neuroscience - How do neurons form new connections in brain plasticity?

I've been reading about brain plasticity and how the brain can "rewire" itself.

One of the things that is not clear to me - how neurons can establish new connections. Does this rewiring mean that neurons can "disconnect" from other neurons to reuse existing dendrites? Or do neurons grow new dendrites to make these new connections, adding to existing ones?

Thank you for your input!

Answer

I haven't read anything particularly about dendrites being reshaped, though I would expect them to be as flexible as other parts of the cells.

The more commonly discussed topic (in my literary experience) is reshaping of the axon's branches before forming synaptic terminals. These branches are not fixed even in adults - neurons can grow new and retract old branches, attaching (synapsing) to other cells in new places and removing old connections (see Wikipedia: Synaptogenesis).

Additionally to this actual change in the number of synapses, individual synapses can be regulated in their signal strength by adjusting the number of neurotransmitter receptors in the postsynaptic membrane (Gerrow&Triller, 2010, also see Wikipedia: Synaptic plasticity)

entomology - How long does it take for a cricket to die at subzero temperatures?

I am on a number of reptile groups and I often hear freezing recommended as a humane way to get rid of surplus feeder insects.

However, they can also revive if you don't leave them in there long enough.

If I had a bag of crickets and put them in the freezer, how long would it take for them to die in this way?

phd - How to work with an overly positive advisor?

I'm in my second year of my PhD and my advisor is very, very positive. She never gives me negative feedback, even when I objectively have made a mistake.

For instance, two months ago, I noticed that I had made a mistake in my code that invalidated about 6 months of work. She wasn't even mad, saying things like "everyone makes mistakes" and "we can fix this." I was able to redo the analysis and still find interesting results, so it did more or less work out. But this is just one example of how positive she is.

Last year she encouraged me to apply to an university internal poster session and strongly suggested that it was highly probable that I would win one of the prizes offered. I did not win, nor do I feel like I was that close to winning. I was a first year student competing against fourth and fifth years.

Additionally, she is always happy with whatever I present to her. I'll admit, some weeks I do not produce much work, sometimes I'm slow to give her abstracts to review for conferences, etc, but she is always happy with whatever I give her.

She praises me excessively for every little thing, no matter how good or bad. She says "excellent work, great job, you're making fantastic progress." I get the same exact level of praise on the weeks I do a lot of work and weeks I do little work.

I'm used to working with PIs were much more blunt. Not necessarily negative, but told me how I was doing. If I didn't do much work, they would say "that's all you've done?" However, if I actually did a good job, I would get a "good job" or something.

It's very hard for me to manage my expectations with my advisor. I don't know if I am actually doing a good job or not. I don't know what my approximate actual chances are for awards and acceptances to conferences.

I'm afraid to ask her to be more blunt, because I'm afraid she would completely switch and criticize me too much.

How can I manage my expectations and work better with my perpetually positive professor?

Answer

I sympathize with this because I had an overly positive advisor for approximately 3.5 years, until I was getting ready to go on the job market and then suddenly he had tons of problems with my work. Others have told you to go elsewhere for criticism. This is probably a good idea, but you should also be able to have a good, balanced discussion with your main advisor who knows your work best.

Two things might be helpful:

1) Ask her what criticisms a reviewer might have of your approach to X

2) Ask her what needs to be done to improve Y

The first may help because she may be able to get in a different mindset about criticism if she is asked to think about the paper like a reviewer (a reviewer's "job" is to be critical, but she may feel that an advisor's "job" is to be encouraging). The second may help because it invites her to frame problems in an encouraging, positive way (which seems to be her comfort zone).

job - What are the negative consequences of slacking off after attaining tenure?

As I was thinking about the question Can I slack off and get a PhD?, I realized that I wanted to say in my answer

If you want to slack off, the best time to do so is after you have received tenure as a college professor, because at that point you can't be fired except for egregious offences.

I am not yet even an assistant professor, so I don't know whether the statement above is really true.

Question: What could the negative consequences be for a professor who has just received tenure, if he were to put in the bare minimum effort needed in teaching just to achieve average teaching performance, and were to just stop doing research and publishing papers?

Note: I don't plan to slack off if I were in the future able to get a tenured position, but I am just curious what happens to those professors who do.

Answer

I think the first question is, what does "slack off" really mean? Many professors do change their priorities after tenure for a slower paced approach to their work more in line with their own intellectual values. Many things that might be perceived as "slacking off" in a pre-tenure professor actually do have value and are much easier to pursue post-tenure, e.g., dedicating more time to students and teaching rather than research, or taking a slower pace to try to figure out an angle on deeper and harder to address issues.

But let's assume that our hypothetical professor is just saying, "I got mine, and I'm going to do the absolute minimum hereafter!" Here are some of the non-firing consequences:

• Not getting grants, it will be hard to have students.


• Their professional prestige will plummet, and anything they do want to do will get harder.


• They may get stuck with unpleasant scut-work tasks for the department, and will have more responsibilities foisted on them because "they have the time" that their harder working colleagues do not.


• They will not get promotion to higher academic ranks, nor the accompanying pay rises.

Some people may actually be comfortable with this, and having a person who's given up ambition and embraced a comfortable position as the organization's reliable clean-up detail can actually be a good thing, as it takes those tasks away from others who have more ambition.

But what if the slacker professor really doesn't care, and does a poor (but not quite enough to be fired) job at all the tasks they are assigned by the department? Well, every organization has ways to make a person's life hell without firing them. In the next departmental reorganization, the slacker professor might find themselves getting moved to a small lightless basement office, the administrative staff may ignore requests for assistance, colleagues may simply treat the person as a pariah. Few people last long in an actively hostile workplace, and it may be harder to fire a person than to make them quit...

Should I learn to use LaTeX to write up a History Masters Thesis?

So I reviewed the "What are the advantages or disadvantages of using LaTeX for writing scientific publications?" question on this forum and am sitting on the fence at the moment whether or not to use LaTeX to write up my masters Thesis.

I get the feeling that it is best suited for Scientific work but my MLitt is in History. I have searched my university website about LaTeX and most results come back from the maths department.

I am a part-time research student so my thesis with be approx. 50,000 words. At the moment I am using Libreoffice (I'm a Linux user -Ubuntu distro) to write up each chapter as a separate document which I was going to bring into a master theses document. I am using Mendeley to manage all my footnotes and bibliography.

I'm going to be meeting my supervisor over the next couple of weeks and would like to discuss the matter with him as to if I should/can use LaTeX. I'm sure how familiarity or usage of LaTeX within the History department will impact on my decision but would also like to prepare my thesis in the best possible way.

Edit 10/04/14: After a meeting with my Supervisor it appear that the History department has no preference on software for writing the thesis. Only requirement is that final thesis before defence is printed in ring-bound cover and the (hopefully!) accepted thesis is a hardback bound copy. My last written piece to my supervisor was done in LaTeX, using Texmaker on Ubuntu then exported to pdf, and other than some tweaking we need to do to the citation styling he was quite happy with the output. His advise was to use whatever software I was comfortable with (although he had never heard of LaTeX).

I would be grateful for answers from people who have used LaTeX in the Humanities area so as to be best prepared for my own decision on whether or not I would like to use it.

(Edit 10/03/2014) Just based on some of the answers, especially in relation to the learning curve with LaTeX, here is some more info that may be useful. Probably about 95% of my thesis will be text but I shall also need to insert some images (maps and photos) and will probably be entering some tables with stats. As stated above I use Mendeley for my refernece manager and have read some blogs where this is compatible with LaTeX so I think I would continue to us it if I go the LaTeX route.

Answer

I'm finishing up a PhD in philosophy that I've written in LaTeX. Here's some suggestions:

• make sure your advisor is ok with leaving you comments in pdf. I suspect he or she will not understand the question and will not be able to give you any feedback unless you submit chapters in word format. This is a deal breaker. Don't make any more problems communicating with your advisor than absolutely necessary.


• lots of academic journals in the humanities still don't accept submissions in pdf or latex source form. If you are planning on submitting your stuff to a journal, you might save yourself time writing in word format.


• there are some tools available to convert latex to rtf, html and other tools. texht is the best.


• If you do decide to go LaTeX, don't get lost in the minutiae of learning how to tweak everything. It's easy to lose lots of time learning new packages and stuff when you should be writing, writing, writing. Use the wikibooks latex guide as your quick start guide when you need to learn how to do something fast.


