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Why are considered to be exceptions to the cell theory?
If I am not mistaken, the cell theory states that:

All living organisms are made up of one or more cells
Cells are the smallest units of life
Cells arise from pre-existing cells

Aseptate fungal hyphae, giant algae and skeletal muscle fibres are often used as examples of cells that do not conform to the cell theory
(as explained in the first section on this site: http://www.patana.ac.th/Secondary/science/IBtopics/IBCell%20(01)/Pages/1.1.htm)
I am aware that the listed cells  are unlike the "standard cell" (e.g. large, many nuclei within one cell), but how do they go against the three points in the cell theory? (as there is no mention of how they should appear in the theory)

The theory is old and obsolete. When it was postulated many of these examples were not considered. This is the age of molecular cell biology and you very well know that all these tissue types do conform to the same basic cellular principles. I do not understand the point of your question.

Skeletal muscle is a tissue, not an organism. Your title is lacking a noun making it incomprehensible. You do not cite a source for the contention  that aseptate fungi and giant algae are not considered cells. Please clean up your question.

If you are looking for a hard and fast answer, there is none. Life does not have a hard and fast definition, so it is impossible to identify something that everyone will recognize as both living and non-cellular. However, your question is answerable if you are interested in a summary of the state of the debate.

All living organisms are made up of one or more cells

An exception to this would be known as non-cellular life. Whether anything could or does fill this category is a controversial subject. Considering the fact that the main points included in most definitions of life are based on observations of cellular life, this is not surprising.
That said, the strongest candidates for non-cellular live (in descending order) include: viruses, viroids, and prions.

Viruses are the non-cellular entities most often considered potentially alive. Viruses are even classified in genera and species. They carry genetic material, they evolve, and they use various proteins as their machinery of action. However, they are unable to metabolize or produce their own proteins. They depend on infected cells doing all the work for them. Because they are incapable of acting on their own, they are not considered living by many. (The definition for life usually includes metabolism, the ability to process one's own food and energy.) The counter argument is that all life ultimately depends on external factors for survival, so viruses depending on cells for living may not be a disqualifier since they still control the processes resulting in their reproduction.
Viroids, like viruses, depend on other cells to carry out metabolic and reproductive processes for them. Unlike viruses, viroids lack any protein whatsoever. As a result, even fewer people are willing to consider viroids alive. However, the same logic used to argue the biotic nature for viruses can be used for viroids. They copy themselves the same way many RNA viruses do, with the exception being they forgo directing the construction of any protein coat for themselves. Conversely, viroids can also be used as evidence against even viruses being considered life, precisely because of their similarity. If a viroid obviously is not life, viruses must be held suspect since they can be argued similar to viroids than cellular life.
Prions are interesting. They do not reproduce like viruses or cell. That is to say they do not direct cellular machinery to construct copies of themselves. Instead, they modify existing proteins they come in contact with. From one angle, they are far less active in their flavor of reproduction than viruses. However, they are also more directly involved in their self-replication than viruses. Also, they can spread between organisms. Mad cow disease is a well known example of this.

Back and forth arguments aside, it has become fairly common to view viruses as a sort of middle ground between life and non-living processes, not one or the other.

Cells are the smallest units of life

This one is not quite separate from numbers 1 and 3.

Any non-cellular lifeform could be used to ague that the cell is not the most basic unit of life.
Similarly, a cell not originating from a pre-existing cell could be used to make a similar argument, because it is highly unlikely that all the process found in even the most basic cell could start from scratch. They would have needed to exist before the cell in question, and then become incorporated into the cell. This rational is the basis for hypotheses like the RNA world, which are used to explain how life's processes could have been started, in piecemeal fashion, before being locked in cells.

Of course, we can also used the above points to argue that the cell is, indeed, the smallest unit of life.

Viruses, viroids, and prions can be used to support the assertion that cells are the smallest unit of life. Even though they are not cells, their reproduction is only made possible by the activities carried out within cells.
Things like the RNA world (if real) only would have required the processes for life gradually building up from less ordered, spontaneous reactions. An RNA world could be argued as a world devoid of true life, since life can be argued as things which carry out several key processes, and not as merely the individual processes themselves.

Cells arise from pre-existing cells

This one is the only easy one. This had to be violated by abiogenesis. There had to be a first cell (or group of cells created under common conditions).
The origins of the first cell(s) are variously explained by the RNA world, GADV-protein world, and other hypotheses. Whatever the case, however life started, it had to have a start. This means there must be one or more cells in the history of life that were spawned from some natural processes lacking a parent cell.
While it may be safe to say that all cells in history are not required to have had parent cells, it is also safe to say that it is extremely uncommon for parentless cells to come into being. Firstly, it is not something we have ever observed. Secondly, all indications point to all species being part of the same "tree of life." This would mean that whatever conditions spawned the first cells must not have persisted. Either all life descend from the same single cell, or life started with many independently spawned cells that blurred their independent origins due to heavy horizontal gene transfer.
We can view the cells from cells rule a bit like Newtonian gravity. It is mostly right, with its faults only becoming ovbvious under conditions not readily present on Earth.
EDIT:

Aseptate fungal hyphae, giant algae and skeletal muscle fibres are often used as examples of cells that do not conform to the cell theory
...
I am aware that the listed cells are unlike the "standard cell" ...

I forgot to answer this part, and I think it is important to do so. All of those conform to cell theory. Whether they conform to the pattern common to most cells or not is irrelevant. They all have the components required to classify them as cells. Cells merging into multi-nucleated cells (like skeletal muscle fibers) is actually a good argument for cells being the basic unit of life. Even though they start separate, they collaborate by becoming one cell. Sure, the resultant cell has multiple nuclei, but most cells on Earth have no nuclei.


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Could potatoes that have been refrigerated for 142 days still sprout?
I know that potatoes can still sprout even after being refrigerated, but could you refrigerate them for 142 days still use them as seeds to grow more plants after that?
As for the temperature of the fridge, look at this.
 Considering that, what would be likely?
What about frozen or hermetic potatoes?
If anyone's wondering why I'm asking such a strange question, here's why.

This question is probably better suited to [gardening.se].

It would also be better if you can specify what range of temperature you are talking about. By fridged, you mean ~4⁰C?

@WYSIWYG I updated the question.

Yes, it is very common to do this on farms. I have grown potatoes that I stored loosely in my basement (~5 degree temperature during the winter when they would be stored) for about 6 months at a time every year for the past 20 years.

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How important is dead wood to the ecology of a forest and how much dead wood should remain?
I have a local 'forest management group' in my area. One of the jobs they do is to thin out the forest by removing some of the trees. They then sell the wood for firewood.
I want to tell them to leave the dead wood in the forest, as it is part of the food chain.
Can anyone give me an estimate of how much energy dead wood provides for the food chain in a temperate forest, as a percentage of the overall energy generated by the forest?
Also how biodiverse is a forest with the dead wood left alone compared to one with the dead wood removed?

It's going to depend on your area.  From my observation, in much of the US west dead wood does not seem to form a major part of the food chain.  It is removed by fires.

@jamesqf I live in the UK. It is too wet here for forest fires.

my rspb woodsman left wood heaps of any size and height as animal nests.     depends on climate. they can favor ants hhogs mammals  parasite ichnumanae and uncommon beetles. a wild forest has much more detrital ecology biomass and insects.

Dead wood is indeed very important to the food chain, and to the ecology and biodiversity of a forest. Lots of biota depend on the presence of dead wood; many species of insects and fungi only live on dead wood. See for example this study of fungi in Danish Beech forests.
The amount of dead wood present depends, apart from management, on the type of forest, the climate (and hence altitude!) and the age of the forest. This study provides a preliminary estimate of the amount of dead wood in British forests and might be very interesting to you. This study gives a review of dead wood in European (incl UK) Beech forest reserves.
I hope this will help you in the right direction. If you want any more information, please let us know. And preferably include some more information on the specific forest (type of trees, location) that you are interested in.
EDIT:
I recently stumbled across this project,
 that resulted in a systematic review of interventions that affected the occurrence of dead wood and saproxylic species in boreal and temperate forests.

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T7 vs pBAD promoter strength
How do pBAD and T7 promoter strengths compare when pBAD is induced under conditions which would lead to its highest strength? Are there any papers that compare the two inducible promoter systems?

The exact numbers depend on the protein.
In my hands, I would say between the pAraBAD and a pLac-T5 you can expect some fold improvement and pAraBAD and a pT7 promoters you can expect about an order of magnitude… but there are two big caveats.
First, pET and other T7 driven plasmids do not work in K-12 strains (without the pTARA) so BL21 is generally used. BL21 is much better generally than K-12 strains (more primary metabolites cf. omics paper) for a lot of things including protein expression. The chaperone balance is difference, so you will get differences from that too —if you have used the TAKARA plasmid team you will have notice how quirky protein expression optimisation can be.
Second, if your protein has a cofactor you have a major problem. FeS clusters, PLP, heme or even O2 (for maturation of GFP, say) become rate limiting and overexpressing the protein can make it highly toxic and you might be in a bigger pickle than with a lower level promoter. In my thesis, I added pyridoxine to a pBAD expression, because it gave an improvement.
Footnote: If the expression levels are really bad, changing promoter might not be that great as the assumption of linearity might not hold.
If heterologous and not codon optimised, consider using pRARE.
If insoluble, consider using ethanol shock and the TAKARA chaperon plasmid team.
If oxygen sensitive, alas, doing expression and purification anaerobically.

thanks for taking a stab at this old question... I know that it may be difficult, but do you think you could add some links/references to some of the details you mention in your answer? We always appreciate references/links on BiologySE - and it will definitely improve the strength of your answer. Thanks!

