CharonY

The use of mono- and dibasic phosphate is used to create a buffer. That being said, yeast as all other organisms require a P-source for survival and there are a quite a range of phosphate transporter that is used by yeast under different growth conditions. There are a range of enzymes that then further utilize the phosphate in a huge range of metabolic and signaling pathways. Phosphorylation/dephosphorylation is a major element of protein activity control, for example, and phosphate is used in many metabolites, including nucleic acids and so on. Far too much to list, really.

In addition, what do you think affects DNA and RNA the most (hint: people that are skilled in isolating RNA also usually have very stable samples, why is that)? Also keep in mind that early nucleic acids do not necessarily have the same structure as the one we have today (read up on PNA, for example). DNA on the other hand is super-stable which is one of the reasons why there was a group that support the DNA first hypothesis. Furthermore, how long do you think does the half-life have to be? Remember cell cycle times do not apply in this situation. The critical rates are the synthesis vs degradation rates. The general assumption is that membranes came much later with peptides and nucleic acids being the early replicating molecules. Lipids are likely a later addition. I agree with Greg, since it is an argument from incredulity, I wonder why it should become a pet peeve. There is quite a body of literature out that one could check out if one really wanted to learn about this topic. While there is no definite answer, it does illuminate the biochemical issues to a certain extent.

I really wonder how you are able to read this into the study (and putting Bayes into everything does not necessary make it more palatable or in this case. any sense). You are now trying to read into the study things that even the authors did not do. Assuming I take their data at face value and make a direct risk assessment it would tell me that when it comes to pituitary abnormalities it is better to have a diet consisting of 33% GMO (8) than no GMO (9) or 11% GMO (23). Likewise, high GMO+roundup or only roundup is as good as control for the kidneys. But if you drop the roundup the risk doubles. It just does not make any sense. I should add, though in vain I suppose, that data has to be treated dispassionately. You start with a sound experimental design, do the appropriate analyses and draw only the conclusions that the data allows you to. If it does not happen to have enough oomph to get published one should not massage it with statistical tricks or omissions. Likewise, as reader one should not hype a publication beyond what it delivers to make a political point. That in the overall scheme of things severely hurts science. If your claim is that we really should need better data to assess long-term effects, you will see no argument from me. If your claim is that the existing data somehow obscurely hints at something then you are reading tea leaves. Edit: messed up a number

Arbitrary arithmetics on top of that. Why are the values multiplied? Why are they not additive? Also according to that, it is always better to have individuals work on a given project as the addition of another person will always lower the score (assuming that the index is a value between 0 and 1).

Curricula can differ quite a bit between universities. However, if you have a basic grasp of cell biology (and/or taken an undergrad course in that area) you have a decent chance to get through. What I would advise you to do is to check out (or ask the instructor) what books are recommended for the course, read a few chapters and figure out if you can follow the concepts.

Like the USA?

Immunology is as a discipline quite different from microbiology. The latter is dealing (obviously) with microbial ecology and physiology and can be either general (i.e. composition of the biota in soil) or very specific (say, physiology and genetic of a specific bacterium). Immunology on the other hand deals with general immune systems and responses of a variety of host systems (and the biomedical part usually deals with humans or certain animal models). There is an overlap when it comes to infectious microbes, but even the the immunology is more on the host side (i.e. how is the pathogen detected, what are the host responses), and microbiology deals with it from the pathogen side (how does it avoid host responses, how does it proliferate inside the host). I am not sure what you mean with:

I will post the link if I can find it. If you have access to the journal of the original paper, it is one of the many follow-ups (as letter to the editor). At least one of them used a revised statistical method and did not find any significant differences. At the very least it was run for the mortality (both sexes). I am now uncertain whether the kidney data and blood data was also discussed there. I have made a note that the statistical method for the biochem data is iffy. In short, they used a partial least square regression method, which, as used does not provide statistical inference per se. However they used a resampling method to generate confidence intervals. It is at this points where things go weird. They compare each of the treatments with the control and generate confidence intervals within each of them for each metabolite. This raises the issue of multiple hypothesis testing, which was not addressed. For the the other parameters, they did not even conducted statistical analyses (and as another potential warning sign it should be noted that the changes were not concentration dependent, if there was, the data would have been more interesting). I am not sure what you mean with Monsanto rats. Sprague-Dawley is a pretty old line from the 70s or so, for which sponteneous tumor formation was described early on; I believe something well in excess of 60-70% (I want to say 80, but am not sure) were described to spontaneously develop tumors (Kimmerle, sometime 70s-80s) when living to an age of 2 years. From the get-go this was a poor choice for anything but short-term studies (say one year). The alternative would have been to start with much more rats or using a less disease prone line (potentially Han:Wistar). That being said, rat models are probably generally not great for long-term studies (note that this is a general comment, not specific to the mentioned study). As a general rule of thumb they do live well to around 16-18 months. Starting from that time point, their mortality starts to rise steadily. The life expectancy is about 2-3 years for rats (and the Sprague Dawley are on the lower end of the spectrum), meaning that a long-term study would sample senescent populations. It would be akin to trying to figure out toxicity on 80-90 year old humans. Trying to figure out exposure effects is extremely hard under ideal conditions (as the sample population is already dying from unrelated causes) and basically impossible with small start population. To make it clear, the data of the study does not support its conclusions. I understand that they probably did not want to repeat the whole experiment and wanted to throw something out, but the analysis had to be more meticulous and the claims much more muted (or at least done appropriately). You are correct in principle, if the effect size is large enough, even a small population may provide some insights. This, however, was not the case in this study. Actually no, The study claims it, but the provided data does not show it. I am pretty sure that they were competent enough to do some straightforward statistical analyses to strengthen their claim, if their data had allowed it.