• Especially if you're on Ubuntu, don't get the LaTeX distributed through Canonical's repositories. It's usually out of date (haven't checked in a while). Just go on and get the vanilla TexLive 2013 distribution from CTAN.


• The tex.SE site is really, really good. Like ridiculously helpful.


• If you are familiar with version control programs like git, mercurial, or svn you can actually keep a very precise idea of exactly how your thesis has grown over time. You can roll back changes, etc. This is kind of advanced stuff for LaTeX, so I wouldn't spend like a lot of time learning this stuff if you aren't already familiar with it, but if you are, it can be really helpful. EDIT: Per @henry's comment below, see the following guide by Roger Dudler to get started with git.

3d structure - DNA topology Linking number vs twist?

Background

Linking number: the linking number represents the number of times that each curve winds around the other.
Twist, called "twist", refers to the number of Watson-Crick twists in the chromosome when it is not constrained to lie in a plane

I read these explanations on Wikipedia and I understand the concept of this (supercoils have to form to compensate the "tension" etc..) however I'm not able to discriminate between these terms while looking at a picture.

(source)

Based on the formula I can easily calculate the twists in the right image. But I'm searching for a way to see/count the twists.
Question
What is the difference between the linking number and the twists (not from a formula perspective)?

Answer

This article (DNA Topology: Fundamentals by Mirkin SM) probably defines and describes linking number better than I ever could:

The fundamental topological parameter of a covalently closed circular DNA is called the linking number (Lk). Assume that one DNA strand is the edge of an imaginary surface and count the number of times that the other DNA strand crosses this surface (Figure 3). The algebraic sum of all intersections (which accounts for a sign of every intersection) is the Lk. Two important features of the Lk are evident from Figure 3. First, Lk is always an integer. Second, Lk cannot be changed by any deformation of the DNA strands, i.e. it is topologically invariant. The only way to change Lk is to introduce a break in one or both DNA strands, rotate the two DNA strands relative to each other and seal the break... Another characteristic of a circular DNA is called twist, or Tw. Tw is the total number of helical turns in circular DNA under given conditions. Since DNA is a right handed helix with 10.5 base pairs (bp) per turn, Tw is a large positive number for any natural DNA. Take a planar, circular DNA and try to locally separate the two DNA strands, i.e. to decrease the Tw. Since Lk cannot change, a decrease in Tw will be compensated by several positive writhes of the double helix (Figure 4). Writhing (Wr) is the third important characteristic of circular DNA, describing the spatial pass of the double helix axis, i.e. the shape of the DNA molecule as a whole. Wr can be of any sign, and usually its absolute value is much smaller than that of Tw. The above consideration can be formalized by the following equation: Lk = Tw + Wr. Note, that while Lk is an integer, neither Tw nor Wr should be such. Also, neither Tw nor Wr are topological invariants and their values easily change...

---

Linking number is simply the number of times one strand of DNA passes over the other. Compare this to:

• Twist - the number of helical turns


• Writhe - the number of superhelical turns

You can, perhaps, think of twist as the passing over of strands within the helix and writhe as the passing over of strands when the entire helix crosses itself. Both twist and writhe involve strands of DNA passing over the other and so both contribute to the linking number. In the absence of writhe, linking number and twist are equal and both describe the same thing: the number of helical turns. However, in the presence of writhe, twist alone is insufficient to describe the topology of DNA because it doesn't account for the passing of the entire helix over itself.

Since twist is the number of helical turns, actually counting it in your image is not necessarily difficult, just time consuming. The image already has the twists numbered, and I've put a red do at every turn of the double helix. I've also circled, in blue, every point where the double helix passes over itself (writhe):

In that image, in may be difficult to visualize (and even understand!) that the strands only cross over each other 36 times (Lk), even though there are 42 helical turns (Tw). While twist and writhe are different modes of strand crossing, it is important to realize that they are interchangeable without breaking the strands. Let's consider a simpler example, where Lk = -1 (Wikipedia: DNA Supercoil):

In the lower image, the strands do not form a helical structure (Tw = 0) but they do both pass over each other together (Wr = -1). Can you imagine that if you were to "unfold" the superhelix (Wr = 0), the two individual strands would still be linked together in a helical structure (Tw = -1)? Neither could I, so I made a video with my shoelaces:

https://www.youtube.com/watch?v=rI3LWIvptf0

---

I hope that answers your question. Let me know if I misunderstood what you were asking or if anything needs clarification. You may also find my shoelace video on the topology of helicase-mediated DNA unwinding informative.

molecular biology - On which DNA strand is TATA box present?

My book (Snustard and Simmons) mentions that TATA box is present on the non-coding DNA strand but that was for the promoter of mouse thymidine kinase gene.

I have tried to find out more information and stumbled upon these in wikipedia articles which actually said something else:

The TATA box can be an AT-rich sequence "located at a fixed distance upstream of the transcription start site. (from a paper "The RNA Polymerase II Core Promoter'')

Then (from a book: Understanding bioinformatics),

The Transcription start site is the location where transcription starts at the 5'-end of a gene sequence.

So my question is, on which strand is TATA box actually (or generally) present?

Answer

TATA box is a feature of organisms that have dsDNA as a genetic material. Like most dsDNA promoter elements, TATA box is basically a section of dsDNA.

However a question like "On which DNA strand is TATA box present?" depends on what you define TATA-box as.

If you go by this definition:

In molecular biology, the TATA box (also called the Goldberg-Hogness box)^[2] is a DNA sequence (cis-regulatory element) found in the promoter region of genes in archaea and eukaryotes

then TATA-box is merely a DNA sequence and is just a representation of a ssDNA region. However, TATA-box is also the binding site for TBP (TATA-binding protein; part of the TFIID complex). While the recognition of DNA elements by the TFs is effectuated by the interaction of amino acid residues with DNA bases and backbone on one or both the strands, the TF binding and its activity has a stereochemistry.

Likewise, TBP also interacts with bases as well as the sugar-phosphate backbone on both the strands (Juo et al., 1996; PDB:1TGH).

_{Figure 2. Stereo pair drawings of the human TBP/DNA complex, with TBP at right and the sharply bent C-G-T-A-T-A-T-A-T-A-C-G duplex at left. (a) Molscript skeletal drawing (Kraulis, 1991). TBP residues that interact with the floor of the minor groove are shown in red with one-letter amino acid codes, and residues that interact with the sugar/phosphate backbone are shown in green. The coding strand of DNA is dark blue, and the non-coding strand is light blue. The two yellow-green base-pairs at bottom are the T1-A2 step that is kinked by interstitial phenylalanine residues, while the equivalent kinked step at T7-A8 is marked in orange. (b) Secondary structure diagram, with a-helices as coils and b-sheet strands as flat ribbon arrows pointing along N-to-C chain direction. Color coding as in (a).}

Considering this, you can say that TATA-box is essentially a dsDNA region.

However, another twist in the story is that TBP can also activate genes with TATA-less promoters (Kamenova et al., 2014) and this is possibly facilitated by other protein partners of TBP in TFIID complex.

Our results are consistent with the model that sequence-specific TBP-DNA contacts are not important at yeast TATA-less genes and suggest that other general transcription factors or coactivator subunits are responsible for recognition of TATA-less promoters. Our results also explain why yeast TBP derivatives defective for TATA binding appear defective in activated transcription.

This, still does not contradict with the fact that TATA-promoters need TBP and the binding is on both the strands.

So, my conclusion is that TATA-box is a dsDNA element but has a polarity in its mode of action.

peer review - How can I clear myself of emotional attachment when reading reviewer feedback?

I have received feedback from reviewers on my latest conference paper (this is in Computer Science). It was an acceptance, which is nice.

I feel I really need to consider in depth the reviewer feedback.

I have consciously noticed I have a emotional reaction. Which is not rational, practical, or useful.

How can I clear myself from it, and handle the review on a factual basis?

Is it better to sleep on it? Perhaps it would help to strip the acceptance from it. Or maybe to rewrite it in my words as if I were reviewing the paper?

Overall I guess I should feel happy, but of course the written reviewer feedback only points at the weaknesses in the work. Which is reasonable, since they can indicate it was generally good with their accept statement. (At the end of the day, an accept is an accept, right?)

publications - What to do when you spot a paper on arXiv with the same essential material as yours?

What do you do when you've sent a paper on your fancy new algorithm to a conference, and before the conference has replied to you, you spot a newly submitted paper on arXiv on the same algorithm?

