Arka Sen Chaudhuri

New Nature Biotechnology paper by @Brown_lab1: detargeting hepatocytes enhanced mRNA vaccine immunity. This raises a key design question: should next-gen mRNA vaccines optimize not only what antigen is encoded, but also which cells express it?nature.com/articles/s4158…

Very interesting new Nature paper by @DelgoffeLab: post-meal lipid metabolism can durably enhance T cell immunity. Raises a key question for vaccine biology: how might obesity or altered lipid metabolism shape immune memory? nature.com/articles/s4158…

A one minute primer on how #AutoSpectral improves spectral flow cytometry unmixing by using per-cell calculation of autofluroscence spectra. Use our R package and get higher quality spectral data, easy as that! biorxiv.org/content/10.110…

This paper took us 13 years and is one of the longest papers ever in Cell. Check it out & judge for yourself cell.com/cell/fulltext/…

Online now!✨RESEARCH: Chen, @VarunDw et al present EMRAge and OMICmAge: biological age measures which integrate electronic medical records, multi-omics and DNA methylation, strongly associated with disease and mortality across validation cohorts nature.com/articles/s4358…

Whole-organ and whole-body atlases at single-cell resolution enable quantitative cellome-wide analysis across development, physiology, and pathology @CellCellPress @hiroking1975 @riken_en cell.com/cell/fulltext/… 🇯🇵

New paper alert 🚀 Our organism-wide single-cell ATAC-seq atlas of mammalian aging is now out in @ScienceMagazine, led by our fantastic graduate student @ziyu__lu from @RockefellerUniv! science.org/doi/10.1126/sc…

Rewinding the Clock: Emerging Pharmacological Strategies for Human Anti-Aging Therapy mdpi.com/1422-0067/26/1…

🚀 Thrilled to launch DeepScholar, an openly-accessible DeepResearch system we've been building at Berkeley & Stanford. DeepScholar efficiently processes 100s of articles, demonstrating strong long-form research synthesis capabilities, competitive with OpenAI's DR, while running up to 2x faster! Try it out: deep-scholar.vercel.app

Generative artificial intelligence is used to design an effective treatment for inflammatory bowel disease in preclinical models go.nature.com/4iteK6Z rdcu.be/eQosr

Pretrained using 335,645 whole-slide images, a foundation model is developed to provide representations for slide- and patient-level tasks. It is capable of performing clinical tasks and generating reports even in data-scarce scenarios, such as rare cancer diagnosis and survival prediction, without further fine-tuning. nature.com/articles/s4159…

