text:
Is the golden ratio of anatomic measurements universally beneficial for sexual reproduction?
It is often claimed, in popular media, that the golden ratio (Fibonacci series) in for example some face measurements is aesthetically (and hence reproductively) attractive with respect to the humans. I wonder if this is particular for today's (maybe since 10,000's of years or so) human anatomy and aesthetics. As in an after-the-fact acquired taste which was culturally psychologically emerged from the actual human ratios of anatomy as it has happened to evolve for whatever practical reasons, way out of intuitive comprehension for human consciousness. Or if it is deeply biological and evolutionary across all lifeforms.
Is the golden ratio as verifiable (if at all) also for the primates who came before us? Some examples are easy to find, but how statistically significant is the golden ratio as an advantage for sexual evolution for non-humans?

Interesting question. However, I'd consider removing the subjective sections in your second paragraph, as the question is not really about what you individually find attractive but what the pool of reproductively capable peers find desirable in a mate.

This question seems fairly opinion-based

Perhaps Cognitive Sciences is more receptive to this kind of flavor of questions.

@AliceD I'm actually interested in the anatomy of animals here. If anatomies in general somehow follow the golden ratio, it would indicate that animal minds do too.

I have some insight into this from an engineering perspective. Years ago, I worked on some computational and search algorithms which used a growth factor to determine how far to widen a search space, or how much more memory to allocate. For some of these algorithms, I ran a series of random trials to search for an optimal growth factor and found that for some, that optimal factor approached 1.618. Completely outside the realm of biology, this golden mean popped up as the most efficient growth factor for some of my algorithms.
Over the years since then, I have recognized that this is an optimal factor for various engineering problems (although in practice I often find it more trouble than it's worth to explain to colleagues why they want to use it). So, I have gradually come to believe that this is why we see this ratio all over nature, in the way seashells grow, the proportions of various body parts, the arrangement of seed structures, etc. It appears that natural processes have ultimately led various lifeforms all over our world to tend toward this ratio to optimize their own functions.
The fact that we find this ratio "beautiful" is a far more fascinating study of what appears to be a meta-adaptation. Consider all of the various ways that the recognition of this ratio is important beyond just sexual reproduction. When we select our food, choosing vegetable or meat specimens which exhibit this ratio most perfectly is more likely to lead us to the healthiest food. Choosing to take shelter under the more "beautiful" tree may also be a good bet that it is the more stable, healthy tree. Making tools, clothing, or structures with this ratio may serendipitously lead us to better formed products. We don't have to find this ratio more beautiful to look at, but because we do, it leads us to better choices in a variety of odd ways. So, those of us whose brains appreciate these things thrive a little more.
Your question was not about underlying causes, but about statistical prevalence. I don't know the answer to that. And I think it would be very difficult to obtain those numbers accurately for another loosely related reason. The human brain is very attuned to finding patterns, and the human brain likes to find this ratio. Taken together, this presents a huge bias for any researcher trying to objectively measure the frequency of this ratio in nature.
However, once we understand that this ratio is the solution to a variety of engineering problems, it becomes clear that it is an indicator of optimization, or proximity to perfection. And even if we cannot say how many species are able to recognize, appreciate, and seek after this ratio, we can say that those who do will most likely find it a beneficial adaptation.

Are those who don't "find this pattern" really less successful?  Our brains do like to find this particular ratio pattern (imagination maybe). Me, I do care. But I wonder if my dog cares about it. If my dog's loved one, which it has only sniffed and never seen, has a Fibonacci nose length to leg length or something. Maybe I'll subject my best friend to some experiments... However I know that it would cheat, just like I would. Could the Golden Ratio be a human cultural construction without relevance to common natural structures?

@LocalFluff Relevance to common natural structures is precisely what I'm trying to demonstrate here. If this number turns up as an optimal growth factor in a context in which the ratio itself is not the target at all, per se, then it certainly carries meaning independent of the observer. The algorithms preferred it. In most contexts, our own preferences muddy the observation, but in this case, I would consider it an objective finding. And if it is objectively useful, then the ability to recognize it must be beneficial (provided said ability does not cost the organism more than it's worth).


text:
Transformation of E.coli cells
As a team of undergraduate researchers we are looking to transform two genes of interest into our competent E.coli cells. Both of these genes are in separate plasmids and we would like to put them into one E.coli cell. Is it possible for a cell to uptake two plasmids in one transformation process? If the last question is impossible, is it possible to transform a cell and then transform the colonies produced by that transformation? Thank you very much.

Do your two plasmids have two different selectable markers?

Yes, we are giving one amp resistance and one kan resistance.

I have done the second protocol (transforming a cell with more than one plasmid) before, and it works. The cells will be resistant to both antibiotics. The chemical competency procedure will have to be carried out with the original transformed cells.

Double transformation (you may want to try searches using that term) has been doable for a very long time, but the efficiency will of course be less than either single transformation below the saturation point.
As for your second question, if the cells are made competent, I think a sequential transformation should work.

The two plasmids have to have different origins of replication. Two plasmids from the same incompatibility group cannot survive together in the same cell.
This quote is from the Wikipedia entry on plasmids:

Plasmids can also be classified into incompatibility group. A microbe can harbour different types of plasmids, however, different plasmids can only exist in a single bacterial cell if they are compatible. If two plasmids are not compatible, one or the other will be rapidly lost from the cell. Different plasmids may therefore be assigned to different incompatibility group depending on whether they can coexist together. Incompatible plasmids normally share the same replication or partition mechanisms.

This is a nice definition/explanation from an OpenAccess article:

A formal scheme of plasmid classification is based on incompatibility (Inc) groups (Novick, 1987). The procedure for incompatibility grouping is based on the introduction, by conjugation or transformation, of a plasmid of an "unknown" Inc group into a strain carrying a plasmid of a known Inc group. If the resident plasmid is eliminated in the progeny, the incoming plasmid is assigned to its same Inc group (Datta and Hedges, 1971). Plasmids with the same replication control are "incompatible", whereas plasmids with different replication controls are "compatible". On this basis two plasmids belonging to the same Inc group cannot be propagated in the same cell line (Datta and Hughes, 1983; Couturier et al., 1988). Inc group identification has been frequently used to classify plasmids. The method has been an important tool to trace the diffusion of plasmids conferring antimicrobial resistance and also to follow the evolution and spread of emerging plasmids (Anderson et al., 1977).

A synopsis of the Datta & Hedges article sited above:

Classification
  In 1971, Hedges and Datta proposed a plasmid classification scheme based on the stability of plasmids during conjugation (incompatibility). The first incompatibility (Inc) groups were IncI, IncN, IncF, and IncP. Currently, 26 Inc groups are recognized in Enterobacteriaceae including six IncF (FII to VII) and three IncI (I1, Iγ, I2) variants. Major plasmid families occurring in Enterobacteriaceae are HI2, HI1, I1-γ, X, L/M, N, FIA, FIB, FIC, W, Y, P, A/C, T, K, B/O, FII, FIII, FIV. IncF plasmids are low- copy-number plasmids, often carrying more than one replicon with at least one replicon strongly conserved.

One way to think about it is: if one prepares a plasmid cloning vector to accept foreign inserts, and ligates in a "shotgun" collection of fragments from some DNA of interest. After transformation, selection and growth on plates, none of the colonies whose plasmids carry inserts ever contain more than one unique plasmid.  In other words, it may be possible for two different inserts to be inserted during ligation into the same vector backbone, but it is not possible to find a single pure colony that contains cells harbouring two different plasmids, each carrying a different insert.
If a cell gets transformed by two different plasmids from the same inc group only one plasmid will "win."
Examples of compatible ori's commonly encountered:

pMB9 (pBR322, pUC, etc., etc.,)
F (fosmids)
bacteriophage lambda (some cosmids)
bacteriophage M13 and relatives (also yields ssDNA phage useful for Sanger sequencing)
bacteriophage P1
pSC101

Here is a nice table, but I cannot vouch for its accuracy.

I've only ever done single plasmid transformations, where do I find incompabtibility groups?

source: Stack Exchange

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

sort: votes


text:
Why does the human brain not overfit when training at some task?

One of the problems that occur during (artificial) neural network training is called overfitting. The error on the training set is driven to a very small value, but when new data is presented to the network the error is large. The network has memorized the training examples, but it has not learned to generalize to new situations.

The human brain does not overfit when training at some task, but becomes better and better instead. Why? Or conversely, why do artificial neural networks overfit when are trained too much, in contrast with the human brain?

What do you mean with "over fit"?

@AliceD Overfitting is when an artificial neural network captures too many extraneous features of the data during training so that generalization becomes nearly impossible.  It's an interesting question, but it's unclear, I agree with both of you.

(parenthetically, I'm not convinced that we don't "overfit" at times, especially in the developing brain, but analogies between ANNs and BNNs are often stretched to the point where such shared features become muddled)

@jonsca - aha, interesting stuff, I never heard of this!

@jonsca - hopefully OP clarifies the question, I am getting curious :)

@emanuele It seems we edited at the same time. Feel free to revert my edit and/or make any changes you want.

@posilon your english is 1k times better then mine :)

any answer at this point would be a speculation...be prepared to be disappointed!

I'm voting to close this question as off-topic because this is not so much of a Biological problem, would be better suited to CogSci SE

I would say the human brain overfits all the time! Gambling addiction,  superstition and anxiety disorders are all examples of overfitting. We are optimized for seeking patterns and avoiding threats. Our brains mess this up all the time! But having said that, one of the major differences between the human brain and a neural network is the amount of information that's being supplied. Neural networks overfit in part because of limited data sets. Our brains have nearly unlimited data sets available, and this provides us at least the opportunity for some self correction.

Do you think that since human brain works on a reinforcement learning basis, this is another factor that outperforms supervised learning in ANN regarding issue of overfitting?