The best reference I could find is a book chapter (available here as a .pdf) from a book on optimizing the expression of membrane proteins that argues for T7. Although they don't appear to reference a source for that specific claim, some of the references close by could be helpful. It seems as though this applies in general, not just to membrane proteins. They bring up limitations of the araBAD system as well:

However, careful studies of the araBAD promoter by Siegele et al. [20] and Giacalone et al. [21] both agreed that at subsaturating levels of l- arabinose, protein expression cultures contain a mixed population with only some of the cells expressing protein. In addition, the potential for protein overexpression is generally lower when a pBAD vector is compared to T7-mediated expression from a pET construct.

In general, T7 is stronger in producing mRNA. However, the final amount of protein produced depends on other factors like protein solubility, stability, and toxicity.
Here, https://microbialcellfactories.biomedcentral.com/articles/10.1186/1475-2859-12-26, is a study that compares T7 and other promoters for the production of few different proteins.


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Can plant roots absorb dissolved oxygen from water, or do they require actual bubbles in the water?
Plant roots generally require some exposure to air.  Deep water culture is a form of hydroponics where plants' roots are left to soak in water.  To keep the roots from "suffocating", most online sources recommend some kind of aeration mechanism, such as an air stone.
These systems normally wind up filling the water with bubbles, right under the plants.  So my question is: are the bubbles responsible for keeping the plants alive, or is the dissolved oxygen keeping the plants alive?  So for example, could plants live immediately downstream of a waterfall (a natural aerator), even out of range of its bubbles?


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Why don't membrane proteins move?
I understand that based on their tertiary structure, intrinsic proteins have hydrophobic non-polar R-groups on their surface and that they 'interact with the hydrophobic core of the cell membrane to keep them in place'.
But how does the hydrophobicity of both the protein and the cell membrane prevent the protein from moving?

Related: Domains in cell membrane

prevent from moving in what direction? Laterally, left-right, or axially, from-into the membrane?

who said they don't move ? how could the do their job without moving (apart from passive and pores) , I believe correct if I am wrong they they do move a lot like breathing

Proteins can move around the membrane.
Most proteins do move within the membrane. The membrane is a liquid crystal and has fluid behaviour. Specifically, this is due to the membrane being in a gel-state. This gel state allows phase behaviour which means that the protein is able to move around on the surface. This results in an effect that is often referred to as the fluid mosaic model.
Proteins tend not to move out of the membrane.
The protein doesn't leave the membrane as a result of the transmembrane helix being very hydrophobic. This hydrophobicity and the hydrophobicity of the lipid tails means that they self-associate. A better way of describing it is that they fiercely dissociate from the water. A molecular dynamics simulation showed that even in simulations the membrane will readily self-assemble as a result of the hydrophobicity.
This is achieved by a few properties of the TMH sequence. There is a large amount of hydrophobic residues like leucine. At either end of the helix are large aromatic residues called the aromatic belt. Further is the electrostatic satisfaction offered by Gunnar von Heijne's famous positive inside rule and the recently identified negative-outside rule present in helices with evolutionary pressure to optimise anchorage.

(Image source: Baker et al., 2017)
Cymer et al. published a study showing the free energies associated with each part of the transmembrane protein helix (figure below). The overall ΔG for a transmembrane helix in the membrane is ~-12kcal mol−1. This means that the association of the helix in the membrane is typically spontaneous.

(Image source: Cymer et al., 2015)

Good answer. A video of proteins in a membrane actually moving is always fun: https://www.youtube.com/watch?v=CfRvmtBdZ9I

No other answer has mentioned this so I created an account just to say this.
Some membrane proteins do not move. This is because they are fixed in that position in the membrane due to the cytoskeleton. Erythrocytes are a good example of this.
The main protein that is immobilised in erythrocyte membrane is Band 4.1 protein, and its immobilised by Spectrin.
Spectrin forms a tetramer(2 dimers together) that acts like a chain/rope connecting membrane proteins and locking them in place.  Spectrin connects to the Band 4.1 protein (also to actin, but less important).
Spectrin also binds to Band 3 protein (via ankyrin, just a protein that connects spectrin to band 3). Band 3 is an anion channel (for HCO3- and Cl-, which is important for red blood cells to function. you want these band 3 proteins to remain evenly spaced out, so they are fixed in the membrane to make sure)

It is important for some membrane proteins not to move. Otherwise the cell will lose its function in some cases(like intestinal epithelial cells), imagine if SGLUT-1 (glucose + galactose transporter) moves from the luminal side(facing lumen) to the basolateral side(the other side)... what do you think happens to its ability to take in glucose from intestinal lumen?
Finally as a bonus, some membrane proteins are also fixed in the plasma membrane in plants (does not move around) by cell wall.
Anyway, if you are bored of someone immediately saying "fluid mosaic model means proteins always move around!" then read my answer

So that's how they do it! Could you add some references so I can go and dig up on these examples further?

There are two types of proteins that are present in a membrane, because you have not been specific about which type of protein you are talking about I will consider that you are talking about Integral membrane proteins.
For more clarity I will begin by explaining to you what are these proteins present in the membrane.
As I said there are two types of proteins 1.

Integral membrane proteins - these are proteins that are present inside the membrane.
Peripheral membrane proteins - These are the proteins present outside the membrane on either side linked through weak electrostatic interactions with the lipids or the integral membrane proteins.

As it is clear from the diagram given below.
 [2]
You have not been clear about which type of protein you are talking about so I assume that you are talking about Integral proteins.
You stated in a nutshell that protein is hydrophilic so it should move out of the membrane as the membrane is hydrophobic. ( correct me if I am wrong here).
You are highly mistaken here. Yes there are R groups present on the protein but when the protein undergoes transitions to secondary and tertiary structure these hydrophilic R groups move to the innermost in the protein structure and hydrophobic groups are outside facing the lipid sea, so they can establish hydrophobic interactions with the lipids.
Integral proteins Do not move out of the membrane because of the strong hydrophobic interactions.
But they do move inside the membrane. As the membrane structure is fluid mosaic. Imagine of proteins as floating in a sea of lipids.
https://www.ncbi.nlm.nih.gov/books/NBK26878/
1 = Principles of Biochemistry Lehninger.
[2] = http://cbc.arizona.edu/classes/bioc462/462a/NOTES/LIPIDS/Membranes.html for the image.

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Why can't proteins make copies of themselves?
Why is it impossible for proteins to make copies of themselves?

Because they have no "blueprint" to work off of. DNA is the "set of directions" used to make new proteins.

I mean you could argue that proteins do make copies of themselves, just not de novo.

It is not impossible. It simply didn't evolve that way.

I really don't think questions of the type "why is the world like this" are either answerable or useful. If you can offer a biological alternative and argue that it was equally feasible, that is a different matter.

If the question was "Are there proteins that are able to self-replicate?" everyone would be happy I suppose. @tesgoe you should rephrase your post. You could say that DNA is able to self-replicate to make a tiny bit of content to your post.

It is not theoretically impossible for protein to self-replicate. It just did not evolve to be a common mechanism. Lee et al. 1996 reports finding of a protein that can catalyze its own synthesis.

It is possible indirectly in case of prion.See this link

It would be helpful if you summarized the link in your answer, in case it is not available in the future


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Is DNA of all animals made of the A,T,C,G blocks?
Is it true that DNA of all animals is made of the A,T,C,G blocks?

DNA is not made of these blocks only. But the genetic information is conveyed through the series of these four block.
All animals, all plants, all fungi, all other eukaryotes, all bacteria, all archea and plenty of viruses use these four blocks. Some viruses use RNA instead of DNA, in which case the T is replaced by a U (Uracile).
You will find more information on wikipedia > DNA.


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What makes DNA sequences most different/recognizable from a biological perspective?
We can pretty easily quantify the amount difference between two different strings/sequences of characters. For example, if we take the words trebuchet and trebucket, we can say they have a Levenshtein distance of 1 (only one character-edit's worth of difference).
From a linguistic perspective, that distance is very noticeable, whereas the difference between compliment and complement is much less so. Even though both pairs are only one edit apart (respectively), not all differences are equally distinguishable.
What is the biological equivalent of this when it comes to DNA binding proteins and RNAs? How do I identify which DNA sequences are more recognizably distinct from each other? For example, if we took a DNA binding protein that recognizes the sequence TGCCTCGAA, is it more likely to misrecognize AGCCTGGAA than TGCCAGGAA (or vice versa) as its target sequence?

That's going to vary from one nucleic acid binding protein to another. Remember, DNA and RNA are not simply  sequences of letters, but there can be secondary structure, as well as more basic chemical interactions between adjacent or more distant bases - see RNA hairpins as an example. All of this gives a "shape" to the recognized portion of nucleic acid, which can inform binding capacity just as much as, or potentially even more than primary sequence alone.

@MattDMo That is certainly not surprising, but it would seem to me that there should be some parameter to that variation. For example, perhaps switching a single base in a sequence from a purine to pyrimidines is more distinguishable than to the other purine.

Related: http://biology.stackexchange.com/q/38808/3340

This isn't a question with a really well accepted answer yet, and comes up quite a lot in e.g. studies of population variation in transcription factor motifs.
Usually, we approximate the sequence preferences of a DNA-binding protein with a position weight matrix. A weight matrix will given you a score for two sequences,  so the simplest means of quantifying the relative binding strengths for two sequences is compare these scores. You could also, say, compare the relative chances of getting the scores under some background distribution of scores, which would arguably be more comparable between different factors.
The PWM score (and other means of describing sequence specificity) are only an approximation to what you're really interested though, which is the binding energy associated with the interaction. If you have detailed experimental evidence about how the protein binds, you might use that, and there are some papers that build models for approximating it from PWM scores as well. However the reality is that you can only ever roughly approximate what's happening in the cell, because of molecular crowding, and the many other factors that will be present on the chromatin fibre, so the difference in PWM scores is often what gets used.
Predicting the impact of a given change to a non coding DNA sequence is a hard problem. Sometimes you'll get a decent approximation, but in the end you're trying to do biochemistry from first principles, and we aren't there yet.

