CharonY

I think what was meant was the turnover rate of the enzymes themselves. E.g. protease activity that, in turn, can modulate other enzyme activities.

I had regulatory roles in mind, but yes, that is another good example.

Not precisely meaningless. In contrast to radioactive decay biological degradation is highly dependent and controlled by the circumstance (e.g. presence of nucleases, medium, etc.). It is not a material constant. But the turnover rate can be of high biological relevance.

Just a little update: http://www.world-nuclear-news.org/RS_Stabilisation_at_Fukushima_Daiichi_2003111.html

There are boatloads of protocols. I assume you want isolate chromosomal DNA? I would recommend a look into Sambrook: molecular cloning for them, if you got access to a library. Purification of nucleic acids from proteins is often used via phenol extraction. The DNA is then pelleted and rehydrated with a Tris buffer. A photometer can be used for purity control. However standard agarose gel will not allow a size determination of chromosomal DNA. It is far to big for it. An alternative is pulse-field electrophoresis.

Actually, it depends on what aspects of tissue engineering you are looking at. Individual mechanisms (e.g. proteins) pertaining to adsorption and cell-cell interaction are closer to molecular biology or chemistry, however in the broader and areas cell biological and cell cultivation knowledge are higher on the list. Other elements include biomaterials and biomechanics and, especially for the applied part, medical sciences.

Indeed. Persisters usually rise when AB treatment is discontinued. IIRC the presence of ABs and other stresses actually promotes the formation of persisters.

Simplified there are three aspects to it. First, the mathematical principles and generalized modeling principles, second the models that have been developed in the frame work of one systems theory or other and third is software development to do the numerical calculations. I am doing mostly the second part (i.e. looking and adapting existing models to my research questions, usually by using or adapting existing software/code) to supplement certain aspects of my research. While the first part is certainly best to build a solid foundation I am too much experimental to delve into it too much. If you want to develop novel approaches it would certainly help (but it would be rather ambitious). I can provide lit for applied approaches and models if you could provide information what you are interested in. I am less suited to evaluate basic principles.

Excellent. The blogpost was nicely written for laymen like me to understand, though with my knowledge I could not easily validate accuracy. Indeed.

Except from a microbial standpoint the toxin argument does not matter as what counts as toxins for us often can be considered food to them. The major issue is the chemical instability of arsenic bonds and the biological importance of phosphate bonds. A simple exchange would be incredibly tricky physiologically. Regarding appleseeds, unless crushed only a limited amount of cyanide will be released from the seeds during normal digestion. While technically correct, it would take quite a bit to be lethal. A few other food sources would be more efficient. It is somewhat well-known that rosaceae produce cyanide compounds, btw.

For biological molecule usually the complete turnover is considered. Here usually also active inactivation of the molecule by e.g. other biological molecules counts against the stability/activity of the molecule. This is a major factor of mRNA decay, for instance.

We are unable to create an absolutely novel virus from scratch. We can, however use existing viruses as template and create recombinant versions. Essentially this is accomplished by standard mutagenesis. Normally a host system is required for production and propagation, though.

I would like to draw a more stronger distinction regarding journalists and neuroscientists.

Basically you are correct. If several parameters are tested it it would be more appropriate to adjust for multiple hypothesis testing. Since the number of tests factors in exponentially, it is usually not appropriate to neglect it. In practice it depends a little bit on whether they really did multiple hypothesis testing. Something that often done is ANOVA f-test followed by the mentioned post-hoc t-test. If the latter only includes two samples (e.g. the groups with the biggest means difference), it does not require adjusting, of course. However, if every combination is tested, adjustments are necessary. A number of post-hocs do control family-wise errors, though. In the end, it depends a little how the authors explain and interpret the data. It should be noted that there are quite some papers around that fail to account for multiple hypothesis testing, though it depends a bit on the field, whether they caught up to it. Especially in molecular biomarker papers many still fail to do it.

Of course. That is what I meant with the difference of basic science that use justification to frame a context and actual applied science that try to develop a finalized product. Between both is usually quite a sizable gap. As well as funding sources, btw.

