), pp.173-178.

Pereira, M.M., Antunes, A.M., Nunes, O.C., da Costa, M.S. and Teixeira, M., 1994. A membrane‐bound HIPIP type center in the thermohalophile Rhodothermus marinus. FEBS letters, 352(3), pp.327-330.

Biochemistry:

Amoozegar, M.A., Salehghamari, E., Khajeh, K., Kabiri, M. and Naddaf, S., 2008. Production of an extracellular thermohalophilic lipase from a moderately halophilic bacterium, Salinivibrio sp. strain SA‐2. Journal of basic microbiology, 48(3), pp.160-167.

I'll note that I haven't found evidence of plants being referred to in this specific manner, but read the example papers below for use of the related phrases "thermohalophyte vegetation" and "thermohalophytes", respectively:

Accogli, R., Nutricati, E., Famà, L., Medagli, P., Manno, D., De Bellis, L., Marchiori, S. and Colasante, M., 2008. Iris revoluta Colas., natural hybrid origin species: characterization and preservation problems. Plant Biosystems, 142(1), pp.162-165.

Vekhov, N.V., 1996. Thermal and freshwater springs of the Chukchi Peninsula: Unique subarctic ecosystems. Part ii. flora. Polar Geography, 20(3), pp.209-220.

Is this word-blending approach the best or most appropriate? Couldn't say, but there is certainly prior  precedence for combining the habit adjectives into "thermohalo-".


Will the interaction of two proteins vary across different tissues? Suppose protein A and B is both abundant in tissue X an tissue Y. Will A and B interact in X but not interact in Y?

I guess A and B could be biomarkers of a certain disease, and in the pathological tissues their amount remains relatively unchanged but no longer interact, thus losing their functionality and causing the disease.

One possible condition would be that A and B don't interact directly but require a third protein C to act as the scaffold to form the complex A-C-B. So when C is expressed in tissue X but not in Y, we would observe this phenomenon.

Are there other mechanisms or examples?

Your example of an interaction mediated by a scaffold protein is certainly one way of controlling protein interactions. This doesn't just happen in different tissues either, it is also used to fine-control the interaction of certain proteins within one cell, the most notable example for this are the kinases in the MAP pathway (see for example this paper).

Another possibility I can see is that A and B can interact, but only if (at least) one of them is modified, e.g. by phosphorylation - so the actual interaction happens with A-p and B. In a case like this it's not unlikely that the protein that modifies A is only expressed (or active) in certain tissues, which then mean A(-p) and B can only interact in these tissues.


Are cell biology and cytology the same science? According to Wikipedia, the terms can be used interchangeably, but according to my college professors, cytology is exclusively the study of chromosomes and identification of their abnormalities.

Your professors are probably using cytology to refer to cytopathology which isn't strictly incorrect, but is a bit confusing since cytology also has a broader meaning.

Overall, the distinction isn't scientifically that important. Biology is so interconnected that you can't really stick strictly within any one "type" of science, they are only soft distinctions. If you need to learn a term for a course, there isn't much point to argue with your professors. Literally, the word "cytology" should refer to the study of cells, so in my opinion the narrow definition doesn't make sense, but like other words it is important to interpret it in context.

In my personal experience, I have usually heard "cell biology" used rather than "cytology" to broadly mean study of cells. If you are at an institution that calls their cytopathology labs "cytology" it doesn't much matter if there are other meanings for the word.


Can the central dogma work in reverse? Theoretically, is it possible to obtain the original gene from the protein’s amino acid sequence as its “template”, as in, the reverse of how gene’s codons were “templates” for the amino acid sequence of the protein?
I’m curious to know if it’s possible to use enzymes such as reverse transcriptase to obtain DNA from a protein in a reversed central dogma model.

Let’s first consider what the Central Dogma[1] actually says. It is precisely summarised in the following figure[2]:

solid arrows represent transfer of information that has been observed directly; dashed arrows represent potential, not yet observed, transfer of information

In the time since the original formulation of the Central Dogma in 1958, we have been able to contribute evidence for one of the dashed lines: RNA→DNA is performed in vivo via reverse transcriptase. By contrast, note the absence of any arrows originating from “protein”. The idea of the Central Dogma was that proteins were an information transfer sink: information could only flow in, not out.
And Crick and his contemporaries had good reasons for assuming such an information sink: translation from RNA to proteins is lossy  because the genetic code is degenerate. A given amino acid can be encoded by more than one codon.
This doesn’t mean that polypeptide chains couldn’t be reverse translated into “a” RNA representation. But it wouldn’t necessarily be the original RNA. Stated like this, it’s obviously possible: we can in fact take a polypeptide chain, sequence it, and synthesise a corresponding RNA.
However, this doesn’t happen in nature — at least, it hasn’t been observed so far, and there are good reasons to assume it doesn’t happen at all. In fact, Crick’s paper[1] explains:

The transfer protein→RNA … would have required (back) translation, that is, the transfer from one alphabet to a structurally quite different one. It was realized that forward translation involved very complex machinery. Moreover, it seemed unlikely on general grounds that this machinery could easily work backwards. The only reasonable alternative was that the cell had evolved an entirely separate set of complicated machinery for back translation, and of this there was no trace, and no reason to believe that it might be needed.

The “complicated machinery” Crick mentions here consists of two distinct parts: On the one hand the ribosome, and on the other hand the aminoacyl tRNA synthetases (aaRSs, singular aaRS). The latter is essentially the physical embodiment of the genetic code in the cell. The former is the machinery necessary to decode RNAs using the codon adapters (= tRNAs) charged by the aaRSs.
Crick correctly notes that, in order to allow the transfer protein→RNA, the cell would require a system analogous to the ribosome and all the aaRSs, redundantly encoding the exact same information. And it would need to ensure that these two distinct encoding systems (translation and reverse translation) never get out of sync with each other.
It’s worth contemplating, briefly, why the cellular translation process isn’t simply reversible, given that it’s an chemical process, and thus pretty much by definition reversible in theory. The reason for this is that translation happens in two entirely distinct, and physically separate, stages: the charging of individual tRNAs with their corresponding amino acids by an aaRS, and the actual mRNA translation by the ribosome.
Simply reversing the ribosome activity would result in a polypeptide being digested from one end, yielding individual amino acids. However, the ribosome actually has no facility for capturing these amino acids, they would simply diffuse away without being attached to a tRNA, let alone the right tRNA. A successful reverse translation would have to physically couple each type of aaRS in turn to the ribosome to ensure that terminal amino acids would pair with the correct tRNA, which, in turn, would pair with a codon. There are two fundamental objections to it, which render it impossible:

Codons, which could be combined into mRNA chains, don’t swim around individually. In fact, short RNA chains are highly unstable and are quickly degraded. By contrast, individual ribonucleotides can be combined into chains — that’s what polymerases do all day, after all. But the ribosome doesn’t possess polymerase activity, and the tRNA anticodon would not provide sufficient physical support to enable a polymerase to dock to it and start synthesising a secondary strand.
Steric hindrance prevents the aaRS enzymes from interacting with the tRNAs while they are being held by the ribosome: the ribosome and the aaRSs would have to physically overlap.

It’s hard to get definitive answers in science purely from theoretical considerations, but this one’s as certain as you’re likely to get: cellular translation using the ribosome is a non-reversible process. Reverse translation, if it existed, would therefore require a separate machinery.


Preferred criteria for new bee colony location As a human I want a house with a roof, indoor plumbing, bug free, and make my wife happy.  I don't want to drive too far to work, and it has to be well-suited for offspring.

What are the criteria that define a "good spot" for a new location for honey bees.  I'm sure it involves water, shade, access to nectar, and defensibility, but I don't know any of the details.

Has anyone ever made measures of this? Beekeepers? Apiologists?  What are the things bees think are important, and what values of those do they think are "best"?