Possible reactions I can imagine:

1. 
   

   You immediately submit your work to arXiv and/or open-source your code to "prove" you were working on it too (or at least as much as that might be worth at this point)

   
   


2. 
   

   You just wait and see if the conference accepts it (but then what?)

   
   
   


3. 
   

   You withdraw your paper entirely -- you "lost"

   
   


4. 
   

   You totally ignore it -- it's not "official" until it's peer-reviewed, so you might still be "first"

   
   

Furthermore, who typically gets credit if:

1. 
   

   Your paper is accepted, and is first to be published outside of arXiv

   
   


2. 
   

   Your paper is declined, and is not first to be published outside of arXiv

   
   

---

Update

I'm reading the other group's paper more carefully (I'd only had a chance to glance at it yesterday, and was alarmed because several of the key words and concepts were exactly the same as ours), and it seems like they might not have discovered the same algorithm after all -- it's difficult for me to tell because their notation and terminology varies considerably from ours, but there's a chance that we've found different algorithms, even though several key concepts are the same. I'll continue looking into it, but just thought I'd mention this to add more context. At least now I'm a little bit more hopeful.

Answer

It seems to me that the best answer is some combination of 1. and 2. Because you submitted the paper for review before the other work -- call it paper X -- appeared on the arxiv, the community will readily believe that your work does not rely on paper X. (At first I wrote "completely clear that your work does not rely", but that's too strong: it's possible that you had some prior contact with the authors of paper X and learned about their work before it was published. But from your description that didn't actually happen, so no problem there.)

So you are in a fortunate situation: because you submitted the work to the conference before the arxiv posting, you have established your independent priority. The fact that the report hasn't come back yet has nothing to do with that. With respect to the submission, it would be reasonable to just wait for the report -- I am assuming that since it is a conference, it will come back within a month or so? If your paper is accepted, then you should include in the published version and also in your conference talk the information that similar (or the same...) work was independently done in paper X.

However it would be a good idea to write immediately to the authors of paper X and let them know about your work. If you are in a field where the conference paper will be supplemented by a later journal paper, then depending upon the degree of similarity you may want to consider a joint publication. If not, then your journal papers should cocite each other: this establishes that "you both have priority", which is certainly possible, and then both works should be publishable. (But in my opinion a joint paper is the better option if the work is very similar: does the community need two versions of the same work? Can everyone be counted on to know about and value the two works equally? Better to join forces: that seals it.) Depending upon the response you receive and the timing it might be a good idea to post your submission to the arxiv as well, with a note explaining the chronology.

I disagree with both 3. and 4. First, it does not matter who did the work chronologically first but rather that each work was done independently and before the other was published. There does not need to be a "winner" and a "loser" here: you can both "win". It is good that research communities operate in this way, much better than your option 4.: no one has control over which referee report comes back first or which paper goes to press first or anything like that, so if this were the standard it would be at the very least quite unfair and in fact open to all kinds of ethical issues and abuses.

Note: One of the comments asks whether the work was stolen. It seems that the only plausible way for this to happen is for there to be some collusion between the authors of paper X and either the conference organizers or the chosen referees of your paper. This type of behavior is in my experience extremely rare, so I don't want to address it in my answer.

publications - How do I withdraw a paper from a journal after it was accepted but before it was fully published?

On the advice of a senior colleague, I decided that I do not want to publish the paper anymore and with that journal. I wrote the editor several times; he has received the emails, but refuses to reply. I know he gets the emails because if its on other matters he has replied but not on the paper. I am ready to forfeit the processing fee I gave them but they refuse to reply and have already published the abstract of the paper to their site but not the full paper. Is legal action an option and do I have the right to sue? What are the guidelines in matters like these?

advisor - Signed up for conference, having second thoughts

My advisor suggested that I should sign up for a conference, which I did - I now have to give a 15 minute talk.

However, the conference is taking place very far from me (+20 hour flight), and I am having second thoughts on whether I should go or not. Here are my thoughts:

Pro

• Meet new people/socialize


• Present my work at a talk, instead of merely a poster session

Con

• Conference is only 4 days, two ~20 hours flights are very exhausting


• The audience are not completely aligned with my field, so I have to make my presentation simpler than what the work actually is


• Will not learn things that I directly need for my research because of the audience (they are in geology and engineering, I'm in math)

Honestly, I don't feel like going because of the above list. But I am worried that

• My advisor will think I'm being annoying by "backing" out


• I can't say no now, since I have been given a talk

I would be very happy to hear your opinions on this matter. Maybe I am putting too much thought into this.

Is there any preference given based on how far in advance of the deadline a graduate application is submitted?

During graduate studies online application submission period, is there any preference given for an applicant if he/she submits his application at the beginning of that period compared with someone who submits his/her application couple of hours before the deadline?

Or as long as the applications are complete there is absolutely no difference!

Answer

Look at all the application rules and guidelines. Unless the registration/application is specifically described as first-come first-served (which would be unusual), the date of submission doesn't matter as long as the submission is complete before the deadline.

human biology - Why do people say that trans fatty acids are bad for your health?

I've heard from several sources that trans FAs are bad for you and their consumption will lead to cardiac problems, and that they are indigestible. But I also learned from biochemistry that they are digestible. What is the reason for this, or is it true at all?

Answer

Trans fatty acids are digestible, but they cause an increase in LDL and a decrease in HDL, which is the leading mechanism for atherosclerosis. So, they increase the risk of a cardiac infarct.

LDL means Low Density Lipoprotein, it's one of the 5 major lipoprotein groups. Lipoproteins carries the fatty acids and cholesterol which were absorbed from intestines (because lipids are hydrophobic and cannot be carried within the blood directly like glucose or proteins.)

So, LDL mainly carries cholesterol and fatty acids from intestines to tissues. (fatty acids and cholesterol are essential for cells.) But in excess levels, they tend to accumulate in vessel walls, which also starts and inflammatory response and makes the situation worse. (mainly includes macrophages and T cells) This is the main mechanism for atherosclerosis.

In the other hand, HDLs serve as a reverse cholesterol transport system, which carries the cholesterol back, mostly to the liver (for bile secretion) or steroidogenic organs such as adrenals, ovary, and testes by direct and indirect pathways. So a high HDL reduces the atherosclerotic process.

graduate admissions - What is the preferred GRE Writing score for MS/CS student?

I am planing to apply to study in graduate school, major in computer science/engineering. I just took a GRE test, this is my 1st time, and the result is pretty...not so good :(

Most schools, the science or engineering related program, reveals the average or preferred GRE scores for quantitative and verbal sections for admitted students. But it seems they don't care about the score of Analytic Writing section. Really?

To be honest, my writing score really bumped this time. I want to know if there's a preferred score range for this section.

Answer

There's a reason it's not listed--there is no preferred score, it doesn't really matter. I am not an expert, but I feel like the writing score is something that can only hurt you, not something that can help you, at least with respect to science or engineering graduate programs.

More concretely, I can see a very low score preventing you from getting into a program, but I highly doubt the admissions committee saying: "Well, he got a great score on the writing section, that really differentiates him from other applicants; let's admit him."

For a foreign student or non-native speaker, doing well in the writing section probably carries a little more weight than it does for native speakers; nonetheless, I believe that the above still holds.

publications - Have researchers any incentive to publish negative or confirmatory results?

Negative results are sometimes seen as failures, and confirmatory results as lack of creativity, even though both kinds can be useful. Have researchers any incentive to publish negative or confirmatory results (put aside the cases when they are forced to, e.g. certain registered clinical trials)?

graduate school - Is there other way to get into more advanced study of math, with a low GPA?

I am a second-year undergraduate majoring in mathematics. In my first year of studies, I didn't do really well on my exams and therefore my results were not good, with a GPA at around 2. I didn't really put time into it, but when I started learning advanced probability, I found that I am interested in it and want to study more and am even thinking of doing research. Also I think I have found my way to study math as I didn't quite do before. In that semester, I got 2 A+. In my school, I heard that people who apply for a master's degree normally have a first-class honors degree. Is there another way to get into more advanced study of math, even with a low GPA?

professors - Hiring academics for very large industrial consulting projects - how to find an arrangement without full-time employees?

As described in Hiring a faculty consultant, we need to hire a faculty consultant. The initial stages are perhaps only 100 hours, but subsequent stages may blossom to ten times that.

Will good professors be able to deliver that much consulting time? Would I be better off with a grad student or postdoc - could they deliver that much time? Or with a less accomplished professor with more time on their hands?

