CharonY

I think the discussion has veered off way into the speculation area. Several points are at best misconceptions. For example: This is inaccurate as certain fish species are hermaphrodites (and obviously vertebrates). This is not how evolution works. Whatever has reproductive success will exist. A predominance of gonochorism in certain groups of organisms suggests that either a) it leads to higher reproductive success than hermaphroditism (under the given environmental conditions) or perhaps that b) there are developmental constraints which limits the conversion from one system to another. Explanations for both have been proposed in a previous post.

I think you are looking at the wrong cause- the combination of rising cost of living plus the pandemic pause has caused a lot of folks to re-evaluate their job situation. Quite a few people have quite (at least for a while) and there is significant amount of folks who are looking for other (better) jobs. Following the the pandemic effect, there was a rapid drop in unemployment, continuing a trend start around 2010 and is at its lowest since the 90s. So in that regard it is small wonder that badly paid position are hard to fill. This effect is also seen in sectors such as academia where postdocs were easy to get in the past, but now it is difficult. Those folks are not typically CERB recipients, either. So the handout is really a narrative without really any evidence (and ignoring much stronger factors). I will also add that I am also teetering at getting annoyed by younglings, which basically just means that I am getting old. But what I have been hearing from students is that increased cost of living basically means that such work is not necessarily beneath them (though for the more urbanized students it might be), but that they would not do it for minimum wage. The argument is that given the current cost of living, other work is a better use of their time. There is simply not a huge segment of folks that would like to take minimum wage jobs (regardless of CERB or not) and for quite some time this has been the domain of immigrants almost everywhere in the world. But with Canada's increase in cost of living, and the rising resentment against immigration (some more, some less justified), this results in a combination of unfilled lower-paid jobs but also record employment rates. I will also add yet another issue to the pile: service jobs are going to be hit the hardest. Generally speaking, Canada has a productivity problem, but certain jobs that are hard to automate, such as faculty but also especially small restaurants, always had the challenge of disproportionately rising salaries. This is one of the reasons why many small restaurants are family operations, for example. With increasing outward pressure (high food and housing prices), these business are unable to keep up with salary demands. You are quite correct. Been in Canada for quite a while- I have been living and working in quite a few countries by now. Also, I have never been an American. I just lived there for a while. And I often do find it curious, if unsurprising, if Americans and Canadians share similar arguments, even with different systems (I guess the cultural impact of USA shows).

This is not how this works. You cannot simply claim something like: While the authors state that: Not only is this misquoting the paper, but it is actually drawing the opposite conclusion. Misunderstanding, or even worse, misrepresentation of citations is failure of science 101. So specifically for the population outlined in OP there are no such conclusions. Again, this is science 101, we do not jump to conclusions by cherry-picking bits and pieces from different papers. The main gist of Pontzer's work is fundamentally that energy expenditure remains constant and that (as mentioned before) PA does not increase expenditure by much. So the key element here is really that energy consumption is the key factor in terms of obesity (or lack thereof). The body has a lot of capabilities to regulate expenditure and it even factors such as stress can increase the expenditure. Now if you look at other papers from the same author in follow-up papers on Hadza populations (e.g. https://doi.org/10.1111/obr.12785) there are several hypotheses:

In other words, it is your claim, not that of the authors. Read the discussion, they come to a different conclusion.

It was a common internet thingy. On IRC you would need to provide a unique nickname and the command for it is nick. Might have other origins, too.

This is a trend in many countries. I believe in the past voting differences were mostly split along factors such as age and education, but increasingly in the young segment there is a split between men and women. With young men increasingly favoring autocrats.

I believe other countries, including the US had similar initiatives. Also the benefits were for a total of something like 7 months, IIRC, so it would be surprising to have long-term impact on employment several years later. There are a range of issues why folks cannot find workers, and we actually see a reflection of it in academia, but it certainly was not due to CERB. There is a general change coming (not all of it good) and I think the older generation (up and including mine) is going to be caught up in it.