According to Thomas Seeley, in his book, Honeybee Democracy, he gives the following as important criteria for honeybees when selecting a nest site when a bee colony moves to swarm:

Larger volume (the minimum nesting capacity was found to be around 14 liters with more preference to nests with a capacity of approximately 30 to less than 100 liters)
Relatively small entrance (10 to 30 centimeters squared)
Nest with the entrance located near the floor of a tree cavity
Direction of the nest entrance (south facing for thermoregulation during the winter)
Nest height (preference given to higher nests for colony defense)
Remnants of previous honeycombs (saves work and energy in building the nest structure)

Interestingly enough, bees did not necessarily display a preference for the shape of the entrance, the shape of the nest, the draftiness or dryness of the nest cavity (they are able to plug and waterproof nests).

Flower/nectar/food availability is not necessarily a direct factor as this changes quite frequently throughout the seasons and honeybees are not able to leave their honey stores (which are necessary to sustain the colony throughout the winter) or take them with them every time the colony has trouble finding food. Honeybees have a complex and efficient system for optimized location and sharing of food sources, therefore distance is not as large a factor.


What's the difference between germ line and germ cells? I'm currently reading some introductory chapters about genetics (in Campbell's book, chapter 13) and I realized that I don't really understand what is the difference between the germ line and the germ cells.

From what I read so far, I understand that germ cells are specialized cells from which gametes are produced, and I suppose that the germ line is the "set" of all germ cells + gametes, but for some reason I couldn't find a good definition of what the germ line is and I was not satisfied with what I found in books and on the internet (because it was not as clear as I thought it could be)...

So, even if I'm ashamed to ask... Could someone explain to me the difference between those two concepts? Thanks in advance!

Germ cells are the cells of germline. Germline is a lineage of germ cells from germline stem cells to gametes. Cells that do not belong to germline are called somatic cells.


Does the nervous system have "routers"? Does the peripheral nervous system have a system of routers that decide where a message is meant to go based on some kind of address, or does a signal from the brain follow a single, unbroken chain of neurons that starts at the brain and ends at one destination?

For example, let's say my brain wants to send a message to my left pinky, telling it to contract. In the first model, a series of action potentials travel down my spinal cord, reaching a node in my chest. The action potentials encode the "contract" command as well as an address. The node in my chest reads the address, and routes the signal down my left arm. It reaches my left hand, where another node reads the address and sends the signal to my left pinky, where the signal reaches the appropriate muscle, telling it to contract.

In the second model, every single muscle in my body has a particular chain of neurons that runs in parallel to my brain. If my brain wants to send that signal, it doesn't have to specify an address, it just sends the "contract" command down the path that says "Leads directly to left pinky and nowhere else".

If the second model is true, does that mean that the large, visible structures that we call "nerves", such as the vagus nerve, are actually bundles of point-to-point connections?

Anywhere I can find more information about this would be awesome. I'm a comp sci/biochem double major, and really love reading about the body as an information system.

The second model is the one. No tags, flags, or routers, just a 1-to-1 system.

A striking example of this is are the different effects of spinal cord injury, which depend on the place where the injury occurs.

And I quote from the apperalyzed website:

  Spinal Cord Injury Overview

  When a person suffers a spinal cord injury, information traveling
  along the spinal nerves below the level of injury will be either
  completely or partially cut off from the brain, resulting in
  Quadriplegia (Tetraplegia) or Paraplegia.

  [...] The brain will still be trying to send messages downwards to the
  muscles in the body, known as motor messages. These messages however,
  will be blocked by the damage in the spinal cord at the level of
  injury. Nerves joining the spinal cord above the level of injury will
  be unaffected and continue to work as normal.

  Quadriplegia / Tetraplegia [occurs] when a person has a spinal cord injury above
  the first thoracic vertebra. Paralysis usually affects
  the cervical spinal nerves resulting in paralysis of all four limbs.
  [...]
  Paraplegia [occurs] when the level of spinal cord injury [is] below the first thoracic spinal nerve [and leads to] paralysis of the legs and abdomen up to the nipple line, [but] paraplegics have full use of their arms and hands

This example shows that there are strict one-to-one connections (2nd model) and that messages are not simply flagged and routed to their destination via the shortest or quickest route available among a bunch of redundant, ill-defined pathways (1st model). Hence, there is only one way from the brain to the periphery; once severed the message cannot be simple re-routed via parallel lines.

Another example is the make-up of the sensory-motor cortex:

The picture shows that the motor cortex (muscle control/motor output) as well the sensory cortex (sensory input) contain a complete image of the body. Each part of the motor cortex and sensory cortex is devoted to the control or sense of one specific part of the body, respectively.

Nerves are indeed typically bundles of 1-to-1 fibers. For example, the optic nerve contains fibers that carry info from one, and only one well-defined and localized pixel from the retina to the brain.

Principles of Neuroscience by Kandel and Schwartz is a great book with a wealth of info.


What is the evolutionary advantage with two lungs (kidneys)? What is the evolutionary advantage with two lungs (kidneys)? Most living beings only have one heart, one stomach. Most internal organs are not doubled and if one lung fails it is not exactly quite transparent due to having an extra lung. Before modern health care one failed lung would kill the individual.
The same can probably be said for kidneys.

Humans and most organisms have bilateral symmetry.  Anything that is on the left side of the body will be on the right also.  Not everything can be in the middle, so ... you end up with two.  This is evolutionarily labile - for example, fish generally have one dorsal swim bladder, though it is derived from the lungs.  In a similar manner, the heart is formed by the fusion of primordia on either side during development - see UNSW for a cute video.


RNA-seq analysis - q-values in cuffdiff I'm using cuffdiff 2.1.1 to look for differential gene expression between two conditions. Each condition has 2 biological replicates. The results I get look promising from a log fold change perspective - lots of genes that I would expect to be affected have the highest fold change values. However, from a q-value perspective very few of the results look promising, having q values > 0.05.

My question is, given 2 replicates, how is the q value calculated? Surely if there was high variance between replicates but the difference between experimental conditions was also large then the q-values would be correspondingly high?

In this case, can I, or should I, really trust the q-values?

q-value is the corrected p-value to account for multiple testing (i.e. you are testing thousands of genes). Those with q-value <0.05 are significant experiment-wise. Cuffdiff uses Benjamini-Hochberg correction to compute FDR (i.e. q-value). The calculation does not depend on the number of replicates it's based on the distribution of p-values those, yes, are influenced by the number of replicates. The more replicates you have the better).


Pathogenesis of Group B Streptococci and C5a I am thinking the pathogenesis of the C5a in GBS.
I think the pathogenesis happens like

  C5a-peptidase in acid environment (Sialic acid, capsule) $\to$ cleaves C'-derived Neu chemoattractant C5a $\to \downarrow$ alternative pathway of Complement.

However, this part cleaves C'-derived Neu chemoattractant C5a is not clear to me.
I think this means that C5a-peptidase cleaves Neu C5a from the previous complement reaction that inhibits the next coming alternative pathway of complement.

How does this pathogenesis of GBS happen?

I think "neu" must be an abbreviation for "neutrophil".

C3a and C5a are both chemotactic peptides that recruit cells to sites where complement activation is occurring. C5 is a substrate for the C5 convertases formed in one of the three complement pathways (classical, lectin, alternative). C5b, the other product of this cleavage goes on to form the membrane attack complex.

C5a peptidase is a Streptococcal product which destroys the C5a peptide before it can recruit neutrophils and other agents of cell-mediated immunity. Streptococci also produce an inhibitor of the membrane attack complex.


How do we know that ammonites are a kind of cephalopod rather than another type of mollusc? On a recent fossil hunting trip, I found a small fossil that an expert identified for me as an ammonite beak. He told me such fossils are not uncommon, but are not normally found with their parent shell due to the decay of the soft tissues holding the beak in place.

