biotechbio
This strategy is how LLM-like models have achieved leading performance on tabular prediction tasks (TabPFN).
The headline reads like Berkeley is suspending authorization of international student workers in general, but this actually seems like a relatively narrow restriction?
The disparity in expected value calculation is due to assuming you can predict the future after joining a company, which is obviously not true. If you can always tell a company is going to succeed after 1 year of working there, you should leave, as you are clearly destined to be the most successful venture capitalist in history
Not to be overly negative, I think this perspective is somewhat misleading as it lets one rationalize valuing startup equity as anything more than a gamble. Which it is, and always has been. Sometimes gambles work out though.
The opportunity is and always has been the possibility of accelerating work. Honestly, if something works (I mean genuinely, actually works) I don't care at all about what craftsmanship or insight went into its creation.
We value these things because they have become correlated with quality. We now have the opportunity to decouple these things; maybe something that took no effort will be just as good as a painstaking human labor.
The risk is if this doesn't come true. If we let our skills degrade and get ahead of our skis, embracing "slop" that superficially appears to "work", we will eventually pay the price. Financially and culturally, it seems like we are already all-in on the bet that it will work.
I hope it does, I just want to solve the problems I am working on.
Does anyone have an explanation for how something like this passes QC at a company with the resources of Apple? Is this video misrepresenting something?
The bar is an imperfect filter. One could study for the exam and pass and still be hugely deficient in ability as an attorney.
I would argue there's no exam that could replace the evaluative and experiential component of 3 years in law school, and accreditation helps enforce at least some standard of quality in the profession. More incompetent lawyers -> more wasteful behavior -> a more bloated and slower legal system -> worse outcomes for everyone.
I think reducing barriers to completing the legal education (part-time programs, lower cost, etc) are better avenues for increasing access.
Most cell types have systems to safely manage replication. Broadly, there are gas pedals (oncogenes) and brakes (tumor suppressors). A classic oncogene is something like RAS, which activates a signaling cascacde and stimulates progression through the cell cycle. A canonical tumor suppressor is something like TP53, the most frequently mutated gene in cancer, which senses various cellular stresses and induces apoptosis or senescence.
Most cancer genomes are more complicated than individual point mutations (SNPs), insertions, or deletions. There are copy number alterations, where you have > or < 2 copies of a genomic region or chromosome, large scale genomic rearrangements, metabolism changes, and extrachromosomal DNA. There is a series on the hallmarks of cancer which is a useful overview [1].
All of the mechanisms that intrinsically regulate cell growth would fall under your "L1 defense". Unfortunately, the idea of reversing somatic point mutations is likely to be a challenging approach to treating cancer given the current state of technology.
First, for the reasons above, cancer is often multifactorial and it would be difficult to identify a single driver that would effectively cure the disease if corrected. Second, we don't have currently delivery or in vivo base editing technology that is sensitive or specific enough to cure cancer by this means. There are gene therapies like zolgensma[2] which act to introduce a working episomal (not replacing the damaged version in the genome) copy of the gene responsible for SMA. There are also in vivo cell therapies like CAR T which attempt to introduce a transgene that encodes for an anti-cancer effector on T cells. These sorts of approaches may give some insight into the current state of art in this field.
Edit: also I should note that the genes involved in DNA repair (PARP, BRACA1/2, MSH2, MLH1, etc) are frequently mutated in cancers and therapeutically relevant. There are drugs that target them, sometimes rather successfully (e.g. PARP inhibitors). But the mechanisms of action for these therapies are more complicated than outright correcting the somatic mutations.
1. https://aacrjournals.org/cancerdiscovery/article/12/1/31/675... 2. https://en.wikipedia.org/wiki/Onasemnogene_abeparvovec
- Sure, cancer can develop years before diagnosis. Pre-cancerous clones harboring somatic mutations can exist for decades before transformation into malignant disease.
- The eternal challenge in ctDNA is achieving a "useful" sensitivity and specificity. For example, imagine you take some of your blood, extract the DNA floating in the plasma, hybrid-capture enrich for DNA in cancer driver genes, sequence super deep, call variants, do some filtering to remove noise and whatnot, and then you find some low allelic fraction mutations in TP53. What can you do about this? I don't know. Many of us have background somatic mutations speckled throughout our body as we age. Over age ~50, most of us are liable to have some kind of pre-cancerous clones in the esophagus, prostate, or blood (due to CHIP). Many of the popular MCED tests (e.g. Grail's Galleri) use signals other than mutations (e.g. methylation status) to improve this sensitivity / specificity profile, but I'm not convinced its actually good enough to be useful at the population level.