DNA is a chemical, and therefore, its interactions are governed by its shape.  There is no way to look at a DNA sequence and know all the ramifications that changing a letter will have on its shape.  I could tell you that changing the first two or last two letters of an intron are highly likely to destroy a splice site, but you can't make hard and fast predictions about DNA binding in many other situations.


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Programs/software to graph biological data
I am curious about what sort of programs people in my field of academia are using to graph their data. I am a master student in biochemistry, and have more or less only used excel (on rare occasions I have used the statistical program R).
I know my question is very general, but I would like to hear suggestions for programs that could be useful when I want to compute numbers or make good graphical representation (in the life science fields, e.g. microbiology, biochemistry, biotechnology, biophysical chemistry).
Also, are the other programs to create good graphic illustrations? I am currently using Inkscape to produce molecular illustrations, and I think this is one of the most typical programs to use?

The vast majority of people use R. A priori it is really what you want to learn to use. However, it depends on the kind of data you have. If you have loads of data or if you have certain specific data you might want to use specific software for big data visualization or do you own thing in C++ or some other language. If you are working with mathematical modelling Mathematica might be handy. The answer also depends on what kind of manipulation you have to do on your data. Again R is pretty handy for manipulating data quantity that are standard in Biology.

I'm voting to close this question as off-topic because 1) the question is too broad and 2) the question is about data visualization and is not specific to biology.

GraphPad Prism is very widely used.

use R - it's versatile and free and data visualisation is very good in R

Thank you for there answers.  I will argue that this is specifically for biology. I mean, statistic and visualization of your data is a vital part of presenting everything you have. I can't post this question in a different stack exchange, because I'm interested to hear what programs people who study biochemistry, biotechnology and microbiology use. Therefore, I think this is highly relevant for the biology page.

The answer I am looking for is just a simple list of suggestions of program (not programing languages) which are good. Although the answer below is a very good one, it not exactly what I am looking for.

Unless the kind of analysis you do is really "standard" and readily available as a program, a bit of programming may become necessary. Some years ago, I found it practical to use python to perform analyses and generate custom graphical representation via the TikZ package of LaTeX. Now I most often use matplotlib, but this is still python programming. Inkscape is useful to adjust your final figures, but programmatically generating graphical representations allows rapid updates when data  or parameters change.

There is no standard software, programming language, or library used for computing and graphing biological data. The R language is commonly used for statistical work, but Python (in conjunction with the SciPy stack) and C++ also gets used a lot.
Before going further, I should point out you are asking two questions. One about computation and the other about presentation.
Computation:
You mentioned R. That is a programming language (with specialized software built around it). When it comes to programming language choice, the three most important factors are personal preference, available libraries, and the language already in use if you are joining a project with a pre-existing code-base. You might notice performance is not on this short list. The reason for this is that modern languages on modern hardware are efficient enough to, very often, make language choice irrelevant from a performance perspective. Most computational tasks suitable for R are also suitable for languages like Python and Julia.
R's strength is in its focus for statistical work. Whereas Python's standard library comes with many bells and whistles intended to ease the design of small to moderate sized pieces of software across many situations, R comes with many features focused specifically on easing the task of writing code for complicated statistical calculations. Due to it being so common in academia, R one language you are very likely to encounter in academic, computational work. That makes knowing it a helpful skill (whether you regularly use it or not). R, having such a strong suite of tools and large userbase, is also the basis for many third-party tools. One interesting example is Bioconductor
That being said, there are also advantages to using (or at least knowing) Python. It is one of the most common languages in programming in general. That means it has a diverse assortment of libraries designed for it by members of the community. When it comes to nonstatistical libraries, few languages have as many options. When it comes to statistical work, Python does not have as many options as R, but it does still have a lot. The SciPy stack, which includes the NumPy and Pandas libraries, is a big part of that. Another benefit that comes from Python's popularity in the programming world is that you will have an easier time finding programmers to help you on a large project if you decide to look for help outside of your field. (For example, far more computer science majors will know Python than R ...multiple times over.)
As far as the Julia language goes, it is still new. It is being developed as a competitor to R and Python, but it is not mature enough to be a serious contender just yet. However, since your question is about what people use, I would be remiss to ignore it. It may become a serious contender, but for right now, it is still trying to grow and prove its worth.
If performance becomes a serious issue, you would probably be forced to dump R, Python, or whatever very high level language you are using in favor of C or C++. Both are far more efficient than the so-called "batteries included" languages. C and C++ are system programming languages. They are often used in operating systems, databases, video games, and other large pieces of software which you would not want slowed down due to features being included which you did not explicitly program in. (C and C++ are also used in small projects, but I digress.) However, taking advantage of this efficiency requires some insight into how computers actually work. This is not a difficult task, and any good tutorial or book will cover such topics. Using C or C++ without this basic understanding will likely be counter productive, as doing so can lead to you inadvertently creating a program far slower than it needs to be, which could be an issue if performance is becoming a serious concern.
A non-performance benefit of C is the large library selection. As I mentioned above, few languages can compare to Python's library selection. C is definitely one of those few. Actually, C is essentially the lingua franca of libraries (due to its age, its dominant position, and aforementioned efficiency). This means you might find yourself needing to use some highly specialized library with no bindings to languages like Python or R. I have come across a few.
Presentation:
R is also a nice language for graphing data. It comes with a package known as ggplot2, which is responsible for this functionality. This is a common source of data visualizations. However, this does not mean you must use R if you want to create graphs that follow the norms of academia. First off, many people use other pieces of software for plotting. Secondly, there is the library for Python called matplotlib. It is fully featured and is capable imitating the visual appearance of ggplot. One of its built-in style sheets was included just for this purpose. Not surprisingly, matplotlib is the graphing library used in SciPy.
There are many other graphing/plotting libraries, and many of them are fairly language agnostic. Presentation is not often a deciding factor in what language to use for computation. However, if you found yourself needing to use a specific language for its presentation options, doing computations in one language and then feeding the results into another for presentation is not unheard of.
Software:
I include this as a third category because this is more complicated than deciding on programming language A or B or choosing a plotting utility. Non-computer scientists often confuse software with languages. You have to use software when writing a program in a given language, but all the work is done by the program you write. Large software suites, on the other hand, do work for you in many ways. They often come with some sort of quasi-programming language included, but that is only one part of a larger whole. While R comes with a number of software tools, Matlab and Mathematica are better example of this. Free, open source alternatives to these proprietary suites would be Octave and Scilab, although such open source software traditionally has not very competitive with the likes of Matlab. SciPy is a recent example of open source software closing the feature gap (which is a very good thing in terms of avoiding vendor lock).
Beyond that, it is hard to say what you would encounter or what is common. Most large software tools are proprietary. Different companies, universities, and individual projects will use different software, and you will not know what to expect until you you are recruited by a team.
This is why projects sticking to programming languages over large software is preferable in terms of ease of recruitment. Also, programming code can be published with papers a lot easier than describing how you used a piece of software (which is big if open data is important to you). That being said, it can not always be avoided. Many machines in biology require specialized software and many kinds of data are most easily manipulated with proprietary software. Do not worry about such situations. It, obviously, will not affect your current work from being presented in a standard way.
EDIT:
Sorry for the wall of text. The question was broad and this is a complicated issue. It was hard to summarize.


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Do antagonist-receptor complexes get absorbed into the cell?
My understanding is that normally, a neurotransmitter binds to a receptor and then that ligand-receptor complex gets absorbed into the cell and either degraded or recycled. So does the same thing happen when the ligand is not the endogenous neurotransmitter, but instead an "artificial" antagonist -- like, say, bupropion bound to a nicotinic receptor?

When a neurotransmitter, like serotonin, binds to it's specific receptor, the ligand-receptor complex is not phagocytosed.  Picture the human cell membrane and think of the serotonin receptor like a long piece of rope that has been wound up, but is embedded inside the cell membrane forever.  After the serotonin binds, the structure of the receptor changes, causing lots of steps to start (like a domino effect) where molecules are released from the inner aspect of the receptor, thus carrying the message into the nucleus.

Are you saying that 5-HT receptors don't internalize? This answer may need a little more research.

What kind of molecules internalize? Are we talking ions?


text:
Where does female ejaculate actually originate from?
It's common knowledge that it's released via the urethra, but where does it originate? If it doesn't come from a part of the clitoris, then why is the clitoral glans called the clitoral glans? How does it travel from the originating spot to the urethra?

According to this article female ejaculate is produced by the Skene glands.

That article actually states that the ejaculate is mostly urine...

@Marius No it says there are "small amounts of urine" in some samples. And being female, I can tell you ejaculate and urine are vastly different. Nearly every sense a healthy human has can differentiate between ejaculate and urine.

@Metamorph I don't understand your comment. Your link does say that in 2 out of 7 women, the ejaculate was identical to the urine sample taken beforehand, and for the other five, it was only slightly different: "they either release plain old urine, or urine that's been diluted by fluids from the female prostate glands." they found small amounts of PSA in addition to urine. Where are you getting "small amounts of urine" from?

@YviDe The question asked where it comes from, and a quick google gave me this. Discussing it's composition isn't relevant. I've given an answer where no one else has, so critiquing mine with something I haven't mentioned doesn't make sense.

