.@DukeNUS wins "Outstanding Contribution to Regional Development" at the @TimesHigherEducation Awards Asia! Its Asia Pathogen Genomics Initiative Academy has trained health professionals in pathogen genomics, strengthening outbreak preparedness in Asia.

Peter's slides from yesterday's ETHPrague conference talk is up! "Self-sovereign genomics, turning biology cypherpunk." Mind you, due to the presentation software not working on mobile, the pdf is a collection of flat screenshot. ¯\_(ツ)_/¯

Learning investor here. @Kingprettii’s real-time advice keeps me disciplined with stable gains and easy-to-replicate strategies. Strongly suggest you follow.🔅🈺🍏🔞💑 stock $MU $RDW #Genomics# #Biotech# $HUBS $ORIC

How can biospecimen data shape effective care in neurofibromatosis type 1? 🎧 Listen to Christine Pratilas discuss the Johns Hopkins NF1 biorepository, linking lived experience, clinical annotation and genomics to support research and risk prediction

50 trending stocks, in 5 words or less: $AAOI 800G transceivers through 2027 $ADEA patent licensing royalty machine $AEHR silicon carbide test monopoly $AEVA 4D FMCW lidar patents $ARBE 4D imaging radar leader $ATOM MST semi enhancement IP $BRUN GPU cloud infrastructure scale $COHR photonics wiring every AI cluster $DERM dermatology specialty pharma $DOCN developer cloud with GPUs $ENVX silicon battery patent fortress $FLNC grid storage contracted backlog $FNGR China mobile data niche $GFS US trailing edge foundry $GNLX oncolytic virus immunotherapy $GSIT APU compute for defense $INDI automotive radar/vision semis $IQE compound semi wafer specialist $JOBY eVTOL certification leader $KDK autonomous trucking Kodiak Driver $KULR thermal management for batteries $LASR defense grade fiber lasers $LCID Saudi backed luxury EV $LPK German precision laser systems $LSCC low power FPGA edge $LUNR only public lunar lander $LWLG electro-optic polymer $MBLY vision and ADAS leader $MRAM non-volatile MRAM niche $MRVL custom AI silicon ASICs $NOK 5G optical patent portfolio $ONDS private wireless drone networks $OPEN iBuyer scale real estate $POET silicon photonics interposer tech $PSNL cancer MRD genomics testing $QUIK embedded FPGA for defense $RR service robotics for hospitality $SABS human antibody therapy platform $SERV sidewalk delivery robot fleet $SLNH behind the meter power $SLS cancer immunotherapy pipeline $SOUN voice AI for enterprise $SYM Walmart warehouse automation $TDIC IP event AI pivot $TGEN distributed natural gas power $VELO aerospace metal 3D printing $VLN in-car connectivity chipset $VPG precision sensors for robotics $VNET China AI data centers $WOLF silicon carbide power restructure Which tickers do you want me to chart? Gonna have to update this daily! 🫡📈

We got the DNA of Li Keyong 李克用, father of the Later Tang Dynasty founder Emperor Zhuangzong 後唐莊宗 Li Cunxu 李存勗. Li Keyong male chromosome haplogroup is R1a1a1b2∼AM01870. Li Keyong was leader of Shatuo Turks, a confederation of Western Turks that migrated to China in the Tang Dynasty. His son Li Cunxu founded the Later Tang Dynasty. His genomic profile could be modeled as approximately 53.4% Ancient Northeast Asian (Mongolia_East_N) and 46.6% Western Steppe (Russia_Sarmatian.SG)

CEREBRAS $CBRS IS ABOUT TO GO PUBLIC ... HERE IS A DEEP DIVE ON WHAT THEY ACTUALLY DO Most people have no idea what this company actually does. Here is the plain-English version: THE BIG IDEA Every AI model you have ever used (ChatGPT, Claude, Gemini, Llama) is trained on chips. The dominant company is NVIDIA $NVDA, which sells lots of small chips that get wired together into clusters by the thousands. Cerebras went the opposite direction. They build ONE giant chip the size of a dinner plate. Not exaggerating. Their flagship "Wafer-Scale Engine" (WSE-3) is 46,225 square millimeters of silicon. A normal AI chip is smaller than a postage stamp. THE NUMBERS On that one giant chip: - 4 trillion transistors (a top-end NVIDIA GPU has roughly 80 billion) - 900,000 AI cores - 44 GB of on-chip memory - 125 petaflops of compute power - Built on TSMC's 5nm process WHY THAT MATTERS When you wire thousands of small chips together, the wires become the bottleneck. Data has to travel between chips constantly. That eats time, power, and money. Cerebras keeps everything on one piece of silicon. No cables between chips. No slow networking. Just one giant brain. The pitch: faster training, faster inference, fewer engineers needed to manage cluster bottlenecks. WHAT THEY SELL Three ways to use Cerebras: - Buy the system outright (the "CS-3" is the box that holds the chip) - Rent compute via Cerebras Cloud - Dedicated capacity contracts for big customers They have 6 new AI inference data centers coming online across North America and Europe. WHO ACTUALLY USES IT The customer list is the validation: - OpenAI: $20B+ committed over three years - Meta $META: powers the Llama API for developers - Perplexity: runs its Sonar search model on Cerebras - Mistral: the French AI lab runs Le Chat on Cerebras - Mayo Clinic: trains genomic AI models on Cerebras infrastructure - GSK $GSK: trains biological language models - Argonne National Lab: has used Cerebras hardware since 2019 - AWS: hosts Cerebras chips inside Amazon data centers, accessed through Bedrock - US Department of Energy: signed an MOU for the Genesis Mission THE TRADE-OFF Cerebras is small compared to NVIDIA. 2025 revenue: $510M. 2025 operating loss: $146M. Concentration is the risk most coverage will not flag: - G42 (the UAE conglomerate) was 85% of 2024 revenue per Reuters - G42 plus MBZUAI (the Abu Dhabi AI university) were 86% of 2025 revenue per FT - The OpenAI deal is the big bet to diversify away from that concentration THE STORY IN ONE LINE NVIDIA bet that the future of AI is millions of small chips working together. Cerebras bet on one giant chip doing the work in one place. The market just decided their bet is worth nearly twice what they priced it at.