@Shelly DeForte Well, forget for a moment such complex behaviors. At simple classifications  tasks, human do not over fit. Or I am wrong?

I'm having trouble thinking of a good side by side comparison. The problem is that we're actually pretty terrible at most things you might use an ANN for because we can't really hold more than a couple of dimensions in our minds at a time. Can you give me an example of a simple classification task that a human outperforms on because the ANN is overfitting and the human is not?

characters recognitions...

I don't think we're better at character recognition because we don't overfit. I think we're better at it because our architecture is immensely better for shape recognition and differentiation. But I admit I don't know how ANNs do character recognition, so I'm not sure what the differences are.

In this fascinating talk by Geoff Hinton, there is the strong implication that the human brain doesn't overfit, because it implements a version of dropout, i.e. of randomly leaving out signals to prevent coadaptation between neurons.
Dropout has been found to be extremely effective in combatting overfitting.
Basically the idea is that the human brain has a stochastic activation of neurons, which is pretty similar to the dropout technique (except that dropout is usually done with the same probability for each neuron).

Like any statistical learner, the human brain surely experiences overfitting. You can convince people of lots of things that aren't true by showing them biased examples.
That said, don't forget that the tasks by which we judge artificial intelligence are largely defined by what human brains happen to be good at. That's part of what makes us call it "intelligence" instead of just high-performance computing.
In other words, in your character recognition example, don't forget that the character system was developed by humans. Presumably we converged on systems of characters that our neural architectures are good at recognizing and discriminating. And in general, the computer vision tasks that we consider "intelligent" often match tasks that our brains and visual systems have evolved to do well.
So, in a sense, the concepts and tasks that serve as our tests are also inherently "overfit" to our brains.

I'm confident we now have a good hypothesis on this topic: dreams are constantly working to reduce the overfitting and improving the generalization. https://www.sciencedirect.com/science/article/pii/S2666389921000945
If this is true, dreaming should be present in much more animals than we currently know.

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

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


text:
How is magnet based bead capture used for indexing-first ChIP-seq?
The context for this question can be found in this paper by Lara-Astiaso et al. Here, to study H3K4 trimethylation in various cell types, they

sort cells
sonicate chromatin
immobilize chromatin on Dynabeads with anti-H3 antibody
index chromatin (barcode)
pool samples
perform H3K4me3 ChIP
sequence

I am interested in the purpose of step 3 and how that contributes to step 4. What could be the purpose of essentially adding another IP step?
The Supplementary Materials (where all the methodology is described) mentions that "magnet based bead capture was used to efficiently add, wash and remove the different master mixes used in the indexing process." I'm somewhat confused as to how this adds efficiency, as I thought barcoding prior to sequencing is typically performed as a ligation step without selection (they perform the H3K4me3 ChIP after the indexing step anyway, so why select for histone H3 chromatin first?).

The anti-H3 antibody will precipitate all chromatin. They could have used an antibody against any histone monomer; that they initially used anti-H3 and then later used anti-H3K4me3 is coincidental. The idea behind the first immunoprecipitation is to immobilize the chromatin on the beads to allow easy recovery of pure chromatin after each step of the indexing process. These steps are seen in the supplementary material (4. Chromatin Indexing). For example, after immobilization, they perform end repair using specific buffers and T4 polymerase and polynucleotide kinase. These enzymes must be removed and the buffer exchanged before the subsequent indexing. The beads facilitate this process.


text:
Independent contrasts with non-homologous traits
I have morphometric measurements for a trait across various animal taxa, and would like to study the relationship between the size of this trait, and the animals' body size. In theory, I can account for the non-independence of the data with phylogenetic methods, such as independent contrasts. However, I have very different groups in my tree (for example insects and lizards), and even within more closely related groups, the structures are often not homologous. Does this present a problem for independent contrasts/phylogenetic least squares? For example, the structure is not homologous between ants and flies, yet every (sensible?) tree would treat a fly as more similar to an ant than to any lizard. Is this justified if structures are analogous? If not, are there any ways to deal with this problem?

When you calculate the PICs of a trait, you are making a statement about how that trait evolved. The purpose of PICs is to account for phylogenetic non-independence. However, when you are comparing two analogous traits, those two traits are in fact phylogenetically independent of each other; they do not share a common origin. Thus, I think it would be an error to perform PICs on analogous traits because those traits do not share an evolutionary origin.
How to deal with this problem would depend on the specific question you are asking. One thing to do would be to perform PICs on each trait independently on just the portion of the tree that that trait occurs so you are not comparing analogous traits.
You've probably already read this, but I recommend that anyone who hasn't check out Felsenstein's 1985 paper describing the PIC method.

Thanks, that makes sense! I think your suggestion is similar to one of the ideas I had: Essentially, I could build groups within which the trait in question is homologous, and connect all these groups to the tree root. As a consequence, the shared branch length between groups across which the trait is analogous would be zero, and thus the data would be treated as effectively independent in a PIC or phylogenetic least-squares, or am I missing something?

@David I'll admit that I'm not entirely sure about this, but I think you should not set the branch lengths between the groups to zero because that would imply that the groups are highly related. On the contrary, setting the branch lengths to be very large would imply that they are effectively unrelated. However, if it were me, I would perform separate analyses on each group and not try to get them to fit into a phylogenetic analysis together

Sorry for being not very clear: I was talking about the branch length shared between groups were the trait is non-homologous being equal to zero, so that there are effectively independent, or unrelated. Does this make more sense to you?


text:
Which tree's root can grow the longest?
In a recent TV documentary it was said that a particular tree root could grow miles long. However, I forgot the name of the tree and I don't remember the name of the TV program as well.
I searched on the internet but I could not get it. Does any one know about this tree and how tall its roots can grow?

Was it Pando? Pando is a single, massive 80,000 year old Populus tremuloides (quaking aspen) individual, that is assumed to have a single, massive root system covering 106 acres.

I suspect it's not.. I heard that the root of this tree can travel many miles

@Learner Since it was a TV production they would definitely state the total lenght of the root system, just like the blood vessels in a human can reach the moon or something like that. It sounds cooler like that.

Possibly yes :)


text:
How do roots obtain nutrition?
I have a city garden (see picture below) and yesterday I was nurturing it and I pulled up a small plant. I saw its root system and laid it in the sun to dry a bit and then scraped of the rest of the earth from it. That gave me a better look at the roots.
Then the question appeared to me. How do roots obtain water and nutrition from their environment. I mean what structures are we dealing with and how does the uptake take place?

In short: root hairs allow the roots to have a massive surface area and thus they can obtain nutrients efficiently through active and passive transport. Most plants also have mutualistic relationships with fungi (mychorrizae) which further effectively increase the area of the soil the roots can obtain nutrients from

@CactusWoman. Thank you for enlightening me. Active and Passiv transport was just the words i was looking for. Im a physiotherapist and i know these terms from physiology. Great to see them take place in plant life too.

One thing to remember when you uproot a plant is that some of the smallest structures break off most readily. What you will usually see after uprooting the plant are the largest parts of the root structure, but there are often smaller parts which have broken off.
Root structure is highly variable, but the general idea is that one or more primary trunk-like roots strike out toward the most important water sources, and from those primary roots, smaller roots branch out to more broadly extend the reach of the root system. Depending on the size of the plant, there may be several levels of branching, just like you might find in the top of the plant.
Generally, the nutrients that the roots need to absorb come to the plant with the water. They become dissolved in the water and the roots receive them along with the water. Dirt is chunky, not usually homogenous. So there may be more of the nutrients a plant needs in one area than another, or water may flow more easily through one area than another. The roots are able to grow in the direction of what they need, which is why you will occasionally find strong roots actually breaking into plumbing to get at your water. Mostly, it is water that they seek out, because the nutrients are dissolved in the water, but the plant may also show preference for a more nutrient rich stream over a larger, cleaner water source. The roots seek out what the plants need over the long term as they grow in one direction or another. For many plants, by the way, this means a simple strategy of simply growing straight down (a tap root), because that's almost always where they find more water with more of the right stuff dissolved into it. This is similar to the fact that many plants grow as tall as they can because that's where they will find the most sunlight, though the general idea is to grow leaves wherever that light might be captured.
Wherever the primary roots land themselves, the smaller root branches extend into the space to absorb as much as they can. They have to be greedy, because there may be days or even weeks when the ground gets very dry, so they have to get while the getting is good. If one of these smaller branches finds itself in a particularly sweet spot, the plant grows that branch more than its neighbors, which is part of how it places its largest root branches in the best spots.
Another very interesting part of the story of how the plant covers the maximum area of soil is symbiosis. I recommend further research into mycorrhizae, especially if you are a gardener. It's absolutely fascinating, and an important part of the story of how plants absorb nutrients and water.
I know less about the microscopic porous cellular structure of the roots. They essentially serve the same purpose as our stomachs and there are some loose similarities to be drawn, but of course, plant tissues, made of plant cells, which have a cell wall are very different from animal tissues.

Thank you so much for this interesting and comprehensive answer Mark. I get the feeling that you have an profound interest for nature, just like myself. I didnt know about this "behavior" of the roots and their abilities or the symbiosis-relationship. Im so happy to have gotten this information. Natures workings is fantastic to unravel. See this news here about plants using quantum physics to harvest light: http://www.bbc.com/news/science-environment-22996054

Oh my god. It says that the fungus colonizes the host plant's roots, either intracellularly or extracellularly. And then this: "This mutualistic association provides the fungus with relatively constant and direct access to carbohydrates, such as glucose and sucrose. The carbohydrates are translocated from their source (usually leaves) to root tissue and on to the plant's fungal partners. In return, the plant gains the benefits of the mycelium's higher absorptive capacity for water and mineral nutrients due to the comparatively large surface area of mycelium. This is fascinating stuff.