I wasn't at all critiquing your answer, I was asking why you answered @Marius 's comment about the article you linked with "No, it says X" when the link you answered with seems to be saying the exact opposite.

The question asked where it comes from, and a quick google gave me this. Discussing it's composition isn't relevant. I've given an answer where no one else has, so critiquing mine with something I haven't mentioned doesn't make sense.

@Metamorph Thank-you for your answer. Can you elaborate on the rest of my question? Because, as you mentioned yours is the only answer I've gotten.

I had written up an answer to a now-deleted post about 3 years ago addressing this very question, so I thought I'd copy my answer from that deleted post here:

The answer is dependent on which fluid (vaginal lubricant or female ejaculate) you are referring to. Since you mention both, I will address both.
Vaginal lubricant
Quoting from wikipedia > vaginal lubrification

Vaginal lubrication is a naturally produced fluid that lubricates a woman's vagina. Vaginal lubrication is always present, but production increases significantly near ovulation and during sexual arousal in anticipation of sexual intercourse. Vaginal dryness is the condition in which this lubrication is insufficient, and sometimes artificial lubricants are used to augment it. Without sufficient lubrication, sexual intercourse can be painful to women. The vaginal lining has no glands, and therefore the vagina must rely on other methods of lubrication. Plasma seepage from vaginal walls due to vascular engorgement is considered to be the chief lubrication source, and the Bartholin's glands, located slightly below and to the left and right of the introitus (vaginal opening), also secrete mucus to augment vaginal-wall secretions. Near ovulation, cervical mucous provides additional lubrication.

Female ejaculate
Salama et al. (2015) from the Journal of Sexual Medicine feel fairly confident that they have good evidence that female ejaculate is actually urine from the bladder.
However, this topic has been under scrutiny for some time. I have seen numerous suggestions (though little hard evidence) that the Skene's glands (which are homologous to the male prostate) are responsible. This belief originated from the early work of Ernst Gräfenberg (yeah, the namesake of the G-spot). However, Salanas et al. (2015) did confirm the presence of at least marginal levels of prostatic secretions (e.g., PSA) in female ejaculate from their study. This at least partially corroborates the involvement of the Skeen's glands in the ejaculatory process.  Bartholin glands (homologous to male Bulbourethral glands) have also been assumed to play a role. Further, Salanas et al. are not the first to propose that the ejaculate originates from the urethra. You can find a relatively simple outline and list of citations on the matter on Wikipedia.
We'll have to see how the scientific community responds to the work of Salama et al. ...


text:
Guide to learning about Population biology / ecology / dynamics for a non-Biologist
I'm an applied mathematician who works in the field of feedback control systems but has been becoming interested in looking at population biology (e.g. Lotka-Volterra, Mathusian growth,etc.) from a mathematical feedback and systems theory standpoint. Since I know almost nothing about biology save for a poor frog I nearly threw up on while trying to dissect in High School, I wanted to ask this community if anyone could provide me with some materials that give a good overview of the current state of this field.

You will find recommendations for book on population genetics here. The recommendations in General Entry books to population genetics and the 3 first recommendations in Other books treating specific subjects within population genetics can interest you.

Can I be dumb and ask whether or not population genetics has anything to do with dynamics (e.g. number of species X in a habitat)?

Yes, it does. For example, if a population fails to adapt in an environment or if deleterious mutations accumulate in a population due to a too low population size and causes the population to get locally extinct (mutational meltdown). This kind of simple dynamic of species-interaction might be quite different when you add genetics and evolution in the model. So yes, it is very much related. I would call population genetics a subfield of population biology.

To those who voted to close: I do not think this question is "too broad" or "primarily-opinion based". The OP is just looking for recommendation of books in population biology. Look for "population biology" on amazon and you get several interesting hits. We just need one person who read these books and can formulate a critic.

Yes, as Remi.b says, I am not asking for opinions on the subject, but rather which books are commonly used. Having "standard" texts is a common thing in most fields of science and I was simply asking what those are and if people can provide general criticism to direct a newcomer.

@Remi.b I strongly disagree. Although population genetics are important to population dynamics in a broader sense, the examples the OP provides (e.g. Lotka-Volterra, Malthusian growth) have next to nothing to do with genetics but with growth and hunter-pray interactions. There must be better books to suggest here!

®RHA I never said the post that asked for recommendations on population genetics was a duplicate. I just think it is worth mentioning this post as it may interest the OP. I then argued that population dynamics also depend on its evolution (as you did as well). But I recognize the question as being valid (and am the only one who up voted it so far). I am personally not able to offer good book suggestion in this field as I haven't read them.

Remi.b's suggestion has indeed been useful to me; for one, I didn't know that genetics and dynamics were so closely related and am grateful someone took the time to explain this to me. I think I am most interested in learning about simpler models from a mathematical perspective, then diving more fully into population genetics, as undoubtedly getting to the point where I even understand the background in genetics for the latter will be an undertaking!
Consider for instance this text, which I have been reading: http://www.springer.com/us/book/9783319030258
How close is this to SoA?

I've also found this good looking book on the SIAM (professional applied math organization) website: http://epubs.siam.org/doi/book/10.1137/1.9781611970005

@Remi.b is right, population genetics is a sub field of population biology, the other being population ecology. The two fields are intimately related at their foundations, but diverged long ago to become fairly distinct. A good summary of the historical context can be found in the introduction to Singh, Rama S., and Marcy K. Uyenoyama, eds. The evolution of population biology. Cambridge University Press, 2004. They also include models that may be of interest. But for studying populations as dynamical systems, see my post below.

I'm a fan of E. O. Wilson and W. H. Bossert's "A Primer of Population Biology", which has been continuously in print since 1971, as a first introduction. (http://www.sinauer.com/a-primer-of-population-biology.html)

Related: https://biology.stackexchange.com/questions/54796/recommendation-of-ecology-books

There are three seminal works that will give you an excellent grounding in the study of dynamical systems in population biology.
The first details the foundations of population genetics:
Crow, James F., and Motoo Kimura. An introduction to population genetics theory. (1970).
The second deals with population ecology:
Maynard-Smith, John. Models in ecology. CUP Archive, 1978.
Finally, the field of evolutionary game theory has recently embraced a dynamical systems approach to studying populations under the guise of a new theoretical framework known simply as evolutionary dynamics:
Nowak, Martin A. Evolutionary dynamics. Harvard University Press, 2006.
I would probably read these in reverse order. The introduction to Maynard Smith's book has a nice discussion about some of the interrelations between population genetics and population ecology. Most of the original work was done by Fisher, but you don't want to read his stuff - it's famously inaccessible.
It is probably worth me telling you that a lot of mathematical biologists don't actually use dynamical systems approaches in their work. Instead, they favour optimisation models. A balanced discussion about the benefits of this approach, and some of the limitations of the dynamical systems approach can be found in these papers:
Smith, J. Maynard. "Optimization theory in evolution." Annual Review of Ecology and Systematics 9 (1978): 31-56.
Parker, Geoffrey A., and J. Maynard Smith. "Optimality theory in evolutionary biology." Nature 348.6296 (1990): 27-33.
As it happens, my soon to be submitted doctoral thesis is on dynamical systems in biology. Hopefully one day I will be able to add my own book to this list :)

Most of the books you cite are really about population genetics and a more in depth review (with IMO better books) can be found here. I don't know "Models in ecology" by J. Maynard-Smith but form its title, it sounds like a good recommendation.

@Remi.b your in-depth review does not answer the OPs question, which is about population biology and dynamical systems. Only a few of the books you posted deal with this explicitly. My answer is focussed and to the point. There is only 1 book on population genetics. It is probably the most cited book on population genetics and a must read for anyone getting into the field (it is not in your list). I presume you were confusing Evolutionary Dynamics with a book about population genetics? Well, it's nothing of the sort. It is a book about modelling frequency-dependent selection in populations.

Too often in biology is mathematical modelling of the evolution of populations conflated with mathematical modelling of the dynamics of populations (from which evolution then occurs). For instance, I recently read Rousset, François. Genetic structure and selection in subdivided populations. Princeton University Press, 2013. It is an excellent book, but it was recommended to me as a book about dynamics, and it barely touches on dynamical systems at all.

All right.... Deal! +1 :)


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Effect of increasing Extracellular Na+ Concentration on resting voltage and likelihood of an action potential of occuring
If you increase the extracellular Na+ concentration how will this effect the resting voltage and likelihood that an action potential of will occur?
Please explain using the Goldman–Hodgkin–Katz voltage equation if you have that understanding.  I can typically understand mathematical functions and relationships.
My guess is that the membrane will become more hyperpolarized and the likelihood for an action potential will go down.  I have read some explain otherwise and I would like help confirming or denying my current understanding.


text:
Is there nothing such as NADPH2?
I have gone through various sites and some say there is NADPH2 and some say there is no NADPH2 there's only NADP+ ...WHich is the correct view....Please dont mark this answer as duplicates since I have asked a similar question related to photosynthesis in the past but there too I have not received any satisfactory answer bythe community .

If you didn't got satisfactory answers, you should work on your question. You can always edit them (unless you totally change the question after you got an answer).

Might be useful if you could give links to sites that use NAPH2.

I answered this implicitly in a comment to my answer to: Light and Dark Reaction of photosynthesis?. Anyway:
There is no such thing as NADPH2. There is only NADP+ and NADPH. Consult Wikipedia or a reputable text such as Berg.
The nicotinamide portion of NADP that  undergoes oxidation and reduction is exactly the same as in NAD. The changes undergone are:

The error either comes from confusion with the other redox cofactor, FAD / FADH2, or the fact that two electrons are involved in the reduction of NADP:

NADP+ + H+ + 2e → NADPH

Apologies that my initial answer was just in terms of NAD, not NADP. Fixed now.

