venu☘️

Taiwan scientists have created a new serum that regrows hair in just 20 days

When and how do different tissue physical structures deteriorate during aging (structural aging)? What molecular changes occur in tissues during periods of major changes? Are there tissue-specific periods of accelerated structural aging? Which organs age early vs. late? Is there cross-organ coordination in structural aging? And, finally, how lifestyle, diseases and genetics impact these organ-specific trajectories? While important, these questions haven’t been answered yet because we lack structural and molecular data from normal aging tissues at scale. We present a framework taking the first stab at scale at these questions using high resolution histology images and omics (+more) from 25,000 post-mortem tissues (public data: GTex). We reason that structure determines function and learning how tissue structure changes with age can help us understand the process of aging in different tissues - a central question with yet little understanding. An example is to visualize these two ovaries histology: young vs. old ovary- young ovaries cortex is intact, with follicles, no fibrosis - basis of its function (partially). First, we extract tissue structure from these organs using their high-res histology images using a pre-trained digpath foundation model (UNI) and asked how much they change with age (Structural Aging Rate)? As an example, the ovary has two peaks around the late 30s and then around 55. First aligns with accelerated follicle loss and second is just after post-menopause. This shows that change in morphology of the ovary captures its functional milestones during aging with no training. Bonus Puzzle: Does anyone know how these two functional milestones of ovary were originally found in the last century? What if we repeat the same analysis on bulk-omics, transcriptomics and methylation, from the same samples? Can they capture this bimodal functional decline? No. (read the paper of our explanation) Can molecular clocks trained on chronological age track this? No. They assume aging is linear. Note: Unlike molecular clocks trained on chronological age, PathStAR learns with no age labels. We simply ask: When and how does tissue morphology change most rapidly during life?

ROGAN: “Did you get COVID?” JENSEN: “I got COVID like six times.” ROGAN: “Of course you did, because you got vaccinated. The people who didn’t get vaccinated got it once or twice.” JENSEN: “Really? … Sh*t!”

I don’t understand who asked for a thinner iPhone

vienna for the week! 👋

We’d also like to address a concern we've heard in the community: We never intentionally degrade model quality as a result of demand or other factors.

🧵 We’re announcing two new updates to Google Translate to make it easier to connect with people who speak different languages, using the advanced reasoning and multimodal capabilities of Gemini models. First: Starting today in the Translate app, you can tap “Live translate” and have a back-and-forth convo in real time with audio and on-screen translations — in more than 70 languages. 🗣️ Available this week for users in the U.S., India and Mexico.

when we introduced image generation in ChatGPT in march, we saw a surge. over 130m users created more than 700m images in the first week. a big portion of that excitement came from India.

Google Chrome was Sundar Pichai's pet project and carapulted him tonthe CEO spot. Why Chrome? Pichai hails from Chromepet in Chennai and has a Metallurgy degree. He named it as a tribute to himself. Now IIT Madras Computer Science boy Aravind wants to buy Chrome. He himself named his co as Perplexity in honor of his favorite dish Paruppu Usili, a Tamil delicacy. This is the power of Chennai in calling shots in the AI age.

We just launched ChatGPT Go in India, a new subscription tier that gives users in India more access to our most popular features: 10x higher message limits, 10x more image generations, 10x more file uploads, and 2x longer memory compared with our free tier. All for Rs. 399. 🇮🇳