CharonY

It does not tell you anything about mental capacities. Just that you have access to more information. The way your brain processes is, is not going to be significantly different from your parents or your grand-grand-grand.... parents. And even if more information is available that does not change your starting position. The next generation would need to start reading from 0 again to catch up to the knowledge of their parents. It is like saying that because I have access to the vast information of the internet I am inherently smarter than Albert Einstein.

Nope. As the training is not passed on its genes, there is no reason to assume that the next generation will be smarter (however one wants to define smartness). They may have access to more information, though, which is a different matter. You are imagining a progression that exist due to accumulation of tools and information, not due to biological mechanisms.

Yes and no. The total capacity is somewhat dependent on binding affinity, but not solely. If you pack the column more or less dense for example, you will change the overall capacity but you will not change the affinity the stationary phase itself. Or if you change flow rate. Again, change in total capacity, but the material does not change. Edit: that being said, keeping all else constant one could reasonably infer that capacity is a function of affinity. But while the elution time is somewhat dependent on affinity, it is again not the the sole factor. Additional effects such as interaction with column material (rather than the resin itself) can change the elution order, for example. This is why these analytical techniques typically require empirical validation as models usually are not perfect predictors. But if your question is more whether there is a correlation at all, then the answer is yes, but depending on the effect you see, it may not tell you enough about what is happening in your system

It depends a little bit on how the scaling is used. If you use a X point scale assuming that the distance between values is equal (e.g. 1-2 has the same interval as 6-7) then it is an interval scale. However if a higher number just indicates e.g. "better" or "worse" with no indication of the distance of the intervals, then it would be an ordinal scale. Or in other words, you could easily shift the numbers, as long as you keep a clear rank. I.e. a four-point ordinal scale that is 1-2-3-4 would be the equivalent of 1-5-6-8 or agree-agree slightly-disagree slightly-disagree. In other words, if the only information in the scaling is that 4 (or 7) points are used does not tell you whether it is interval or ordinal.

I am not entirely sure what you are asking, as your title and OP actually refer to different things. Generally, the binding capacity (which most often refers to dynamic binding capacity) refers to how much target molecule a packed column can bind before breakthrough of unbound analyte happens. So in that regard, the capacity in itself does not influence elution behaviour if you load less than the total capacity. If you overload the column the result is that unbound analyte will elute fast, providing no real separation. The strength of the affinity between analyte and stationary phase on the other hand, does determine the elution behavior (and is pretty much the basis of this separation technique).

Also, it is useful as a platform to discuss why certain views are held, by testing assumptions. In this thread, for example, it has been proposed that governmental structures such as, say EPA or research funding programs have been bloated and steadily increased. As a consequence, they have to be pruned back regularly. I have provided a counter example of plateauing or even declining funding. Likewise I could point out that the EPA's budget has declined since the 80s as part of federal budget or that the workforce and expenditure have been constant since the 90s but started declining since ~2013 (i.e. under Obama). Because then we could move the discussion regarding what size these structures should have, and what possible consequences are (for example). Everyone of us has some preconceptions but with some luck we will find someone to challenge them (ideally by being familiar with them or having data at hand). However, opposing a view just for sake of opposition is not terribly productive.

I don't intend to speak for him, but I think he addresses many of these questions in post #4.

This is a very good question. Broadly speaking, both theoretical frameworks are more suited to explain the origins of a given event, i.e. whether there is a founder cell that it can be attributed to or whether are tissue level interactions or disruptions that initiate the proliferation process. However, they are not necessarily good for categorization of specific events as they are not precisely opposite to each other. Your example is actually a pretty good case which could be broadly categorized to TOFT as the reaction to growth factors is on the tissue level, whereas the proximate cause could have been a mutation. The fact is, TOFT does not make any assumptions about how tissue-level disruptions arise. As both theories are more conceptional rather than mechanism-driven I see them more as complimentary explanatory models that can be used to frame a given set of events. Depending on the type of study you describe (e.g. tracing individual cells vs looking at larger interactions) either one or the other may be more applicable. At this point, it is more a matter of perspective rather than a precise categorization (which nature cares little about, anyway).

Actually it seems that this is not actually happening.