The uptake system in plants is much more complicated, as Mr.Baily opened the anatomy and structure of root I will only explain the uptaking system.
When plant absorbs water it absorbs some elements too, but there is a broblem which is soil particles as the clay is a cloidical substance of soil wich does contain negative charge and most of essential elements are cathionic forms sothey won't solve in water. When a plant want to absorb nutrients it releas protons in soil ,these protons will be replaced by cathions and that's when plant can absorb them .
For negative charged elements it is much more easier cause they don't need proton exchange.
Plants also need some sugar, bigg ionic compounds,and organic matters ( mostly acids ) to uptake these paticles it's even more complicated, as there is some protein channels wich use energy to insert these materials inside the roots ( last system works based on aqua-porous)
Then plant never uptake cathions more than it needs,elemental and metal poisoning is based on flaccid and turgor pressure and parallel exchange or protein channel damages

Thank you for an in-depth scientific answer. Is it really true that plants release protons? Thats amazing. They behave quantum mechanically!


text:
Gene frequency problem
1/330 of white people in south Africa have a disease called Porphyri.
What is the frequency of the gene if it is inherited autosomal recessive?
This one I understand. The genotype frequency is $q^2 = 1/300$ so the gene frequency has to be $\sqrt{1/300}$ as it is inherited aa.
What is the frequency of the gene if it is inherited autosomal dominant?
Here's where I run into trouble. So if $p$ is the dominant allele and $q$ is the recessive allele the allele frequency has to be $2pq+p^2$ right? As we can either see them as $Aa$ or $AA$
But my book says it is $2pq+q^2$ and then solves it algebraically by pretending $q^2$ is so close to 0 that it's not in the calculations. I strongly disagree that $p^2$ is very small if I am right with my assumption. Which one is right and why?

I think you pretty much nailed the problem. If I understand correctly, you are just confused about the approximation that your textbook does, an approximation that is correct but not needed. Let me restate the solution to the problem first.
Resolution of the problem
As you said the frequency of sick people $\frac{1}{300} = 2pq + q^2$ in this case, where $q = 1-p$ is the frequency of the (dominant) allele causing the disease. It is a simple quadratic equation with one unknown so one doesn't even need to make any approximation to solve for $q$ (or for $p$). Rearranging the equation and expressing $p$ in terms of $q$ (p=1-q), you'll find that $q ≈ 0.00167$.
Are the results intuitive?
Note that the frequency needed to create a frequency of $\frac{1}{300}$ sick individuals is $\sqrt{\frac{1}{300}} ≈ 0.05$ if the allele causing the disease is recessive while it is only about $0.0016$ (31 times less frequent) if the allele causing the disease is dominant. It shows that indeed a lower frequency of a more dominant allele is enough to create more sick people.
Is the $q^2$ term negligible?
Given that $q ≈ 0.00167$, the term $q^2 ≈ 10^{-6}$, while the term $2pq ≈ 10^{-3}$. So, indeed the term $q^2$ is rather negligible compared to $2pq$.

You write 1/300 = 2pq+q^2 like my book. I say that it's 1/300 = 2pq+p^2 because p is the dominant allele and 1 out of 300 people has Aa or AA. 2pq + p^2. I don't understand why you have +q^2 in the end as we're not counting the recessive aa's ?

It is just a matter of what you want to call q and what you want to call p. They are totally interchangeable. In my example, I called q the frequency of the dominant allele causing the disease. But if you feel more confortable the other way around, you can call p the frequency of the dominant allele causing the disease.

Ah, okay, I thought it was the norm in literature to denote p as the dominant allele and q as the recessive allele, but I guess I am wrong. Thank you.


text:
Walk me through microsatellite markers and PCR

Three polymorph microsatellite markers are used to try and narrow down the location of a disease locus, with the use of PCR with 2
  flanks on each side of the actual polymorphic area. The PCR product is
  then put on gel.

I need help understanding exactly what is going on here. I understand polymorph as "two or more phenotypes in the same population" as by wikipedia. I don't understand what they mean here.
How is the PCR different with 2 flanks from a normal PCR?
I do understand how the gel works.

you should probably include a link to the paper you are reading.... it will make the question easier to answer :)

It's actually very simple. You misunderstood the term polymorph in this situation.
A microsatellite is a sequence composed of a short repeated DNA motif. A polymorph satellite is then simply a microsatellite varying in length between individuals, i.e. composed of more or less repeats of the motif.
The flanks of the microsatellite are the DNA sequences targeted by the primers.
In essence, using two primers flanking the microsatellite you will amplify this specific DNA region and because it varies in length between individuals you can detect that by simple gel electrophoresis.
Note: Microsatellites can also change in sequence (also called polymorphic regions) as they are prone to a high-mutation rate but in this case you would need to sequence it to know how the sequence differs between individuals.


text:
Which are mobile and immobile elements in plants?
I am confused with this element or nutrient classification in the plants, since some authors set elements like $\ce{S}$ and $\ce{Ni}$ as mobile and other as immobile elements (Citation 1, 2, 3, 4, 5). Then, I would like to know if there is a fixed and official classification.
I found a classification only for those elements:
Mobile: $\ce{N}$, $\ce{Mg}$, $\ce{P}$, $\ce{S}$, $\ce{K}$, $\ce{Ni}$
Immobile: $\ce{B}$, $\ce{Ca}$, $\ce{Mn}$, $\ce{Fe}$, $\ce{Co}$, $\ce{Cu}$, $\ce{Zn}$.
And what about the rest of elements like $\ce{Sr}$, $\ce{Mo}$, $\ce{Ba}$, $\ce{Pb}$, etc?

Can you cite a source for the classification you provided?

Here the citations, I couldn't put those in my question because at the moment my profile doesn't accept more than two links.
[1]: http://www.gettextbooks.com/isbn/9780824759049
[2]: http://msue.anr.msu.edu/news/knowing_nutrient_mobility_is_helpful_in_diagnosing_plant_nutrient_deficienc
[3]: http://broome.soil.ncsu.edu/ssc012/Lecture/topic17.html
[4]: http://lee.ifas.ufl.edu/Hort/Videos_and_Powerpoints/Presentation2013.pdf
[5]: http://nrcca.cals.cornell.edu/soilFertilityCA/CA1/CA1_print.html

What does it mean for an element to be mobile or immobile in plants?
Generally speaking, mobile elements are those that can be moved from older to newer tissue in the plant, while immobile ones cannot be.  For example, calcium is incorporated into the cell wall, so it is immobile; it cannot be relocated later.
Plants with a deficiency of mobile elements (e.g. nitrogen) will exhibit damage to older tissues as the nutrients are moved to new growth.  Conversely, plants lacking immobile elements (e.g. calcium) will exhibit stunted new growth while the older tissues stay healthy.  Some elements (e.g. sulphur) fall somewhere between these two extremes.
To illustrate this concept, here is a plant with nitrogen deficiency (source).  Notice how the older leaves are dying while the newer leaves are relatively normal.

Is there an official list of mobile and immobile elements?
No.  You will probably have to survey the literature yourself to come up with such a list.
Why are some elements not mentioned in the sources?
Elements like Pb or Sr do not appear in either list because plants generally do not use these elements at all.  Only a few elements are present in any appreciable quantity in plants.  The main elements used are C, H, O, P, K, N, S, Ca, Fe, and Mg. Additionally, some other elements such as Cu, Mo, Al, and Cl are used in trace amounts as enzymatic cofactors.  Si is used for structural purposes in some plants.
However, even for "useless" elements, it is possible to study how these elements are absorbed transported through a given plant.  For instance, Rediske and Selders (1953) studied the absorption of strontium by bean plants using radioactive labeling, and concluded that the element was not relocated once absorbed by the plant.  This would make it an immobile element.
References

Feldman, Lewis J. (23 February 2015). Water Relations, Mineral Nutrition, Fruit Development (YouTube). Biology 1B. UC Berkeley. At 32:20.
Lewis, Helen (9 January 2012). "Mobile vs Immobile Nutrients". Mastering Horticulture.
Rediske, J. H. and Selders, A. A. (October 1953). "The Absorption and Translocation of Strontium by Plants". Plant Physiology 28 (4): 594–605. PMC 540423.


text:
What does this sentence about angina mean?
I do not understand this sentence

Angina can be treated ambulatory (in out-patient) in stable angina.

I think ambulatory refers to ambulatory ECG monitoring.
So I understand this that you can treat stable angina based on ambulatory ECG.
How do you understand this sentence about "ambulatory" angina?

Ambulatory means there is no emergency and refers to where the patient is and not to the disease itself (here the patient is out of hospital): for example there is no need to perform urgent PTCA or other procedures related to unstable CAD (coronary artery disease).
Another option is - to hospitalize the patient to perform urgent or planned procedures if the patient is unstable and there is deterioration in his/her status which may worsen and threaten the life.
The key word in this sentence "stable".


text:
Are there examples of now-living species where one is descended from the other?
I'm not a biology person so please forgive me if this question is formulated badly :)
I'm curious. Are there any species that has an ancestor species that is still alive today?

http://www.quora.com/Is-there-any-species-living-today-that-is-the-ancestor-of-any-other-living-species

Wolves and dogs.. They are still considered the same species though

No - Current thought is that today's domestic dog did not descend from today's non-dog wolves. Today's domestic dog is thought to descend from a now extinct ancestor that shares an ancestor with the latter group. (Source.) They are sister taxa.