One alternative we've considered is to hire a fresh Ph.D. looking to enter industry. The problem is that we are not ready to commit to a full time employee - it's only if we get good results in the initial stages (and our investors agree!). Is there a solution for this?

One last alternative is to work out a research arrangement, where the professor would direct some of their research towards our needs. They'd get to publish the papers (with the application details removed, but the algorithms there) and we'd get ownership of the IP and control over the research agenda. Is that an option?

lipids - Why are triacylglycerols broken down before being absorbed?

So when dietary fats are in the small intestine, they are emulsified by bile salts in order for action by lipases to occur. Lipases degrade the triacylglycerols into monoacylglycerols, diacylglycerols, free fatty acids, and glycerol, and these are taken up by intestinal epithelial cells. Once taken up, these are reformed into triacylglycerols.

My question: why do these have to be broken down in order to be absorbed, and what is the driving force for reformation of triacylglycerols once absorbed?

Answer

Your question does not have a clear answer yet, as stated in Metabolic regulation: a human perspective / Keith N. Frayn. – 3rd ed (2010) on page 39.

There is still debate about how fatty acids cross cell membranes.

On that page they claim that a simple diffusion transport is possible through a 'flip-flop' mechanism, where the fatty acid inserts itself in the membrane and then experiences a reversal movement. The kinetics of the process seem to be in agreement with the observed utilization rates.

Also, it is claimed that there is growing evidence specific fatty acid transporters like FAT and FATP (fatty acid translocase and transport protein) play a key role in the process. They might join forces with the fatty acyl-CoA synthase, consuming ATP. ATP consumption activates them, i. e provides energy, for the acyl-CoA formation (not in order to move against gradient). The activated state would enable them to both move into the cell and regenerate a triacylglycerol.

Why do these (triacylglycerols) have to be broken down in order to be absorbed?

I would say that the hydrolyzed products (fatty acid and glycerol) are more readily internalized because their structure is simpler. Fatty acids can perform the flip flop mechanism, but whole triacylglycerols might not (I am not sure). There is a fatty acid binding protein that may help maintain the fatty acid importation gradient too.

What is the driving force for reformation once absorbed?

It is the secretion of chylomicrons loaded with regenerated triacylglycerols. Since they are secreted, they don't pile up inside the enterocyte (intestine cell) and the equilibrium in the synthesis route (the monoacylglycerol esterification pathway) favors the production of triacylglycerols.

Remarkably, not all fatty acids are reesterified, those of short length (< 14 carbons) are not compatible with the acyl-CoA synthase and enter the plasma in a free state. That happens when digesting dairy products.

evolution - Key points of Wagner's theory of innovation

What are the key points of Andreas Wagner's "theory of innovation" from his book The Origins of Evolutionary Innovations?

Specifically:

• What is new in his theory?


• What problems in evolutionary theory does it address*?

Answer

Wagner does not propose any new theory. He uses the word innovation to mean evolutionary adaptations that manifest qualitatively different traits. In fact, he has himself not defined it properly in the mentioned book. This is what he has written about the term innovation:

It may be difficult to define rigorously what an evolutionary innovation is [538,616]. However, these and countless other examples show that it is usually easy to recognize: a new feature that endows its bearer with qualitatively new, often game changing abilities. These may not only mean the difference between life and death in a given environment (just think of biosynthetic abilities), they may also create broad platforms for future innovations, as did innovations of photosynthesis and of complex nervous systems.

However the examples he cites in the introduction are not as obvious as he says. Lets say the evolution of nervous system. The innovation that would have led to evolution of nervous system could have been the evolution of electrically excitable cells.

Actually, throughout the introduction he does not actually clarify the meaning of innovation. In page-3 he does list some features of innovation but it is all still vague. Without a rigorous definition nothing is easy to recognize and classify (especially in theoretical terms).

I do not have the book and cannot look through the entirety of it. However, based on what I have read in some of his papers I can comment something about what he possibly means by innovation.

In certain dynamical systems, some parameter changes can lead to qualitative differences in the output (bifurcations). Steven Strogatz's book on Non-linear Dynamics and Chaos is a nice and easy read on this topic.

If you look at some of Wagner's research papers, you would understand this idea better. Some of these papers are about how some parameter changes can affect the overall outcome (which may be drastic). For example, in this paper the authors talk about how differences in cell division rates can affect the outcome of a cell differentiation event.

He also talks about metabolic innovations which refer to changes in metabolic reactions and network leading to new products/optimized growth etc. Details of this can be found in his lab's papers on Metabolic Flux Balance Analysis.

There is nothing new or drastically different about all of this. This is just an addition that was unknown during Darwin's time. All that he says is still quite in agreement with the current evolutionary theory. (But personally, I find the opening of the book too vague).

As far as empirical corroboration is concerned, you may have a look at his lab's research papers.

Is it a good idea to email the admission committee after a suboptimal interview?

I didn't do well in a part of a technical interview for a master's program, which was a question in the field X.

The most important subfield of my major for that program is X and X is not particularly my strength, because I took more advanced courses in other subfields during the last two years of my undergrad. But that is exactly why I want to do that master's program. I've had the necessary elementary courses in the field X, but did not go deeper in those topics, simply because there is not enough time to take all the helpful and good courses as an undergrad. That's why I decided to take some other courses as an undergrad and leave advanced courses in X for my graduate degree. I also want to do a Ph.D. after my master's and the field I want to do my Ph.D. is quite directly related to the master's degree I was interviewed for and involves a great deal of X.

To me, it makes perfect sense to apply for a master's program in a field that I'm not strong in, because otherwise, I wouldn't need to take more courses in that field and I will learn whatever I need, quite easily, through self-study. Is it a good idea to email the admission committee (or the professor who interviewed me) and explain this to them now? (Please keep in mind that I could have mentioned this point in my SOP, but I didn't. Probably a bad decision...)

citations - How can I cite my dissertation in a paper that will be double-blind reviewed?

A chapter in my dissertation has not been published in an archival conference. I am writing a paper based on the chapter for a conference that does double-blind paper review.

The paper will include pretty much the entire chapter, which presents a method, and will perform additional analysis of the method. The analysis on its own, without the method, is not enough to merit a paper. In the interest of the full disclosure, I should cite my dissertation. How do I do that without revealing my identity?

Is citing my dissertation without name and institution, just the title, appropriate or not?

I know there were similar questions recently, but none asking about the dissertation. The dissertation is different because it is a publication, a literature search will return a hit, but does not count as one, and is considered OK to publish chapter from in conferences and journals. The field is Computer Science.

Answer

The challenge here is seems to be to ensure that if the reviewers do stumble across your thesis, then the failure mode will be penetrating blinding rather than accusations of plagiarism.

It is my belief that with an "extract" paper like this, the thesis should be cited in any case. In most cases, there will be some connection to other portions of the thesis that could motivate such a citation (e.g., a motivation or an application). I also think that it is good to explicitly acknowledge the relationship to the thesis, e.g., "This manuscript is based on work also presented in [cite]", though the customs of your field may differ.

Then you can appropriately blind the citation to the thesis, e.g., "Ph.D. thesis, blinded for review." This makes the relationship clear without violating blinding. At that point, you are preserving blinding to the best of your ability, and while a reviewer can certainly try to penetrate blinding if they want, you certainly won't run into any problems with misunderstanding about plagiarism.

publications - Is there any referencing style analyzer?

As different journals and Conference proceedings have different reference styles, it is very much important to quickly identify the specific reference style they prefer.

To do this, is there any reference style analyzer which can analyze a given example reference style and give the name of the style?

Answer

Unfortunately the name of the reference style is in general Journal X's style. Even journals that claim to stick to a standard style (e.g., APA, MLA, Chicago) often throw in little twists. In general I find that if you get close, journals are pretty forgiving and will copy edit you to the correct style.

Basically you want your in text citations to follow either an author-number (Vancouver) or author-date (Harvard) style. As for the list of citations, just make sure it has all the information that the journal uses (e.g., DOI if the journal uses it).

deferral - Deferring admission to a computer science graduate program

Background: I have the opportunity to pursue a Computer Science graduate degree at a well respected program (around top 20). I spoke with the professors at the university and I feel like I am a good fit with their research program. My ultimate pursuits are to complete a PhD in CS. I've done research as an undergraduate and it has only confirmed my notion that I want to pursue a research track career.