I know that under some unusual conditions, (i.e. the Burgess Shale) the soft tissues of animals can be preserved. And a quick google search reveals that there are rare belemnite fossils with their tentacles in place.

However I can find no reference to any ammonite fossils, nor any of the orthocone clades, with soft part preservation.

Shells of similar shape and structure are not unknown in other groups of mollusic (i.e gastropods). So: what characteristics of the shell make taxonomists sure that ammonites and orthocones are cephalopods rather than some other kind of mollusc?

It really is mostly the siphuncle as @augurar mentioned. The siphuncle is present in modern ammonites, cuttlefish, and Spirula(a deep-sea squid with an internal chambered shell). It links shelled cephalopods neatly with other cephalopods(both extant and extinct) and distinguishes them from other shelled things like snails.


How is the enzyme glycogen synthase regulated? How is the enzyme glycogen synthase regulated in regards to glycogen synthesis? I think I understand that phosphorylation decreases its activity (through glycogen synthase kinase?), but what role do molecules like insulin, glucagon (or epinephrine), protein kinase A, and Protein phosphatase 1 (PP1) play? Do they activate or inhibit glycogen synthesis, and how so?

The answer to this question can easily be found in standard biochemistry texts, e.g. one available online (albeit an old edition): http://www.ncbi.nlm.nih.gov/books/NBK22444/. Presumably this is why this question has not been answered previously. However I think it worth providing a summary answer in order to stress something that I feel perhaps does not receive enough emphasis in texts, and is ignored in the question.

This is that the regulation of glycogen metabolism (and indeed other aspects of carbohydrate metabolism) differs in different tissues because of the different situations in which its synthesis and breakdown is appropriate in relation to the integrated metabolism of the organism. If we just consider skeletal muscle and liver, the muscle stores glycogen to use itself when it needs to generate ATP to power contraction, whereas the liver stores glycogen so that it can provide other tissues with glucose when they require it either for work (e.g. the skeletal muscle) or in starvation (e.g. erythrocytes and brain).

The appropriate integration is provided in the first instance by the hormones mentioned (so-called first messengers), and the presence or absence of receptors in the tissues so that they will respond to these hormones or be refractory towards them, as appropriate.

Both liver and skeletal muscle have receptors for epinephrine (adrenaline) which is secreted as a stress response. This results in the breakdown of glycogen — used internally by the skeletal muscle and released as glucose by the liver, as already mentioned.

Liver also has receptors for glucagon, the hormone which is secreted in starvation or fasting, and also causes the breakdown of glycogen. The skeletal muscle does not have glucagon receptors because the nutritional state is irrelevant to its energy requirements.

Both liver and skeletal muscle have receptors for insulin, the hormone of the fed state, and respond to this by increasing the synthesis of glycogen to build up supplies to be available when needed.

How do these hormones work?

Glucagon and epinephrine (adrenaline) both work by activating adenyl cyclase (a so-called second messenger) in a G-protein-coupled manner to generate cyclic-AMP. This acts by a phosphorylation cascade to activate glycogen phosphorylase and inhibit glycogen synthase as described in any standard text. (Protein phosphatase 1 is also involved.) Glycogen breakdown is this enhanced.

Insulin acts differently. Binding to its receptor activates the protein's intrinsic tyrosine kinase, and there is a chain of subsequent protein self-phosphorylations culminating in the phosphorylation of protein phosphatase 1. This causes it to dephosphorylate both glycogen synthase and glycogen phosphorylase leading to the activation of the former and the inactivation of the latter. Glycogen synthesis is thus enhanced.

It is important to understand the molecular mechanisms of metabolic control, but it is also important to see them in the context of integrated tissue metabolism. So I will end with a question for the reader. The brain (which is absolutely dependent on glucose for part of its metabolism) stores a small amount of glycogen. How would you expect the brain to respond to the three hormones mentioned, and what would you expect to be the fate of the product of glycogen breakdown? If you have understood the principles above for liver and muscle you should be able to make rational predictions for brain (even should the facts turn out to be more complex).


How can some animals see ultraviolet or infrared light? I know that some animals like birds, bees, and fish can see ultraviolet and infrared light. Whether it to detect flowers that bare nectar, or the urine trails of prey. But what I don't understand is how they see these wave lengths. What is different about their eyes or brain that allows them to see different wavelengths than humans?

Short answer
In mammals dedicated UV cones have been found, as well as photoreceptors with secondary peak-sensitivity in the UV range. In fact, human blue cones are sensitive to near-UV.

Background
In humans, the visible spectrum is generally accepted to range from 390 to 700 nm. Figure 1 shows the spectral sensitivities of the various photoreceptors in humans.

Fig. 1. Spectral sensitivity of the four receptor classes. Source: Wikibooks; Sensory Systems

In invertebrates sensitivity to near-UV is quite common. However, some rodents (mice, gerbils and gophers) also feature a peak in sensitivity at 359 - 511 nm. In these rodents and some marsupials sensitive to UV, it is thought to be attributable to a specific dedicated type of cone sensitive to near-UV (Jacobs et al., 1991; Winter et al, 2003). Near-UV is referred to as UV-A, encompassing 315 - 380 nm.  Likewise, birds seem to feature a dedicated, fourth cone class to detect UV (Benett & Cuthill, 1994).

In the color-blind flower bat, UV sensitivity has been attributed to a photoreceptor with two peak sensitivities - one in the green range and the other around 365 nm and running down to 310 nm (Winter et al, 2003). Hence, UV sensitivity can be conferred by cones with broad sensitivities ranging into the UV range.

In fact, the aphakic human eye (eyes with the lens removed after cataract surgery are aphakic) has been shown to be sensitive to near-UV. As can be seen in Fig.1, the blue cones are actually pretty sensitive to near-UV. It appears that the lens absorbs much of the UV, rendering UV light useless to humans with healthy eyes (Griswold & Stark, 1992).

Infrared (IR) sensitivity in snakes is mediated by pit organs and not via the eyes. Basically they are heat sensors and likely don't mediate vision as such (Newman et al., 1982). I was not able to find any relevant information on IR vision, or how it should be mediated. In fact, I doubt IR is used for vision at all, i.e., to reconstruct the visual scene. IR vision is used more in the form of general heat detection.

References
- Benett & Cuthill, Vis Res (1994); 34(11): 1471–8
- Griswold & Stark, Vis Res (1992); 32(9):1739-43
- Jacobs et al., Nature (1991); 353: 655-6
- Newman et al., Sci Am (1982); 246(3): 116-27
- Winter et al, Nature (2003); 425: 612-4


What is the purpose of segments in citrus fruit? What is the purpose of segments in citrus fruit? In my experience, each segment usually contains from zero to two seeds. I may have seen more but only rarely, if I remember correctly. What is the significance of having more than one seed in a section (twinning, perhaps)?

The citrus fruits belongs to the type hesperidium, which is a highly specialised form of berry specific to citrus fruit.The hesperidium is derived entirely from the ovary, other features of hesperidium includes leathery exocarp, spongy mesocarp and juice filled endocarp.

A hesperidium has an aromatic, leathery exocarp (the "rind") and mesocarp and endocarp compartmentalized into sections (each representing a carpel), each of which contains one to several seeds. This type of berry is unique to the genus Citrus, which produces fruits such as oranges, grapefruit, lemons, limes and tangerines.
Reference

The edible flesh of a mature citrus fruit is divided into segments, each derived from an ovary locule.The number of segments varies widely but is typically between 10-15.Each segment is surrounded by tough endocarp membrane and filled with tightly packed juice sacks or vesticles.
Reference

There are several major ways that plants have evolved for constructing
fleshy fruits.  Oranges etc. belong to the family Rutaceae, which is
unique in filling its "locules" (the spaces in which seeds are produced)
with "juice sacs" that are attached to the outer walls.  Each of the
locules probably represents a structure, known as a "carpel" that was
at one time distinct, but now has fused together with its neighbors.
The outer skin of most citrus fruits shows no sign that the interior
is sectioned, but this is a result of the fusion occurring early in
the development of each individual fruit (rather than early in the
evolution of the Rutaceae).
Reference

Segmentation inside the citric fruits are due to its development from the ovary, as each of the segment is evolved from the ovary locule, the number of segments varies according to species(also there are variations in single species). The evolutionary advantage of segmentation inside the fruit is a debated topic.A digestible theory is that, each segment of the fruit contains 1-3(in the case of orange) seeds, due to the evolution by mammalian resources, keeping segments inside the fruits helps in the dispersal of seeds by different agents. When a ripe fruit falls down, its individual segments are broken apart and eaten by different animals/birds, this in turn helps in dispersal of seeds to geographically distinct(distant) locations. With each segments featuring seeds inside them, its a good adaptation to produce a single fruit which can be distributed by different agents.