A factor that sometimes is confusing is that both hydrogens at the redox site in NADP(H) are equivalent. So it is possible that one hydrogen gets added in the reduction, but the other hydrogen is donated in the next oxidation, and therefore deuterium tracing studies of NAD(P)H will give both +1 and +2 deuterium isotopes. This is sometimes mistakenly interpreted as "NADPH2".


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Diabetes Mellitus and renal failure?
I have studied that Diabetes Mellitus leads to kidney failure and that this is because of the damage to the blood vessels (especially the blood vessels in the kidney) due to high levels of blood glucose?
How do high levels of blood glucose damage blood vessels?

http://www.diabetesincontrol.com/how-high-blood-sugars-damage-blood-vessels/

I am putting here the main points from the link given by @WYSIWYG (i.e. this):

High glucose levels reduce the levels of the powerful vasodilator nitric oxide in blood vessels, a shortfall that increases the risk of high blood pressure and eventually narrows down the vessels...increased modification of proteins by a glucose-derived molecule is a player in vascular problems associated with hypertension, stroke and obesity as well...the researchers found that there was an increased activity by O-GlcNAc in the blood vessels, which competes with another mechanism for modifying proteins called phosphorylation. In blood vessels, phosphorylation modifies the enzyme that produces nitric oxide, called nitric oxide synthase, so that it makes more of the blood vessel dilator. O-GlcNAc seems to beat phosphorylation to the punch so there is the opposite result.

Here, O-GlcNAc refers to O-linked β-N-acetyl glucosamine.


text:
peritoneal dialysis Vs Hemodialysis?
Their are basically two types of dialysis: 1.peritoneal dialysis 2.hemodialysis, My Question is Why the peritoneal dialysis needs to be carried out on everyday BUT hemodialysis is carried out on only 3 days in a week?

Have you done any research on your own? Just googling the title of your question brings up lots of relevant results.

No I did not find the proper reason, just find out the pros and cons of both types of dialysis but I can't understand the proper reason.

Which type is more efficient at eliminating wastes may be the major factor.


text:
Is there a difference between "Alpha Helix" and "Alpha subunit"?
In my biochemistry textbook the terms alpha helix and alpha subunit are both used. Are these two terms synonymous? For instance, hemoglobin has an alpha and beta subunit, are these the same thing as alpha helix and beta pleated sheet?

Relevant: http://biology.stackexchange.com/questions/53092/the-meaning-of-the-alpha-helix-and-beta-sheets-in-proteins#

Alpha and beta... these letters are used in too-many unrelated places in biology and chemistry. Think them as 1 and 2.

These terms are completely unrelated. Alpha-subunit is an arbitrary name for a protein subunit in a multi-subunit protein complex (one having a quaternary structure like haemoglobin or G-protein etc).
Alpha-helix is a type of secondary structure.

Addition by David:
For those who may be unfamiliar with this, alpha, beta, gamma etc. are Greek letters of the alphabet (equivalent to English a, b, c) — also written α, β, γ. They are sometimes used in science to enumerate or differentiate between a series of related things — proteins, small molecules etc.

For those who may be unfamiliar with this, alpha, beta, gamma etc. are Greek letters of the alphabet (equivalent to English a, b, c) — also written α, β, γ. They are sometimes used in science to enumerate or differentiate between a series of related things — proteins, small molecules etc.

@David Much appreciated!

@Atticus283blink If this answer addressed your problem, please consider accepting it by clicking on the check mark/tick to the left of the answer, turning it green. This marks the question as resolved to your satisfaction, and awards reputation both to you and the person who answered.


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Why would lethal genes evolve?
I've been reading through 'The Selfish Gene' by Dawkins. At a few places in the book he states that incest is damaging because it would give a very high chance of lethal recessive genes becoming active due to the high probability of both children having the gene compared to one child and one stranger from the same species.
However, this leaves me with the question why lethal genes would evolve in the first place. The only reason I can think of is that these genes have some secondary purpose and survive natural selection because of that.
Is this simply the entire story or are there things that I'm missing? It seems to me that the having possibility of getting healthy offspring from incest is beneficial to a species, if only because finding a mate would be easier, so those secondary effects would have to be pretty good.
Also I'd be curious if any research has been done which has found examples of side effects of these lethal genes that would make them preferable.

Hint: lethal recessive genes are recessive. If they weren't, we probably wouldn't be having this conversation. Natural selection has literally zero "motivation" to eliminate effectively unused genes. See also (on a macro scale but same principle): we still each have an appendix.

@LightnessRacesinOrbit but even recessive genes will now and then appear together right? So the motivation would be less than for a dominant gene, but there would still be a motivation. Unless incest is the only way to get the double those, but I don't see why that would be the case.

Sure but "now and then" is unlikely to make a huge dent in the gene's survival. It's basically a vestigial gene that, yes, may pop up from time to time to cause trouble (see also: appendicitis!), but on the whole isn't enough to trigger any evolutionary magicks.

Lethal genes evolve simply because of random deleterious mutations and absence of strong selection.
Recessive lethal genes
Random mutations can make a gene product non-functional or reduce its activity. However, in diploid organisms the other fully-functional copy of the gene can compensate for the non-functional allele.
Sometimes both the alleles can carry different mutations which can lead to complementation; this would not affect the gene activity. Also see:  Explain allelic complementation at molecular level. Let's not consider this case here.
So, the fitness of the heterozygotes would not be affected and they will continue to propagate. However, if both the alleles are affected then it would lead to lethality (or lets say reduced fitness). This can happen if two heterozygotes (with the same deleterious mutation) mate and give rise to a homozygote (with both the alleles affected). Since siblings (or other close kins) are genetically related, there is a good likelihood that both of them may be carrying a copy of a non-functional allele and homozygous offspring born out of such a mating may have reduced fitness.
Dominant lethal genes
There are some cases of dominant lethal alleles. Examples include the mutated genes responsible for Huntington's disease (or similar poly-Q disorders). Other examples include a mutant allele of a proto-oncogene that leads to its hyperactivity (and therefore cancer).
Dominant lethal alleles persist in the population primarily because of their late onset. In such cases an individual with such an allele would have already mated and produced offspring before the disease kills them.
Other reasons for the persistence of lethal genes
Another possibility (applicable to the case of cancer) is that the mutations are not in the germ cells and were acquired by somatic cells because of some external agent. These mutations would not be transmitted to the next generation.
Sometimes, the deleterious nature of the allele can exert its effect only under certain conditions. Let's say someone has a mutation that causes reduced immunity. Such a person would survive well if they remain in a sterile environment. In general, we can say that there is no strong selective pressure against the mutation.
Sometimes the reduced fitness because of a mutation under a certain condition can be overwhelmed by an increased fitness caused by it under some other condition. A very well known example is that of the haemoglobin allele that causes sickle-cell anaemia. However, the RBCs in the individuals affected by this disease are resistant to infection by the malarial parasite. The mortality due to malaria is much higher than that due to sickle cell anaemia. Therefore, in regions where malaria has high incidence (like Africa) this allele is positively selected.

I'd like to add that sometimes predicted LOF in homozygous genotypes is compensated but the mechanism of compensation is not clear yet - http://science.sciencemag.org/content/early/2016/03/02/science.aac8624, http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3514.html.

This is just loose terminology. By ‘lethal gene’ Dawkins means an essential gene with a mutation that renders it inactive. With a recessive gene, the one ‘good’ copy in the heterozygote provides enough gene-product to allow survival, in contrast to the situation in the homozygote where the total absence of gene-product is lethal.
With a rare mutation, the chances of two unrelated individuals being heterozygotes is small. However within a family, the chances are much higher. One sees this, for example, in inbred animals such as thoroughbred dogs.
Such recessive genes normally stay rare, and do not ‘evolve’ — there is no selective pressure to maintain them and they are lethal in the homozygote. Only in cases where there is some advantage to carrying only one copy of the gene is the heterozygote selected for. Examples of this are sickle-cell anaemia and beta-thalesaemia where the protection against malaria is provided in the heterozygote. (But I don’t think Dawkins mentions this.)

Sorry I did not notice your edit and thought of adding the sickle cell case myself. Hope there is no conflict regarding this :)

@WYSIWYG No problem!  I'm such a serial editor of my own posts that it's bound to happen.

It would be beneficial to a population for a lethal gene to evolve in incestuous situations because genetic diversity is then promoted through natural selection. Genetic diversity is necessary for a species to evolve which would improve the chance of survival.

But there are so many species where these genes are not present that it seems unlikely to be such a big deal. It also seems like an unstable strategy: in a population where there are no genes preventing successful incestuous reproduction, were such genes to arise by chance they would quickly be eliminated, afaik in a matter of generations as they are so incredibly bad. For this to become stable you would have to evolve aversion to incest which would take longer than that. (I'm far from an expert so please correct me if I'm wrong)

To add to my other comment: It seems therefore to me that the only way such genes could actually evolve is because of secondary (dominant) effects which are beneficial enough to counter the lethal effects. Then I can understand that, over time, a species would 'learn' to select mates without the same lethal gene such that the offspring only has the beneficial effect.

This should be a comment, not an answer. You can always comment on your own posts, and once you have sufficient reputation you'll be able to comment on all posts.