More background: I applied to jobs (in case I was rejected to all the programs I applied for) prior to hearing back from my respective graduate programs. I accepted a position for an industry job (this was to secure I wouldn't be both unemployed & not in school).

Dilemma: Turns out my top graduate school is very interested in me, and I'm very interested in them. I would rather go to graduate school than work in industry forever. The industry job pays very well. I am split between A) working for a maximum of 1 year(It would only be 1 year, seriously I do not care about the money enough to work past a year) and B) going straight to graduate school.

My concerns are as follows, if I choose A):

• Could I potentially defer my admissions?


• If I am not allowed to defer my admissions, would I have a good chance of re-applying and being accepted a year later?


• Would my potential advisers look down on me for deciding to work a year?

I already made my mind up it would be a 1 year gig if I decide A). I know some people say once you make money, you may not be able to readjust to the graduate salary pay. But I don't think that will apply to because I'm going to live very frugally with or without industry pay.

career path - What academic functions does a Professor Emeritus usually serve?

I for one have never understood the distinction between a regular prof with a prof Emeritus/Emerita aside from the obvious fact that a Prof. Emeritus is someone who is usually fairly elderly and has spent quite a bit of time in the University.

I am currently spending a working with a Prof. Emeritus who is the a supervisor/advisor to my project and conducts biweekly meetings (which are really brief chats about life) and am in need to make a serious request. I feel uncomfortable because he may have a lot of other work on the side. I do not know whether has classes on the side nor am I aware of any research activities.

How does a professor become Prof. Emeritus (is age-status a must?) and do Prof. Emeritus usually still actively conduct research, teach classes? What is the distinction between a retired professor and a Prof. Emeritus?

Answer

To the best of my understanding, the primary function of a professor switching to emeritus status is that it frees up a faculty slot for a new hire. Emeritus is essentially retirement without giving up affiliation. An emeritus professor can ramp down their duties, go part time, etc. In some cases they may still do some teaching and supervising, and may have office space, but I believe they are typically no longer paid and no longer expected to fulfill normal faculty duties. That said, when they are still active, their advice and participation is often still quite valued by the active faculty.

funding - In the US, if I cannot finish my PhD in 5 years, will I lose my stipend and have to pay for my own living costs?

The contract says financial support is for 5 years. I am not able to support my own living cost.

botany - Are there any fruits that rely on dispersal of their seeds using excrement?

Are there any fruits that have seeds which can survive the digestive entirety of an animal's digestive system and remain germinable once passed on in animal excrement?

Perhaps this is the primary function of all seeded fruits and I'm just fundamentally unaware of the intended biological processes. If so, then why do poisonous seeded fruits exist?

conservation biology - What is projected to go extinct first?

Though a number of animals have been going extinct at alarmingly quicker rates (so called anthropocene extinction), a number of 'star' threatened animals are still alive. Which of the following charismatic endangered species is modeled or expected to die off soonest: polar bear, panda, black rhino, african elephant, mountain gorilla,bengal tiger/ tiger.

Where can I find population projection models for each of these species?

Mountain gorillas have the current smallest population, but polar bears are clearly in a lot of (and quickly widening) trouble due to melting ice. African elephants are seeing species loss due to poaching, and drying conditions making food/water scarce, which could also speed their decline, but they have the largest of the populations.

Since WWF and other conservation organizations use these charismatic species to 'sell' conservation and raise interest, awareness and funding, losing one (or many) of these species could affect conservation considerably. This will either be positively via a reactionary disappointment by the public or negatively by losing a "poster species".

molecular biology - How to relate human metabolic model reactions and cancer drug targets / reactome items?

Trying to find a way to take a cancer drug (from CancerDR, for instance) and infer the metabolic reactions that are affected by it in the Human Metabolic Model.

Essentially, I would like to know which HMM reactions are affected by a certain cancer therapy drug, especially the ones found in the Cancer Cell Line Encyclopedia.

Example:

Searching for the reactions that are associated with the HSP90 protein, which is targeted (to some extent) by the 17-AAG agent. That way, I can trace back the relevant section of the HMM and mark it as 'affected' by that agent.

This could be a very broad or naïve question, but I am afraid I don't know enough to be able to determine that.

If someone can only provide some tip or guidance, that will be greatly appreciated as well.

Thanks very much!

Answer

Short answer:

For drugs with targets having a direct effect on reactions in the model:

Find the gene name for the target protein -> Find the Entrez Gene ID (or other gene identifier, depending on the model) -> Search the model file or database for reactions having a gene association with the gene identifier.

AND/OR:

Find the EC number for the target protein. -> Search the model file or database for reactions associated with the EC number (likely to miss many reactions or be imprecise).

EC numbers can be found in the UniProt database, while the HGNC database at http://www.genenames.org/ can be used to look up genes by name and find their Entrez Gene ID and other resources, including links to UniProt. Many other databases can also be used.

For drugs with targets having an indirect effect on reactions in the model:

Model/estimate in-direct metabolic effect yourself by using data on known regulatory effects to create new gene-reaction associations or modify the bounds on reactions affected by the drug.

Long answer:

To knock out or constrain a reaction based on data about a drug, you have to go from an entry in a drug database to an EC number or gene identifier consistent with the annotation in the model you are using. However, There is currently no standard annotation nomenclature for gene-protein-reaction (GPR) associations in metabolic models, and in general only direct GPR associations are encoded in model files.

Assuming that you have a metabolic model in SBML format (for example the RECON 2 model), reactions may have a gene association and/or be associated with an Enzyme Comission (EC) Number.1 However, several model reactions catalyzed by distinct isozymes (different enzymes catalyzing the same reaction) may be associated with the same EC number, and many reactions may not be annotated with an EC association. Therefore you might want to use the gene associations directly. Both gene associations and EC numbers are recorded in the "notes" tag for each reaction in the SBML file, and is not part of the SBML specification. (A proposal has been published for improving support for gene associations in the SBML format.2)

Using EC numbers:

Using EC numbers is the simplest, but not the most precise or possibly even usable solution. It is simpler than using gene associations, because genes may described using any of a number of identifiers depending on the database while any reaction is associated with at most one EC number. Once you know the target protein, you can look it up in the UniProt database, and find its EC number (if any) in the field "protein names". A simple example: The DrugBank entry for the cancer drug Fluorouracil (http://www.drugbank.ca/drugs/DB00544), links to the UniProt entry for one of its targets, Thymidylate synthase (http://www.uniprot.org/uniprot/P04818), where the EC number is given as 2.1.1.45. Searching the RECON 2 SBML file (or searching in BiGG at http://bigg.ucsd.edu/bigg/main.pl) for occurences of this number will show that it is associated with the "TMDS" reaction.

The IUPHAR database (http://www.iuphar-db.org/index.jsp) may also be useful.

Using Gene associations:

Genes are conceptually at least one step removed from the reactions their products catalyze, so one might argue that using EC numbers which actually correspond to reactions would be better. However, it is thinkable that a drug might affect only one of several enzymes which catalyze the same reaction and thus share an EC number. Furthermore, annotation of reactions with EC numbers in metabolic models may be poor. Thus you may want to use gene associations to determine which reaction are affected by a drug.

Unfortunately, due to the large number of databases, a single gene may be described using many identifiers or symbols in different databases. You thus have to identify the gene coding for the product which is the target of the drug, and obtain the correct identifier for that gene which corresponds to the nomenclature used in the model.

Assuming again that you are using the RECON 2 model, it is an update of RECON1 which was made using the BiGG database3. Gene protein reaction (GPR) associations are therefore recorded using the gene names in BiGG. When ignoring the decimal and any following numbers, BiGG gene names appear to correspond to Gene IDs in the NCBI Gene database.5 A question on how to identify BiGG reactions was also discussed at BioStar.6 However, you have the opposite problem: Finding a BiGG gene name from another gene identifier.

Using Fluorouracil as an example again, the DrugBank entry lists "Cytochrome P450 2C9" as a targeted enzyme, with the effect being inhibition. Following the link to the UniProt entry P11712 (http://www.uniprot.org/uniprot/P11712), we find that it has an EC number 1.14.13.- However, this EC number corresponds to a class of reactions (or rather, enzymes). There are also listed several additional EC number at the single reaction level, but as these may not cover or be associated with all relevant reactions, we'll use the gene association. Going back to the DrugBank entry (or just reading on the UniProt page), we find that the gene name is CYP2C9. Looking up this name at a suitable database, for example the HGNC database of human gene names at http://www.genenames.org/, we find the entry for CYP2C9 (http://www.genenames.org/cgi-bin/gene_symbol_report?hgnc_id=2623) and that its Entrez Gene ID is 1559. Searching for this gene identifier in the BiGG database, we find that four reactions are associated with this identifier (The actual association is to "1559.1"): P4502C9,P4502C92,P4502C93 and P4502C94. Note that none of these reactions have an associated EC number.

