CharonY

What I often feel is that biological knowledge has not kept up with medical innovation. Right now for more steady improvements it is important to understand the biology of cells in more detail rather working on the end of the assembly line (i.e. finding cures for cancer). Unfortunately this kind of basic research is much harder to get funded. Innovations need good foundation. Unfortunately, the work on the foundations takes time, tend to be uneventful, get ignored by most of the public and gets much less funding.

In general small metabolites are not very good in causing immune responses. Thus, normally you won't have good antibodies available. For these kind of analyses one would use something like GC or LC-MS.

Way of the Dragon. By grabbing Chuck's chest hair. I think in Texas getting shot is more likely than roundhoused.

What DrRocket said. 10-30 mins are basically the rule for these kind of talks, so it is important to know what is best to present for the given audience, instead of showing off everything you did. The way you intend to do it (focus on impact) is a good way to go, especially when hard data is added. Note that in most institutions the PI is hiring you personally, so it is most important to make yourself attractive to him/her. Other than that you may want to try to figure out what your job is going to be. For instance, are you working on an existing project or are you supposed to bring new lines of investigation to the group (postdocs are usually not truly independent, just expected to be able to get stuff done with little or no supervision)? Depending on this you have either to emphasize why your stuff is important and why one should investigate it more or demonstrate that what you did makes you a good candidate to work on that particular project. That being said, if you are already invited and are a nice guy, chances are very high of getting the position. More often than not it depends on whether you want it or not.

Considering that handwriting is a skill with extremely high elements of learned behaviour (just consider different languages), it is extremely unlikely.

Non-coding, not non-genomic . Considering that the OP states that it just wants to create DNA with an artificial sequence, yes you can. DNA synthesizers can synthesize around 100 bp of DNA last time I looked (it could be longer by now) but you can use PCR to stitch them to longer sequences, if you wish. If they are to be translated you need at least a ribsome binding site and, as mentioned, a start-codon, both of which require G and C. However it would be achievable using the 90% limitation. If the question was whether the DNA is stable with a high AT content, then yes it is (there are repetitive sequences known consisting of long runs of simple repeats). While they have a lower melting temperature (and repeats make it hard for sequencing/assembly procedures), they are physically stable.

In addition, the market also seems to be sensitive towards that. When gas prices rose, suddenly cars with better mileages were suddenly more attractive. My link Note that between 1990 and 2006 the mileage only rose by 2 mpg. Between 2006-2010 it by over 3 mpg. The same table also shows that imported cars consistently had higher mileage, i.e. that the development of new technologies was not the critical factor. In other countries where gas is taxed higher, higher mileage cars have been introduced earlier, which may offset additional costs (and provide additional benefits in terms of pollution). Where (and when) in the heck in Europe have you lived? To my eyes the cars at least in Germany actually were of better quality than you get them in the US. Or do you think that this is a tricycle, for instance? Interestingly the same model appears to have less MPG in the US than in Germany, I am not sure why. What you get, however, is a high-efficiency central heating system and (way) superior insulation.

Re OP: No one really knows at this point. Few disregard the possibility of a genetic influence (after all certain genetic defects inhibit proper brain development), but how strong the genetic link is cannot be properly quantified at this point. It is a very complicated trait, requiring a lot of interaction with the environment to develop. Not to mention the limitations what IQ test actually measure, as e.g. someone can be illiterate but solve certain problems faster and better that do not require literacy. So, as already mentioned, there is likely a genetic basis, but how much it shapes the overall potential is still unknown. Re Marat: If one grouped all people according to their IQ, would races emerge (i.e. does IQ measurements have predictive powers for subpopulations?). Skin color is a lousy indicator except for very broad distinction, btw, as the greatest genetic variation is found within a broader population in which one skin colour is prevalent.