A few things should be added to lay the foundation for further discussions. First gonochorism (the term to describe a sexual system where there are male and female members) does not always have to be linked to sexual dimorphism (the term to describe differences in appearance between male and females of a species). Sexual dimorphism is often a consequence of the respective reproductive strategies. Among hermaphroditic species, one can actually also distinguish between various forms. The one OP is thinking about is considered simultaneous hermaphroditism, i.e. all individuals producing sperm and eggs, but there are also species who are sequential hermaphrodites. I.e. producing egg or sperm at different points in their life. Studies trying to figure out fitness benefits have been investigating closely related species in which all three strategies are found, e.g. in certain worms. Here, it was found that the different species had different reproductive characteristics, that likely have benefits under different conditions. Generally, they found a trade-off between fecundity (how much they reproduce) and survival. Simultaneous hermaphrodites had the highest survival rate, but least fecundity (and smallest eggs, indicative of lower maternal investment), whereas the opposite was found for sequential hermaphrodites. The gonochoristic species was somewhere in-between. Taking that all together (survival rate, reproduction over total life cycle etc.) it seemed that the dichoristic species had overall the highest fitness. They had higher fecundity in the early stages of life cycle. They outperform simultaneous hermaphrodites, which have lower fecundity. While sequential hermaphrodites are more fecund, they are delayed until their female phase, and during the whole life cycle they are not able to compensate the early advantage. Essentially they are able to reach sexual maturity faster, likely as they only need to produce one form of gametes. The disadvantage of that gonochoristic species pay is that they produce males, that cost the same as females (as eggs) but do not directly contribute to future generations (the limiting factors are the eggs). Hermaphroditism is speculated to be a primary advantage when population densities are low and it is difficult to find a mate. There are also evolutionary developmental consideration. Transition from hermaphrodite to gonochoristic species is comparatively easy, as it could be reasonably executed by suppressing the development of one sexual function. Conversely, there are more steps involved in transition from gonochorism to hermaphroditism. I.e. once gonochorism outcompetes hermaphroditism in the evolutionary history of species, it is very unlikely that they hermaphroditism will develop, even if it became more advantageous.

I agree to some degree. It is a bit of a pity that ultimately the conservative plan of the Harper government ultimately won out and that others did not come up with a better plan. There have been some federal initiatives, but certainly not such ambitious one as Biden's and given the tenuous relationship between Provinces and Feds, I suspect it is difficult to create effective frameworks at scale. But it would be a bit unfair to say that Canada is only using stick- the carrot is there, but it is not that big and much seems to be based on tax credits in Canada (though not certain, perhaps I am underestimating the scope). However, the existing subsidies in Canada for green energy have been criticized as corporate welfare. How effective these are is not quite clear to me, especially as there is massive political infighting between provinces and feds, and with at least some performative pro-oil and gas dance in certain provinces. I also would add that some provinces as well as states have separate carbon pricing initiatives, so it is not quite as straightforward. But I would say is that in Canada, pretty much regardless who is in power, far-sightedness does not seem to be the main strategy. And much of the fight is outright silly and fraught with targeted amnesia.

Please quote the part in the study where the author make this claim.

ELISAS are the standard for such tests. They can be ordered by health provided, but are not that well suited for home use.

That has mostly been the state in the last ten years and so far there has been (to my knowledge) no smoking gun studies who are able to clear these things up. Small wonder, metabolism is hugely complex. Especially as the effects are diffuse. Inflammation is one of these issues, for example. Almost everything modulates inflammation one way or another. In a way it is just how our system interacts with the environment and to change in general. Predicting what is going to happen and how it impacts overall health (and heck, even health can be elusive to define once we go down to the molecular level), is near impossible. Even for individual bacterial cells we have fairly crude models, despite a glut of data (certain things are now somewhat predictable, but add a complex environment and the the models often go crazy). Medical studies on the other hand are empirical and look at overall outcome and not the mechanisms. There the issue is to identify the components that affect the outcome, which for metabolism can be fairly complicated.

Definitions can be useful for specific contexts. A bit of an issue with fascism is that it definitions are slightly diffuse, and the folks who considered themselves fascist are a bit deliberate vague in their ideology. There are a few common elements that one can use to distinguish them from e.g. communist ideologies (e.g. ultranationalism, capitalist system and belief in natural social hierarchies, for example). But if the point is regarding the dangers of autocratics systems, these elements might not be relevant. A modern danger in the Western world is the momentum of more diffuse, but populist and grievance-based sentiments, where folks are willing to trade freedoms for an autocratic system that punishes those oppose to them. It remains an old play book where fears of immigrants, certain races, homosexuality (or basically any sexuality not considered the norm), transgenderism and so on is seen as an existential threat and a perceived strong man (Orban, Trump etc.) is their saviour. What can also be seen is that those sentiments are coordinated (to a big degree by social media) and can be traced to certain strategist (e.g. Bannon) who have especially mobilized young white men who feel disenfranchised.

Since it has popped up a couple of times, I want to stress that the role of the gut microbiota is unclear. Especially relating to energy consumption, activity and weight gain/loss, it is very unclear whether changes in the composition are just associated with larger changes or whether they are affecting it. There have been fecal transplantation studies, but not on a scale necessary to deal with these high dimensional data sets. I.e. it is one of these areas where I think hype might have outrun the evidence a fair bit. There is also a bit of an issue with incongruent approaches as there are studies that measure things very differently (e.g. microbiologists vs nutritionists) leading to uneven data interpretation. This is generally not an issue in the long run as at some point the more robust results get elevated but more than a decade in, I still a lot of noise.