How would an unknown mechanism violate causality? I assume you misunderstand the random or stochastic part which just means that one has not found that the stain target specific cells (just a random subset of the tissue). The hypothesis you put forward is a big vague, considering that metals can adhere to quite a range of biomolecule and proteins under the right conditions. Or rather they precipitate at it. You should also realize that the stain is applied to fixed tissues. I.e. the cells are dead, proteins are severely cross-linked etc. There are no biological processes involved but rather physicochemical interactions are behind this. It could be as easy that a random formation of a silver chromate nucleus promotes to formation of further precipitates, thus filling a cell that by chance was leaky enough during the fixation process that the silver could enter it.

It appears that you are switching positions from the original OP. There your argument was that whether evolution would render pathogens harmless before they could kill off their hosts and as a consequence: I was arguing against that scenario as the selective pressures to become less virulent would require large time scales and hence if the cold was given to everyone it would just as it did before. Pathogen populations do not rapidly undergo vast changes (though their generation time is obviously on the scale of other bacteria). I.e. if everyone got the cold there are likely a couple of bacteria in the total population that are more or less virulent, but then you have the massive amount of bacteria who are not. It will take time until the frequencies shift perceptibly.

Those acids work well at room temperature. Others such as acetic acid are also used but require heating. And some other acids are more likely to hydrolyse proteins too fast resulting in soluble breakdown products instead of visible turbidity. I assume one could adapt it to quite a few other precipitants, but these are the commonly used ones. Another thing to note is that urine also contains other components and varies a bit in pH. Care must be taken that the normal constituents do not precipitate either, otherwise one would not be able to distinguish between presence or absence of proteins. If you mix a large amount of proteins into urine and run Heller, you will get positive result even in the absence of albumin. I.e. if you have leaking of other proteins in urine in significant amounts (which would be bad) Heller would still give a positive signal. The ring is less a property of albumin but to having a interface (between urine and the nitric acid) at which the proteins precipitate.

One should probably add that not all chromatographic interactions are based on polarity. The general principle is that the analytes are retained by the stationary phase (which can be due to ionic interactions, hydrophobic interaction, size etc.). One can model that by modeling it as a series of discrete equilibrium processes in which the analyte is either in in the mobile phase or at the stationary phase. I.e. one could imagine it as a series of partitioning processes in which a given concentration of the analyte is distributed between the two phases. The equilibrium constant of this interaction, called partition coefficient in this case, is a measure of how well an analyte gets retained. In chromatographic columns (as opposed to TLC) the constant flow of mobile phase will eventually elute the analyte from the column, and the time necessary (retention time) for a given analyte to come off the column depends on the partition coefficient of an analyte for a given phase mixture.

On the list, one is not a bacterium. And considering the selection I am wondering about the context of this question (as it does not make a lot of sense to me).

It is true that over any significant length of co-evolution pathogens tend to mellow out a bit (at least it appears to be the case in viruses), partially because killing the host is not a very good long-term strategy. However, pathogens generally do not modulate their virulence as response, it is merely a matter of selection. Adaptation is in essence a passive mechanism, i.e. those strains that kill their host fast tend to spread slower than the others over evolutionary time scales. But if spreads fast, there is a chance that it kills of its host before any kind of selective pressure could kick in. That being said, whether that could happen in any population depends on a lot of other factors, including size of the host populations, migration and so on. Humans are quite abundant and wide spread, so there is a good chance that pockets of survivors will exist and/or that there are people who will be immune, for example

What may happen is that technology makes certain skills obsolete, but in turn requires new ones, whereas the field continues to exist and/or branches into new technology-enabled areas. For example, computer skills were not needed 50 years ago. Today it is a basic skill in many areas of science (and everyday life, for that matter). In molecular biology being able to crate and run nice sequencing gel was highly-sought after. Nowadays, you can to the whole things with kits and cartridges. Has molecular biology become obsolete? Quite the opposite, precisely as swansont described.