Defining your own GPR associations

Keep in mind that even in genome-scale metabolic models, a large number of reactions and processes are missing. While the term metabolism in its widest sense may be used to refer to all reactions occuring in a cell, metabolic models have so far been mostly limited to covering transport and inter-conversions of small metabolites. Processes involving signalling molecules and regulation of enzymes have been mostly or completely left out, along with critical processes such as DNA replication, DNA repair and apoptosis. Because of the limited scope of the models, gene-protein-reaction (GPR) relations do not generally take regulatory effects into account, being limited to describing direct relations between one or more genes encoding (part of) a protein which directly catalyzes a reaction.

One could also model a very large number of indirect regulatory effects, but as current metabolic models generally do not specify these, you may have to construct them yourself. To find reactions in a metabolic model which are affected by a certain drug, you may have to "travel" several steps of interactions from the original target of the drug. You may consider using protein-protein-interaction network (PIN) data to help in this. You will have to consider the strength/importance of the interactions, something which may require a high degree of manual curation.

For example, if your drug affects HSP90, which is needed for correct folding of certain enzymes, you may consider creating a GPR stating that the gene for HSP90 (or rather, HSP90 itself) is needed for any reactions catalyzed by a specific enzyme to be active. This would be an indirect GPR, as HSP90 does not itself produce any part of the enzyme that catalyzes the reaction. As an alternative to on/off regulation through a GPR, you may consider searching for data on the ratio of correctly folded and misfolded enzymes in the presence and absence of HSP90, and apply bounds on reactions catalyzed by the affected enzyme to reflect this.

What percentage of DNA do people share?

If you picked two people in the world at random, sequenced each of their entire genome, and compared their DNA, what would the expected percentage match be?

Answer

Based on fine scale mapping of human genome structural variation, which is expanded on here, according to this study, the amount of genome structural (nucleotide diversity) ranges from 0.1% to 0.4% (look under section "Fine-scale map of human genome structural variation"). Hence humans have an up to 99.9% nucleotide similarity. And according to this study the average proportion of nucleotide differences between a randomly chosen pair of humans (i.e., average nucleotide diversity, or pi) is consistently estimated to lie between 1 in 1000 and 1 in 1500.

botany - What is the mechanism behind plants losing their leaves?

Do plants that lose their leaves (i.e., deciduous plants) do so because of external conditions (e.g., drought, cold), or because of an internal process?

Another way of looking at it: if you take a deciduous plant that loses its leaves during cold winters with long nights (e.g., Canada), and plant it in an environment where it is always warm and sunny (i.e., at the equator), would this plant continue to shed its leaves and re-grow them according to the seasons in Canada, or would it behave as an evergreen instead?

Answer

The process of a deciduous plant losing its leaves seasonally is known as abscission. It occurs when a layer of cells known as the abscission zone elongate and weaken, causing the leaf to fall off. It is mediated mainly by the hormones auxin and ethylene.

The functional purpose of abscission is to remove leaves when they are no longer producing a net gain of biomass. Leaves decrease in productivity during drought, decreased day length, and when under the effect of herbivory.

In some species (according to the day-length source), "When the daylength exceeds a certain 'critical' duration, growth may be maintained continuously for at least 18 months under favourable tempera­ture conditions, e.g., in Liriodendron tulipifera, Robinia pseudacacia". So, to answer your final question, some deciduous species (but not all) may indeed retain their leaves if they are grown in favorable conditions. One reason why not all deciduous trees retain their leaves even in favorable conditions is that internal ageing can also induce abscission: "In such species, growth is determined primarily by the endogenous ageing process and is only modified by environmental factors, including daylength"

mathematics - Referee says the proof is wrong, but it is not, what to do?

About 3 months ago, my co-author and I submitted a mathematical paper to a journal. Yesterday, we got the editor's response and the paper has been rejected. The reason is that the referee thinks that our results are wrong or trivial.

However, we carefully read the referee's report and we conclude that the "errors" pointed out by the referee are not errors at all and that our proofs are correct. In all modesty, we think that the referee understood very poorly even the statements of our theorems.

Personally, this is the first time that such a situation occurs to me. I already have four papers published in such journal and the reports I got from the referees have been always really accurate.

What do you think is the best thing to do? Forget about and try another journal? Write an e-mail to the editor explaining why we disagree with the decision? Other ideas?

EDIT: Thank you very much for all your answers. At the end, we submitted our paper to another journal with only some slight edits we have already planned to do independently by the referee's report.

Answer

I must disagree with some of the answers.

Some assume that it is a problem with the person asking the question and giving suggestive answers with a very limited number of fixes ("I would strongly advise performing a major revision on your presentation of the proofs"; "two potential problems. One is that you may be in error, [...]. The other is presentation[...]").

If we are attentive enough, we all know well what the source of our problems are: The magic word is feedback. If people complain consistently that our papers are hard to read, the suggestion to work it over has a point. If again and again referees find errors in the manuscript, we will take much more care to avoid errors (proof-read, double-check, triple-check).

But if we get an outlier answer and cannot fathom what exactly should be the problem, it is very, very likely that we are not the source of the problem. Working the paper over in this case is a waste of time.

The reason(s) that a referee rejects a paper are virtually unlimited. He is wrong, she does not care, he is busy, she is right, but explains it badly. Whatever.

But what you can do is to read very carefully the answer and ask yourself under what circumstances a person would give the answer. Is it aggressive, dismissive, bored, sad ? It is rather short or more detailed ? Does it look like the referee invested time or it is hastily written with boilerplate passages ? On this you could often discern what the real reason for the rejection is.

Everyone will sooner or later have a paper rejected for inexplicable reasons, it is normal. Nothing to be afraid of.

That said, the best strategy already mentioned by other answers is to cut the losses and simply go to another journal. I would advise to challenge the decision if and only if the editor made it clear that the decision is not final and then if and only if it is objectively important to have it published in this specific journal. In that case do everything in your ability to check and recheck your paper and hone it to perfection because you will only get exactly one chance to convince the editor.

botany - How will rising carbon dioxide levels in the troposphere affect photosynthetic producers?

Much discussion has been had about the affects of climate change on plantlife, but how will rising carbon dioxide concentrations affect the photosynthetic process itself? Since CO₂ is a reagent in photosynthesis, would we expect higher CO₂ to mean an increased rate of photosynthesis in a real-world context? Has there been any research on this?

I am thinking more of large-scale field tests rather than lab plants.

Answer

There are several key ways in which rising atmospheric CO₂ concentrations will affect photosynthesis, and these are related to the different types of photosynthesis. In order to properly answer your question, I'll provide some background about photosynthesis itself.

Photosynthesis evolved in a high-CO₂ atmosphere, before the oxygen-enrichment of the atmosphere (which actually happened as a result of photosynthesis). Most plant species operate C3 photosynthesis. In these plants, carbon dioxide diffuses into the cell where it is fixed by Ribulose-1,5-bisphosphate carboxylase oxygenase (RuBisCO) into a 3-carbon molecule (hence C3), which is then polymerised to make sugars. A crucial fact about RuBisCO is that it has both carboxylase (carbon-fixing) activity and oxygenase (oxygen-fixing) activity. This means that oxygen and carbon dioxide compete for the active site on the enzyme complex, leading to RuBisCO being quite inefficient and slow at fixing carbon in higher oxygen concentrations. That didn't matter in the high-CO₂ atmosphere of the early Earth, but in todays atmosphere O₂ concentrations are high enough that they severely limit the productivity of C3 plants.

However, plants haven't just been growing slowly all that time - several mechanisms for increasing photosynthetic efficiency have evolved. The most influential systems involve concentrating carbon dioxide in a particular area, excluding oxygen, and concentrating RuBisCO in that same area. This avoids the oxygen competition for the active site and allows RuBisCO to operate more efficiently. The key adaptation here is C4 photosynthesis - the system which is present in most grasses and many of the most productive plants on Earth (e.g. maize, sugarcane, Miscanthus). It has evolved at least 62 times independently. It works by having RuBisCO concentrated within 'bundle sheath' cells which are surrounded by a layer of suberin wax. This layer prevents CO₂ escaping and O₂ from getting in. CO₂ from the atmosphere is then fixed in different cells - 'mesophyll cells' - by another enzyme - Phosphoenolpyruvate carboxylase (PEPC), resulting in a four-carbon molecule (hence C4). This 4-carbon acid, (malate or oxaloacetate depending on the system) is then shuttled into the bundle sheath cells. There, the CO₂ is released again by a variety of enzymes depending on the system, creating a high CO₂ concentration in the cell where RuBisCO can then work efficiently.

