CharonY

Are we talking about publications? It really depends on the type of review. Obviously the intro should lay out what the review is about.I is it an exhaustive review on one topic, does it provide a general broad overview, is it a critical review, does it focus on outcomes or methods, is it more a perspectives paper, etc. How the rest is crafted is highly dependent on the focus of the topic. What most have in common is at least a rough outline of the state of the art and then expand or dive into details from there. The important bit is that all good reviews at one point or another start to narrow down the scope to the critical lit that are to be discussed and put into perspective. The latter is often lacking if someone junior is writing a review (or worse, if it is from a student and the PI is either not familiar with the topic or just does not care).

I should probably add that R&D in industry tends to be the most vulnerable positions. In crunch times they are one of the first to go (as they are not directly related to revenue generation).

Off-topic, but my deepest condolences.

If you are thinking experiments you are almost certainly thinking academic research. There is comparatively little in experimental research going on in industry with the exception of certain set goals. Examples include specific target screens or formulation optimization. But in either case, considering you are an idealistic student, I will tell you that typically once you reach these kind of position (academic or industrial) your actually duties will barely revolve around the bench. And that is only if you are successful. If you start a PhD, five years from now you are likely have to do a postdoc for another 4 years or so (though the time is creeping up due to budget cuts). Then, if you apply for a proper job, you are likely to compete with 100-300 other applicants for tenure track position. If you are the lucky one, your time will be eaten up by teaching, managing your lab, training students and tons of academic responsibility (including grant writing) so your actual time to do proper research can be very limited. And if you get a bad crop of students in your lab you may be fighting your way out of a downward spiral towards your tenure application. To summarize, an academic career (but increasingly also industrial) are highly competitive at the PhD level, you have to be aware that you are not doing full-time research after your postdoc and if either of that is interesting to you, you better be good at networking.

It is not clear to me what you intend to do. If you want to do basic research, you typically need to get a PhD at some point. If you want to be a technician/research assistant a BS/MS may suffice. That being said, enzymology is a rather general field and there is broad range even in industrial job that are somehow associated with it. For example you could be an analyst that validates the composition for product control in pharmaceutical production, or you could assist in performing assays to look for therapeutic targets or part of a production team. Or maybe an application specialist in that area etc. The relevant bit is that is basically a topic, deeply rooted in biochemistry that is not necessary connected to a particular type of job.

A) Evolutionary theory as other scientific theories are constantly expanded, revised and refined. B) It has nothing to do with your arguments and certainly does not require the presence of some ominous unknown entity. There does not seem to be a lot of grounds for a scientific discussion here.

That is not the the point. B. subtilis is a harmless model organism for Gram+ bacteria. However, isolation of spores is also applicable (to some extent) to the pathogen. What John brought up is that a lab that is doing standard analyses should either already have protocols in place or have some sort of guidance on how to find them, as it is not precisely obscure knowledge for microbiologists. It is quite possible that it is a legitimate question by a student, though as usual I wonder why the question is not directed to the adviser first. Note that there are a lot of other normally harmless microbiological techniques that can result in increased scrutiny due to their potential of abuse.

Hmm, good point, actually...

I certainly do not agree to sweeping statements, especially in areas with a lot of unknowns and especially outside of my area of expertise.

There are a lot of methods for which you should be able to find literature rather easily. Note that most are older, so you may not find them by quick googling (unless you got the right keywords). However, in almost all methods you will find some extent of bacterial contamination, unless you repeat the purification steps. Edit to remove details, although they should be easy enough to find in the literature,

Depends quite a bit on what you have access to. However, there tons of possibilities in terms of data modeling/analysis that can be split up either way (a field/lab group to do analyses, a data analysis group to investigate the data etc.). Or share the same system/organism and do complementary experiments. Basically, tons of research activities are decentralized but it may be tricky to organize it on the student level.

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.