First, you probably want to have a look at this answer who offers an introduction to phylogeny.
There are few subjects that yield to as many semantic issues than the definition of species and the definition of life. Your question is at the border of the species definition. Therefore the question is not so much about Biology than about Philosophy, so let me advertise: The following answer is nothing else than a semantic discussion (philosophy) and has no relevance of our understanding of evolutionary processes. Note however that semantic discussions might be very helpful to evolutionary biologists as well.
Sexual organisms
Dogs and wolves
Commonly, in sexual organisms, species are defined by the concept of reproductive isolation. To address @WYSIWYG's comment, wolves can breed with dog. The offspring is called a wolfdog and it suffers from little inbreeding depression.
Is the MRCA contained in the species of one descendent
But I'll use the wolf example anyway and make some fake assumptions. There are probably better examples I could pick though. Let's assume that wolves and dogs can't interbreed, that they are therefore separate species. Let's assume as well that somehow we manage to recreate the Most Recent Common Ancestor (MRCA) of wolves and dogs (which has some issue due to parental effect incl. culture and epigenetic) and this MRCA can breed with wolves and but not with dogs, then we could eventually say that the MRCA and the current wolf (there are actually 3 species of wolves that are probably not real species) are the same species. One could then say that dogs descend from the wolf.
Feeling that we talk about improvement over another species
I think that much of the issue in saying that the dog descend from the wolf is that some people would understand things like "dog is more evolved than the wolf", which makes no sense. Both the current wolf and the current dog has evolved for exactly the same amount of time. Any living species on earth has evolved for exactly the same amount of time (almost 4 billion years).
Temporal constraint in the definition of species
Therefore, it is eventually possible to say that one node on a phylogenetic tree is of the same species that the previous and the next node. I think that in reaction to the misleading feeling that early evolutionary biologists and current non-biologists people have in hearing that one species descent from another one, we tend to make a temporal constraint to the species. A species is what it is only at the exact moment we talk about it. This prevents us to consider a MRCA as being of the same species of any descendent.
Note that the definition of species yield to various issues such as the case of ring species.
Asexual organisms
Same issue as above
Now, if we talk about asexual species. There is no clear definition of what is a species there. Of course if we were to use the definition based on reproductive isolation, then any asexual individual would be a new species. We therefore define a species when we see a group of phylogenetically related individuals that all share some feature that we find "interesting enough" (very arbitrary) to describe this group as a different species from the some other group of individuals. In asexuals, you may see one lineage that accumulate 10 mutations, while the other lineage accumulates 1000 mutations (this other lineage may eventually live in a different environment than the MRCA of the two lineages). Therefore, you get to the same issue when thinking about asexuals than about sexual organisms. If one lineage ressemble more their MRCA than the other lineage, then is their MRCA part of the species of one lineage?
Is a species a thing?
Well... the concept of species is useful in the sense that it is often not ambiguous to classify one individual into one species or into another. The concept of species allows us to better communicate.. so yes I guess it is useful.
The concept of species existed way before we had any understanding of natural selection, mutations, genetic drift and other basic concepts in evolutionary biology. Aristotle used the concept of species (he also thought without having any good evidence that life evolves). This concept of species has been very well conserved because of the static view we had on what a species is before Darwin. Now that we have a better understanding of evolutionary biology, ecology and reproductive biology we discover that the concept of species is not a natural category but is a man-made unclear category.
Other discussion on the subject
In this post I made an overview of the issue related to the concept of species. I did not go into the details but tried to list important concepts.
You will find many websites that address the concept of species in philosophy. I particularly like discussion in the concern of how current bad semantic may yield to bias in the way we think nature and may yield to bad decision in applied science. Here is a podcast (in french) on the origin and consequences of the concept of species.

Very interesting, thank you. The thought that species is a man-made categorization had never occurred to me before. Non parlez francaise I'm afraid though :(

Very nice breakdown, +1. One nitpick, however: the unit of "evolvedness", if such a thing could be said to exist, would not be years but generations. A bacterial species is way more "evolved" than a mammal since they've had several orders of magnitude more generations in which to evolve. While I agree that saying "X is more evolved than Y" is nonsensical, if you do choose to make such a statement, you need to think in terms of generations and not time. Time is irrelevant, it is only the number of generations that counts.

There's a TetZoo podcast where one of the two pod-casters, I think it was Darren Naish, observed that species as a taxonomic grouping are inherently paraphyletic.


text:
Plasmid gene knockout
How do delete a single gene encoded in an operon on a plasmid using E. coli? Could you use the same principle as when knocking out a gene from the bacterial chromosome which is e.g. suicide vector-derived allelic replacement through homologous recombination?
Thanks for your response.

So you have a plasmid and you want to delete a section? Why not do the deletion outside the cell with PCR or something and then transform into new cells?

Well the simplest way is to cut the gene at the both ends from operon by means of proper restriction enzymes but in case that you get more than one recognition site then you can use microRNA or proper oligo to make a hairpin loop and break that part
If you only want to silence the operon then you just need to cut it nearly at middle of the gene and ligate it with a nonsense sequence, then it will loose it's functionality.


text:
Pedigree problem and type of inheritance
Please observe the following pedigree and come up with a conclusion as to which type of inheritance is most likely

Now it should be obvious that it's either autosomal dominant or autosomal recessive. BUT it could also be X-recessive if the women I2 and II5 are carriers.
Anyways, if it's autosomal dominant than about 50% of children should be sick according to a punnet square.
If autosomal recessive about 25% should be sick according to the same logic.
33% of children are sick so what do you answer? My book states a very clear and only answer without explaining how it could be other forms too, autosomal dominant. Why not autosomal recessive just as likely?

1. Please read our homework policy. 2. Demanding answers generally does not go over too well. 3. Look up "incomplete penetrance". Even if a trait is autosomal dominant doesn't mean it will occur in exactly 50% of the carriers all the time. Unfortunately, Gregor Mendel fudged a lot of things, and Punnett Squares ≠ reality.

It's not really homework, I'm preparing for exams and I already have the answer as stated in the question.

That is included in the policy. Please read it.

You should use better titles... "Very easy pedigree problems annoying me heavily": what does this mean? Are you asking why some easy problems are annoying you? Keep your title to the point.

Some edits have been made. I'd prefer not to have it closed as I did receive an answer that I am satisfied with,

Let's consider all the options.
X-linked recessive:
I:2 is a carrier and II:5 is also a carrier, and I:1 is affected. Everyone who marries into this family(except for II:2, lucky guy. He better not have any sons though.) is a carrier for the same genetic disorder. While it does work, it's vastly less likely than the other options. Observe: II:4 is affected, but only because I:2 is a carrier. I:1 is entirely irrelevant.
Autosomal recessive:
I:2 and II:5 are still required to carry the same genetic disorder, but at least I:1 contributes.
Autosomal dominant: The only individual required to have the disease genotype is the affected I:1 individual, and everyone else inherits from him.
The problem asks which inheritance pattern is most likely and not which inheritance pattern is possible because of cases like this. Most genetic disorders are rare, and it's far more likely that random chance had 25% instead of 50% of four children affected than this small family inherited two sources of this genetic illness.


text:
Is there a way to examine amount of red/white muscle fibers without biopsy?
I want to conduct some research on red and white muscle fibers, why do some people tire during certain movements while others do not. Obviously I can't get a biopsy on the subjects so I wanted to hear if anyone here maybe had a suggestion to a reliable method to test for amount of red vs. white muscle fibers?
Maybe some strength measurements, endurance tests etc. I'm looking for something that is documented to work.

Indeed, a paper was published in 2011 regarding the same for humans by using a magnetic resonance spectroscopy to measure carnosine content.

A significant positive correlation was found between muscle carnosine, measured by 1H-MRS, and percentage area occupied by type II fibers. Explosive athletes had ∼30% higher carnosine levels compared to a reference population, whereas it was ∼20% lower than normal in typical endurance athletes.
Muscle carnosine content shows a good reflection of the disciplines of elite track-and-field athletes and is able to distinguish between individual track running distances. The differences between endurance and sprint muscle types is also observed in young talents and former athletes, suggesting this characteristic is genetically determined and can be applied in early talent identification. This quick method provides a valid alternative for the muscle biopsy method. In addition, this technique may also contribute to the diagnosis and monitoring of many conditions and diseases that are characterized by an altered muscle fiber type composition.

Baguet, Audrey, et al. "A new method for non-invasive estimation of human muscle fiber type composition." PLoS One 6.7 (2011): e21956.

Sounds great but not very helpful, logistically speaking. I don't have access to the kind of equipment needed. But definitely a good answer.

@Paze Anything preventing you from using model animals instead of humans?


text:
Viruses selected by evolution
I had a conversation with a family member who is a bio-computer-scientist and he talked to me about what his colleagues (biologist) are doing and I feel confused about it.
They work in Chile and are studying plants that live in the desert.
If I understand correctly his team is trying to explore the idea that some viruses might actually be good for the plants they are infecting and might have been "selected" and are now "part" of the species.
The way I understand it is that they collect samples of plants and somehow by analyzing their DNA notice that these plants are all infected by some kind of virus and they tell themselves that maybe if all the plants have the virus it's because it's good for the plants.
My questions are:
1) Does any of this make sense?
2) If it does how would the selection process happen ? Combining my (old) high-school knowledge and Wikipedia I see a virus as a piece of DNA that goes into a cell and then uses the cell's tools for it's own purpose, but it is not part of DNA. So how can it be treated as part of the plant ?
3) Do you know any good references (preferably for an enthusiast layman) about this subject (i.e. the potential good of some viruses and maybe potential applications in bio-engineering) ? I find especially fascinating the idea that maybe evolution isn't just the result of mutations but also of "contamination".

Have a look at "symbiosis"...

Thanks for you answer. I don't really understand the link though. Obviously this just means i'm dumb not that your answer is wrong. Would you mind elaborating a little bit ?