Why and when did the misconception about the Panther being a species came to be? People usually call Panther to any big black cat, I did so until I watched Apocalypto and saw they were calling the black cat Jaguar which confused me. (I have no biology knowledge)

It was kind of a shock to discover that Tigers and Lions, among other species, where Panthers too, and it surprises people when I tell them.
Does anybody know why and when this started to happen?

* I realize this might not be related to biology but actually to popular culture, but I couldn't find a stackexchange site about that.

Tigers, lions, jaguars and leopards are all different species. Panthera is a genus, a broader classification than a species. Tigers are Panthera tigris, lions are Panthera leo, and jaguars are Panthera pardus. In each cases the formal species name is the genus followed by the species identifier. Common usage rarely gives a hoot about the subtleties of biological classification.

Your initial impression was correct: any member of the Panthera genus that has a black coat is called a black panther. In South America they are jaguars, in Africa and Asia they are leopards.


When glucose production is low, the brain begins using ketoacids as energy... how does that work? Can someone very generally describe how the brain consumes ketoacids/ketone bodies when blood glucose has been completely depleted?

Human body is a glucose driven machine which intake carbohydrates and converts to glucose. Energy is yielded from the glucose and glucose is stored as glycogen. When the carbohydrate intake is somehow reduced then body will shift its mechanism and uses the fatty acids to produce energy. Liver synthesis ketones from fatty acids in our diet or from body fat. Ketones(acetoacetate and acetone) are released into the blood, which is absorbed by the brain and synthesis energy from it through their mitochondria.

  The brain gets a portion of its energy from ketone bodies when glucose
  is less available (e.g., during fasting, strenuous exercise, low
  carbohydrate, ketogenic diet and in neonates). In the event of low
  blood glucose, most other tissues have additional energy sources
  besides ketone bodies (such as fatty acids), but the brain has an
  obligatory requirement for some glucose. After the diet has been
  changed to lower blood glucose for 3 days, the brain gets 25% of its
  energy from ketone bodies.After about 4 days, this goes up to 70%
  (during the initial stages the brain does not burn ketones, since they
  are an important substrate for lipid synthesis in the brain).
  Furthermore, ketones produced from omega-3 fatty acids may reduce
  cognitive deterioration in old age.
  Reference

When the body starts using the fatty acids for energy production, one problem arises in the brain and that is the blood-brain barrier( barrier that separates the circulating blood from the brain extracellular fluid in the central nervous system )Brain cannot use long chain fatty acids because they cannot cross the blood-brain barrier due to their bondage with albumin. As ketones are medium chain fatty acids which can effectively cross the barrier. So brain uses the ketones for energy production by a process called as ketosis. The ketone bodies are then incorporated into acetyl-CoA and used in the citric acid cycle.

  The citric acid cycle – also known as the tricarboxylic acid (TCA)
  cycle or the Krebs cycle – is a series of chemical reactions used by
  all aerobic organisms to generate energy through the oxidation of
  acetate derived from carbohydrates, fats and proteins into carbon
  dioxide and chemical energy in the form of adenosine triphosphate
  (ATP).
  Reference

Metabolic pathways to produce energy


What is a proximal deletion breakpoint? I am reading a journal paper about the relationship between NCAM2 and autism. I have come across the following statement in the paper:

Based on analysis utilizing the UCSC Genome Browser (hg18, build 36),
there are a total of 19 genes deleted (Table I). The proximal deletion
breakpoint falls within the NCAM2 gene, deleting 17 out of 18 exons,
and the distal deletion involves 16 out of 17 exons of the GRIK1 gene.

I am not sure what is meant by a 'proximal deletion breakpoint'. I have tried searching for the definition online but I have not found anything useful. Any insights are appreciated.

Let's break down the phrase as one of the commenters mentioned:

"proximal": in the context of chromosomal position, this means closer to centromere, whereas "distal" means further from the centromere (the middle part of the chromosome).
"deletion": referring to the genes that were deleted as a result of the chunk of the 21st chromosome being missing.
"breakpoint": points at which the chromosomes was broken leading to the deletion.

Putting it all together, this says that the deletion breakpoint closer to the centromere falls within the NCAM2 gene, whereas the deletion breakpoint further from the centromere falls within the GRIK1 gene, with the other 17/19 genes falling between the two.
To verify this interpretation, we can use the USCS Genome Browser as mentioned. We can see that if we compare the position of the NCAM2 gene and the GRIK1 gene on the 21st chromosome, the former is closer to the centromere than the latter. We can also do a spot check to verify that ADAMTS1 one of the other 19 deleted genes, lies between the two of them.


What are some examples of animals who return to their group when threatened? Based on my (limited) understanding, some types of animals use the power of numbers for protection. I assume, then, some species instinctively respond to threat by immediately seeking out and joining their group/hive/pack, not just to hide or get lost within the group (eg., school of fish), but to also take advantage of the power of the organized group (eg., swarm of bees).

Is this correct? If so, what are some examples?

[Note: It is my understanding that bees don't flee to their hive in response to threat, but rather sting and release a pheromone to draw the other bees to the area and create a swarm.]

The specific description you have made is almost the definition of mobbing behaviour. Some animal species protect each other and especially their offspring by cooperatively attacking and harassing a predator. Individuals can't cope with the predator alone, but by working together they increase the chance of discouraging the predator.

African buffaloes, bison, gulls show mobbing behaviour.

Wikipedia article

The Royal Society for the Protection of Birds article


How does myopia develop, exactly? Recently I was reading about myopia and I understood a few basic facts about it:

Its initial cause is a constant spasm in the ciliary muscle.
To do less work, the eyeball elongates a tiny bit.
During adolescence, because of the growth of the body, the eyeball elongates accordingly even more (i.e. the myopia is progressing).

I don't really understand the exact mechanism responsible for the transition from 1 to 2. Is the ciliary muscle capable of elongating the eyeball on its own, or is there some other relationship to another part of the eye?

It's weird how myopia is so common these days and I wasn't able to find the precise mechanism behind it.

What is myopia?

Myopia, a.k.a. near-sightedness, is a refractory error. Refraction is the process by which the optical system focuses images of objects on the retina, which are then transmitted to the visual cortex of the brain to be interpreted as vision. The major components of refraction are the cornea, lens, and axial eye length (distance from the front to the back of the eye). The problem in myopia is that images are focused anterior to the retina, like this:

Causes of myopia

You ask

  I don't really understand the exact mechanism responsible for the transition from 1 [ciliary muscle spasm] to 2 [elongation of the eye].

Swelling or spasm of the ciliary body (the circumferential tissue inside the eye composed of the ciliary muscle and ciliary processes) can be seen after trauma or in inflammatory conditions. Contraction of the ciliary muscles causes decreased tension in the suspensory ligaments that connect the ciliary body to the lens. The results in the lens becoming more rounded and/or moving anteriorly. Both processes also bring the focal point more anterior, i.e. further in front of the retina, resulting in a configuration as in the picture above. You could call this "elongation of the eye" if you wanted. This 25 second video explains the process succinctly with pretty pictures.