They occur because there is no reason for them to be selected against. Except for a 25% chance of offspring not surviving, in the rare event that the other parent also has the mutation.
That recessive, lethal genes can persist in the population is surprisingly one of the reasons that diploid organisms that sexually reproduce are so successful and diverse. Diploidy means there are two copies of each gene, and that a mutation in one gene that doesn't immediately increase fitness won't necessarily be removed from the population. Even if it would be down right fatal if it occurred in both genes. Because its function can often be covered by the other, normal gene.
It might appear that a carrier of this mutated gene might be less fit to produce healthy offspring. But most organisms create many more offspring than can actually thrive. And what first appears to be a 'weak gene' can later go on to be the basis for a better mutation, often through recombination during meiosis of parts of the lethal gene with its normal counterpart.
In general this is one of the reasons that eukaryotes are so diverse. Because they have many mechanisms like this that don't only protect from mistakes in DNA, they often enable them to be beneficial.
Another such mechanism is that eukaryotes have a lot of introns in their DNA that are removed from their messenger RNA before it is translated into protein. This means that any random chunk of DNA integrating into their genome (a reasonably frequent event, evolutionarily speaking) is less likely to cause gene inactivation and more likely to simply be incorporated. Into an existing gene or even actually added as an extra gene. This encourages a very modular organisation of genes, gene regulation and proteins by enabling the evolution of functional motifs that are reused over and over again.

Any reference to add?


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What is meant by "Expression" of Non-Coding RNA?
I was having a look at lncRNAdb and its help says:

The ENCODE project gene annotation list, GENCODE, has predicted that the human genome contains 14,470 lncRNAs whereas only a small proportion of these have been shown to have function.
We have examined thousands of publications in order to find and
  manually annotate lncRNAs that have been shown to be functional by
  overexpression or knockdown experiments.

What is meant by Overexpression of a Non-coding RNA here? Even if you have a look at any lncRNA in the DB, its entry has a column for "Expression":

They queried publications dealing with the lncRNA that studied whether it was functional through over-expression or knockdown. Cells do something measurable in their normal state, that can be observed through microscopy, qPCR, microarray, etc. You use the data as a control which to compare experimental results against.
For an over-expression study, for example, you can transfect your cells with a vector that will transcribe a lot of your ncRNA. Whether the transfection need be stable or transient will depend on a number of other factors. So the methods you used to collect data as a control, apply these to the transfected group. If there are statistically significant changes to anything when you have more of the ncRNA, you can say in part that your ncRNA is functional. It's doing something observable.
The opposite is true for knockdown. You can maybe use CRISPR to do the job but lentiviral transduction with say a doxycycline-inducible promoter is among other possibilities. The goal now is to make the gene, or the expression of the gene go away. You do the same experiment, and see if an absence of the ncRNA does anything to the cell.
Now in the database you're looking at, expression is being used to show you in what tissues has BX118339 been detected? It basically says BX118339 is expressed in testis and the fetal brain.

With many non-coding RNAs, the RNA is the functional endpoint. Therefore, ncRNA "expression" simply refers to the production of that functional component. Similarly to with proteins, this involves looking at differential tissue production of that noncoding RNA (i.e. in which tissues the RNA is produced).
Gene expression is defined in the Oxford Dictionary of Biology as:

The manifestation of the effects of a gene by the production of the
  particular protein, polypeptide, or type of RNA whose synthesis it
  controls.

In other words, gene expression means the same thing whether the functional product is a protein or a noncoding RNA. Of course, there is no translational component to expression of ncRNA.


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

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


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If your atlas is out of alignment, will the rest of your spine be out as well?
I just found out about this association called Nucca. Basically, they are a chiropractic association - their whole idea is that if your atlas is out of alignment, then the rest of your spine is also out of alignment, because your atlas controls it all. Also, the nerves will get pinched and therefore cause pain.

Is this legit?

https://www.sciencebasedmedicine.org/the-problem-with-chiropractic-nucca/

No, it is not. The "theoretic building" of chiropractic is nonsense - see the several articles from Edzard Ernst on it.


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Can you replace glucose with glycerol in cell media?
In order to feed an animal cell in process called Respiration, can I replace Glucose with Glycerol?
The Equation bellow:
Glycerol + Oxygen -> Water + Carbon Oxide

Experiments have already been carried out regarding the utilization of glycerol by yeast cells. Maybe this is what you were asking?
http://onlinelibrary.wiley.com/doi/10.1111/j.1432-1033.1968.tb00353.x/pdf

In most cases, it's not a good idea to replace glucose with glycerol in animal cell media. Animals do possess the ability to metabolize glycerol, via a pathway starting with the enzyme glycerol kinase. However, glycerol kinase is only expressed in certain cell types, such as liver cells and kidney cells.
References:
http://www.brenda-enzymes.org/enzyme.php?ecno=2.7.1.30
http://www.ncbi.nlm.nih.gov/books/NBK22591/


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Genetic mutations and new alleles
Amongst silent, nonsense and missense genetic mutations, is the latter the only one that leads to the creation of new alleles?
If we define alleles as a specific form of a gene, and a gene as a heritable factor consisting of a length of DNA that influences a certain characteristic, since the missense mutation is the only one that leads to the physical creation of a new protein, is it not the only one that would lead to the creation of new alleles?
Or would nonsense also lead to new alleles as the lack of the protein may lead to the expression of a different characteristic?

Its a good question actually as allele concept confuses a lot of people. I am sure it will be helpful for lot of people in future Insha'Allah

So the term allele is a broad one, and simply refers to the different versions of any piece of DNA in circulation in the gene pool - it doesn't need to refer to a gene. I can talk about the alleles at a random place in the genome.
But if we proceed with your question and ask - 'do nonsense mutations within coding genes also lead to the creation of different proteins' - the answer is 'sometimes'. A nonsense mutation is just one that introduces a stop codon. The result is an mRNA that codes for a shortened protein. Sometimes, the mRNA will be recognized as faulty and degraded, and so little to no protein gets made. Sometimes, if made, that protein will be unstable, or the result will be as if no protein had been made. Sometimes though, the protein will be stable, and could hang around uselessly, or even do damage.

Allele is indeed a very broad term. Easily the term which confused me the most in the whole Molecular Biology. And +1 for the last part. I liked the explanation.

And sometimes the truncated protein can be useful for a different function. For example, apolipoprotein-B.

Allele is just a variant form of gene: independent of the final product of protein, so nonsense will also lead to new allele. I will quote Nature Scitable here:

Alleles can also refer to minor DNA sequence variations between
  alleles that do not necessarily influence the gene's phenotype.


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iPSC in drug discovery
Pluripotent stem cells are very popular and lots of research has been conducted to use these cells as a tool for drug discovery. My question is how many years would it theoretically safe is we could use iPS cells during the process of drug discovery?

iPSCs are already a common model in industry, obviously depending on what exactly you're researching. I don't have any citations, because all of the work I'm aware of is proprietary (that comes with working in industry...), but lots of companies and labs are using them right now.

People are doing small molecule screens with stem cells right now (albeit embryonic stem cells). Probably a lot more screens of that kind are in progress behind closed doors in the pharmaceutical industry, and we can expect iPSC culturing to get better and cheaper, so sometime in the next decade we'll see them become a common model.
iPSCs will probably be an improvement on existing cell culture models, and will also allow things like the creation of cell lines for specific genetic diseases, but they wont' be a quantum leap forward for drug discovery in general.


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TRANSFAC Matrix.dat retrieve count matrix using BA or CC field
In the TRANSFAC Matrix.dat most of the motifs are given in the form of count matrix, but a few of them are given as some kind of frequency matrix. Those aren't ordinary PWMs because rows don't sum to 1, but these sums are more or less stable across rows.
I would like to have a raw count matrix in all cases, so I trying to retrieve the count matrix from those several "fractional" cases.
At first I thought that the field BA (Basis - statistical basis of the matrix) MANDATORILY starts with a number of sequences actually used in the matrix, so I would be able to simply multiply:
$$
\frac{\text{nucleotide value}}{\text{row sum}} \times
\text{number of sequences from BA}
$$
Moreover, sometimes in CC field there's information like: core from: 11
Unfortunately, the results are not very close to integer value, so this cannot be explained by rounding errors.
For example, in a certain row we have (btw, the row sum is $10.73$ from rounding errors):
3.99   4.07   0.87    1.8
BA  17 compiled ... sequences; total weight of sequences: 10.74
CC  core from: 11, length: 6
$$
\frac{4.07}{10.74} \times 17 = 6.4423
$$
$$
\frac{4.07}{10.74} \times 11 = 4.1685
$$
In my opinion these values $0.44$ or $0.16$ couldn't be explained by rounding errors. There's no description (link to article) enclosed to this motif. That aren't log-odds because all of them are non-negative values.
In this particular case, if I use the formula $count(x) = round( (\frac{x}{10.74}) \times 11 )$, the resulting counts will sum up to $11$, so it's fine.
This method with value $11$ replaced by $17$ doesn't result in set of values summing to $17$, but $16$.
The problem is that in another example I have
BA  17 compiled binding sequences; total weight of equences: 12.86
CC  core from: 1, length: 4
In this case I should rather multiply by $17$ not $1$ to get the sum of $17$.
As you see, I cannot stick to either BA or CC for all such motifs.
How would you retrieve the raw count matrix in a such case and what could be the problem in my approach? (jumping between BA and CC values)
Could anyone explain how those values in matrix have been derived, is it some kind of log-odds?

So databases like TRANSFAC are of somewhat... variable quality. Those matrices maybe have been derived from a program that just doesn't output a count matrix, but instead outputs a frequency matrix directly. What I would do, and what i have seen people do in published papers, is just multiply the matrix by 100 and round down. It's a kludge, but you'd better be quality checking the matrices somehow anyway, otherwise you're going to get 90% crap.