It seems to me that you are making a conclusion that the authors are not making. The authors found similar TEE, but your assumption is because physical activity should increase TEE, but it doesn't, it means that the basal metabolism is lower (thus preserving energy). However, this is not what the authors conclude. Rather (and in agreement with other studies) the conclusion is that physical activity has little effect and that differences in adiposity is linked to calorie intake. I.e. it does not support the notion of a difference in energy expenditure (and certainly states nothing about specific anabolic or catabolic functions). This is not what I was referring to. Studies have shown that folks with active lifestyles, on average have a slightly higher baseline metabolism (a random one is here, but there is a glut of papers like those out there https://digitalcommons.wku.edu/ijes/vol11/iss2/2/). However, the effect size is often fairly small and relies on longer-term activities (but it is possible that over even longer time, the body adapts). But as a whole this really just suggests again that the largest determinant is usually calorie intake. Another factor is that BMR is also dependent on body composition and I think studies in athletes suggest higher BMR in athletes in part because of higher fat free mass. But I am not sure what the current state of the art there is. There is of course also a whole area where folks discuss the issues with calorig measurement techniques, but this is probably beyond the scope of this discussion.

Aside from the issues with the use of "randomness" to explain phenomena, the science community typically does not ignore findings. Often things are misinterpreted or oversold in popular science articles or folks are just unable to tease them apart (e.g. due to technological limitations). We first build tools in order to look at things we want to look at. Every biologist is well aware that things are more complicated- we start off with simple models and then add on. To a layperson that might sound like being surprised all the time, but really we all know that we are just working on different pieces of a puzzle. Every now and then there is a paradigm shift, but the frequency has gone down considerably over the last decades. We are more filling out blanks than finding complete new puzzles. But that does not excite the public so often stories come out how things are going to revolutionize things (and to be fair, young scientists often think that way). But folks who have been around longer tend to see it as further parts of the puzzle and just continue (up until something really unexpected happen). Nothing I have seen in these threads fall under that category, though.

Off the top of my head I don't know specific papers, but this is a somewhat known but in clinical practice also somewhat ignored issue. You are right that the medium choice will affect outcome and it goes beyond issues such as auxotrophic effects. It is close to something that my group is working on, but ultimately one has to recognize that clinical practice is driven by pragmatism. I.e. standard media are used because they are (for the most part) informative and allow direct comparisons. If one wants to understand cellular resilience against ABs, things get exponentially more complicated (as even different strain can have different metabolic adaptations that can change their susceptibility to a large degree). And you can complicate it further as the growth history can play a role. I.e. cells grown under nutrient replete or deplete conditions and then exposed to AB under certain conditions can be more or less susceptible, too. This is basically why these assays are run under standard conditions to mostly eliminate (or ignore) these issues, allowing a simplified characterization. If wondering from a microbial perspective there are different areas to look into, all of which are unresolved. These includes stress generation and how cells deal with it when exposed to AB (oxidative stress was a assumed to be a main hub, but I have a slightly different view on that matter) and increasingly, metabolic diversion and flexibility. An example is given here, but it is important to note that these effects are not universal. Using a different bacterial species you do see quite different outcomes. http://dx.doi.org/10.1016/j.chembiol.2016.12.015

Also, that was not a broad consensus. Most researchers had hopes that more disease markers could be identified (and to be fair, some have been), but especially folks working more closely in the areas of physiology were highly skeptical about the benefit of such an approach. In these communities the complexity of biological systems is very apparent. There is a big differences in what one sees in the news and popular science publications as there you need to find grappling headlines. Saying that "stuff more complicated, just as some folks expected, but others hoped it wouldn't be" somehow does not quite engage the public. Exactly, this is a bit of a dirty secret for studies that primarily generate minable information. We really do not know what we get (otherwise we wouldn't need to look), but it just sounds so much better if it is linked to something the public understands. A running joke is that everyone is somehow curing cancer.