Today, we’re announcing Kosmos, our newest AI Scientist, available to use now. Users estimate Kosmos does 6 months of work in a single day. One run can read 1,500 papers and write 42,000 lines of code. At least 79% of its findings are reproducible. Kosmos has made 7 discoveries so far, which we are releasing today, in areas ranging from neuroscience to material science and clinical genetics, in collaboration with our academic beta testers. Three of these discoveries reproduced unpublished findings; four are net new, validated contributions to the scientific literature. AI-accelerated science is here. Our core innovation in Kosmos is the use of a structured, continuously-updated world model. As described in our technical report, Kosmos’ world model allows it to process orders of magnitude more information than could fit into the context of even the longest-context language models, allowing it to synthesize more information and pursue coherent goals over longer time horizons than Robin or any of our other prior agents. In this respect, we believe Kosmos is the most compute-intensive language agent released so far in any field, and by far the most capable AI Scientist available today. The use of a persistent world model also enables single Kosmos trajectories to produce highly complex outputs that require multiple significant logical leaps. As with all of our systems, Kosmos is designed with transparency and verifiability in mind: every conclusion in a Kosmos report can be traced through our platform to the specific lines of code or the specific passages in the scientific literature that inspired it, ensuring that Kosmos’ findings are fully auditable at all times. We are also using this opportunity to announce the launch of Edison Scientific, a new commercial spinout of FutureHouse, which will be focused on commercializing our agents and applying them to automate scientific research in drug discovery and beyond. Edison will be taking over management of the FutureHouse platform, where you can access Kosmos alongside our Literature, Molecules, and Precedent agents (previously Crow, Phoenix, and Owl). Edison will continue to offer free tier usage for casual users and academics, while also offering higher rate limits and additional features for users who need them. You can read more about this spinout on our blog, below. A few important notes if you’re going to try Kosmos. Firstly, Kosmos is different from many other AI tools you might have played with, including our other agents. It is more similar to a Deep Research tool than it is to a chatbot: it takes some time to figure out how to prompt it effectively, and we have tried to include guidelines on this to help (see below). It costs $200/run right now (200 credits per run, and $1/credit), with some free tier usage for academics. This is heavily discounted; people who sign up for Founding Subscriptions now can lock in the $1/credit price indefinitely, but the price ultimately will probably be higher. Again, this is less chatbot and more research tool, something you run on high-value targets as needed. Some caveats are also warranted. Firstly, we find that 80% of Kosmos findings are reproducible, which also means 20% are not -- some things it says will be wrong. Also, Kosmos certainly does produce outputs that are the equivalent to several months of human labor, but it also often goes down rabbit holes or chases statistically significant yet scientifically irrelevant findings. We often run Kosmos multiple times on the same objective in order to sample the various research avenues it can take. There are still a bunch of rough edges on the UI and such, which we are working on. Finally, we are aware that the 6 month figure is much greater than estimates by other AI labs, like METR, about the length of tasks that AI Agents can currently perform. You can read discussion about this in our blog post. Huge congratulations to our team that put this together, led by @ludomitch and @michaelathinks: Angela Yiu, @benjamin0chang, @sidn137, Edwin Melville-Green, Albert Bou, @arvissulovari, Oz Wassie, @jonmlaurent. A particular shout out to @m_skarlinski and his team that rebuilt the platform for this launch, especially Andy Cai @notAndyCai, Richard Magness, Remo Storni, Tyler Nadolski @_tnadolski, Mayk Caldas @maykcaldas, Sam Cox @samcox822 and more. This work would not have been possible without significant contributions from academic collaborators @mathieubourdenx, @EricLandsness, @bdanubius, @physicistnevans, Tonio Buonassisi, @BGomes_1905, Shriya Reddy, @marthafoiani, and @RandallBateman3. We also want to thank our numerous supporters, especially @ericschmidt, who has been a tremendous ally. We will have more to say about our supporters soon!

After two years of work, we’ve made an AI Scientist that runs for days and makes genuine discoveries. Working with external collaborators, we report seven externally validated discoveries across multiple fields. It is available right now for anyone to use. 1/5

Just out - our major review piece @ImmunityCP , summarizing couple of decades of the research on human immune aging and providing highlights of the latest advances in the field. Led by Marina Terekhova, truly encyclopedic depth (327 references) sciencedirect.com/science/articl…

In 1992 Peter Ratcliffe received this rejection letter from Nature. His findings were not "a sufficient advance in our understanding". 27 years later he won the Nobel Prize for the same discovery. Don't lose faith in the things you believe in.

The proteins in our blood as fingerprints for 59 diseases and health @ScienceMagazine science.org/doi/10.1126/sc…

Our new research identifies the transcription factor TCF4 as a master regulator of alveolar macrophage (AM) stemness, similar to TCF1 of T cells. Targeting TCF4 can promote AM regeneration and limit post viral lung conditions like respiratory long COVID. biorxiv.org/content/10.110…

Current i.m. COVID and Flu vaccines induce good immunity in the blood, but not well in the mucosa. Our new study @ImmunologyTang @arka_sc uncovers how mucosal boosters drive powerful IgA responses, essential to block infection at the site of entry. biorxiv.org/content/10.110…

#WeekendRead #InterferonPower! Hegde, @MiriamMerad &co show @Nature that #tumor-derived factors drive NRF2 activation in bone marrow myeloid progenitors, dampening IFN-I/-III responses & favoring emergence of immunosuppressive monocyte-derived macrophages nature.com/articles/s4158…

Hi @elonmusk @xAI, I’m a postdoc researcher eager to use Grok 4’s advanced reasoning for my work! Can xAI offer free access to postdocs to drive scientific breakthroughs? #Grok4Research