In general, C4 plants are much (about 50%) more efficient than their C3 counterparts, and they are particularly well adapted to high temperatures and moist environments. So, to answer your first question: as atmospheric CO2 levels continue to rise, C3 plants will gradually be able to photosynthesise more efficiently. Interestingly though, C4 plants are predicted to also benefit from increased atmospheric CO₂. If global temperatures rise as predicted, both C3 and C4 plants will be able to operate more efficiently than they currently do, up to a maximum temperature beyond which enzymes will begin to denature faster and efficiency will drop. One consideration is that the difference in efficiency between C3 and C4 systems will decrease, which may significantly alter the makeup of plant communities around the world.

This is a vast oversimplification, but it is accurate for the predicted overall effects. Localised effects (i.e. productivity changes in a particular region or for a particular crop) will depend on habitat, physiology, etc.

Some key papers to launch you into the literature:

• Leakey, A.D.B., Bernacchi, C.J., Dohleman, F.G., Ort, D.R. & Long, S.P. (2004) Will photosynthesis of maize (Zea mays) in the US Corn Belt increase in future [CO2] rich atmospheres? An analysis of diurnal courses of CO2 uptake under free‐air concentration enrichment (FACE). Global Change Biology. [Online] 10 (6), 951–962. Available from: doi:10.1111/j.1529-8817.2003.00767.x [Accessed: 31 January 2012].



• Morgan, J.A., LeCain, D.R., Pendall, E., Blumenthal, D.M., Kimball, B.A., Carrillo, Y., Williams, D.G., Heisler-White, J., Dijkstra, F.A. & West, M. (2011) C4 grasses prosper as carbon dioxide eliminates desiccation in warmed semi-arid grassland. Nature. [Online] 476 (7359), 202–205. Available from: doi:10.1038/nature10274 [Accessed: 31 January 2012].

publications - Why are CS researchers reluctant to share code and what techniques can I use to encourage sharing?

While researching a topic area I have come across a number of papers that claim to improve on the state of the art and have been published at respected outlets (e.g. CVPR, ICIP). These papers are often written in a way that obscures some of the details and their methods can be lacking in detail. Upon contacting these authors for more information and asking if they would kindly make their source code available they stop replying or decline the offer.

Why are computer science researchers reluctant to share their code?

I would have expected that disseminating your source code would have positive effects for the author, e.g., greater recognition and visibility within the community and more citations. What am I missing?

For the future, what are some better ways to approach fellow researchers that will result in greater success at getting a copy of their source code?

Answer

Why researchers might be reluctant to share their code: In my experience, there are two common reasons why some/many researchers do not share their code.

First, the code may give the researchers an important advantage for follow-on work. It may help them get a step ahead of other researchers and publish follow-on research faster. If the researchers have plans to do follow-on research, keeping their code secret gives them a competitive advantage and helps them avoid getting scooped by someone else. (This may be good, or it may be bad; I'm not taking a position on that.)

Second, a lot of research code is, well, research-quality. The researchers probably thought it was good enough to test the paper's hypotheses, but that's all. It may have many known problems; it may not have any documentation; it might be tricky to use; it might compile on only one platform; and so forth. All of these may make it hard for someone else to use. Or, it may take a bunch of work to explain how to someone else how to use the code. Also, the code might be a prototype, but not production-quality. It's not unusual to take shortcuts while coding: shortcuts that don't affect the research results and are fine in the context of a research paper, but that would be unacceptable for deployed production-quality code. Some people are perfectionists, and don't like the idea of sharing code with known weaknesses or where they took shortcuts; they don't want to be embarrassed when others see the code.

The second reason is probably the more important one; it is very common.

How to approach researchers: My suggestion is to re-focus your interactions with those researchers. What are your real goals? Your real goals are to understand their algorithms better. So, start from that perspective, and act accordingly. If there are some parts in the paper that are hard to follow or ambiguous, start by reading and re-reading their paper, to see if there are some details you might have missed. Think hard about how to fill in any missing gaps. Make a serious effort on your own, first.

If you are at a research level, and you've put in a serious effort to understand, and you still don't understand ... email the authors and ask them for clarification on the specific point(s) that you think are unclear. Don't bother authors unnecessarily -- but if you show interest in their work and have a good question, many authors are happy to respond. They're just grateful that someone is reading their papers and interested enough in their work to study their work carefully and ask insightful questions.

But do make sure you are asking good questions. Don't be lazy and ask the authors to clear up something that you could have figured out on your own with more thought. Authors can sense that, and will write you off as a pest, not a valued colleague.

Very important: Please understand that my answer explaining why researchers might not share their code is intended as a descriptive answer, not a prescriptive answer. I am emphatically not making any judgements about whether their reasons are good ones, or whether researchers are right (or wrong) to think this way. I'm not taking a position on whether researchers should share their code or not; I'm just describing how some researchers do behave. What they ought to do is an entirely different ball of wax.

The original poster asked for help understanding why many researchers do not share their code, and that's what I'm responding to. Arguments about whether these reasons are good ones are subjective and off-topic for this question; if you want to have that debate, post a separate question.

And please, I urge you to use some empathy here. Regardless of whether you think researchers are in right or wrong not to share their code in these circumstances, please understand that many researchers do have reasons that feel valid and appropriate to them. Try to understand their mindset before reflexively criticizing them. I'm not trying to say that their reasons are necessarily right and good for the field. I'm just saying that, if you want to persuade people to change their practices, it's important to first understand the motivations and structural forces that have influenced their current actions, before you launch into trying to browbeat them into acting differently.

---

Appendix: I definitely second Jan Gorzny's recommendation to read the article in SIAM News that he cites. It is informative.

graduate admissions - How old is too old for a PhD?

Are there any age limits (formal, informal, or guidelines) that schools use when deciding to accept someone into a PhD program? I'm most curious about the upper age limits. For example, will most schools accept someone in their 40's? How about their 50's?

Answer

I would imagine most institutions would consider it discriminatory to judge on age, and in some places it would just be illegal. The real consideration is always whether the prospective candidate has a reasonable chance of success. Having appropriate education, or a reasonable substitute is of course the main criterion, though demonstrating research-level ability always helps (research publications etc.).

Personally I have seen PhD candidates of all ages. There's some skew to the distribution across disciplines (business and history for example often have higher proportions of older doctoral candidates than, say, mathematics), but this seems to be more a socio-cultural thing than any institutional influence.

peer review - How to respond a reviewer's reply when he is asking about much more to be discussed?

I got review reply with three reviewers comments. Most of them are workable. I can address them while revising the manuscript. However, one reviewer is asking much more work, and based on my understanding, I feel that can be a another work. Thus, I want to politely answer that query with proper reasons. However, I am wondering if there is any formal way to address this issue?

I can say that, we totally agree with the reviewer's concern. However, this study which itself is a novel work, is a part of our future work.

Answer

One thing to make clear at the outset: it is the editor who controls whether your paper is published or not. Your task is to convince the editor, not necessarily the reviewer, that your paper merits publication.

Most editors will not blindly require you to implement all the reviewer's suggestions to get the paper published. After all, it's not like the reviewer necessarily knows better than you do what is going to make your paper publishable. (If multiple reviewers all make the same suggestion, that's a different story and an editor will put more weight on that.)

If you have a good reason not to do something suggested by a reviewer, you can not do it and point out in your response to the editor why you made that decision. The important thing is to make it clear that you didn't simply decide to ignore part of the reviewer's report. If you're going to avoid implementing any of their recommendations, you do need to justify it. It then falls to the editor to decide whether the paper is still publishable without that particular suggestion being implemented.

I'd add that saying that an addition to the paper would be too large and would justify an entire followup study on its own is a valid response. Even a reasonably common one, I would think. Reviewers haven't done the research themselves; they don't know how much work an extension would take, and if you say it would be too involved, there's a pretty good chance the editor believes that you know better than the reviewer on that point.

evolution - Why does the gc content deviate from 50% in prokaryotes

I have read quite some articles but I can't figure out the main reason for gc content deviation in prokaryotes. In eukaryotes I can understand it, because the genome isn't composed at random, like TATA boxes and CpG island, because they are important for functioning in the production of proteins.