And even if years down the road someone figures out that something is not quite correct at some point, many of his findings have been put to good use in the last (and I am sure also the coming) decades. It would be like calling out Alexander Fleming an idiot because he initially did not think that penicillin would work in vivo and because he was unable to purify it in sufficient amounts. Considering the amount of lives being saved I would gladly be such an idiot.

The temp differences could be a reason. Most manufacturers provide minimum lifetimes under shelf conditions and in reality it a bit worse. But did I understand correctly that it only survived 18 months? I do not know that logger, but according to the specs it should be around for 6-8 years? I would call the manufacturer to troubleshoot the issue, they know their product best and may actually have some ideas how to improve lifetime.

You are reading the paper wrong. I will get into the retraction part later, as that is contentious. However the overall data is inconclusive on basically all counts. Cancer is one aspect, but small sample size is an issue that goes for everything. The result was that the study design would not allow for finding any differences (including mortality as well as kidney issues). Among the many flaws was the aspect that the control rats they used had an usually high mortality rate, which makes them unsuitable for long-term studies. OECD guidelines state that for two-year experiments 50% survival is the minimum after 104 weeks (and at least 50 animals per sex group, which also was not the case). This would be the case with many rat lines, but the one they used only had 30% survival rate of male rats and less than 50% for females after that time. That alone invalidates any finding of long-term effects. Other more suitable rat lines would exhibit well above 70% survival rate. In addition, there are odd statistical test designs (though quite possibly down to insufficient statistical knowledge rather than intent). Other researchers have redone tests on the same data and found no significance in the other indicators (such as mortality, kidney failure, maybe also hormone levels, but am unsure about that). While one may always suspect the hand of some lobby in these kind of public incidents, strong data is usually an excellent defense in the scientific community. However, if the data is weak to begin with there will be little support and, rightfully, a lot of criticism. Now with regards to retraction. Technically it is a flawed study, though often this is insufficient to force a retraction. Theoretically it should not have passed peer-review or possibly only presented with proper statistical analysis stating that the data is inconclusive and that more research is needed. In fact, the latter would have resulted in much less contention and one could even propose that there may be an effect if more are tested. Alas, the authors decided to go the high-risk, high-reward route. But obvious overselling is rarely taken kindly in the scientific community. Especially because it makes other researchers vulnerable to people like lobbyists and pundits. The retraction part does cause ripples in the tox community. While many researchers would disregard the study as valid, few would outright push for retraction because of flawed interpretation. By this standard quite a few more papers should have been retracted. I guess it is an unfortunate mix of PR gone wrong and possibly the wrong editorial decision due to that.

The precipitation mechanisms are slightly different, but results are similar. I.e. nitric acid will not specifically precipitate a particular protein. There is a variation of the SSA method that allows a colorimetric and thus more quantitative method. The reason why it is used for albumin (+globulin) testing is that normally that these two are the highest abundant proteins. However, if there are other proteins present in significant amount, they will interfere with the test. The methods are as such not terribly selective, other than that some protein species are more prone to precipitate than others. Generally, the SSA method is reported to be slightly more sensitive than Heller but otherwise yield very similar results (each are somewhat prone to a different set of interfering substances, such as e.g. certain antibiotics, and are sometimes used in tandem).

It is an unspecific test for proteins and based on the denaturation of proteins in presence of a sufficiently strong acid.

There is no best place per se. Rather you would look at research groups with interesting topics that fits your interests and try to get positions there.

Who are you calling professional??? Those are fighting words around here!

Honing my skills to confuse people. Also smoked beetle legs.

I would be hesitant to say plenty, as I would not state that for practically any science job, currently. I know a few people outside of academia that work in the broader area of wildlife assessment in Canada/USA they are quite split between provincial/state jobs and consulting companies. The latter pay quite well and are still recruiting (last thing I heard was up to 85k for someone with a master's degree, but with practical experience). Their job is often to provide data for wildlife and vegetation assessment to companies and ensure that companies can survive governmental audits. One thing that often pops are are also fisheries (if you have specialized in aquatic biology a bit). Again, these job are often not PhD level, but I tend to be surprised by the amount of jobs found to these more traditional biologists than in my line of work (where there is considerable competition from the chemistry/medical/pharmacological area, depending on the precise specialization). Of course there is also academia where you will have always at least one position in the area of zoology and animal phys or ecology that could be filled by such a person (depending on specialization, as wildlife biology is a bit broad). But getting tenure is always a bit of a tricky beast, regardless what you do. That may be the case though there is usually no discipline dedicated to that particular task. One of the things could be biodiversity research during which new species may be found accidentally, so to speak.

Interest in a topic will be the most important part. Imagining doing something at this early point is not terribly useful as you will likely have a very imprecise idea what a researcher will do in a day-to-day basis (especially as it will dependent strongly on what you are doing and where you are). If you are fascinated by a subject so much that you cannot stop thinking about it, you have decent starting point. But as others have said, you should read around and find that thing (even if you do not end up in the field).