Although interesting to think on, this is not the most common cause of myopia. Myopia is an extremely common condition, occurring in ~25% of the US population. Myopia can be classified as axial or refractive.

Axial myopia: The refracting power of the eye is normal but the length of the eye is too long.
Refractive myopia: The length of the eye is normal, but the lens refracts too much for a given axial length.

Both axial and refractive myopia are usually caused by failure of emmetropization, the process by which a normal eye has coordinated growth of its refractive components. This process takes place mostly during the childhood and adolescent years, which is why myopia most commonly develops during that time. There are certain disease conditions that increase lens power (e.g. osmotic effects in diabetes) or axial length (e.g. posterior staphyloma) that may also cause myopia at other points in life, but these are less common.

References:

1. Neil J. Friedman and Peter K. Kaiser. Essentials of Ophthalmology. © 2007, Elsevier Inc.

2. Albert, Daniel M., MD MS.Albert & Jakobiec's Principles & Practice of Ophthalmology, Third Edition. © 2000, 1994 by W.B Saunders Company, © 2008, Elsevier Inc.

Image from:
http://en.wikipedia.org/wiki/Myopia


What does completing the course of antibiotics accomplish? This article from the WHO states:

If you stop treatment early, there is a risk the antibiotics won’t have killed all the bacteria that made you sick and that it will mutate and become resistant. This will not happen to everyone – the problem is that we don’t know who can safely stop treatment early.
By taking the full course prescribed by your doctor, even if you start to feel better earlier, you increase the chances of killing all of the bacteria and reduce the risk of resistance.

A clarification: When the WHO says “[bacteria] will mutate and become resistant,” they are implying that using antibiotics will create a selective pressure for bacteria with antibiotic resistance, arising most likely through mutations, correct? Bacteria don’t mutate into antibiotic resistant strains in direct response to a drug; rather, antibiotic resistance is a phenomenon of natural selection. I’ve seen so many articles about antibiotic resistance worded this way and I feel it’s simply wrong.
Presuming I am correct about my first point, here’s my true question: Say you’re prescribed a course of antibiotics. It kills the bacteria that are not resistant to the drug and leaves those that have developed resistance behind. What does it matter if you finish the whole course? Taking more of the drug is not going to affect the resistant strain. The only rationale I can think of is that it would kill off any residual non-resistant bacteria and prevent them from gaining resistance (via conjugation) from the resistant kind. However, is this the only mechanism whereby completing the course prevents the rise of antibiotic resistance? How effective is this at all at preventing the rise of antibiotic resistance in the bacterial population in general (i.e. beyond the host)?

As anongoodnurse has mentioned in the comments, an incomplete course can allow weakly resistant stains to expand their population. As you mentioned, the antibiotic does not cause the bacteria to mutate but it kills all strains that do not carry the mutation that provides the resistance. This mutation arises randomly (in certain cases it can be acquired by other mechanisms such as lateral gene transfer).

Many antibiotics may not kill the resistant strains but may still impose some burden on their growth. Now, with a complete course you would have eradicated a lot of strains and only a few resistant strains would be left. The probability of extinction/eradication depends on how many individuals are present in a population. When you do a complete course, then the chances of extinction of the resistant strains due to random birth-death process (and attack by the immune system) would be high because of their low numbers. However, with an incomplete course these bacteria would quickly expand their population once the antibiotic is gone. Now, with the next round of antibiotic treatment you would have more number of resistant cells to kill. As again pointed out by anongoodnurse, this will lead to further selection of resistant strains.

There are additional factors:

Microbial community (colony) is more than just a bunch of cells. They can protect each other.
There are some cells called persisters which are not really genetically resistant strains but tolerate the drug because of adaptive phenotypes. Again, the microbial community plays some role here.


Design primers for PCR from given DNA sequence Given the sequence: 5’-ACTGACTATGTAGAA………GGCCCTAAGGGCCAA-3’ (1)

  You wish to do PCR of this dsDNA to add “overhangs” on the ends of it. On the 5’ end of you will put
  the restriction site for NdeI enzyme (5’-CATATG). On the 3’ end you will put the restriction site for
  MspI (5’-CCGG).

  Write the sequences for the “forward” and “reverse” primers to do this. Each primer should be
  16-18 nt, with 12 nt for hybridization and 4-6 nt for “overhang.”

I am a little confused as to how to write the forward and reverse primers for the sequence. From videos I have seen that I should write the complementary strand of my giving strand. Which would be:

3' -TGACTGATACATCTT........CCGGGATTCCCGGTT-5' (2)

Then from sequence (1) I should get the reverse primer and from sequence (2) the forward primer. However, this doesn't allow me to add both restriction enzymes because both primers would be from 5' to 3'.

Or should I simply take the given sequence and write the primers only from that? So that the primers would  be:

Forward primer:
3’-GGCCTGACTGATACAT

Reverse primer:
GGATTCCCGGTTGTATAC - 5’

where the bold nts are the restriction sites.

Thank you!

As alluded to in the comments above, remember that you are amplifying double-stranded DNA, so two new reverse-complementary strands should be generated from each cycle (so that they can hybridize with eachother). As written in your answer, both of your primers would hybridize with your given strand, and the amplicons would not overlap (one binds at each end, but both would amplify in the same direction).

To fix this, you need one primer that binds to the target sequence, and one that binds to the reverse complement strand, which you have already generated. The new amplicons extend 5' -> 3' (from the 3' end of the primer not the template), so your primers need to bind at the 3' end of their respective strand. This link has a good visualization of that.

The 3' end of the primer needs to hybridize tightly with the template, since this is where extension happens (discussed in this guide). So your flanking restriction site sequences will always be on the 5' end of either primer (notice that the question actually hints at this by indicating the 5' end of each flank).

The primer given as your "reverse" primer will actually bind to the 3' end of your given sequence in the appropriate orientation, but you may want to put your 3' flank onto the 5' end of that primer sequence, as this will be theoretically be the 3' end of your amplicon. Your other primer will need to bind the 3' end of your reverse complement (which is the 5' end of your target), so it will have the 5' restriction site flank.

By convention, primer sequences are always given in 5'---3' format. So make sure your answer follows that convention. (i.e. 5'-Flank-primer-3').

If all of this sounds really confusing, that's because it is. I do this for a living, and I still always double check my primers in a program like Primer3


Why don't we consciously know how our bodies work? Our brain is responsible for regulating our entire body. However, we don't consciously know how our bodies work — our brain has to learn about biology, medicine, and so on.

Since our brain controls our bodies at the cellular level and below, why do we still have to learn about these processes to become doctors, biologists, etc.? Shouldn't we born knowing about them?

We aren't even conscious about most of what goes on in the brain; consciousness is still a poorly understood phenomenon, but we know that lots of brain activity happens without conscious awareness. Most of the regulatory functions of the body are included in that unconscious activity, but so are some processes closer to what we do in day to day life.

For example, if you are experienced driving a car, you are able to do most of the general functions (staying in a lane, for example, or starting to brake at an intersection) without much conscious input, allowing you to have a conversation simultaneously. You only need to be strongly aware of what's happening in an unexpected situation.

Similarly, muscle movements come from executed programs that are learned through experience (especially for humans; some of these movements are innate). When you raise your arm above your head, for example, there are muscles in your arm that move, but also muscles in your back and legs have to adjust to prevent you from tipping over with the new weight distribution, you might lean your head out of the way, all of this is done without you thinking about each one of those individual muscle movements: you are just conscious of the whole general movement.

Regulatory control of things like your heart rate or digestive system is even less out of conscious control.

Brain-computer analogies are often fraught with peril, but in this circumstance I think it is reasonable to use one. Your computer is able to perform all sorts of calculations, it even has software that allows you to communicate with a network of other computers to bring you StackExchange. But even if you download computer science books onto your computer, it has no idea how to assemble a new computer from scratch. It can display the text that could allow someone else to do it, but it is completely unnecessary for its day to day functioning to know how to replicate it.

