CharonY

Or you may succumb to your hardship. Either way, it is a saying and not meant to be literal.

Half of these are pretty useless post-mortem and unleashing methods onto subjects with not good idea of how to do analyses is pretty much worthless. In the end you end up chasing spurious associations. That being said, there have been numerous brain imaging analyses of transsexual people. But MRI studies are also prone to false-positives. I am no expert in this field but from what I understand the studies are somewhat inconclusive but point to small distinct neuroanatomical pattern. But with the issues of neuroimaging, it is not clear whether these finding will survive larger sample sizes (which is the major limit of the studies).

On the note of housekeeping genes, it is important also to check whether the ratio of the proposed housekeeping genes are constant. Also, as a rule of thumb if your growth diverges massively from your "reference" condition, there is a good chance that your housekeeping genes are also going to be altered. The problem here is that it is typically unknown how the change looks like in reference to your genes of interest. If you go to tissues it is even worse.

What precisely do you mean with recovery? I am only vaguely aware on studies looking at the effects of caffeine in a variety of exercise performances, though. One proposed effect was that it may alleviate the influence of low amounts of pain.

It depends largely on how much you want to infer from your runs. There are several levels of normalization but I will limit the discussion to the use of reference runs for now (other aspects are well covered in literature). Theoretically, the best way, which no one does, is to use artificial mRNA of your target gene in known quantities to create a calibration curve. This would take care of the RT reaction (which is somewhat underexplored as a source of error) as well as the actual qPCR run. A second method, which is cheaper is to use cDNA of your gene in known quantities. In some cases people utilize genomic or plasmid DNA to do that. While the RT bias would not be included and there is going to be some deviation from the true results, in many cases one assumes a constant error, which still allows for comparisons. What most people are doing, however, are simply the use of reference genes, which are assumed to be expressed independent on the variables you are testing (i.e. housekeeping genes). Many only use one, which is rather bad practice. But even if using a panel there is the general issue that there are no true housekeeping genes. In fact, the more omics data you generate and look at, the more you will note that almost everything can change under given conditions. This can be due to external sources (e.g. nutrients, stressors) or interior signaling (e.g. cell aging, cell cycle). So one should validate whether this reference genes are truly stable in your test conditions. Even then, there is a lot of uncertainty and everyone has to decide what level of accuracy one is aiming for (or can afford).

A couple of things. In general, if you go the kit route, there is not a huge difference between them (on average). Typically, familiarity and practice with a given protocol is a stronger determinant than the kit itself. If you got only one system, such as yeast and under mostly standard condition, RNeasy is fine. If you have different cells and/or may run into situation of low cell count and increase of interfering substances a phenol-type extraction (e.g. trizol is more flexible and does a better job at cleanup). Microarrays by now are cheaper, but more restricted in many ways, I would probably go for NGS for an easier sell. At least for now, considering there are still statistical issues that have not been ironed out completely. Though that does not mean that microarays are better in that regard, it is more that their issues are better known. Crucial, as usual, are biological replicates, which still are going to be expensive. Validation: that is somewhat of a biggie and people often do get it wrong (but still get it published). In the end, it depends on the conclusions you intend to draw with your data set and in some cases it is sufficient to run some qPCRs with sufficient biological replicates. Unfortunately some use the original RNA extraction, instead of growing new batches which ends up just to be a comparison of RNAseq to qPCR data, with little power to infer biological effects (as an example). Also there is the huge fight over how to properly normalize and calibrate qPCR data in the first place, especially in the low abundance regime.'

That is pretty much it. Theoretically one could try to target specific compounds using immunoprecipitation, but that would be costly, has low throughput and the result would not be safe for ingestion (although you will end up with tiny amounts, anyway). There are much cheaper alternatives to that.

It is a matter of concentration. As I noted above, it is unlikely that you can ingest sufficient arginine so that it would have any effect on NO levels. The only effect that has been observed were in subjects that had coronary diseases, ischemia etc. In healthy subjects there was no effect observed (see e.g. ). From what I understand there is no good reason for arginine as exercise supplement as NO production is induced by exercise-related stress and unless you have a deficiency, ingesting more would have little to no effect.

Uh, L-Arginine is an amino acid whereas nitric oxide is a gas. While one degradation pathway of arginine produces NO, the rate is not huge and I doubt that you can actually ingest sufficient arginine to significantly alter NO production (nor whether that is a good idea, considering that it is involved in a number of signaling cascades other than vasodilation).

The easiest way to buy them is from ATCC (US) or maybe the DSMZ (located in Germany, but they are usually cheaper). If you are lacking the budget you can always ask local researchers to have some for you. The latter is the easiest options as you can just pop in and get a plate. At least in theory, as some researchers may have unknowingly signed a material transfer agreement that prohibits them from distributing them further. An example is the MTA from ATCC, which requires ATCC's written agreement. DSMZ does not seem to have that issue, I believe. Either way, if you want to receive strains via shipment you have to make sure that you are following proper shipping and receiving biological material. If you have to something already in place (as e.g. for the E. colis) be sure to document that you followed it. You really do not want to get into trouble with receiving Bacillus even if it is a harmless one. Sometimes it raises flags that can make things difficult (not saying that it is likely to happen, but a few years back reseachers got into serious trouble for failing to follow procedure).

