Somebody has to pay for the trial. Drugs are expensive and the amount needed to dose a single person is orders of magnitude more than mice. So who funds the study?
If it's the government or philanthropic fund, you have to put in a grant and show that it's competitive in terms of preliminary results etc.
If it's the drug companies, you have to deal with lots of complicated things with combination therapies. Drug companies don't like their drug paired with other drugs that aren't in their portfolio. They also need to see a way to make a profit on it, which means they need to evaluate whether this is the most likely successful trial, assess bang for buck etc.
If you want to go compassionate use, then you need to get the pharma to donate the drugs or insurance to cover it. This is spain, so I guess insurance is just the government since they have universal healthcare. I have no idea how that works but I am guessing it doesn't move fast.
Somewhere between $500K and $2M for an mRNA vax is my guesstimate. You don't have to hypothesize about what the rich might do, though. The co-founder of GitLab was diagnosed with osteosarcoma a few years ago. He relapsed, leaving him without any standard of care treatments. He's spent the last couple of years throwing loads of time, effort, and of course money at lots of experimental treatments: https://osteosarc.com/ -- even released all the data gathered on his tumor over timepoints.
For all the folks complaining about "it's only in mice! things never work in humans!" -- I work at MSK and we definitely have seen success treating PDAC in humans: https://www.nature.com/articles/s41586-023-06063-y
"Why don't I see these treatments hitting the general public?" Because trials like these are phase I/II. Then you need a phase III that takes a long time to recruit a large cohort and has overall survival as an end point so you need a long time to measure the actual outcome you care about. And most trials fail in phase III because the surrogate end points used in phase II studies, like progression free survival (ie how long did patients go before their disease advanced in screens), are not necessarily great predictors of improved overall survival.
Specifically for cancer vaccines, this paper was a driving force behind MSK establishing a cancer vaccine center to scale up these personalized neoantigen mRNA vaccines. It's very very difficult to do and extremely expensive right now.
PI at a cancer center here. These two ideas are not mutually exclusive. Cancer is indeed not 1 disease but many countless ones. At the same time, cancer diagnoses are based on site of origin and histology (how it looks under a microscope). But often what drives a cell of one tissue toward pathogenesis is the same mutation or other molecular malfunction as a cell of another tissue type. In those cases, we can develop drugs that target that specific component and it may work across both cancer types.
Unfortunately, there are countless ways things can go wrong in cells. There are also rarely drugs that truly span a large swath of cancer types effectively. That's because even though two cancers from different sites may have the same driver, how they respond to treatments can differ. The difference in cell state may allow one of the two cancers to adapt to the treatment, such as by activating an alternative growth pathway, whereas the other cancer type's cell of origin may not have such an easy road to therapeutic escape.
"Off-Topic: Most stories about politics, or crime, or sports, unless they're evidence of some interesting new phenomenon. Videos of pratfalls or disasters, or cute animal pictures. If they'd cover it on TV news, it's probably off-topic."
- bio startups rise, ag tech startups rise, food startups rise-- everything to do with engineering life.
- second and third tier cities rise in the US while first tier cities ebb. Places like Austin see continued growth, while cities like Baltimore and Cincinnati begin to really revitalize as local market become more important than global markets.
- mobility increases as people are less tied to their jobs/families
- Google slows on the innovation front. FB is increasingly weak as social networking just isn't as profitable anymore. Amazon keeps churning away. Netflix wins best picture at the Oscars.
- AI progress slows. The ML community fragments again. Neurips is no longer where the best ML researchers publish.
- At least one climate protest with over 5M people involved nationally, calling on Congress to act now
- China, mired in internal political upheaval, faces a lost decade. They will either no longer be one of the two biggest economies or they will be on a clear trend down but third place is a long way to fall
- twice as many people consider themselves vegetarian or vegan. Foods for this demographic have gotten much better and more diverse. Consumption of meat is still high, but the trend in 2030 will be clear: the meat industry is shrinking rapidly in the US and Canada. Other nations will lag here, meaning almost all of the innovation will happen in North America
- NYC will have built only two new subway stations
- a third political party will gain at least 5 seats in the House
- there will be a recession. Likely due to housing again. As the boomers die out, their millennial children inherit their suburban homes. Unfortunately they don't want to live in the suburbs and selling the house would pay off their student loan debt. But who is going to buy all these houses?