So back to your original question: why don't we consciously know how our bodies work? Because it's completely unnecessary to know this for normal day to day operation in the context in which animals evolved.


Why doesn't the Tsetse fly wipe out all animal life in its range? The Tsetse fly, which is native to interior West Africa, carries the protozoan that causes sleeping sickness, a disease which was apparently invariably fatal before the advent of modern medications. Since there is no way to prevent being bitten by this insect, why did it not kill off all animal life in its range?

Firstly, as Remi.b points out, although Trypanosoma brucei infection is invariably fatal in humans, the same is not true for all host species. Domestic animals, particularly cattle, are an important reservoir of T. brucei rhodesiense and can also be infected with T. brucei gambiense (although it's not clear how important a reservoir they are for gambiense).

Secondly, just because all hosts in an area get bitten by tsetse does not mean they all become infected with trypanosomes. Even in endemic areas most vectors never become infectious. The transmission cycle for trypanosomes and other insect-borne pathogens requires an incubation period in the insect vector which is often longer than the median lifespan of the insect, so even if all insects get infected early in life, most will never survive the incubation period to become infectious. The later the average time of first infection gets, the higher the fraction of hosts that will have the opportunity to reproduce before dying.

Finally, it isn't entirely accurate to say "there is no way to prevent being bitten"; it is possible to reduce exposure to insects behaviourally. For example, in most endemic areas local residents will know which areas are most heavily infested and should be avoided.


How to search NCBI in bulk for a list of accession numbers? I have a large ( >100 ) list of accession numbers I want to look up and match to searches in NCBI (nucleotide); mainly for getting a tentative organism to match to the accession number.

ex:

KJ841938.1 would match to  Setoptus koraiensis
...
FJ911852.1 would match to  Uncultured eukaryote
...

I googled for tools, and I found this site. However, it is not what I really want since it doesn't list my queries in the same order as my list, which means I cannot match.

I also attempted to write a script in biopython using Entrez E-tools, but was unsuccessful due to a lack of coding skill.

Does anyone have any way I can go about this?

EDIT:
Via this tutorial,
I attempted to use this code sample:

from Bio import Entrez
Entrez.email = "Your.Name.Here@example.org"
handle = Entrez.efetch(db="nucleotide", id="AY851612", rettype="gb", retmode="text")
print(handle.readline().strip())
# expected output : LOCUS       AY851612                 892 bp    DNA     linear   PLN 10-APR-2007
handle.close()

But modified to take any list instead of the variable id, as below:

import Bio
print (Bio.__version__)
from Bio import Entrez
import time

Entrez.email = "Your.Name.Here@example.org"
id_list = ["KJ841938.1", "FJ911852.1"] # real list is about 500 elements

x = 0
while  x < len(id_list):
    handle = Entrez.efetch(db="nucleotide", id=id_list[x], rettype= "uilist", retmode="text")
    #print(handle.readline().strip())
    print(handle.readline())
    handle.close()
    x = x + 1

Output is:

1.69
673539906

283462561

However, I do not believe I am using the right "rettype" parameter in the .efetch function as I keep on getting GI numbers, where as I was hoping to get something like a species name directly. Unless I can then search with these GI numbers in batch with more code or a tool to produce a ordered list?

After some trial and error in an interactive python shell and some documentation checking, I found that the relevant information is present in the genbank-formatted output (rettype="gb"), and this can be parsed using Entrez.read provided it is returned in "xml" mode (retmode="xml").

The following code that seems to work:

#!/usr/bin/env python3

from Bio import Entrez

Entrez.email = "Your.Name.Here@example.org"
id_list = ["KJ841938.1", "FJ911852.1"] # real list is about 500 elements

for accession in id_list:
    handle = Entrez.efetch(
        db="nucleotide", id=accession, rettype="gb", retmode="xml")
    gb_record = Entrez.read(handle)
    handle.close()
    organism = gb_record[0]['GBSeq_organism']
    print("{}\t{}".format(accession, organism))

(You don't need a while loop here, by the way: python has the more convenient forsyntax to loop over the elements of a list.)

Output:

KJ841938.1  Setoptus koraiensis
FJ911852.1  Gaeolaelaps aculeifer


Do free computer programs exist that can simulate drug permeability in bacterial membranes? I am currently trying to make derivatives of a molecule in hopes of making it more permeable to bacterial membranes. I wanted to know whether there are any bacterial membrane simulating programs where I can dock my molecule and see its predicted permeability. Preferably, I would like to use a free program that is known to be trustable/accurate.

I currently work alongside many biologists that are working on simulations of anti-microbial peptides in the membrane. These seem to have a similar mechanism to what you're aiming for — permeating the lipid bilayer.

Presumably you have already come to the conclusion that molecular dynamic simulations are probably a good shot for exploring what may happen at an atomistic and energetic level. Software does exist, but an accurate answer from a project like this may take months of work and time on an HPC cluster.

The only software recommended by PDB (trustworthy) explicitly for membrane systems is GROMACS (and it's free!). This has documentation on membrane simulation which I have used in the past. However you will need to be very careful in both designing the simulation and analyzing the output, and an expert guiding hand may be required.


Is it normal for corn to have multiple ears in one place? I have (out of a mistake) one corn plant in my greenhouse and it made 4 cobs in one place. Is that normal?! I never saw something like this before.

This is an example of "bouquet ears", or, what Rob Nielsen coined in his 1999 paper2 as "MESS Syndrome" (Multiple Ears on Same Shank).

Although not a regular occurrence, MESS syndrome is not unusual. According to Nielson in an online article in 2014 [my emphasis included]:

  The fact that multiple ears sometimes develop from a single ear shank is not, in and of itself, physiologically unusual. Ear shank development essentially replicates the developmental pattern of the main stalk of the plant.

  The ear shank is comprised of nodes and internodes just like the main stalk (Fig. 7).
  Each node of an ear shank develops a leaf just like the main stalk, although we add the adjective "husk" to describe them.
  The ear shank terminates with a reproductive organ (the female ear), akin to the main stalk terminating with a reproductive organ (the male tassel).
  Secondary ear shoots can develop from individual nodes of the ear shank just like secondary ear shoots develop from individual nodes of the main stalk.

  Normally, hormonal apical dominance exhibited by the primary or apical ear on an ear shank (or simple competition for photosynthates) suppresses the initiation or development of secondary ears at lower shank nodes. However, under certain conditions or with certain genetic backgrounds, one or more secondary ear shoots not only initiate successfully, but also continue to develop to the point that their presence is clearly noticeable. While the appearance of one or more additional ear shoots with silks emerging from lower husk leaves may not be attractive, I do not believe there is any real detriment to the plant by their existence as long as the primary ear is "normal".

Bonnet (1966)1 described this phenomenon in the 1960s and reports of occurrences happen periodically. In 2006, Nielson3 coined the "bouquet" terminology to describe multiple reports of out-of-proportional growth of multiple ears from single shanks in numerous midwestern farms.

According to Nielson:

  The common factor in affected fields was some sort of damage to the development of the primary ear prior to or during silking that seemed to "set loose" the continued development of the secondary ears on the same ear shank.

An Ohio State University article suggests that the cause of this phenomenon is unknown, but Nielson is probably the expert:

  Some hybrids may be genetically prone to developing multiple ears on a single ear shank. A threshold genetic trait may be triggered by particular stress events that occur during primary ear formation. Dr. Bob Nielsen at Purdue University has proposed that more than one external “trigger” may enable the development of multiple ears to occur on these hybrids. He has observed that the “bouquet” effect is more prevalent and severe in late plantings where silk clipping by rootworm beetles prevented kernel set on the primary ear (i.e., possibly minimizing or negating apical dominance against secondary ears).