I guess Remi has answered this extensively below now :)

Welcome to Biology.SE!
About your title
Let me first react to your title: Viruses selected by evolution.
Evolution is any process of genetic (and eventually phenotypic) change in a population through time. Selection (Natural Selection for example) is one of such process. Genetic drift is another such process. So, evolution doesn't select viruses unlike your title suggests. Viruses are selected and therefore evolution occurs. When saying "viruses are selected", it means some viruses have a higher fitness (fitness is a function of both survival and reproductive success) than others.
Species interactions
Generalities
Species interact. Species actually interact a lot. One can classify species interactions into categories. Here is a table of possible biological interactions (comes from wiki), where a + indicates "beneficial", a - indicates "detrimental" and a 0 indicates it has no effect on fitness.

Note about symbiosis - comes in reaction to @Gerhard's comment
Different authors use the word symbiosis differently. From wikipedia:

The definition of symbiosis is controversial among scientists. Some believe symbiosis should only refer to persistent mutualisms, while others believe it should apply to any type of persistent biological interaction (i.e. mutualistic, commensalistic, or parasitic).4 After 130+ years of debate,5 current biology and ecology textbooks now use the latter "de Bary" definition or an even broader definition (i.e. symbiosis = all species interactions), with the restrictive definition no longer used (i.e. symbiosis = mutualism)

Blurry limits
Generalities
It may seem pretty obvious that two species should have either one type or another type of interaction. However, the reality is often more complicated than that. An interaction might fall under one set of environmental conditions and fall under another category under another set of environmental conditions. In addition to that, it is not uncommon that due to selection on the host genes due to the presence of a parasite, the host evolve so to tolerate the presence of the parasite up to the point of actually having a lower fitness in the absence of the parasite.
Example
Parasites often have different life cycle of alternance of generation. That is a parasite sometimes need several hosts such as the parasite causing malaria for example that need both mosquitoes and a primate.
Consider for example a snail which is infected by a parasite. Let's assume (I think it is a real world example but I am too lazy to find out a reference) that the parasite need the snail to be eaten in order to reproduce in the body of the predator. In order to improve the chance of its host to be eaten the parasite is selected so to diminish the thickness of the snail's shell. In response to that, there is selection (which intensity depend also on the proportion of infected snails in the population) for having thicker shells. But now, snails that are not infected have shells that are so thick that the can't move as fast as they'd need to forage. So, by the end infected snails may have a higher fitness than non-infected snails. And therefore the interaction between the snail and its parasite is not a parasitic interaction anymore but a mutualistic interaction.
Can viruses have non-parasitic relationship with their host?
Well... why not? There is nothing so fundamental about viruses that would prevent them to have mutualistic relationship with their host. Actually I am aware of a few relationships. For example, female parasitoid wasps (parasitoids: parasite that kill their host). parasitoid wasps lay their egg in other insects larvea (host), the egg (and later the larvea I think) develop in the larvea (host), eat the larvea (host) and once the host is dead, the individual metamorphose to its adult form and fly away to find a mate a parasite the next larvea. Some lineage of those wasps have viruses in their ovary that infect the host to reduce the immune defence of the host to protect the growth of their egg (Flemming and Summers 1991). This is an example of mutualistic relationship between the wasp and the virus.
Sequencing to know if the plants are infected
You said

they collect samples of plants and somehow by analyzing their DNA notice that these plants are all infected by some kind of virus

Most likely, they make a PCR (amplification of DNA, that is increasing the number of copies of a given DNA sequence) on viral DNA and look for a signal. In other words they are not sequencing (=reading) the plant DNA but only seeking for viral DNA. If they find viral DNA, then the plant is infected if not, the plant is not infected.
What is unclear to me from your text
From your post, it is unclear to me yet how your "family member" plans to decide the type of interaction there are between viruses and plants. Knowing the frequency of infected plants won't tell you much about the interaction between the plant and the virus.
They will likely have to make growth experiment and try to measure the fitness of infected and non-infected plants.
EDIT - Following the first comment
Knowing that every plant individual is infected doesn't tell much of the virulence of the virus. The frequency of infected is not an information that can help understand the "type of biological interaction" (paratism, mutualism, etc..). One will need to actually make growth experiment and measure fitness (or measure phenotypic traits that are known to be good proxy for fitness).

One question about your answer is : can we really speak of a virus as a species and especially does your table really apply in this situation ?

The concept of species is not a natural category. It is a man-made, unclear, sometimes useful, category. Typically, it is hard to even talk of species for organisms that don't have sexual reproduction (which is the case of viruses). I wrote this other answer today about the concept of species, you may want to read it. Here is another post on the subject. Btw, talking about semantic issue, viruses are often not considered as alive. All those semantic issues don't have any impact on our understanding of biological processes.
But yes, the table applies to viruses as well. This table is indifferent to the concept of species. Consider just two genotypes (two individuals), one plant and one virus, the existence of the virus has (or has not) an impact on the fitness of the plant (and vice-verse) and therefore, you can classify the relationship between those two genotypes (individuals) into one category of the table. As a reminder, the nature of those relationship may depend on the environment. Typically, a photosynthetic endosymbiote might be very beneficial to the "host" if there is some sunlight but might be quite detrimental in the absence of sun light.

Wow, thanks for your great answer. I have no idea how they decide what kind of relation there is between the plant and the virus but what I understood was that every plant has the virus somehow, like now it is part of their DNA (which doesn't really make sense hence the question) and so that's why they think it must have a beneficial effect (in other word it has been "naturally selected"). One question about your answer is : can we really speak of a virus as a species and especially does your table really apply in this situation ?

One interesting thing I found on google was : http://news.sciencemag.org/2013/05/friendly-viruses-protect-us-against-bacteria

I guess in this example what I don't understand is the relationship between the virus in the mucus and the human-being itself. Do we produce this virus ourselves ? In this case is it really still a virus  since it doesn't seem like we are "contaminated" by it. I understand that the fact that I ask this questions probably reflects my lack of understanding of the subject so I apologize.

I don't understand why you talk about human. Are you talking about the parasitoid wasp example? I don't know in details how the virus come to exist in the ovary and I don't know if anybody knows the answer. Might be an interesting question for another post.

I was talking about the example given in the link I gave : http://news.sciencemag.org/2013/05/friendly-viruses-protect-us-against-bacteria

ah ok... hum...I don't really feel like reading the whole link. I think you may have better to open a new post for that.

Ah I believe I have a found the anwser to my question : http://en.wikipedia.org/wiki/Provirus. Basically the genome of a virus can be integrated in the host's genome allowing it to be "selected". Then the host can create the virus itself.

You may want to read about the lytic and lysogenic cycles. Viral DNA that is not integrated into a eukaryote genome is not free from selection. However, if integrated repeatedly into some eukaryote genome, its expression and changes between cycle can be under selection both in the eukaryotic genome and in the viral DNA (as it may gain through hitchhiking).

As a terminology note, "lysogenic" is a term used for viruses of bacteria, not eukaryotes, although there are analogous concepts for eukaryotes.

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


text:
X-recessive disorders and genetic markers
Please observe the following pedigree of a family with a x-recessive disease (bleeder disease).

The A's are genetic markers so close to the disease gene that recombination is negligible.
I understand it like this: They place markers that cut the DNA at the locus of the diseased gene. If you have the disease, your gene snip will be different in size than someone who does NOT have the disease and this will be visible on a gel.
So if A1 is the disease marker, how come II:1 is not affected? He only has 1 X-chromosome and has the A1 marker?
EDIT: Another problem:

Is the coming child affected? Here I don't understand how the mother A1,A1 is NOT affected when the child A1 is?! Obviously A1 is the diseased marker and the mother has two of them...So she should be sick? Unless it's spontaneous?
Am I misunderstanding something fundamentally?

You might also want to read this: http://genome.wellcome.ac.uk/doc_WTD020778.html

Genetic markers don't cut the DNA. They're simply regions of DNA sequence that happen to be variable between individuals (see my answer to your previous question). They might be measured using restriction enzymes (i.e. RFLP) to identify the exact difference, but it's not the marker that's cutting DNA. Furthermore, they don't necessarily cause disease. They're used in pedigree analysis to find regions that are linked with various phenotypes, among which is disease. However, linkage does not imply causation, especially with so few individuals.
To answer your first question II:1 isn't in the affected family - he married into it. In the affected family, A1 has become linked with the disease mutation. It is not necessarily linked with the disease in other families.
This is also the answer to your second question. I:2 has A1 marker on one chromosome associated with the disease allele, and one that's not. II:3 presumably inherited an X chromosome with the disease allele.

So the unaffected individual who is married into the family has the same marker on the gene, called marker A1, in other words, if there's a sequence AGTAGTAGTAGT in the affected family and it is called marker A1, this means they have a disease, but he also has this sequence but not the disease because he doesn't have other traits the family members have? Am I finally understanding this right? (I'm really trying!)

Almost, but not quite. There is a marker A1 that has sequence AGTAGTAGTAGT where individuals with A2 with have sequence AGTAGTAAAAGT at the same A locus. Some very short distance away from the A locus, there is a disease locus - when individuals have only the m allele instead of the M allele, they have the disease. Because the A locus and the M locus are so close together (i.e. linked), meiotic recombination is very unlikely. So if the M locus becomes mutated on one chromosome that happens to have the A1 allele, the disease m allele becomes linked to the A1 allele +

(cont'd) and you will see that the disease goes along with the A1 allele in successive generations. However, it could have just as easily been the M locus becoming mutated on a chromosome that has the A2 allele - and in some other families, this might be true for the same disease. In that case, the disease will be associated with the A2 allele for that family, for the very same disease. The marker allele is only informative within the context of a single pedigree - you don't know which allele is associated for unrelated families. What you do know is that the marker is linked to the disease +

(cont'd) because the two loci are so close by -- it could just be a different allele in different families. Does that make sense?