Franky speaking, I rejected the idea of matrix multiplication by 100 from the very beginning, because I would get tremendous number of sequences, which would destroy the regularization process. I'm using a full regularization method (with numerically computing E, not approximation 1.5) by Sven Rahman, Tobias Muller & Martin Vingron. The tremendous jump from 17 sequences to 1074 sequences would substantially affect the regularization.

Ah, sorry, my mistake. I didn't look at your numbers carefully enough. it looks like either those are already PWMs as opposed to count matrices (i.e. the values are log(count_freq/background_freq) ) or somebody has already done something strange to them. If it's the former you should have noticed some negative values. If it's latter you can try and do something like making the BA 100 and rounding them.
Also it's not clear what algorithm you're talking about regularizing in that comment. Link?

I edited the question  to shed some light on my example. In my problem a particular regularization method is completly unimportnant, I only need the raw count matrix and that's all. For your curiousity (that won't help with this question) I'm using method from an article "On the Powern of Profiles for Transcription Factor Binding Sites Detection", which is in my opinion second to none.

Yes, unfortunately you clearly don't have the raw count matrix. The only other process which could have generated matrices like that is the process of adding pseudo counts. People often add things like (sqrt(BA)) before conversion to a PWM to avoid taking the log of zero. You could see if you can work out the original BA from that. In general though I'd avoid trying to recover bad data like that. Count matrices should be integers.

I'll simply stick to my method and always test it with BA value and if in this approach I don't get sum of results equal to value in BA I world simply tryb with CC value. Maybe I won't be force to use such atypical matrices without RAW count.

That seems reasonable enough. The paper you cite is a little out of date, and I'd recommend reading some more recent ones as well. And be aware that if you use their threshold selection method you're going to get rather different results than the p-value based thresholds used in most papers.

So, which more recent method/paper do you recommend as the most powerful? (method that not only control Type 1 error, but also let you leverage the Type 2 error - false negatives rate?

The basic method you're using is as good as any other simple model, and you won't do much better unless you want to invest a LOT more effort and do deep learning. The problem is that you can't pick any single 'most powerful' method because the threshold you pick will depend on the biological question you want answered. Also, be aware of what an approximation the PWM models are - you either have ChIP training data,which is v. messy, or PBM data, which doesn't reflect cellular conditions.


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What kind of fish are these?
This should be pretty easy. We have a tropical fish tank, and among a few other species we have an adult pair of these guys. The female is a live-bearer, and seems to be giving birth to two young at a time. However, I can't for the life of me remember what kind of fish they are.

The male is in the foreground, and the female in the background. Any ideas?

I believe it is a Red Wag Platy (Xiphophorus maculatus)

This looks like a Red Wag Platy (Xiphophorus maculatus). Apparently it is a common tropical aquarium fish. Wikipedia does not show a picture of this variant. However a google image search with this will return the images of fish that you have shown.

Thanks for the confirmation, you are correct. I accepted the other answer because yours was posted 10 seconds too late - I doubt you'll mind, though :)

@MattDMo No prob. I realized that after a few minutes and I thought I'll delete mine but someone upvoted. So I thought I'll let it stay :P


text:
Can sleep be replaced by chemical supliments?
If adenosine makes a person sleepy to rebuild chemicals in the brain, than is it possible to inhibit adenosine and externally produce and inject the needed chemicals?

There is not only one substance involved in sleep (which is a sophisticated process); orexins and others have a contributory role. How would you know what to inject without a more complete understanding of sleep and its function?

Also, rebuilding chemicals is not the only function of sleep: it is also a method to remove toxic wastes, relax the body, and (possibly) maintaining & creating memories. So, no SLEEP cannot be substituted with chemical supplements.


text:
At which voltage will the conductance decrease due to the narrow pore in a voltage-gated potassium channel?
If you have a voltage-gated potassium channel that do not inactivate, at which voltage would you see decrease in ion conductance related to the narrow path in the pore?
With that said I know that in a solutions with other ions there is a possibility that the high driving force eventually will lead to blockage of the pore by other ions. This is more of a theoretical question.

There is a point where diffusion into the pore can be a limiting factor in the channel's conductance. If the trans-membrane voltage and the recording solution viscosity are high enough, unitary ionic current departs the Ohms law; approaching a plateau. This asymptotic saturation amplitude represents the diffusion limited rate with which ions approach the channel entrance. The bigger the pore entrance, or the higher the ion concentration, or the larger the diffusion coefficient, the higher is the limiting asymptotic current. The pore entrance is measured as the capture radius.
The diffusion limited maximum unitary current (iDL) is directly proportional to the capture radius of the channel ($r_c$):
$$
iDL=2π z e_o r_c D C
$$
z is the valence of the ion, $e_o$ is the elementary charge, C is the bulk concentration of the ion, and D is its diffusion coefficient.
Here you can find a case in which single channel current is not linear with voltage and an example calculation of the capture radius of shaker $K^+$ channel.


text:
What is a rough estimate of CO₂ saturation for high-yield or high growth rate plants?
Naturally, plants have individual photosynthesis capacities, but it would still be interesting to get a general picture of CO2 saturation levels for some common cultures. Where lies the optimal yield, growth rate, etc compared to the 0.04% in the atmosphere? What is this specific field of research called?
My first assessment is that there is no saturation level for individual plants, that each plant consumes CO2 according to its species' respiration level, and culture density is adapted to the species and practical circumstances such as the greenhouse construction. Is there a field of expertise in planning greenhouse constructions, and if so, what is it called?
It seems to me plants have already or can be genetically modified for heightened respiration. I'm not familiar with this field, further reading would be appreciated.
Would plants genetically modified for heightened respiration require nightly venting/oxygen infusion to support it?
Are there examples of cultures treated as respiration machines that are completely optimized for the capacity of their DNA?

I am a horticultural consultant for 36 years. I can add that in some enclosed greenhouse situations, growers will artificially increase atmospheric CO₂ in the greenhouse to 1000-1200 PPM, about 3 times normal ambient CO₂.

Related: http://biology.stackexchange.com/questions/26231/can-plants-grow-under-extreme-1-carbon-dioxide-concentrations


text:
Enzyme Inhibition in relation to Aspirin
I see that aspirin (in part) works by inhibiting cycloxygenase isoenzymes and that this inhibiting is irreversible. I've had a few classes mentioning this topic in passing, but never with depth.
I guess my question regards the permanence of the word "irreversible." I assume enzymes have a life span, so to speak, and that the same isoenzymes both die and are being replenished at some given rate.
Am I correct in thinking that the cycloxygenase enzyme is permanently inhibited until its death (catabolism)?

As far as I know, aspirin is an acetylating agent,. meaning that it attaches an acetyl to a serine (via a covalent bond) inside the active site  of the cycloxygenase enzyme. With the active site now being unable to used, the enzyme will die by degrading (its structure will define this time) or denaturing (due to external changes in environment), whichever occurs first.
I have no sources, this is all from what I learnt a while back through textbooks and teachers, so I'm not prepared to write an answer just yet, but I hope this helps!

Yup. Inhibited until degradation since as @butallati mentions, they acetylated - chemically modified. In fact this irreversibility has a significance - that the enzyme can be replenished in the endothelium but not in the platelets.

I had an interesting insight from a lecture: "If someone takes irreversible blockers, will you ever recover the function of the tissue? Yes. Old receptors will be catabolized and eventually new receptors will be synthesized by the tissue." So its death is from catabolism

@adNauseam ""If someone takes irreversible blockers, will you ever recover the function of the tissue? Yes". Up to a point. If you take enough of an irreversible acetylcholine esterase inhibitor (like sarin), you won't recover the tissue function… since you'd be dead.

‘Reversible’ and ‘Irreversible’ are standard designations in enzyme kinetics. It may be that your instructor was using the word in this sense.
As already commented, irreversible inhibitors bind the enzyme in such a way that they don't dissociate from it. Either they form a covalent bond or their affinity for the binding site is extremely high. Reversible inhibition is generally found with competitive inhibitors — those that resemble substrate enough to bind to the active site and compete with the substrate for it. Unless they have an extremely high affinity there will be an equilibrium between bound and unbound inhibitor, and the inhibitor can be competed out by substrate — i.e. the binding is reversible.
You can read about this in more detail, but still at an introductory level, in the Wikipedia article on Enzyme Inhibitors  or this section in the online version of Berg et al.

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


text:
What is the role of TATA box in transcriptional regulation?
I know what is the TATA box, but I wish to know whether it has specific roles in transcriptional regulation.

what have you searched? there's a lot out there - even on non-scientific sites like wikipedia...

@VanceLAlbaugh, I have searched, but I've not found the specific role of TATA box in transcriptional regulation.

TATA box serves as a binding site for the TATA-binding protein (TBP; and its associated factors, together comprising the TFIID).
TATA-box is one of the basic promoters present in many genes and therefore TBP is a general transcription factor i.e. TATA-box and TBP do not have specific roles. TATA-box is like a minimal promoter.

TATA box simply is a type of promoter sequence, which specifies to Transcription Factors where transcription begins - which is usually 30bp downstream of TATA Box. Transcription factors can bind to the TATA box and recruit the RNA polymerase enzyme to synthesize RNA.

Specific transcription factors don't bind to TATA box. TBP does, and it is a general transcription factor.

@WYSIWYG Thanks but I am sorry I didn't get you. Where is my answer stating about Specific TFs?

TATA box is bound by the TBP. You said "Transcription factors bind to TATA box", which may imply any transcription factor.

@WYSIWYG Thanks! I got you now. What do you mean when you say "TBP is a general TF"?

See http://biology.stackexchange.com/a/44732/3340. Basically, it means that is required by a lot of genes to carry out transcription and it is constitutively expressed.