You can see an image of a buoquet from one of Nielson's studies below:

 Source: Purdue Dept of Agronomy; Credit: Bob Nielson (2014)

1  Bonnet, O.T. 1966. Inflorescences of Maize, Wheat, Rye, Barley, and Oats: Their Initiation and Development. Univ. of Illinois, College of Ag., Agricultural Expt. Sta. Bulletin 721.

2  Nielsen, R.L. 1999. What A MESS! (aka Multiple Ears on a Single Ear Shank). Corny News Network, Purdue Univ. http://www.kingcorn.org/news/articles.99/990823b.html

3  Nielsen, R.L. 2006. A Problem with “Bouquets”. Corny News Network, Purdue Univ.  http://www.kingcorn.org/news/articles.06/Bouquets-0912.html.


What is the silencing mechanism of AGO1,AGO3 and AGO4? I read that this proteins lack of slicer activity, so what mechanism do they use to perform gene regulation?

Paper by Zinovyev and colleagues in the paper "Mathematical modeling of microRNA‐mediated mechanisms of translation repression" find that there are reports to 9 different mechanisms of miRNA-regulation.

Here we are reviewing available experimental data on all reported mechanisms of microRNA action, grouping them in a way which elucidates the main details supporting each of these proposed mechanisms.

Here is a list of the mentioned mechanisms:

M1: Cap-40S Initiation Inhibition
M2: 60S Ribosomal Unit Joining Inhibition
M3: Elongation Inhibition
M4: Ribosome Drop-Off
M5: Co-translational Nascent Protein Degradation
M6: Sequestration in P-Bodies
M7: mRNA Decay
M8:  mRNA Cleavage
M9: Transcriptional Inhibition Through microRNA-Mediated Chromatin Reorganization Following by Gene Silencing

Also here is a link to the Pubmed entry of the article:


Do we see protocells forming in nature today? I understand that the current theory of chemical evolution (aka origin-of-life, abiogenesis) involves lipid-based protocells that enclosed RNA and perhaps some other compounds as a first steps to life.

I'm wondering if we see them forming today in nature, and if not, why not?

Are there successful experiments creating them in the lab?  If so, what are the required conditions?

we don't think so, some parts are easy. liposomes form all the time, and can be created in lab quite easily. They quite readily encapsulate any material present when they form, including DNA. So once you have the right stuff (likely RNA) it is very easy to enclose it in a lipid bilayer.

RNA has been formed from scratch in a lab it is even easier than we thought it would be. It may form in nature today but is unlikely , the current atmosphere is very oxygen rich and bacteria are not just willing to eat RNA but also basically all the chemical building blocks as well so it is difficult to build up a concentration of the precursors in nature today.

Worse evidence of it forming today would be ridiculously difficult to demonstrate because there is a constant contamination of biologically produced RNA from decaying organisms. There is basically no place on earth today with liquid water that does not have life in it.

Here and here is a review of our current models for the origin of life, Be warned the first link is a file.There are many steps involved to get a cell but it all starts with the first molecule capable of making copies of itself.

Forming self catalysis strands in a lab would be the best evidence but has one major problems, Money. Earth had an ocean sized laboratory and millions of years to stumble one of the strands with one of the right codes through random chance (remember a self-replicating code only has to happen once) which likely could only be duplicated by doing millions or even billions of identical laboratory experiments.

Finally while the Urey-miller experiment was historically important it is by no means the best nor most modern experiment you can find much more information about the variety of experiments at this question.


Why do rod cells not recover after bleaching upon exposure to bright light? I'm confused as to why, when exposed to bright light, rod cells cease to function.

I understand that exposure to bright light bleaches all of the rhodopsin, but...so what?

Why doesn't the bleached product (opsonin/retinal) simply get re-synthesised back into rhodopsin as it does in dim light, and therefore continue the cycle and enable impulses?

In rods, the pigment Rhodopsin can be thought of as a receptor protein with a pre-bound chemical agonist. The receptor protein is called Opsin, and the pre-bound agonist is called retinal. The absorption of light causes a change in the conformation of retinal so that it activates the opsin, causing bleaching (changing the wavelengths absorbed by rhodopsin). The bleaching of rhodopsin stimulates a G-protein called transducin in the membrane, which activates phosphodiesterase, which in turn breaks down cGMP that is normally present in the cytoplasm of the rod and is necessary for the continued operation of the sodium channel sending the neural signal.

In the dark, the channel is open and the Rods are active. In bright light, due to the amplification from cascade effects, all of the channels will be closed, stopping the Rods from sending their normal signals until cGMP levels are able to compensate.

My guess is that the bleaching ceases fairly quickly and the proteins return to their original conforms shortly, but it's the cGMP levels (remember, cGMP is destroyed, so any new cGMP replacing the old must be synthesized first) that take a long time to recover as the signal to destroy the cGMP initiates a cascade that results in strong amplifications.


Why violets die in closed space? I'm going to engage into making florariums (a garden in the bottle). I've heard, that violets die in closed space. Do anyone have an idea, what is the reason for that? Lack of oxygen or CO2 or moisture control issues?

I believe it is because of the violets' propensity to collect mould. They seem to like warm, humid space, but in a closed-air type system, it is a breeding ground for mould.

There could be another reason as to why they die, but I would assume it would be an infection.


Forward versus backward numerical simulations in population genetics My question is closely related to this post.

There are a number of existing platforms to perform numerical individual-based simulation in populations genetics. An almost exhaustive list of such platforms can be found here.

Some program (like NEMO or SFS_CODE for example) perform forward simulations while some others (like SIMCOAL2 for example) perform backward (coalescence) simulations.

I am asking for a general comparison between those two methods.

Is one faster than the other? Why? (Intuitively I'd expect backward simulations to be faster).
Does backward simulation allow for simulating selection, recombination, gender-specific mutation rate, complex demography, etc..
Why/when would one use a backward/forward simulation model rather than the other type of model?

Your intuitions all seem correct. Coalescent simulation should be faster, because you don't track the entire population history over all t generations as you do in the forward simulation. Rather, as you work backwards in time you are tracking a smaller and small population. And with coalescent simulations it is probably very hard to incorporate the full range of evolutionary processes whereas doing so is more or less trivial with forward simulations.

A 2008 paper by Carvajal-Rodriguez reviews some of the issues in more detail that I have here.


Dnase data for GRCh38 This question below turned to be completely faulty. I don't have to do anything with DNase data for GRCh38.
I asked it because of the file count difference between hg38 and hg37, which I thought to be too big.
For hg38 there're 95 files *Peak.txt.gz. For hg37 there're 236 *narrowPeak.gz, but after merging pairs PkRep1 & PkRep2 (probably FASTQ(SE/PE) reps) we get only 123 files. Finally, this difference (123 & 95) no longer seems to be big and we have even cleaner situation without PkRep1 & PkRep2.

One again: there's no problem with DNase data for GRCh38 assembly and only my question was misleading. I'd like to apologise for the confusion I introduced.

I'm interesed in transciptional activity, thus I'm willing to use DNase hypersensitivity sites to detect regions where transcription factors are allowed to bind.
In previous genome assembly GRCh37 / hg19 I used to use narrow peaks files from these two sources (University of Washington and Duke University, respectively) (files with suffixes .narrowPeak.gz):

http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeUwDnase/
http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeOpenChromDnase/

With the most contemporary assembly GRCh38 there're also some annotations attached (files with trailing Peak.txt.gz):
http://hgdownload.soe.ucsc.edu/goldenPath/hg38/database/

And here four complementary question arise:

Consider only datasets, which come from University of Washington.
For GRCh38 / hg19 I counted 236 narrow peak files, whereas for newer
GRCh38 there're only 95 files. How to explain this differene? Do the datasets represent exactly the same coverage, but with much lower granularity / precision (datasets that come for several tissue lines are merged into fewer files)?
With GRCg37 / hg19 we have both narrow peaks as well as broad peaks,
whereas GRCh38 comes with only one type of of file *Peak.txt.gz.
Does it mean that with the newest version we have only narrow peaks? Are the broad peaks hidden somewhere else?
With GRCh37 / hg19 we have two separate sources of DNase data: UofW and Duke. For GRCh38, it seems that only UofW datasets are availabe. Is any other source of DNase data available, maybe stored separately (Duke or other lab)?
Let's suppose that you're in my place and you would like to determine cis-regulatory areas. What type of data can be used to do so? Mabey DNase datasets but from other source or even completly different type of data (NOT DNase)?