I have no idea about the real background, but this is odd http://geniusnow.com/2011/03/15/the-strange-case-of-josef-oehmen/

Thing is, money is going to dry up regardless. Publications may put one on track to get the next grant, however, there are other factors that (especially for the big initiatives) are more likely to succeed. Publish or perish is more relevant to pre-tenured faculty.

First of all coffee is hardly a diuretic at all (as formerly believed). There are a number of studies regarding health benefits (including reduced risk of stroke, for example) of coffee but at also at least one where I think heart attack risk was higher. Most were association studies, IIRC and while in case of reducing type II diabetes risk there are proposed mechanisms, the details are unknown. Tea leaves contain more caffeine but in the usual way it is brewed less will be extracted per cup than coffee. Withdrawal effects to caffeine depend on a number of factors, including genetic disposition. I am a heavy drinker, but going without for months at a time did not result in any symptoms or cravings (as I was simply not able to get decent coffee for a long while). But as usual, you mileage may vary.

Lemur, you are totally missing the point. Even basic research is usually framed within an applied context. The difference is that there usually is no defined path to that application. Sure, genomics may lead to elimination of all diseases and make us immortal. But find a way there. In industrial settings they are interested in a product that can be pushed to the market. Things are usually bought off from unis (or result in startups) if they get close to that point. Before that it is mostly hands-off nowadays. There are exceptions in areas which promise high rewards, as e.g. with biofuel development from bacteria. But even then after the initial results were rather disappointing many companies pulled the plug. But from what I heard from friends and colleagues in companies, the real world comment is less about funding or doing theoretical work, but is rather about the difference between university and corporate culture. One case is job experience, for example. In corporate settings you are expected to get better with experience (for the most part) and more experience can result in better positions. In academia, on the other hand, there are positions that run parallel to a career. Doing long postdocs, for instance, does not count as experience past the first few years, but at some point makes you actually less attractive as a job candidate (either in or outside academia). Also, non-tenure track positions carry a similar stigma (for slightly other reasons). Also there is the corporate belief (which is true to a limited extent) that academics are less familiar with the concept of deliverables. One could argue something similar on the management level for industrial positions, too, though.

I do not think that sequencing is really an issue per se. As long as the service is provided with a disclaimer that the data should not be used for diagnostic purposes. For the latter definitely a consultation with a physician would make sense as they could recommend additional tests. It is likely, however, that sequence services may sell the sequence as a kind of personalized diagnostics, something that we are not going to get anytime soon. Proper physicians should acknowledge that currently the genome(again, with the exception of the available and properly validated biomarkers) offers only little diagnostic value. Regarding GWAS, there are statistical issues that almost always result in a vast overestimation of effects. Based on my experience this is not going to change in population studies, without additional molecular data. It all boils down to costly and time intensive validation studies. That, however is not done often enough due to time and money constraints as the outcome is often questionable due to the aforementioned issues. Currently GWAS studies far outweigh validation studies. And the latter are often too limited to provide sufficient statistical evidence.

This is not quite trivial. Genomic data is hugely complex and its implication as a whole is, not comprehensible to anyone right now (certain piecemeal exempted). The current risk assessment using genomic markers is quite unreliable on the whole and even academic professionals in that area trip up on a regular basis when it comes to the assessment of biomarkers. So my basic concern is really about anyone trying to make health assessment using genomic data except for those that have been well established. I am especially concerned that someone would eventually try to make risk assessments based on association studies. While I am generally all for free sharing of information, my feeling (as evidenced by literature) is that using that data you are going to be wrong more often than not. Again, exceptions are markers with well known functional associations. But then one could identify them easily with a limited test rather than with sequencing (especially with a lower error rate).

QTF. Lemur, interestingly all the examples you gave are applied sciences (regardless whether they apply to biotech applications or benefit people in the Amazonas). Basic research tends to be way more, well, basic than that. Of course, still justifications are being made on the potential impact, but no one seriously expects that it will lead to it in the foreseeable future (if it did it would venture into the realm of applied science).

So it is a pairwise comparison with replicates? A t-test should do the trick.

I doubt that there is reliable data out there. For instance, there is a gay community in Iran (there was a report on it a while ago on PBS, I think). Their non-existence is just the official line....

This journal does not appear to be peer-reviewed and half of the paper does not support its main points. To call the presented evidence weak would be flattering.