- political divide in the US heals, but it's not pretty and it all feels chaotic. Political parties weaken in favor of some new form of factions.
- CS undergrad enrollment declines but CS course requirements pervade nearly every STEM field. The common wisdom will now be that interdisciplinary jobs, not vanilla software engineer, are the growth sector, especially in bio/medicine/food/agriculture.
- Medicare age lowered to 50 as a transition toward single payer that will take another decade
- the world has missed its chance to avoid global warming. The schism in the debate will now be about what to do. Radical positions (open borders for mass refugees, a trillion dollar climate change R&D bill) will become more mainstream.
- Cities all over the US will be calling for federal infrastructure to build new train and tran systems, obviating the market demand for autonomous taxis. Uber and Lyft go under. Long haul autonomous vehicles are at 10% of all interstate traffic
- a common political point will be about how the US needs to stop subsidizing corn. As crops becomes more important in this decade (ag tech, rise of vegetarianism, climate change) it will be clear that corn is an over investment but political inertia will not let things change this decade.
- A dominant Canadian tech company will arise that rivals Google/FB or is at least rising rapidly
- towards the end of the decade, robotics is starting to reach the early-success stage. This will have impacts on all sectors as co-working spaces pop up with access to reprogrammable devices that can prototype commercial products. Think: Roomba for X. 2030 is still early days for this, but there's buzz, articles about robo-spaces Wired, etc.
Yes. Exactly this. Building models that assess risks and potential gains. A quant is a catch-all term though, so one person may be working on models that predict some sector of the market and another person could be looking at online allocation algorithms for maximizing risk-adjusted returns.
The problem with the idea of cheap screening tools is Bayes' theorem. If doctors go ordering this for most people since it's just a blood test, and if only 1% of people ever really have cancer when tested then the 1% false positive rate means there's only a 50/50 chance you have cancer given the test is positive.
Except it seems there is good case law to show that in fact suspected cannot be forced to open a combination lock, as it falls under fifth amendment protection. They can, however, be compelled to provide a key if it is a key-based lock. This applies similarly to biometric-based locks.
It's hard to believe that an encryption key is any different than a combination lock in this "encryption is like a safe" metaphor.
The usual way they reduce variance in these man vs. machine poker showdowns is to do "pairs" play. You have two humans playing simultaneously in isolated locations. The decks for both humans are the same, but player 1 and 2 are swapped for one human. That way, the bot strategy has to play both hands.
It does totally eliminate variance, but they also take that into account and correct for it when looking at final outcomes usually. Right now the bot is up by something like 800K over 60K (out of 120K) hands. If that rate continues, it will win by around 1.6M or 400K per human. The blinds are 50/100, so that would equate to roughly 33 millibets (thousandths of a big blind per hand). That isn't too far off from standard win rates in bot vs. bot tournaments [1].
I'd say it's likely that the results of this tournament will be a statistically significant win for the bot.
Educate them to do... what, exactly? What could Uber do with two million mostly-uneducated truck drivers that are scattered all through out the country? Most do not want to move, do not have the patience/desire/grit to go through long retraining periods for a vastly different job, and are currently making something in the neighborhood of $55K/yr. How could any company possibly be expected to help that large a workforce not take a dip in its standard of living, when literally the only skill they have is about to become nearly worthless? And how could you do it while still upholding your fiduciary responsibilities to your shareholders?
Stan (http://mc-stan.org/documentation/) is arguably the most advanced language. It's especially pushing the bounds of doing automatic variational inference, for the scenario where your model does not have a nice conjugate form that would be amenable to Gibbs sampling. It's not quite reached what I would say is production-quality, but some of the best people in the world of computational Bayesian methods (e.g., Michael Betancourt, most of David Blei's lab, etc.) are working on it.
> The data is useless unless it is annotated (e.g. a human labels where the lanes are, where the obstacles are, what the bicylist is doing, etc.) - that's the bottleneck, not collecting large amounts of raw sensor data + driver actions.
Not sure what the source is on that chart, but I'll assume it's credible. If that's the case, then despite what the article says, it's hard to believe that most of this decline is not accounted for by the decline in smoking. Surely the decline in smoking accounts for the vast majority of the lung cancer decline, and the peaks for colon and prostate cancer are very close to the same time.