I think you may be misunderstanding a few things here. First, there is codon bias. That means that not all codon variants for a given amino acid are used at the same frequency. You may want to check wikipedia or some other common resource for a short overview. Then, the abundance of a tRNA with a given anticodon does also vary, but in a much more dynamic way. A simple situation is if a certain amino acid is lacking, of course. But even for a given amino acid the various tRNAs carrying it vary in abundance. The regulation of these pathways can be rather complex. Typically (but not always), there is a correlation between tRNA abundance and the particular codon usage. The result is that genes using these high-abundant codons can be translated faster than if other codons are used. This is typically the case for genes that can become rate-limiting growth in unicellular organisms. Also note that codon bias and aa usage can vary significantly between organisms. Most of our knowledge is derived from model bacteria, yeast, and Drosophila but especially in more exotic bacteria things can be markedly different. On top of it the number of tRNA genes also varies, allowing for further regulatory control of translation rates and aa usage. And even more control can be exerted via the tRNA synthetase activity. But I think the regulatory circuits are quite an advanced topic and there are still gaps in our understanding of the underlying regulation. Suffice to say that a simple 1 on 1 interpretation as outlined does not reflect the actual mechanism.

Wait, why does training have to be free? The guns are not free either and gun purchases can be taxed. So I do not see that paying for training would be an infringement.

Why do you think you can generalize how people use and react to photos? Or to anything for that matter? And why extrapolate from there?

Note that tRNA abundance is quit diverse. Together with codon bias the false pairing rate is much lower for common and lower for rare pairings. This, btw. is another way to control protein synthesis rates.

I do not think it is glossed over. You will hear pretty much everywhere that it is one of the essential elements of life. You may have heard of CHONP referring to the major elements required for life.

You are confusing different elements to a degree. First, you must recognize (and I think implicitly you do but may not be quite clear about the ramifications) that the need for the lagging mechanisms is because replication happens on both strands at the replication fork. The polymerase has 5'-3' activity and can therefore only proceed without lagging on one of the strands. However, if you transcribe a gene only one strand is read at any given time, so the RNA polymerase can continue to move. Moreover, the polymerase reads everything during replication, however transcription is only initiated at open reading frames. They can be on the one, or the other strand though. I.e. if the recognition is one the one strand, it will bind there, if it is the other one. I.e. there is not one strand that is completely sense or anti-sense. It all depends in relation to the transcription initiation. Note that in most eukaryotes ORFs are only a tiny proportion of the genome and typically you won't find many examples of genes that overlap at a given locus. I.e. gene products are generally only produced from one or the other stand from a given site. In smaller genomes, especially viral ones, this is not the case, however. None of this changes the genome size. The number only refers to the number of base pairs.

Yes. The polymerase can only work 5'-3' but it can go on either strand. Leading and lagging strand is relevant with respect to replication, not transcription (with the caveats mentioned above).

Transcription can occur in either strand. As mRNA is of much shorter length and is synthesized as a single continuous molecule it does not have the same issue as DNA-replication. That being said, there are interactions e.g. of the transcription and replication apparatus that can result different properties, depending on which strand the gene is being transcribed.

In addition to the misunderstanding what races are (or not), the largest genetic diversity is found within Africa.

Others have touched by it, but it is the very consequence of the definition. To be precise, she is the most recent common ancestor of all currently living humans in a matrilineal descent. We trace the line back so far that it converges (by definition). And as pwagen already mentioned, the offspring of other women of that time are likely to be around. Only they have a broker maternal line.

You do not get criticized for stating that she was/is infected, but the rest of your statements and especially your conclusions ("billions are going to die" really?). And if you think that level of scrutiny is considered crucifixion, well your threshold for criticism is fairly low then. Certain people like to see data that point to issues rather than extrapolating from a random point and let their gut do all the data analysis. Otherwise you may end up with politicians that are publicly in favor of euthanizing infected people. Oh wait...

From what I saw on the website this seems to be the case. Part of the description makes me think that they do not even need to affect each other, They only say that there are interactions on the level of mutants. For example, mutation in either A or B has little effect, but A/B at the same time is lethal. That would indicate that they have some common function, but as long as one is active the organism survives. As I said, it is a very broad definition essentially only meaning that on one level or another, the functions of gene A and B have some sort of intersection. Thus, A/B mutations will look different than mutations in A and B alone (in my example). The issue I have with that point of view is that if we take a global perspective (say, genome or proteome-wide) pretty much everything intersects at some level with something else. There are very few (if any) pathways that are so specialized and isolated that there are no effects whatsoever. More likely it is just not observed under a given test condition (e.g. in laboratory culture).

And in areas where we have less maths (e.g. due to complexity of the system, or maybe just incompetence ) we tend to overthrow models very fast once new experiments roll in.

Well, I am not sure what parameters they measure, but as an example, you got your mutant A and measure some parameter (could be expression, could be something else). Then, they look at mutant B and do the same. Finally they look at mutants with deletion in A and B. Say, they look at expression. If you delete A, B goes up, if they delete B A goes up (and in that case AB would just be a negative control). Looking at their definitions they would consider that a genetic interaction (could be direct or indirect via different regulators). The same could be done by looking at other parameters, such as a different target gene could be used. They use a very broad definition for genetic interaction (as opposed to physical ones).

What seems to be the issue in understanding it? I.e. what does not "click" with you?