I think we have a very different definition to what "down pat" means. Bits and pieces, yes. Some neuronal structures, too. While I am not expert in this area, I do come across neurobiological reviews fairly often and nothing in the recent years have suggested that there is a full understanding of the mechanisms from the cellular process to full physiological consequences (i.e. memory consolidation). In fact, a tonne is still missing to fully understand the concept of memory itself. What you may be thinking about is some of the basic processes that happen that are either associated or are a consequence of the respective sleep phases (such as controlled activation and deactivation of certain brain areas). But how precisely the various functions lead to memory consolidation is far from known. Rather, it is known that certain disruptions in those pattern can interrupt the process somehow. However, if you came across studies that have those details, I would be more than happy to read them. However, the link you provided is not such an example. It names a conceptional model of uncertain usefulness (as it lack quantitative properties) and not much else. Having hypotheses (and there are several of them around) is not quite the same as having it down pat, which I interpret as actually understanding the mechanisms.

Any source claiming a clear understanding of the process(es) is almost certainly wrong. Either that, or simplified to a degree that it does not actually answer any questions. Roughly two things are assumed. The first is that sleep is somehow required for memory consolidation (whch is failry certain as it goe) and the is that the there are different contributions of REM and slow-wave sleep (SWS) to the process. After that it gets very speculative. A common hypothesis is that SWS is important for the formation of declarative memories, whereas REM has a stronger contribution to non-declarative memory. This would run counter to a merely supplemental role of non-REM sleep. And there is quite a bit of experimental data to support the role of SWS for this process. But clearly, far more research is needed to actually figure out mechanisms and form a coherent theoretical framework.

Yeah, but the question is: which language?

I am a biologist, not a medical specialist, but from what I heard exercise is the way to go. That can actually lead to a release of growth factors that promote stem cell proliferation. However, that is likely not to be relevant for the typical ailments and is more investigated in actual rehab after major injuries. Stem cell therapy is something else entirely, more like trying to cure a mild headache by performing rocket-powered brain surgery.

The reason why the UA-attempt failed was that they did not follow policy. You divert prospective GPs do a different terminal and then perform extreme recruiting. Otherwise the policy is sound. In fact, that is how I recruit my international students.

Yeah, it is difficult to distinguish these two forces experimentally, without a good model and the question is often how precise measurements have to get to be able to accurately reflect the respective contributions (and even more difficult to figure out whether they are biologically relevant). The model proposed in OP does indicate that melting behaviour can be explained by base stacking alone. Theoretically so do base pairing models, though they are less accurate when they do not take a least a fudge factor from the stacking into account..

Yes indeed, if we are not interested in the actual details such as relative contributions of the forces, media influence, complex conformational changes that occur in vivo (and under influence of proteins), then it becomes almost trivial. Who knew?

Precisely what I am getting at. In a way they make you force more to about thinking about the image. Of course, you can put the constraints on yourself otherwise, even with a modern camera, but in the former case you will have no choice (or pay a lot for bad pics). To be honest, though, I will admit that due to time constraints I am a bit more of a gearhead than photographer. Getting good at the latter takes more practice than I am able to invest and I really never really got above technically competent picture taking myself (though to be fair, in case of wildlife you may not have much choice other than just shooting away and hoping for the best, unless you can really spend days or weeks at a spot). That being said I do love the feel of the Olympus OM series (the viewfinder especially). But from a purely technical viewpoint I would not be adverse to try out one of the new mirrorless medium formats. But currently I shoot (when I can) using M43 as I can slip them in my bag in the (forlorn hope) that I may find some time somewhere just to look at things a bit more.

Actually quoted as it is, I find the statement in OP rather thoughtful. It is open to interpretation, but my first thought is rather:"yes intuitively it seems so, but why is it so?" With the latter being what sets scientists off to this journey (and rocky careers). To me, any statement that makes you ponder is a worthwhile one to have and I have no issue with that. It would be different if they misstate facts, or make wrong definite statements, for example.

Oh please do that. Teach them kids with their cellphones or their 15fps machine guns how to capture decisive moments Also working in the darkroom is fun (as long as you knock over the bottles because you couldn't see them...)