- The cost-effectiveness of most follow on screening is not viable for the given sensitivity-specificity profile of MCED assays (Grail would disagree). To achieve this, we would need things like downstream screening to be drastically cheaper, or possibly a tiered non-invasive screening strategy with increasing specificity to be viable (e.g. Harbinger Health).
In this study, the authors demonstrate pretty convincingly that erythropoietin (EPO, a hormone that stimulates red blood cell production in the bone marrow) reduces the recruitment of tumor-cell-killing T cells to the TME. It does this by acting on tumor macrophages, another type of immune cell, and changes the state of these cells to facilitate accumulation of immunosuppressive cells.
They work out the mechanism largely through mouse models and associative analysis in human tissue samples, but I thought it was interesting that this finding aligns with the clinical observation that cancer patients who receive recombinant EPO for treatment of anemia frequently experience tumor progression.
After reading this, I am going back to check out EPO expression in old datasets that I worked with haha.
I used VSCode as my default IDE so the switch was very natural.
I am working on machine learning in bio, and many of the tools, methods, and data structures are very domain specific. Even so, the agent feature is good enough that for most tasks, I can describe the functionality I want and it gets me 80% of the way there. I pay $20 a month for Cursor and it has quickly become the last subscription I would cancel.
What are others' general level of recall for this stuff? Am I a charlatan who never was very good at math or is it just expected that you will forget these things in time if you're not using them regularly?
Historically, cancer was treated with therapies that are toxic to all cells, relying on the fact that cancer cells divide quickly and are unable to handle stress as well as normal cells (chemotherapy, radiation).
The last couple of decades we've seen many targeted cancer therapies. These drugs generally inhibit the activity of a specific protein that lets the cancer cells grow (e.g. EGFR inhibitors) or prevents the immune system from killing the cancer cells (e.g. PDL1 inhibitors).
This mechanism is way more interesting. The gene BCL6 is usually turned on in immune cells when they are mutating to recognize foreign invaders. This process involves lots of DNA damage and stress, but BCL6 stops the cells from dying and is therefore important for normal immune function. Unfortunately, this makes BCL6 a gene that is often co-opted in cancer cells to help them survive.
The method cleverly exploits the oncogenic function of BCL6 not by inhibiting it, but by turning it into a guide, enabling the delivery of activating machinery to the targets of BCL6 and reversing the inhibitory effects on cell death.
The whole field of targeted degraders, molecular glues, and heterobifunctional molecules is a growing area of interest in cancer research.
IANAL, but this sounds pretty standard and I doubt OP will be able to fight the legitimacy of the claim. Re: a forward sale, sounds like it blatantly violates the agreement and would expose OP to some obvious risks. Agreed OP should talk to a lawyer.
Is anyone else surprised by OP’s indignation? Startups are risky, options aren’t a guaranteed payday, exercising is gamble, and liquidity events are regulated for a reason. Sometimes you lose money. Have we forgotten this?
You could also take the bottom-up approach of asking what DOES cause certain cancers. That's a whole other discussion.
Considering all this, if you still have doubts that "viruses do not cause the majority of cancers", I think you will likely be skeptical about pretty much all of biology.
"Searching large pan-cancer genome and whole-transcriptome datasets enabled the identification of a high percentage of virus-associated cases (16%)".
Far from majority.
This was a hypothesis in the field for a number of years, and has been disproven. If you want a good overview of the history of cancer, I recommend “The Emperor of All Maladies” by Siddhartha Mukherjee.
What happens when you exercise? Blood pressure increases, blood vessels dilate. I honestly would not be surprised if what these authors observed was just existing T cells in capillary beds being kicked up into circulation, only to adhere and begin to intravasate a couple minutes later.
Tl;dr from the article, they used flow cytometry to quantify different cell populations in the blood, and noted that the proportion of CD8+ T cells (cytotoxic, the kind of immune cells that recognize and kill cancer cells, among other things) increases after exercise and returns to baseline 30 min later.
Claiming that this supports the idea that cancer patients could benefit* from exercise is a pretty big overstep of scientific inference.
* = in the immune response to their cancer
Knowing how to step through backpropagation in a neural network gets you pretty far in conceptual understanding of a lot of architectures. Imo there’s no substitute for writing out the gradients by hand to make sure you get what’s going on, if only in a toy example.
Look at the last 12 months. Multiple sub-sectors of public equities down >80%. Do you think people saw that coming? If they believed it enough to bet on it, the opportunity to profit was huge. That's to say nothing of private valuations.
And yet every startup with some traction and a proof of concept PMF seems confident an exit is on the horizon. Maybe for parts of the last decade this was true, but now...