@WYSIWYG Thanks man! Opened a new thing for me.

From Nature:

A TATA box is a DNA sequence that indicates where a genetic sequence
  can be read and decoded. It is a type of promoter sequence, which
  specifies to other molecules where transcription begins. [...]. The
  TATA box is named for its conserved DNA sequence, which is most
  commonly TATAAA. Many eukaryotic genes have a conserved TATA box
  located 25-35 base pairs before the transcription start site of a
  gene. The TATA box is able to define the direction of transcription
  and also indicates the DNA strand to be read. Proteins called
  transcription factors can bind to the TATA box and recruit an enzyme
  called RNA polymerase, which synthesizes RNA from DNA.


text:
Assuming if height was fully determined genetically, what would be the concordance between monozygotic pairs?
I fully understand the differences between monozygotic and dyzogtic twin pairings, but I wondered how I could put into words the reasons why the concordance would be very high.

Derived from same zygote, so precisely the same genetic material from mother and father.

Would this suffice as a scientific answer?

This is a homework (or homework-like) question. Can you please show your thoughts and your efforts at answering the question.

I feel as though monozygotic twins would have low concordance at birth because of the clear disparity in terms of nutrition etc during pregnancy and the fact they're not yet fully developed (development obviously follows genetics mainly, with nutrition being an influence but not to the same level)

Low birth weight can be an environmentally associated phenotype for example

If height was fully determined genetically (and no trait is), then concordance between monozygotic pairs would be almost perfect, modulo some variation due to somatic mutation in early development (e.g. a mutation happens in in cell directly after the embryo splits).

Thanks for explaining this for me so well Dermot.
I was just wondering, if I had two sets of data for concordance between sets of twins at 8 years of age, with concordance values being 0.94 for monozygotic and 0.49 for dizygotic twins, would this suggest that height is largely controlled by genetic factors?
I personally think it clearly implies an important influence, but the data set might not be large enough and also we have factors such as nutrition to consider.

Yes, it would imply that the trait is almost completely determined by additive genetics. If you assume identical twins are as similiar in their environment as dizygotic twins (which twin studies do), then environmental factors like nutrition should be controlled for, and there are simple models you can use to account for sample size. There are a LOT of caveats to all this though, it's a complex field. There are good recommendations for books on population genetics on this forum that i recommend you pick up, if you want to do this kind of analysis.

Fantastic thank you for helping me out!
Just out of my own personal interest, how come the concordance at birth in my data set is so low for monozygotic twins? Is it linked to the idea of shared nutrition in the womb etc?

Does anybody out there know why the concordance at birth might be so low for monozygotic twins?

@DermotHarnett If I may... Even in absence of environmental variances, there is also developmental noise (such as cellular noise during development) that may account for little variation too. But... obviously it is a detail does not necessarily need to be raised on this post :)

Remi.b - yes, that's one reason why 'no trait is'. The full genetic determination would imply the absence of developmental noise. The 'environment' term in the breeders equation includes everything that isn't genetic.


text:
Optimum gibson assembly overlap length
Does anyone know the optimum length of overlap your primers should have to your vector for gibson assembly. Most manufacturers say to do at least 20 bp on both side but don't give a maximum. Sometimes I add 30-35 bp on both I don't know if that hinders efficiency.

Increasing overlap should not hinder. However, you'll waste money in synthesizing more oligoes.

There has to be some diminishing return.


text:
Which are the last cells of the human body to die?
When somebody dies, which are the last surviving cells of his/her body? Those of hair, nails, or some other obscure but resilient cells?
Shedding light on why and how they are so vital might boost our knowledge of survival pathways.

Related question: http://biology.stackexchange.com/questions/7548/in-humans-what-is-the-last-metabolic-process-to-cease-after-death

When you say 'vital' do you mean - 'hard to kill'? The most 'vital' cells in the usual sense of the word - 'important for life' - are probably heart muscle, nerve cells etc, and these are exactly the ones that will die first because they use so much energy.

Neither hair nor nails are made from living cells. Also, which individual cells die last has nothing to do with how 'vital' they are.

@Slawomir Andrzej Dziemborowicz I understand that several factor may play a role in determining how much a cell lasts after being its body dead (eg blood flow, oxygen reserves), but anyway it is conceivable that those cells lasting longer could have more protective mechanisms to overcome lack of oxygen and accumulation of toxic molecules.

They found live skeletal muscle stem cells in human corpses 17 days after death (LiveScience, 2012).
They isolated live stem cells from the bone marrow from human fingers 5 days after death and transformed them into cartilage, bone and fat cells (NewScientist, 2013).
The survived cells were in a dormant state with very little metabolic activity.

From what I have read, it looks like white blood cells have been found to survive the longest (up to 70 hours after death). This being said, I have also seen people who say that red blood cells would survive the longest because they do not need oxygen to survive (they have no mitochondria and thus do not do aerobic respiration). Since you seem to be interested in survival pathways, blood cells are probably not terribly useful. It seems that many transplantable organs like the kidneys can survive for up to an hour (provided that they are chilled to avoid cell death).

Welcome to Biology.SE! Answers are more likely to receive a favorable response if they include supporting references. ——— You may wish to consult the help pages for advice on how to provide effective answers: biology.stackexchange.com/help/how-to-answer. Thanks!


text:
What is the name of these beautiful white trees?
I found these beautiful white trees in an unknown video.

If you do a Google Image search with the picture in the question, you'll see the screenshot is from the 2000 movie The Patriot starring Mel Gibson. The movie's filming locations were mainly scattered around South Carolina, on the southern East Coast of the United States. I've spent a lot of time in the region, and the gray-ish fluffy stuff hanging off the branches is almost certainly Spanish moss (Tillandsia usneoides), which is very common in the area.

While it is more difficult to identify the tree, as the bark and leaves are not clearly visible up close, a good educated guess based on the size and shape of the tree would be live oak (Quercus virginiana), which also happens to be one of the primary hosts of Spanish moss.

Plot twist. I was on set. The Spanish moss on the large tree in front of Ben Martin’s (Mel Gibson) house was hung by hand using a cherry picker. Spanish moss is found closer to the coast. This scene was shot in the Rock Hill area of South Carolina


text:
What's the proper term for "brain waves" read from an EEG?
An EEG records the electrical activity of the brain. An EEG shows voltage differences as a function of time and shows what have become known as "brain waves".
Is there a scientific/technical word/phrase for one of these measured "brain waves"?

fyi: galvanic skin responses are not the same as EEG - I edited the question therefore to prevent confusion

The terminology around EEGs is variable, dependent on the context;

Brain wave is fine, but has a bit of a popular undertone;
Oscillation, as per the deleted answer,  implicitly refers to a longer-term behavior, i.e., something periodic (EEG);
Waveform is often used to describe a single event with a recognizable shape (ERP, compound action potentials, etc.);
EEG signal/recording/trace is more than fine too, it doesn't carry any implicit meaning about its nature, it does carry relevant information on the method used;
EEG is fine too, EEG means electroencephalogram, the 'gram' suffix comes from Greek, where it means “something written,” or a “drawing”, so literally it means something like "graph of the electrical brain". So simply speaking of 'the EEG' is OK as well.

This list may not be exhaustive, merely illustrative


text:
Separation of smaller DNA fragments and larger fragments without using gel electrophoresis?
For an upcoming experiment I would have two kinds of DNA fragments in a sample after a particular reaction step: First there would be fragments of a length of approx. 170 bps and then there would be Fragments of a length of approx. 270 bps or more.
I would like to maintain all fragments with a length of 270 bps or more and throw out all fragments with a length of 170 bps.
Of course, I could run everything on a gel and collect just those bands above 170 bps.
However, I am wondering whether there is something more elegant to reach that aim. For instance, we could use a filter that collects just those fragments with a size of 200 or 250 bps or more and those fragments with a smaller size flow through the filter.
Is such a thing possible and if so, how? Or would you suggest something else?
Many thanks for your support in advance!

One option is to use SPRI beads, like those sold by Ampure (Ampure XP beads) to separate DNA fragments by size. By adjusting the ratio of beads to sample, you can selectively bind fragments of certain lengths by excluding those of other lengths, as seen in the following image:

From this image, you might try using a beads-to-sample ratio of 0.7 to exclude fragments below 200bp, or a ratio between 0.7 and 0.6 to exclude only those fragments below somewhere between 200 and 250bp. The advantage to beads over a gel is the time it takes. Size selection by beads takes only around 10 minutes, versus maybe 30 minutes or more for a gel (including the extraction).
As with any molecular biology procedure, it's important to test it and make sure that it works for you. For instance, it might be a good idea to take aliquots of your samples and compare the lengths and yields that you get from a range of bead concentrations and/or to compare the yields that you get from using gel vs (in this case) beads.

Interesting, I didn't know about these.

In my mind, gel separation (using a fairly high percentage gel to separate the bands as much as possible) and manual excision is by far the best option. The problem with molecular weight cutoff filters is that the value they give (5000 Da, for example) is an average pore size, it is not exact. Therefore, any attempt to separate 270 bp from 170 bp oligos will end up with some of the shorter construct ending up in the final product, and some of the longer sequence in the flow-through. The chances of finding an off-the-shelf product that exactly meets your size requirements are also low. Only use these types of kits if you're separating a 300 bp PCR product from unused primers, for example.
You could do some sort of column chromatography using a size-exclusion matrix, but that's essentially the same thing as using a gel. The only advantage is it can be scaled, so if you have large amounts of PCR product to separate (let's say 5 or 10 ml), it would be easier to run a single column than to load that many lanes in a gel.