However in prokaryotes there is such a variety, GC% is ranging from 20% up to 70%. Most of the time it depends on the environment, high temperature needs a stable genome (= high gc content).

I also read the answer to this question How does GC-content evolve? however it's still not clear to me. I hope someone can explain the deviation a little bit more.

Question

Prokaryotes have an AT drift, but what mechanism causes some of these bacteria to higher their GC% instead of lowering it.

Answer

I wonder whether it is just some sort of functionless drift. I don't know about bacteria, but think the example of some human viruses may be instructive. The human herpes simplex 1 virus (causes cold sores) has a very high GC content, whereas the quite closely related (in terms of gene repertoire and organization) human varicella zoster virus (causes shingles) has a bias towards AT. Now these viruses depend completely on the host cell's translational machinery, which will be similar (e.g. distribution of tRNA species) in the cells these two viruses infect (as will temperature, ionic strength etc.) (The viruses encode their own DNA replication enzymes but not the systems for transcription or translation.)

So if there is a functional reason for the differences in these alpha-herpes viruses, it is difficult to discern, and one might assume that the situation in bacteria might well be similar.

Clearly, there must be mechanisms for the accentuation in a GC bias once established. One imagines there could be a selective advantage in either replication, transcription or translation, but molecular explanations do not jump to mind (at least not to mine).

Can a conference paper be an extended version of a previous conference paper? Will it face copyright issues?

I am currently reviewing a conference paper, which I found is just extended version of a previous conference paper. I have heard that a journal paper can be a new version of a conference paper with some new stuff added, while the paper I am reviewing is something like this. Would this paper be rejected simply because of the copyright issue?

Answer

It would seem that you should contact the organizing committee of the conference and get their advice. To me it seems kind of low to write papers that have very similar content but it does happen. If you are questioning it that much I would bring it up with the Organizing Committee and see what they say. You don't even have to mention names or specifics but just get their general opinion.

application - TOEFL IELTS GRE scores expired but with work experiences in English speaking countries

If one wants to apply for grad school in the USA, and one's TOEFL, IELTS, GRE scores all expired, but one has all the following experiences able to prove ones' English proficiency:

one did an exchange student program in an English speaking country; one has work experiences in English speaking countries; one has published many papers in English.

Will these be sufficient reasons to persuade the admission office and pass their English requirements?

Human evolution: Where *exactly* did the first human come from, whose parents were not?

Layman here. So I have never really quite understood this facet of human evolution, (or any other for that matter), in that, I understand the evolutionary process, but I get lost on the 'border' cases.

For example, we, as humans, evolved from monkeys, (to use the colloquial term, I am not a biologist by any measure).

My question is, doesn't this mean that at some, discrete point, there had to have been a human, whose parents were not? If that is true, how does that work, in the sense that we now have species1 giving birth to species2.

If not, then how exactly does this border case work? The only other alternative I see, is that the borders are 'fuzzy', but then that necessarily means that the definition of a species is itself fuzzy, which I understand is not the case.

Thanks!

Answer

Actually, your last paragraph is more the case than not.

There are currently three common definitions for delineating discrete species:

1) Phenotypically different from related species (looks or acts differently).

2) Produces viable offspring in the wild.

3) Some % of genetic difference.

There are strengths to all three:

1) Very easy to ascertain and measure.

2) Most common conception of a species.

3) Genes control the first two, so genetic divergence gets to the heart of the matter.

There are also weaknesses to all three:

1) Is notorious for mis-labeling and missing species.

2) Some species which can mate and produce fertile offspring under enclosed conditions do not do so in the wild (Tigers and Lions, for instance).

3) The amount of divergence has, thus far, been completely arbitrary. If there is a certain % or patterns of mutation required in the genome, science hasn't yet discovered it.

The fuzzy definition of species, combined in the not-exactly-intuitive generational-type thinking required for understanding evolution, and the answer to your question is (at least to the best of my understanding) the following:

Yes, at some point one of our ancestors gave birth to the first Homo sapien that was somehow genetically different from its parents. However, the magnitude of the difference is probably not as great as you might think.

We've already observed our closest evolutionary cousins, the Bonobos, making basic tools through flint napping: http://www.newscientist.com/article/dn22197-bonobo-genius-makes-stone-tools-like-early-humans-did.html

It's also possible that disputes between male chimpanzees are mediated by an older female: http://www.cpradr.org/Resources/ALLCPRArticles/tabid/265/ID/121/Primates-and-Me-Web.aspx

And that both Chimpanzees and Capuchin monkeys can be taught the concept of currency (which, somewhat comedically, they then used for prostitution): http://www.nytimes.com/2005/06/05/magazine/05FREAK.html?ei=5090&en=af2d9755a2c32ba8&ex=1275624000&partner=rssuserland&emc=rss&adxnnlx=1118160068-1EGJuan4FJH1LooxHYd5/g&pagewanted=all

Then there's the everlasting impact of Koko, the Silverback Gorilla who was taught - and perfectly capable of replying in - sign language: http://en.wikipedia.org/wiki/Koko_%28gorilla%29

The idea that humans jumped onto the scene with unforeseen amounts of intelligence and capability probably isn't what happened. Obviously we are capable of constructing and using the most advanced tools on the planet, but this is after several thousand generations of innovation. The very first human might have been more intelligent (or at least had the capacity to be), but otherwise probably fit in pretty well with its parents and other relatives since the vast majority of what we learn comes from our parents and personal experience.

Then over time the number of individuals with the capacity for higher modes of thinking increased as a result of the genetic inheritance of whatever mutation created the first human. The first human, to put it simply, was successfully able to pass on their mutation which gave them our unique traits, and their offspring were also successful - until you have an entire population of humans living amongst each other. Eventually our innovative capacity lead, step by step, to our dominant position on the planet.

Even now humans are yet evolving. Lactose tolerance (the ability to consume dairy products after childhood) is a very new trait among humans (and unprecedented among all mammals) only a few hundred generations old (roughly 10,000 years) that evolved twice in separate populations of humans (North Africa and Northern Europe). Our jaws are getting progressively smaller (which is why some people have to remove their wisdom teeth to maintain a straight smile - and some people don't have wisdom teeth at all), some muscles are disappearing (the Palmaris Longus is one example - it's present in about 80% of humans), and other subtle changes are occurring.

Just don't make the mistake of equating "evolved" with "superior." Evolution is dictated by the ever-changing demands of the environments we find ourselves in, and what's beneficial today isn't guaranteed to be beneficial forever.

research process - How should I proceed if the author of a paper doesn't reply to my query?

During my literature survey for a project, I came across papers that have very interesting results and have decent number of citations. Unfortunately, the sub-area I am exploring doesn’t have any standard datasets on which I can test my implementation of the algorithm presented in their paper. The authors have generated the dataset by applying certain actions on a subset of publicly available information, which are Flickr images in my case.

So I have contacted the authors, asking whether they can provide me with their code or the dataset that they used for their implementation so that I may use it to perform my experiments and possibly try to extend their approach. But almost a week has passed by, and I haven’t received any response from them. Does that mean that,

1. My method of asking was not correct? (In that case, what would be appropriate?)


2. They don’t have the code or dataset available? (The papers were published in 2004.)



3. They are not interested?

Would it be polite to remind them again?

Answer

The reason for no response may be anything from what Anthony Labarre mentions to what you list. I would wait at least a month before writing off an answer; I have received answers even later than that. It is possible they are working on improvements themselves as well. In short, if you do not get a reply, you can only try again and perhaps express your request differently.

You seem to have contacted all the authors. Sometimes the project leader may be too busy to pick up on questions and the younger team members may be more inclined to answer. They may, however, want to talk it over among themselves, and it is probably not a high priority.

So try again in a couple of weeks. If you do not get any response, then there is probably not much you can do. You probably need to think about what can do to improve the chances for a reply. The word "collaboration" is sometimes a good way to "sweeten the deal". Sometimes, just to give a perspective, I get requests for data that are of the sort, give me this or that, give me everything, and I often end up asking myself, why should I? having laboured to generate the data. I am not saying you must follow such an approach to be successful but considering why the other researchers would want to help you may also help towards solving the communication problem.