It all makes sense and I'm drawing it up right now but it would help if you clarified what you mean by "m" and "M" allele as it is the only thing confusing me and I need that information to read the rest of the answer.

m = disease-causing allele, M = wild type allele.


text:
Chromosomal translocation notation
A reciprocal translocation is denoted for example: t(4;12)(q32;q24) indicating that q32 on chromosome 4 being swapped with q24 on chromosome 12 if I understand this correctly.
My question is: How do I know whether this is a balanced or an unbalanced translocation?

This translocation is balanced: if it was not there was a specific sign for specific unbalanced event, for example "del" for deletion or "+" and "-" signs etc.

Chromosomal aberrations have defined notations you can find here.
The notation rule for multiple genetic abnormalities is to concatenate them. In the presence of an unbalanced translocation two abnormalities will be indicated as you have both a translocation and another event (e.g. deletion, duplication, ...).
In your example, as no other abnormalities/events are indicated, this is a balanced translocation.


text:
How can omnivores be in more than one trophic level?
I'm learning about the ecosystem and its relation to organisms, So organisms in a food chain can be put from producers to tertiary organisms. I just don't get the part where an organism can be labeled into 2 trophic levels ? how is it placed in the pyramid if so ?

You just represent a species twice, once at each level of interest. In reality a food network is much more representative than the fake hierarchical food chain represented in pyramids (and you would not have to represent one species twice).

I am not sure I fully understand where is your misunderstanding. I think your whole misunderstanding boils down to the representation of food chain. Actually, let's talk about food web as the name gives an intuition of what is happening that is more correct than the term food chain.
The conceptual view of the food web in a pyramid is super misleading. This is not how ecosystem work. But anyway, if one might want to make some ugly approximations and represent food web into a pyramid, then this person is totally free to represent one species twice, at two different levels. It makes sense that a species can eat things at several level in the same time. Just like you. You probably eat vegetables, salads, fruits, rice, corn, bread, etc... but also eventually fish and meat. Note also as indicated by @fileunderwater in the comments, that some species have different relationship with their ecosystem (typically eat different things) at different life stages.
Here are few examples of more or less realistic food webs. As you can see, one can roughly make category of trophic level but it is not all that clear.

Also note that a species can use different resources at different life stages, so that e.g. juveniles in a species of fish might eat plankton while adults are piscivores - hence the species as a whole functions at two trophic levels.


text:
120-year-old gene regulation problem independently solved by a computer. How?
My Background:
I'm a mathematics graduate student with a physics background.  I have a very little biology knowledge and a little knowledge of machine learning and statistics.
Topic:
I recently found out about the following computational biology breakthrough: A computer managed to independently develop an explanatory theory for a 120 year old problem in biology. It is detailed in the popular media here and here and in great detail in this paper.
Questions:
I lack domain expertise but I'm very curious as to how this was possible and why it happened now. For example:

Were these methods novel (from a machine learning perspective), or does the novelty lie in using old tools/techniques and adapting them to a
workable problem?
Mathematically speaking, what kind of models were produced by the computer? Are these standard in this field of genetics/biology? Is this area particularly mathematically tractable?
What about this particular biological problem made it amenable to this machine learning approach?
What other kinds of low hanging fruit (in biology or science in general) could we expect to see tackled with these techniques?

Popscience blogs have a flair for exaggeration. This is not the first application of GA and machine learning has been used to solve a lot of problems in biology. I am sure that those who have written the blogs have not even read the article.

Agreed. However, my first exposure to the topic was through the popsci articles via social media so I figured I'd throw those in to give a picture of how I came to understand the topic.

I find this question to be of low quality. Details are hidden in links, and premise is speculative at best.

The fruit, sadly, does not hang so low.
Short version
Lobo et al (the work you refer to) is a nice and not especially novel application of basic Systems Biology modeling approaches to the wound healing system in flat worms. The main barrier to the wider application of such work is the lack of the necessary experimental data. Lobo et al themselves don't really have enough data to justify their models.
Long version
Lobo et al  can be thought of as consisting of two separate parts: the models themselves, and their genetic algorithm based search of modelspace.
The Models
The models aren't novel. Their basic form is extremely common in Systems Biology work. Take a look at the equations in the methods section of their paper and in their Supporting Information. For example:

This is a very standard way of representing a set of coupled biological "species". Some of those species correspond directly to the concentration of some specific protein (ie βcat), whereas other species are more vaguely constructed (ie head) and presumably correspond to some generalized set of biological elements (eg proteins, DNA, lipids, etc).
The first term in each of the equations is a production term. It represents how the given species is increasing with time. Each of the fractions takes the form of something called a Hill Function. These come up all over the place in biological modeling. The important thing to know about Hill Functions is that they vary either from 0->1 (for activators) or from 1->0 (for inhibitors) as the concentration of the relevant species varies from 0->infinity, with a smooth transition region in between.
The min and max operators are meant to stand in for the Boolean operators AND and OR, respectively. In other words, if two of the Hill Functions are related by a min operator, they both have to be in an ON state in order for the overall term to be in an ON state. If the two Hill Functions were instead related by a max operator, only one of them would need to be in the ON state for the term to be in the ON state overall.
The second term is a degradation term. It represents how the given species is decreasing with time. The particular form of the degradation term (directly proportional to species concentration) derives from the fact that the main sink for generic biomolecules is dilution via cellular growth and division.
The third term (the thing with ∇² in it) is a diffusion term. This describes how the species moves around the in the worm, assuming that it is free to do so (not all proteins/species can exit the cell). Not all of the species have this diffusion term.
The Search of Modelspace
Their modelspace search isn't novel either, although some very specific aspects of it may be.
the basics of modelspace search
The basic problem of modelspace search works like this: imagine a generalized form of your model in which every species interacts with every other species in every possible way. Each of the equations will have a (large) set of free parameters associated with it. You then take a set of experimental observations and-using any one of a number of methods-find a set of parameter values such that your model will reproduce those observations. This is called parameter fitting. In order to find the unique set of parameters that describes the "real" system that you're trying to model, in general you need at least as many independent experimental observations as you have independent parameters in your model. If you have more parameters than you have data, then you will be able to find infinitely many different parameter sets that reproduce your experimental data.
Here's where things start to get complicated, and a bit fuzzy. What separates modelspace search from standard parameter fitting is that you usually employ some version of the Principle of Parsimony in modelspace search. The Principle of Parsimony can be thought of as equivalent to Occam's Razor: the simplest model is usually best. In practice, what this means is that when you don't have enough experimental data to uniquely parameterize your model, you should favor versions of your model with as few species-species interactions as possible.
The Principle of Parsimony is one specific example of a broad category of modelspace constraints known as "prior knowledge". This refers to everything you know in advance about what your final model "should" look like. Prior knowledge tends to be specific to the particular modelsearch at hand, and it can be incorporated into the search algorithms in many different ways.
With enough prior knowledge the modelsearch can be constrained to an extent such that, in theory, it should become possible to identify the one true parameter set, even when you only have a relatively small set of experimental observations. Good luck constructing a formal proof for that, though. In fact, for a complex system it's usually impossible to quantify the extent to which a given piece of prior knowledge reduces the number of experimental observations required to uniquely fit the parameter set.
the modelspace search in Lobo
For the type of model used in Lobo, a rough calculation says there's going to be
$3n^2 + 4n$
independent parameters (where n is the number of species). Further, there's going to be a set of parameters that describes whether each species-species interaction is activating or inhibiting, and another set of parameters that describes the Boolean logic of those interactions.
The "comprehensive" model in Lobo has 14 species, so there are at least
$3\cdot14^2 + 4\cdot14 = 644$
644 free parameters to fit during the modelspace search. Against those 644 parameters they have exactly 16 independent experimental observations.

The paper by Lobo and Levin is an attempt to learn a model that represents the inner workings of a biological system by fitting parameters to data. This is a common topic in "systems biology", a model-based approach to studying biology that is popular in some fields.
Even for small systems, this is a phenomenally hard problem. Unlike most machine learning problems, achieving good accuracy (on independent test sets) is not enough; the model structure itself must be correct, meaning that it should reflect the actual mechanisms operating in the cell or organism. It's like trying to identify the actual structure of an integrated circuit by only observing inputs and outputs --- not only identifying the logical rule that the system exhibits, but the actual wiring diagram that implements that rule. It is of course quite possible that alternative models exist that perfectly captures the behavior, and yet is completely different "on the inside". In other words, the system may not be identifiable from the data --- two different models may produce exactly the same behavior. So there's no way to know beforehand if these problems are even theoretically solvable (let alone practically feasible).
Lobo and Lewin claim to have accomplished this task for a small system of gene products. As far as I can tell, the central piece of evidence is that the model they come up with does capture some mechanisms previously known to exist. Whether this is just plain luck, overfitting, or a special case where (parts of) the system is actually identifiable from data is hard to tell. But it's notable that Lobo and Levin fall back on demonstrating predictive accuracy in the end, which is not enough to support their thesis. The critical test is to take a new prediction from their model and actually test it experimentally, to prove their (strong!) claim that their method can actually infer new mechanisms. Doing so is expensive and time-consuming, and much more difficult to get right than tweaking models to fit mechanisms you already knew beforehand --- which is exactly why I want to see that evidence. If the authors really believe that their method is a breaktrough, those experiments should have been done.
Finally, these type of algorithms are applied to models containing only a handful of components, and typically to systems that have been studied for decades and are very well-characterized to begin with. In most of biology, the problems are orders of magnitude more complex, and often we don't even know what the components are. So I thik there are a limited number of cases where this approach is feasible.