That is not quite correct. Catabolism simply refers to break down more complex to simpler molecules. Energy is released in the process, but does not necessarily generate biologically usable energy (e.g. simply released as heat). Only certain chemical steps (e.g. substrate level phosphorylation) recover energy, rest has to go through additional processes such as respiration. The metabolites are not just simply fuel, they are also building blocks for other molecules. In my mind this is overcomplicating things a bit. Metabolism is in the simplest form conversion of one biomolecule (metabolite) into another. Depending on the individual reactions it can require or release energy and the energy might be lost (as heat) or recovered biochemically. The sum of all processes (or metabolic activities) at rest is referred to as basal metabolism, which sometimes is conflated with the energy expenditure for said metabolic activities. Technically it would be more correct to refer to the energy portion as basal energy expenditure (BEE). Importantly, BEE is the largest energy expenditure (something in the order of 70-80% of total energy expenditure). I.e. doing exercise does only requires a fairly limited amount of extra energy relative to the basal metabolism. The interesting but well-known bit is that basal metabolism is dependent on a lot of factors, including age, activity, but also body weight. For example, maintaining a highly active lifestyle is associated with an increase in basal metabolism (and hence, BEE). But sometimes burst of high activities (and higher associated energy expenditure) can partially be compensated by a reduction of BEE, so that the total expenditure does not change proportionately to the activity. Folks with less body weight, or after losing body weight also have a reduced BEE(which might contribute to the yo-yo effect). Also, I think the attempt to link overall energy expenditure to specific metabolic processes is flawed. There are many, sometimes competing activities going on at the same time involving different cell types, tissues and so on. There is no way way to measure them in vivo and unfortunately quite a few folks start to speculate about them without really having the data to support it on that level of detail. This has resulted in a lot of proposed diets, supplements, exercises and whatnot which for the most part simply not evidence-based. I.e. we cannot gain a deeper understanding of processes by speculating harder. At some point we first have to think about a) what data do we need and b) how can we get them (or is it possible to get them in the first place).

Have you considered the fact that a photo is a frozen moment in time and that depending on conditions and effort, the outcome can differ vastly? In contrast, in a mirror you can pose until you like what you see?

My pleasure.

Added two reviews in the post above. What I heard as student was, I believe, from a textbook related to bioenergetics, but I cannot recall the title (too long ago, likely in German). I only remember because it was fairly counter-intuitive, considering what was taught in high school. But considering what I learned about cellular efficiency since then, it makes a lot of sense. Here are the abstracts from the links above:

Not really a plant person, but I think it is a bit of a misconception that plants do not use green light effectively. It is just utilized slightly differently. One important finding is that green light penetrates deeper into tissue, providing energy for CO2 fixation in deeper within leaves. Studies also suggest that because of that (and because of the rather steep absorption profile, this deeper penetration is more effective rather than trying to absorb more blue/red light closer to the surface. I think there are quite a few studies out there regarding the overall quantum efficiency at different wavelengths and different intensities in relation to plant architecture and distribution of chloroplasts, but I think the the idea that the rather simplistic idea of low absorptance of green light and associated low levels of CO2 assimilation that is sometimes still taught is school is a fair bit outdated (and goes back to studies in the 70s, I believe). But again, not my field of expertise but there is a rather large body of literature on this topic. Edit: found two reviews on the topic (only skimmed the abstract, but rings a bell to what I heard as student): https://doi.org/10.1093/jxb/erx098 https://doi.org/10.1093/pcp/pcp034

This is an extremely poor argument, especially in the context of racism. Are you arguing that if folks respected racist opinion more, folks would somehow be less racist? We see how well that works under Trump and other reactionaries. Also it seems that you have no idea what happened in the 2000s. The issue around that time was a massive effort by evangelists to influence science teaching in the US by trying to put creationist view next to evolutionary science. At that point folks atheists, including Dawkins became fairly vocal to resist such influence to diminish science teachings and putting belief over facts. Still no idea how the various thoughts on Dawkins, cancel culture, racism and whatever has been brought up by OP connect, though.

There is one thing that always strikes me as odd and where the cancer analogy might fail a bit (though it depends on the level of detail). Often, in the discourse anything in the autism spectrum is set apart from the "normal" population, in part using quantitative measures. This of course requires to set a normative standard and to complicated things, the measures within the spectrum categorization are notoriously hetereogenous. There are attempts to better characterize individuals to better understand population heterogeneity without pre-assumption of what is normal, which, in my mind, would be a better approach to try to identify categories. There of course challenges to this approach and in the area of medicine it has been sought after for so long that in the academic community folks have rebranded it multiple times to keep it relevant (and I have been long enough around to recognize the same argument, even if the newer generation think that they discovered it for the first time). Many of these terms (personalized medicine, precision medicine, normative modeling) fundamentally recognize the heterogeneity within a population and seeks to find the level of detail to provide better diagnoses or treatment, if necessary. It also tries not to predefine categories by being more data-driven. Obviously, it is also way more challenging and the current flavour of research is to add machine learning or AI to it.