Well, there is a field-dependent minimum most expect. In most experimental fields one or two publication during your PhD is expected, more in engineering and computational fields, for instance. But even a single publication on the right topic can be sufficient. No publication could be assessed as a warning sign. Postdoc positions are often tied to existing or planned grants, hence (in contrast to faculty positions that is) most are tied to a certain topic/technique. Thus one has to demonstrate abilities in that area, which is usually done with having a publication in that area. The numbers are secondary. Demonstrating the ability to work independent is a plus, however most are more interested that one can contribute meaningfully to a project or, even better, provide knowledge in a field in which the PI wants to expand. But again, this is mostly for postdoc levels. Service or faculty positions follow different rules, for instance.

Indeed. I think that most lungfishes actually have atrophied gills and would eventually drown (at least from what little I remember). Which demonstrates that single traits are often insufficient to make precise phylogenetic distinctions, especially over large distances.

For junior scientists (i.e. one the postdoctoral level) the number of citations is usually not a good measure. The h-index mostly only comes in when hiring faculty. Sometimes as a rule of thumb the number of papers in very similar fields can be compared. However, for most intents and purposes (e.g. getting a postdoctoral position or postdoc grants), even the absolute number has usually (relatively) little relevance. Depending on your field often the technical abilities are more sought after, or the topic of your phd. Essentially postdocs are not ranked per se, but they would more likely ask the advisor whether someone could get something done (within a project). Theoretical fields may follow slightly different rules than experimental ones, though.

Well, direct comparisons are not easy as there are a host of different enzyme assays, each with different ad- and disadvantages. In general terms immuniblots can be adapted to all kind of proteins, provided you can get a good antibody, whereas enzyme assays require that the protein under investigation has some quantifiable enzyme activity.

There are things like that out there, too. Faculty of 1000, for instance, consists of well-established scientists that pick out papers they find remarkable and rate them. Also many journals have a news or top pick section in which they highlight remarkable papers. However, only a small fraction of the scientific body will be reviewed. Similar to, say, a nature publication this will highlight individual papers, but will not be informative for the vast majority of work that is being done.

I think LaCie uses Hitachi (now bought by WD) and Seagate mostly. But regarding failure rates, I would check the individual series rather than the brands, especially since now the market is basically reduced to Toshiba (for 2.5 that is), WD and Seagate. Also note that not all hard drives are compatible with all NAS, Usually they have documentation regarding tested drives.

Ah, how to quantify scientific productivity, the eternal question. Short answer: there is no accepted means. It is extremely field dependent. In engineering and bioinformatics more publications are expected as e.g. in biological and medical sciences. Publishing in high-impact (for your field) journals is especially relevant for review panels that othwerwise are not familiar with your work but want to get an idea whether the stuff you do is of interest to a given scientific community. As a rule of thumb original work outclass reviews. Especially as not all reviews are invited. There are very notable exceptions, of course. In the end, even within a field productivity is not easily compared. Often the h-index is a guideline, but in the end the evaluation of individual scientists depends heavily on context. For many evaluations of scientific merits the number of publications and citations may not be that much relevant, unless you are clearly above or below average. In the end it depends a lot on the context of the evaluation. In hiring processes, for instance, candidates may be ranked according to h-indices, but the ideal candidate is often not the one with the highest citations. From what I heard mathematically paper take a long time to get cited. They tend to get a torrent of citations once someone found an application for it, for instance. But the turnover is often counted in decades. In other fields you can expect citations in the first few years after publications and then (almost) never again. But it can be very fickle. I published something 5 like years back that was never cited until new groups started to work on it about a year ago. Since then the citations came rolling in...

I would also argue that at least in undergrad teaching one of the more important bits is to get exposed to different topics, concepts and ways of thinking rather than factoids. It is also the time to recognize whether you enjoy getting exposed to new concepts on a regular basis. To me, this broadening of perspective is one of best elements of universities. After high school this can be quite liberating (though for many it can be a source of anxiety and uncertainty, being used to spoon feeding and rather clear-cut tasks). Unfortunately it is easier to create an exam that tests the latter. Unis are not a factory. You do not go there to work, but it is more of a giant toolbox. In that regard I agree with the OP that being an expert on a certain field does not necessarily makes a better lecturer for more basic concepts. The real advantage will be in the field overlapping with actual ongoing research. For many junior lecturers (e.g. assistant prof level) graduate level classes are far easier to teach, as it is closer to what they actually do. Getting the skills to do good undergrad teaching takes a lot of more time.