Christian, great idea to ask this question here before taking important decisions.
Are those media articles a hype?
Yes. Over the last 10 years I constantly see those hype stories in media about "revolutionary" large-scale-study/big data projects with mind-blowing numbers (gigabases, teraflops, terabytes, thousands of papers and hundreds of genes).
Journalists are competing for readers with pompous titles as well as scientists need to promote their results. I recall that ~7 years ago Russian national TV came up with nothing less than "Scientists unravel the mystery of Russian soul", when one of our scientific institutions, close to government, finally managed to actually launch (not buy and put on hold as usual) a next-generation sequencing machine and sequence a full genome. Curiously "Russian soul" belongs to half-jewish, half-armenian guy. :)
Though it's nice that in media papers journalists and scientists managed to explicitly articulate the problem we can't solve: how to predict morphology using molecular biology data. This work is not even close to doing that. It essentially predicts a binary trait, "heads or tails".
Is this a new breakthrough approach in science, connecting areas that noone could imagine to connect?
No. This approach is called Systems Biology. It's been getting popular, I'd say, since late 90s-2000s. This paper is not like "look, I've invented a DNA computer/quantum computer" etc. It's an ordinary systems biology study, its quality is a matter of discussion.
Is this a good systems biology paper?
I don't claim that it's bad. But some things sound strange to me:

I don't think that de-novo predicting the network of interactions should be done by systems biology, when some facts are known from literature and could be enriched by Omics. If you're modelling a molbiol/biochem network, too much freedom might render your network totally different from reality.
Robustness test is unconvincing to me. Ok, "We dropped 3 crucial papers" and still network holds. Ok, network predicted some results from papers, not taken into account upon its construction. But this is not a systematic test. Split the data into two parts, optimize the network on one part and test it on another one.
Small number of genes involved in the described process and binary trait in consideration make the authors do overambitious claims, IMHO.

Can a smart and diligent person as yourself make a better career, doing systems biology than something else? Will your higher IQ and/or mathematical background give you an edge?
I think, no. I think, this area downplays the difference in intellect between people, while you might prefer a more discriminating area, even if you allow the assumption that you're not the single brightest person to ever live on Earth. :)
You should have worked out (or have from birth) some mental habits (e.g. habit to consider simple examples with small numbers of elements; use modular thinking; not to get stuck with a single approach - switch tricks; etc. etc.) that less smart people lack. Systems biology often works with too complex objects to imagine, thus your edge might be lost.
Complexity:
In reality cascades of biological pathways include huge numbers of players involved. Moreover often a single protein has several seemingly unrelated functions. I was a bit shocked at first, when aconitase, a Crebbs-cycle protein turned out to be at the same time an iron-responsive element and regulator of mRNA turnover. What a mad engineer could've created this?! Later I heard that many Crebbs-cycle proteins have seemingly unrelated "part-time jobs".
Stability of solution:
Look at any pathway in KEGG. Or this one: a famous cascade of cell signal transfer: . Although, it's not that complex, when you know all those proteins by heart, it might be hard to work with it quantitatively. Solutions might be unstable. Lack of experimental data may lead your simulation in a wrong direction. For instance, famous p53 protein (in this cascade it has 3 arrows or so) in reality has more than a hundred counter-agents, loss of interaction with one of them might lead your simulation to a state, critically different from reality.

Publish-ability:
Another problem for  systems biology papers is lack of comprehensible results. People are usually interested in a paper, when they can build something on top of it or at least when it's an interesting special case to know about. To be honest, I can't make myself read them.
What's the scientific takeaway of this paper for instance? It doesn't exist. I can't use it as a scientist.
Systems biology guys often speak of holism approach as opposed to reductionism. To me it's an empty word - the only comprehensible (and thus publishable) way to approach complex entities for human being is modular thinking - you just replace a complex cascade with a black box with a set of inputs/outputs and rates and treat it as a single object, reducing the complexity that way.
I feel that systems biology approach is better suited for commercial use in industry than for small-lab science. You need tools to mine data, to visualize results etc. Companies can afford building them for themselves.
As for the results, in industry you don't need them to be publishable. In agricultural industry you can optimize producing organism's metabolism with systems biology means to make it output more product of interest e.g. in cows or chemicals-producing organisms (though same goal can often be attained with pure genetic engineering easier). May be pharmacology can be interested in this to optimize their drugs to find best way to modify particular coefficients in particular reactions.
Practical achievements based on fundamental biological science still make use of reductionist works. They stem from little facts that cervical carcinoma is caused by a specific virus and vaccination against it seems (silver bullet warning) to prevent it. Or the fact that CCR5 mutants are seemingly immune to AIDS or similar results for causative agents for stomach cancer or hepatitis. Or useful discoveries of tools, such as siRNAs, Crispr/CAS9 or Tal effectors.

If you still decide to give biology a try, it won't take you too long to grasp the molecular biology. You can mostly work with it without knowledge of biology or chemistry. In a month you'll grasp the basic concepts such as replication-transcription-translation, in a year will be familiar with most key processes, in ~3 years will be a good specialist in some area.


text:
Imidazoline receptor agonist in clonidine?
I am thinking which pathway is more important in hypertension, ADHD and withdrawal of clonidine:

alpha-2 agonist
imidazoline receptor agonist (maybe)

Alpha-2 is the classical one. Pubchem starts to have more more info about imidazoline receptors.
How does clonidine work in hypertension, ADHD and withdrawal?


text:
Doxorubicin's intercalant and topoisomerase inhibition in leukemia?
I am thinking the role of doxorubicin's pathways in cancers such as leukemia and lymphoma:

topoisomerase inhibitor and inhibit DNA activity
intercalant - intercalant DNA bases and inhibit DNA activity

I am interested in which of these mechanisms are most relevant in leukemia and lymphoma.
How does doxorubicin work in leukemia and lymphoma?

isnt it just case of chemotherapy, that is treatment directed to block cell proliferation thus stopping progression of cancer?

I think you are right! It seems that it is not used without chemotherapy? At least this is right: Used in Combination Cancer Chemotherapy: Leukemia, lymphoma, solid tumors, soft tissue carcinomas. Please, make your comment an answer.

I need to search literatures to answer this. What I know is the first explanation about the mechanism is that it works as an intercalator perhaps because there was not much knowledge about topoisomerases at that time. However, DNA fragmentation was described at the same time. I think interacalators themselves do not have abilities to fragment DNA. It is easier to come up idea that topoisomerase inhibition would be responsible for DNA fragmentation. Having said that, I do not have any knowledge to deny contribution of intercalating effect for anti-cancer effects.

@Masi that is chemotherapy. Chemo is a cocktail of drugs

Doxorubicin is part of chemotherapy drugs. Most of them are targeting proliferation of cells, e.g. by blocking cell division or DNA replication.
Intercalants and topoisomerase inhibitors are powerful in interfering with DNA replication (not DNA activity). Other chemo drugs might inhibit proper operation of cell division machinery, such as through interfering with cytoskeleton functions.

What do you think role between intercalation and topoisomerase inhibition?

I do not have the complete answer, but the links below could be useful.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2748742/
The article above says etoposide, which is top II inhibitor and effective against cancer, is not a intercalator.
http://www.ingentaconnect.com/content/ben/cpd/2001/00000007/00000017/art00004?crawler=true
You would find the description below in the article. I just quote their description here.
"As with many other DNA intercalating drugs,
topoisomerase II-dependent DNA cleavage is
thought to be the critical event responsible for the
anticancer activity, [101] whereas free radicalmediated
DNA cleavage is probably more closely
related to the cardiotoxic side effects [118]."
http://www.degruyter.com/view/j/aiht.2013.64.issue-4/10004-1254-64-2013-2371/10004-1254-64-2013-2371.xml
The chemical, MLN944 (XR5944) described in the link above was designed as Topo inhibitor, but the topo inhibitory effects were less regardless of anti-tumor activity.
There is another type of anti-cancer drugs intercalating into DNA: cisplatin. Interaction of DNA with cisplatin result in formation of DNA adducts. Therefore, it may be difficult to explain anti-cancer effects by only intercalation. For the reference of cisplatin: http://en.wikipedia.org/wiki/Cisplatin

Doxorubicin and etoposide are both inhibitors of DNA topoisomerase II.
When chromosomes replicate as a cell divides, the helical nature of DNA results in the daughter molecules being tangled up (catenated).  Topo II is an amazing enzyme; it binds to DNA, cuts a gap in both strands, and then passes another DNA molecule through the gap.  Thus it untangles DNA.
If you inhibit Topo II, two things happen.  Firstly, the inhibition usually takes place after the first DNA molecule has been cut.  Thus inhibiting Topo II causes severe double stranded DNA damage, which is hard for the cancer cell to repair, and it dies.  Secondly, the physical entanglement which remains stops the clean separation of the chromosomes during mitosis.  Depending on the Topo II inhibitor in use, this can trigger a cell cycle checkpoint which stops the cell dividing Downes et al, 1994, or can cause the cell to rip the chromosomes to pieces as it tries to separate them anyway.  I worked in the same lab as Downes et al, above, and have done this experiment with several Topo II inhibitors, including etoposide and doxorubicin.  It's quite spectacular.


text:
Are there any culinary fruits that are not botanically fruits or accessory fruits?
Many culinary vegetables are botanically fruits. For example, the tomato, bittergourd and the cucumber are generally considered culinary vegetables, but they are botanically considered fruits.
Does the reverse also hold true, in that are there any plant parts that are considered fruits culinarily, but are botanically not considered fruits or accessory fruits?

I'm voting to close this question as off-topic because it's not about biology but about what chefs consider a fruit.

I think this would be better suited to [cooking.se] (though I'm not sure about that site's scope, you should check first). You're not really asking anything biological here, you're asking about misnomers that chefs might be using.

I'm afraid the question would be pointless on Seasoned Advice too, because our users don't know enough biology to be able to compare both categorization systems. From our point of view, you wouldn't be asking anything about cooking :( I won't close it outright if you ask it, but I don't know how it will be received by the community in terms of voting, close votes and, most importantly, providing answers.