Thank you in advance for your answer.

I am unsure that how much does it answer your question, but have a look at the homepage of DeltaSVM - In his work, Lee et al. have used similar datasets (only for GRCH37 I reckon) and I am sure they will address some of your queries.

As you yourself can better determine which dataset(s) are ones you need.


What is the significance of the amplitude of brain waves? What does the amplitude of brain waves represent and to what neuronal activities is this amplitude related to?

For example, in a hypothetical situation, the frequency of brain waves is kept the same, but the wave amplitude is increased or decreased by some means. What would be the effect on human brain and its activities?

Can resonance related to waves effect human brain waves?

Brain waves are a colloquial term for EEG recordings. EEG recordings are gross potential recordings, in other words, they represent the responses of thousands of neurons together. Much of the background is stochastically determined, meaning it represents, basically, random activity. In other words, oftentimes neurons fire stochastically and they cancel out each others contribution to the EEG, resulting in random ('white') noise.

However, certain parts of the brain do exhibit synchronized activity in certain frequency bands (Fig. 1). Oftentimes a time-to-frequency analysis is necessary to visualize these activities, e.g. by FFT. Certain stages of sleep, most notably slow-wave deep sleep, is characterized by gross synchronized, slow-wave activities across the cortex, showing up in the raw EEG. In fact, simply closing ones eyes has marked effects on the cortical synchronization on the raw EEG. This is caused by the fact that certain swaths of the cortex become synchronized, resulting in more clear responses.

In general, to come to your question, gross potential recordings like EEG thus are plagued by background noise. Synchronized activity increases the amplitude of brain waves and hence increases their amplitude. Amplitude is hence directly related to the level of synchronization. This shows up as larger peaks on the FFT.

What synchronization exactly does to the brain boils down to down to is the classic interpretation of EEG frequency bands (Table 1).

Fig. 1. EEG waves. source: Neurosky

Table 1. EEG frequency bands and their associated mental state. source: Liang et al. (2016)

Reference
- Liang et al., J Sensors (2016), 1831742


Is there a downloadable list of all species along with their traditional classification? I'm looking for a downloadable list of all known (or better said, online documented) species in this straightforward format, as an example the European Frog:

Kingdom: Animalia
Division: Chordata
Class: Amphibia
Order: Anura
Family: Ranidae
Genus: Rana
Species: Rana temporaria

I'd like to have this list offline in a structured text or database format that I can parse. If such a list has other useful attributes (credits, distribution, common names), it is welcome, but the above is the absolute minimum I need. I know there are more taxonomy levels in the above system (Species 2000 & ITIS Catalogue of Life: April 2013 ), but I only need these basic 6.

I have been looking at several collections and am having difficulty getting the data that I need. Either the database is incomplete, not accessible as a database, has complicated data structures, or has poorly described and slow web APIs that I cannot rely on.

Conceptually, it seems to me that http://www.catalogueoflife.org comes closest to my needs, yet their download is a tool, not a database. I do see the download has a MySQL folder containing lots of files, but I don't know how to reconstruct it into a database. It seems also that they pulled the page to describe the database download.

As simple as my question seems (I'm not a biologist, so I assumed it was simple), I have wasted several hours coming up empty. I was wondering if anybody has done the work to provide basic species info in an understandable format as requested?

As for species completeness, the 1.3m of Catalogue of Life sounds really good, as I'm currently basing my system on Wikipedia, which only has 180k species.

ITIS
To expand on the answer from @Rodrigo, the Integrated Taxonomic Information System seems to be a reasonable answer. It gives you a database of every taxonomic node in the Tree of Life™ and each node has a Taxonomic Serial Number (TSN). You can download the data as a database or even just use the tables as pipe-delimited text files.
For example the file vernaculars tells us that the TSN for European frog is 173444. By looking in the file longnames, we can see that the complete name for 173444 is Rana temporaria. Finally, the file hierarchy, tells us that the complete hierarchy for 173444 is:
European frog:
→ Animalia
→ Bilateria
→ Deuterostomia
→ Chordata
→ Vertebrata
→ Gnathostomata
→ Tetrapoda
→ Amphibia
→ Anura
→ Ranidae
→ Rana
→ Rana temporaria
You may download the ITIS data in several different formats from https://www.itis.gov/downloads.
Example data from some of the files.
If you downloaded the MySQL Tables data from https://www.itis.gov/downloads/itisMySQLTables.tar.gz, here are some of the useful files you'll find.
vernaculars
The first two fields of vernaculars contain the TSN (Taxonomic Serial Number) and the common name for a node. Some common names map to a single TSN and not all TSNs have common names. Some examples:
50|bacteria|English|N|2015-03-02|159942
14092|mushrooms|English|N|2004-01-28|11529
14092|pore fungi|English|N|2004-01-28|11534
14092|rusts|English|N|2004-01-28|11539
183798|Domestic Cat|English|N|2007-08-15|111781

You can read the file dropcreateloaditis.sql to see what the other columns mean.
longnames
The file longnames contains only two fields, the TSN and the complete "scientific" name of the item. For example:
50|Bacteria
14092|Basidiomycotina
183798|Felis catus

hierarchy
The first field of the file hierarchy is the entire hierarchy, from tip to tail, for the TSN in the second field represented as a sequence of TSNs separated by a hyphen (-). E.g.,
50|50|0|0|14789
555705-936287-623881|623881|936287|2|415
202423-914154-914156-158852-331030-914179-914181-179913-179916-179925-180539-552304-180580-552363-180586-183798|183798|180586|15|0

Of course, those TSNs can be looked up in the longnames table to convert to something human readable. Using that, here is what the first field of the last line above actually says,
Animalia → Bilateria → Deuterostomia → Chordata → Vertebrata → Gnathostomata → Tetrapoda → Mammalia → Theria → Eutheria → Carnivora → Feliformia → Felidae → Felinae → Felis → Felis catus

synonym_links
The synonym_links file contains a list of obsolete TSNs and the current TSN which should replace them. For example, the TSN for the vernacular mushrooms is given as 14092 (Basidiomycotina). However, that TSN is not in the hierarchy file. Instead, synonym_links points us to use 623881 (Basidiomycota), which works. Here are some synonym entries:
51|50|2015-03-02
14092|623881|2004-01-28
552455|180586|2007-08-15

Example script
If you have access to a UNIX machine (MacOS may work), you can look up any common name by using a simple script I wrote. It will print a taxonomy, just like for European frog above. You can cut and paste the following commands into your terminal to try it out.
① Download the data
    wget https://www.itis.gov/downloads/itisMySQLTables.tar.gz
    tar -axvf itisMySQLTables.tar.gz
    cd itisMySQL*/

② Download my lookup.py script
    wget https://tinyurl.com/yyld2mm4 -O lookup.py
    chmod +x lookup.py

③ Run the script giving it a common name
    ./lookup.py human
    ./lookup.py domestic cat
    ./lookup.py love-lies-bleeding

④ Sample output
    human:
     → Animalia
     → Bilateria
     → Deuterostomia
     → Chordata
     → Vertebrata
     → Gnathostomata
     → Tetrapoda
     → Mammalia
     → Theria
     → Eutheria
     → Primates
     → Haplorrhini
     → Simiiformes
     → Hominoidea
     → Hominidae
     → Homininae
     → Homo
     → Homo sapiens