I have used quite a couple of them. Had no problems with several types of the Readynas series, although the fans on the NV+ were a bit loud. If you only need Raid1 the Duo is much more tolerable in that regard. For secondary backups I am also using a mediasmart server. Especially in terms of quick non-redundant backups the WHS fit the bill better. Some users have reported problems, specifically with the corruption of the backup database, but I have regular dumps of them (on a different storage) and have no problems with it yet. The reasoning for the final selection of Netgear and HP was that I could get more redundancy as compared to the NAS from synology (which were much more expensive when I was about to buy one).

Dependent on the audience it can be a little bit tricky. The stronger your evidence the better it is, though. I was in a similar spot in grad school and at least to my audience (mostly senior scientists towards the end of their careers) had to be slowly convinced. Acknowledge the existing expertise in the audience, slowly build up to the parts that could not be satisfyingly explained by existing theories. Basically make them agree with each step you take and then build up to the point where you show the flaw. Do not drop a bombshell, but rather try to ease them into it. Also use "we" instead of "I", especially if you are junior (you can highlight your contributions, if appropriate during 1-1 discussions afterwards). Be prepared for questions regarding alternatives, though. Based on what you said it would have to be an evasion of some sorts, which is a polite way to say "no bloody idea". But most of the time it is OK.

There are decent part-time PhDs. Usually in the applied field, in conjunction with a company funded-project. Part of it was to improve a certain thing, and another part was to provide the guy in charge (very capable chap btw.) the possibility to get his PhD. Of course time management is highly important there, but with sufficient industry experience one should have learned that, anyway.

So your age is close to 40? Getting a PhD is basically not a problem per se. Especially with work experience it could be easier for you then for younger people. Chances for an academic career are most likely (depending on precise situation) not very beneficial. Same could go for industrial career if you do the PhD full-time.

From how I read you it appears that you may confuse organization with advancement and relate advancement with evolution. This connection is not there. First of all, whether certain structures are more or less organized depend on their function. In order to sense gradients there has to be strict control on where the receptors are located, especially considering the small gradients they have sense (as most bacteria are not mace larger than a micron). It depends a lot on the type of sensors, though, as some, in contrast, have to be distributed more equally across the cell to work properly. Thus the amount of organization is not a matter of advancement, but just a manifestation of their function. To be more precise, it is probably somewhat erroneous to consider the receptors more organized, both, motor and sensor proteins depend on specific localization within the cell, moreover motor proteins require a higher level of organization in terms of protein-protein interactions to work properly. It is dangerous to overlay simple observations with simple narratives and build hypotheses from there, as biology tends to be at least several orders of magnitudes more complicated than on the first (and second) glance.

Well, from that article: It basically calls for careful calibration of the estimates (at least I read that from it, I am no expert on that field).

I have not read the article, but from the blurb the approach seems to be somewhat similar to this: http://www.sciencefo...-in-hiv-patient Edit: eh, it is the same article, it appears.

Hum, must be a Freudian slip.

The critical point is joining the right group as a grad. Ivy league universities do not produce high quality scientists, they are just able to attract and hire high quality faculty at a higher rate than mid-range unis. They do not necessarily produce top-quality scientists (that depends on the individual). However, they may give young scientists a head start due to a) the pedigree and b) open up some networks (though the important ones depend more on divisor rather than uni, for the most part. Also one should not IMO focus too much on the big shots. Going into science depends a lot on passion (as you will unlikely get much else out of it). You should be willing to put enormous amount of effort into something, knowing that whatever you do you may still fail (basically around 80%) and do not expect a huge outcome, but still live love (almost) every minute you do it. In the long run that is more important as where you got your degree.