I think there aren't any, with two tentative positive results. Of course, it is hard to prove a negative. But here are my arguments:

Logic. there is little reason for this to happen. A culinary fruit is supposed to be sweet, and plants tend to store sugars in their botanical fruits, not in other parts (although by this logic, there should be tuber-fruits too).
Source checking. I went through two books intended as comprehensive reference works in the kitchen, The professional chef and McGee On Food and Cooking. They don't list a (culinary) fruit which I could identify as a botanical non-fruit, with one contentious case. Note that On Food and cooking's chapter on vegetables is structured according to plant part, and it has a subchapter called "fruits used as vegetables" - but it does not have a chapter of "vegetables used as fruit" or similar.
Language. People tend to mix up the two categories of "culinary fruit" and "botanical fruit" until the difference is pointed out to them. While they don't follow the correct botanical definition of fruit, a plant part seems to have to check the "used by the plant for reproduction" check box to be called a fruit, to the point where many words related to "fruit" evoke a harvest-like image such as "to come to fruition", or also words for "barren" in many languages which literally translate to "without fruit", e.g. the German unfruchtbar. So even when a food fulfills the "plant part" and "sweet snack" criteria, it is not called a fruit if it's not (commonly perceived as) a seed container.

The contentious case coming from the books is rhubarb. The professional chef lists it as a fruit, while McGee calls it "a tart stand-in for fruit" in the vegetable chapter, and mentions it again in the fruit chapter, as "a vegetable that often masquerades as a fruit". Kitchen terminology is by no means standardized, so if you were to question random cooks about rhubarb categorization, I guess you'd get a mixture of "a fruit" , "a vegetable" and "I don't know" answers.
An idea I had while writing the answer: maybe you can count ginger. Ginger has many uses in the kitchen, but it is available as a candied snack, which is else only common for fruit. McGee lists it as a spice, although he explicitly speaks of "the dried spice" without giving a category for the fresh rhizome. I think most cooks won't dub ginger "a fruit" but would be willing to include candied ginger in a bowl of mixed candied fruit.
Some petals and leaves are also made into jams, which would support a fruit categorization. Nevertheless, I've never heard of them being referred to as fruits. Prominent examples are rose petals, violet petals and verbena leaves (although one could argue that preserving verbena produces a herb jelly, not a jam).
This answer encompasses Western cooking traditions. It is possible that other cuisines have a different view of what a "fruit" is - for example aloe is frequently used in sweet preparations, maybe the leaf flesh is eaten as fruit somewhere.
So in the end, it comes mostly to your willingness to stretch the definition of a culinary fruit. It is certainly not the case for the most commonly eaten fruits.


text:
Is dinoprost PGF2-alpha or PGE2?
In many places it says PGF2-alpha.
Now, I am thinking PGE2 is a substype of PGF2-alpha.
However, I am not sure.
Is dinoprost PGE2?

Prostaglandins A,B,C,D,E,F represent different non-inclusive classes of prostaglandins and all have different chemical structure. Hence (by classification), PgE cannot be subtype of PgF:
PgE have an oxo group at C9, while PgF have hydroxyl group at C9.
Back to Dinoprost, it is PgF2alpha and not PgE2 which is Dinoprostone.


text:
Why can't this be 1st meiotic division non-disjunction as well?

The child has gotten 2 "sick" chromosomes from his dad and 1 healthy from his mom, leaving it with trisomy 13.
I can see how this can happen in the second meiotic division of the father but I can ALSO see how this can happen in the FIRST meiotic division by non-disjunction.
Let me illustrate both ways which I see it and you can tell me where I go wrong as my book only illustrates that is the second division:

The black is the healthy chromosome and the red is the "sick" one. As you can see, we should end up with a double sick chromosome in both cases??

I'm starting to believe that my professors/whoever made the material has misunderstood nondisjunction as everything I read seems to indicate that it can happen in both 1. and 2.? Example: http://genetics.thetech.org/sites/default/files/NonDisjunction12.gif
If the "small" chromosome is the blue one you can notice that it can happen in both. Anyone else agree?

A) you are combining trisomy with translocation B) your final question is a bit unclear— are you asking why non-disjunction cannot happen in first division?

How am I combining trisomy with translocation? Nobody mentioned translocation. The small allele is the product of translocation, yes but it has nothing to do with the way the question is formulated. The final question asks if you can get trisomi from a nondisjunction in 1st meiotic division.

So you can remove the reference to the "sick" chromosome. It is unnecessary and confusing.

It's there to illustrate the fact that these are 2 different variations of the same chromosomes. Else people may think it was a different chromosome adding even more confusion. I don't know why this confuses you. I answered my own question anyhow.

This was a little confusing to answer because there is no explanation of why you thought non-disjunction could not happen in meiosis I. Another difficulty highlighted by @WYSIWYG is that you tackle both a specific disease resulting from non-disjunction and general non-disjunction diseases in the same question. I've tried to lay out my answer accordingly. Personally, I always find meiosis is a tricky one to visualise so the more we talk about it the better!

Non-disjunction and trisomy 13.

Why can't this be 1st meiotic division non-disjunction as well?

It absolutely can be 1st meiotic division non-disjunction as well. Patau syndrome can occur as a result of either meiotic non-disjunction event, and furthermore a mosaic form of the disorder can occur after mitotic non-disjunction.
Non-disjunction generally.
Non-disjunction can occur in meiosis I, but it's not necessarily the same result as non-disjunction event in meiosis II. This image helps describe the formation of the gametes from non-disjunction events.

(source: bioninja.com.au)
The most obvious point is that there are more aneuploidy cells born from a meiosis I non-disjunction event than a meiosis II non-disjunction event.
Furthermore non-disjunction occurs between homologous chromosomes in meiosis I or between sister chromatids in meiosis II. Was this subtlety perhaps what your lecturers/teachers were trying to describe?
Confusion somewhere in the question perhaps.
I think the problem comes from your first diagram, which appears to be an inaccurate sketch of what trisomy 13 looks like in a karyotype. This is what trisomy 13 actually looks like:

(source: iupui.edu)
How non-disjunction leads to a disease karyotype.
Lets pretend that the 'long' chromosomes in the first diagram of this answer are chromatids of chromosome 13. Thinking about the fusion event between gametes - the aneuploidy (n+1) gamete formed from either meiotic non-disjunction event and a normal (n) gamete could fuse to make the trisomy 13 result shown in the karyotype above; there would be three 'long' chromatids rather than two.

Your illustration is wrong. During second meiosis the sister chromosomes go to each their cell, not the same cell.
This would correspond to switching the colors up in your illustration like this:

What textbook are they using for your genetics course in the First year of medical school?


text:
Why does our eyes got wet when we feel emotional or whenever we are hurt?
I was thinking like whenever when we feel emotional or whenever we got hurt we got tears from our eyes but why is that happen ? Why our eyes got wet ?

the other answer does not discuss specific nuclei or larger structures within the brain

The other question is only about emotions.

The lacrimal gland, the major source for tear production, is situated just above the eye. It sends it's secretions to the eye via the lacrimal duct. Tears secreted collect in the conjunctiva of the upper lid. There is also a nasolacrimal duct which drains the tears from the eye into the nasal cavity. Hence the sniffles when we cry.
How is it controlled? The lacrimal nucleus in the brain is the major source for innervating the lacrimal gland, is a subnucleus of the superior salivary nucleus in the tegmentum of the pons (part of the brain stem). The lacrimal nucleus is connected to the lacrimal gland by the facial nerve (more specifically the greater petrosal nerve). This is parasympathetic (meaning it is part of the autonomic nervous sytem) and the neurotransmitter transmitting the signal from nerve to gland is acetylcholine (the receptor is muscarinic subtype of the cholinergic... though strangely wikipedia says nicotinic subtype is involved as well, but maybe this is simply poorly worded and is just refering to the ganglia stopover between pre/post ganglionic parasympathetic neurons), this information helps explain how some drugs affect lacrimation (tearing up).
Taken from source (1) [I do not know how reliable this website is]: How is the lacrimal nucleus told to start lacrimation? The lacrimal nucleus receives projections from the autonomic nervous system as well as from various brain structures, including the frontal lobe, globus pallidus, thalamus, and hypothalamus. The projections from the frontal lobe are thought to be important in human psychic lacrimation (crying because of emotions).
What I know: Another brain area called the limbic system is involved in production of basic emotional drives, such as anger, fear, etc. The limbic system [in the case of sympathetic nervous system it would be the hypothalamus specifically, but this is irrelevant here since parasympathetic nerves cause lacrimation] also has a degree of control over the autonomic system. As I under stand it, the frontal lobe usually inhibits the limbic system.
Again from source (1): While humans across the world produce psychic tears in response to both positive and negative emotions, and while similar emotions have been identified in other animal species, no reports exist of other animals showing psychic lacrimation.
according to wiki (3): Compared to tearing for physical reasons,  tears for emotional reasons have s different chemical composition. They are composed of more protein-based hormones, such as prolactin, adrenocorticotropic, and leucine enkephalin (a natural pain killer), which is suggested to be the mechanism behind the experience of crying from emotion making an individual feel better.
sources:
(1) http://carta.anthropogeny.org/moca/topics/lacrimation-tearing
(2) http://en.wikipedia.org/wiki/Lacrimal_gland#Innervation
(3) http://en.wikipedia.org/wiki/Tears

you are explaining in great detail the process of How, but not the Why.

good catch... not only do I have no idea on "the why", but some of the info in my answer way be inacurrate for "the how"... I'm hoping someone posts a better answer and then the credit is given to them...

don't Dogs and Cows shed tears? I thought they do

maybe, but i only know about humans

Without having anything but intuition, I would tend to think that it is a communicative signal, i.e. laughter the other way round. It helps individuals in social groups to infer the state of mind of other group members.