It was indeed. The first time I saw them I really thought they were contemporary and then one notices the mid-century subjects and it got confusing in a really exciting way. He was certainly up there with Cartier-Bresson and colleagues. Normally I am not a fan of composite photos but even those (which he probably did not do himself) were quite neat, to a large extent due to his choice of subjects but also the incredible use of patterns.

Yes, in many cases the rough models do not seem to make much of a difference in practical terms, but once you get into details it is quite complicated and I run out of expertise rather quickly to properly evaluate the data. It really becomes more a physics/physical chemistry question. In most molecular classes we tend to teach the abridged version as it is far more intuitive to understand and most of the time it gets us reasonably close to where we want to be. But at night I do wonder about how much we get wrong and how relevant it may (or may not be) for biological questions.

If you like photography I would advise you to look at the works from Fan Ho (who is not featured). Hi photos from the 50s are incredibly modern while depicting the remnants of a bygone age.

While "destruction" is clearly not the correct term to use it is pretty clear that the rate we are looking at in terms of temperature increase. Being a biologist (really?) does not make you an authority on climate (i.e. appeal to authority fallacy). Unless, of course you are specialized in the area, but then one would expect a bit more background knowledge. Also mechanistically this argument is inherently weak. What would cause those cycles? Currently we know that it is driven by CO2 and we know that humans are the major source for the observed increase. To state that it is somehow cyclic one would have to argue that in absence of anthropogenic sources the Earth would somehow suddenly decide to increase CO2 or other greenhouse gases. There are no alternative theories to explain all the observed climate signatures, which is why people actually looking into it pretty much agree on the human-CO2-climate change link. That being said, there is a cause to be had regarding use of meat as an important contributor to CO2.Current estimates (Pitesky et al 2009, Adv Agronom) pin it about 14-18% (with fossil fuel use making up ca. 60%). So it is significant and it is almost certain that stopping industrial production would reduce CO2 emission. What the actual impact is going to be is more difficult to assess, however. Even shifts from beef (which contributes most) to other livestock could reduce emission significantly. But obviously, it would not change the big chunk of fossil fuel use.

I can provide figures on R&D funding and if you have any questions with regard to the grant process, I am more than happy to share. One thing of note is that the competition is fierce in academia. Typically success rate are around 10% and most of them are going to those that already have grants. Meaning that if you are unsuccessful in securing funds, you can end up in a quick death spiral which, from what I have heard from colleagues in industry is to some extent even less forgiving as you can end up there despite hitting all the metrics. So for young researchers (i.e. assistant profs with no records of funding) some mechanisms have success rate below 2%. I am aware that there is some perception out there that academic research gets to waste a lot of money, but truth of the matter is that unless you are successful, you are likely to penny pinch a lot. In that regard it is closer to underfunded start ups in operation. While social sciences has a bad rep in some circles I will say that a) their research is usually much cheaper and b) some of the studies are of significant impact in terms of how to apply things that we find out in STEM. And considering the big division that apparently comes up every time gender or ethnicity comes up, I am happy that someone is trying to gather data that we could use as a basis for discussions, rather than, say, yelling at each other. There are structural advantages of industry, of course, but they are generally a function of not being that cash-strapped and being able to rely on a work force rather than having the mandate of training students (on top of doing research). On the other hand, there is no else that would do research that has not immediate payoff.

In that regard I would say that it is pretty difficult to predict which findings are going to open up new venues. Even experts have trouble predicting impact of proposals, so I see at as a general challenge. In fact I would not be against more unfocused research spending as a means to figure out novel problems (though that may generally fall under fundamental research). However with regard to research spending, as a portion of the federal budget the R&D funding has declined since the 60s and pretty much plateaued since the 80s. Which is a bit worrisome considering the increase in college graduates. Concurrently investments by industry has increased massively, but obviously the focus there is mostly applied and proprietary research, leaving fundamental funding in the dust. So overall, there is not really a funding creep, if anything it is either steady or declining, (according to some figures the inflation adjusted research budget of the NIH has decreased over the last decade for example). I can dig out some more tables if needed, but would you consider a revision of the assumption that research funding is in a constant uptick (at least as function of budget)? Edit: meant as rely to post #15