RosethornRanger (it/its) (TTV)An ounce of prevention is worth a pound of cure. If you want to beat capitalists you must give away all your extra resources to get other people out of this cycle, and let others do the same with you.
Dong-Ha Oh
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- 191 Posts
Bioinformatics data wrangler; (plant) comparative genomics; he/him; opinions are my own
naturepoker오랜만에 눈마새를 정독중에 이 부분을 다시 읽었다.
자기완성을 위한 인생을 조심하라. 나중에 라수가 독자를 위해 풀이해 줄때 까지 정말 알쏭달쏭한 문장이었는데.
젠장. 한국밖의 사람들이 이 소설을 읽지 못하고 살아가야 한다는 사실은 굉장한 불의가 아닐 수 없다.
그럼, 내가 모르는 언어와 문화들의 시와 이야기들도 이처럼 재미있고 인생들을 흔들어 놓는 존재들 이라는 것이겠지.
인생이 어둡고 갑갑해질때 이런 생각을 하면 좀 가슴이 시원해지는 느낌이 든다.
Zoom Earth
Ming 'Tommy' Tang1/ If you're a bioinformatician and think "protein abundance = protein function"…
You're wrong.
Dangerously wrong.
You're wrong.
Dangerously wrong.
Terence TaoAny complex project tends to have both explicitly stated and implicitly unstated target goals. For instance, a Lean formalization project may have as its explicit goal the task of obtaining a formal proof of some mathematical claim X; but there are often unstated goals, such as also formalizing key subclaims and definitions X_1, X_2, ... to X in a fashion that would be suitable for upstreaming to the Mathlib library; learning how to use various collaboration tools and distribute tasks; organically discovering insights to the finer structure of the proof of X that might not be emphasized in previous informal proofs; giving real-world training and experience to novice formalizers; and more generally building a community of humans expert in the art of formalization.
In the past, it has generally not been necessary to state these implicit goals because of a strong empirical correlation between the achievement of these goals and the achievement of the explicit goals. In the example of the formalization project, pretty much any human-centric effort to accomplish the explicit goal will end up naturally also achieving most of the implicit goals stated above. So the explicit goal effectively becomes a viable proxy for the broader range of actual goals. (1/2)
The situation is however now changing with the advent of capable AI tools, that use very different methodologies than human equivalents. Such tools can be directed to solve an explicit goal without necessarily achieving any of the implicit goals that would have been also likely to be achieved if the task were performed by a human team. In fact, the nature of AI optimization algorithms is such that they may even achieve high performance at the explicit goal at the *expense* of all implicit goals. (Cf. Goodhart's law: "When a measure becomes a target, it ceases to be a good measure".)
Given the increasing deployment of these tools, this suggests to me that project organizers will now need to make more strenuous efforts to explicitly state *all* the goals of a project, and not just the nominal one. In some cases, these goals may not even be obvious initially to the organizers themselves, and may require some discussion amongst the participants. And external parties who are interested in testing their AI tools against such a project would be advised to coordinate with the organizers in advance, in case they have missed one or more key implicit goals that their tool would not be optimizing for. (2/2)
Grégory Gutierez 🌻🎸🐧"Despite rhetoric painting liberal big cities as lawless war zones, the most dangerous places in #America in terms of gun violence are often deep-red states and rural towns. Federal health data reveal that states with conservative leadership consistently have higher firearm death rates than their blue-state counterparts. In 2021, eight of the 10 states with the highest gun death rates per capita were won by #Trump in the 2020 election." #GunControl #GunViolence
https://theintercept.com/2025/09/12/charlie-kirk-gun-violence-red-states/
bioRxivContext-dependent regulatory variants in Alzheimer's disease https://www.biorxiv.org/content/10.1101/2025.07.11.659973v1?med=mas
Context-dependent regulatory variants in Alzheimer's disease
Noncoding genetic variants underlie many complex diseases, yet identifying and interpreting their functional impacts remains challenging. Late-onset Alzheimer's disease (LOAD), a polygenic neurodegenerative disorder, exemplifies this challenge. The disease is strongly associated with noncoding variation, including common variants enriched in microglial enhancers and rare variants that are hypothesized to influence neurodevelopment and synaptic plasticity. These variants often perturb regulatory sequences by disrupting transcription factor (TF) motifs or altering local TF interactions, thereby reshaping gene expression and chromatin accessibility. However, assessing their impact is complicated by the context-dependent functions of regulatory sequences, underscoring the need to systematically examine variant effects across diverse tissues, cell types, and cellular states. Here, we combined in vitro and in vivo massively parallel reporter assays (MPRAs) with interpretable machine-learning models to systematically characterize common and rare variants across myeloid and neural contexts. Parallel profiling of variants in four immune states in vitro and three mouse brain regions in vivo revealed that individual variants can differentially and even oppositely modulate regulatory function depending on cell-type and cell-state contexts. Common variants associated with LOAD tended to exert stronger effects in immune contexts, whereas rare variants showed more pronounced impacts in brain contexts. Interpretable sequence-to-function deep-learning models elucidated how genetic variation leads to cell-type-specific differences in regulatory activity, pinpointing both direct transcription-factor motif disruptions and subtler tuning of motif context. To probe the broader functional consequences of a locus prioritized by our reporter assays and models, we used CRISPR interference to silence an enhancer within the SEC63-OSTM1 locus that harbors four functional rare variants, revealing its gatekeeper role in inflammation and amyloidogenesis. These findings underscore the context-dependent nature of noncoding variant effects in LOAD and provide a generalizable framework for the mechanistic interpretation of risk alleles in complex diseases. ### Competing Interest Statement The authors have declared no competing interest. Cure Alzheimers Fund PA Cure, 4100087331 NIH, DP1DA046585, F30DA053020
captain acab 
datum (n=1)"In 1982, an interviewer for a science fiction magazine asked Le Guin what she would do to save the world. She answered impatiently: “The syntax implies a further clause beginning with if…What would I do to save the world if I were omnipotent? But I am not, so the question is trivial. What would I do to save the world if I were a middle-aged middle-class woman? Write novels and worry.”"
https://lithub.com/the-way-of-water-on-the-quiet-power-of-ursula-k-le-guins-activism/
