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Prime editing has continuously improved since David R. Liu's lab introduced it in 2019. 3 new studies from his lab advance prime editing systems, addressing key bottlenecks by increasing the efficiency and improving its potency when delivered into the body.

23,280 Aufrufe • vor 1 Monat •via X (Twitter)

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EXTREMELY CONCERNING 🚨 PRIME Drinks is going through a lawsuit. “The lawyer who tested their drink is claiming it has 3x the amount of forever chemicals a human can safely have in their lifetime” What exactly is it that the FDA even does in America? “PRIME is now getting sued and you should be seriously concerned if you have any prime since it released. So prime is now going through a new lawsuit after it was discovered that their drink has PFOs which is forever chemicals but what's really concerning is the fact that the lawyer who tested their drink is claiming it has three times the amount of forever chemicals a human can safely have in their lifetime and the lawsuit is claiming they found these forever chemicals in the grape flavored prime drink. However, it also seems like other flavors might also have these chemicals because other prime flavors are being tested. And we're going to find out after the lawsuit is approved. One lawyer on TikTok actually spoke about this and said he had a 10 year old and his mother talked him about how her son got leukemia after drinking prime. Now knowing that prime contains these chemicals, it's very much likely because of it. These chemicals are known to cause cancers and deteriorate your health since they're forever chemicals and your body can't get rid of them. Prime has 3 times the amount of these chemicals a person should have in their lifetime. —So if you still drink Prime or have any make sure you dispose of all of it until we find out what's going on”

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InstantDrag Improving Interactivity in Drag-based Image Editing discuss: Drag-based image editing has recently gained popularity for its interactivity and precision. However, despite the ability of text-to-image models to generate samples within a second, drag editing still lags behind due to the challenge of accurately reflecting user interaction while maintaining image content. Some existing approaches rely on computationally intensive per-image optimization or intricate guidance-based methods, requiring additional inputs such as masks for movable regions and text prompts, thereby compromising the interactivity of the editing process. We introduce InstantDrag, an optimization-free pipeline that enhances interactivity and speed, requiring only an image and a drag instruction as input. InstantDrag consists of two carefully designed networks: a drag-conditioned optical flow generator (FlowGen) and an optical flow-conditioned diffusion model (FlowDiffusion). InstantDrag learns motion dynamics for drag-based image editing in real-world video datasets by decomposing the task into motion generation and motion-conditioned image generation. We demonstrate InstantDrag's capability to perform fast, photo-realistic edits without masks or text prompts through experiments on facial video datasets and general scenes. These results highlight the efficiency of our approach in handling drag-based image editing, making it a promising solution for interactive, real-time applications.

AK

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How can generative AI and Robotics help advance drug discovery? 🚀 Excited to introduce LUMI-lab! A foundation model-driven Self-Driving Lab (SDL) for autonomous ionizable lipid discovery in mRNA delivery 🤖🔍 🔬 What is LUMI-lab? LUMI-lab integrates molecular foundation models with autonomous robotic experiments to efficiently explore new LNPs (lipid nanoparticles, mRNA delivery vehicles) with minimal wet-lab data. 🔥 Key Highlights: - 🧠 Foundation model trained on 28M molecules using a three-step strategy: - Unsupervised pretraining to capture broad molecular knowledge - Continual pretraining to specialize in lipid-like molecules - Active learning fine-tuning within a closed-loop experimental system - 🤖 1,700+ new LNPs synthesized & tested across 10 iterative cycles - 🧪 Brominated lipids autonomously identified as a novel structural feature that enhances mRNA transfection—an insight previously unrecognized in LNP design - 🏆 20.3% in vivo CRISPR gene editing efficiency in lung epithelial cells—the highest reported for inhaled LNPs 🚀 Why it matters? LNPs are the backbone of mRNA therapeutics, yet discovery has been slow due to data scarcity. LUMI-lab shows that AI-powered autonomous labs can accelerate mRNA delivery innovation🚀💡 🌐 Beyond mRNA drugs, LUMI-lab exemplifies a scalable framework for AI-driven molecular discovery, pushing boundaries in material science & drug delivery. 📜 Read the preprint: 🔗 💻 Code available on GitHub: 🔗 #AI #DrugDiscovery #mRNA #LNP #SyntheticBiology 🙏 A huge team effort behind this work, with special appreciation to Bowen LI for driving the project. Kudos to Haotian Cui, @YueXu1995, Kuan Pang, Gen Li, and Bettycat567!

Bo Wang

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Mr. Ajay Banga, President, World Bank Group (WBG) calls on Prime Minister Muhammad Shehbaz Sharif Pakistan acknowledges the World Bank Group’s valuable support which has been instrumental in advancing Pakistan's economic development and reforms. Prime Minister Mr. Ajay Banga, President, World Bank Group (WBG) calls on Prime Minister Muhammad Shehbaz Sharif in Islamabad today. Prime Minister welcomed Mr. Ajay Banga at his first official visit as President of the World Bank Group. The Prime Minister appreciated the World Bank Group for its long-standing partnership with Pakistan and its commitment to supporting the development priorities of the country especially through the 10-year World Bank Group Country Partnership Framework (CPF). The Premier also appreciated Mr.Banga's leadership in transforming the World Bank Group into more impactful development partner. Prime Minister stated that Government of Pakistan is vigorously working on economic reform agenda with multi-pronged comprehensive structural home grown program aimed at sustainable economic stability. The Prime Minister appreciated the support of the World Bank Group in resilient infrastructure, agribusiness, digital development, energy, human capital, fiscal reforms, and increasing productive private investment for job creation and growth. Prime Minister and Mr.Banga reiterated the need to fast-track implementation and ensure strong oversight to deliver impact at speed and scale on CPF-aligned priorities. These measures would duly assist Prime Minister’s initiative to address and resolve Implementation Bottlenecks in development projects. The Prime Minister expressed the government’s commitment to structural reforms that would unlock job-rich growth and further strengthen investors' confidence. Mr. Ajay Banga expressed his gratitude to the Prime Minister for his reception and hospitality in Pakistan. Mr. Ajay Banga commended Government of Pakistan’s ongoing reform efforts and reaffirmed his commitment to deepening cooperation through a One World Bank Group approach. He added that greater leverage of private resources, in addition to strong coordination with development partners, is necessary to meet the ambition of the government’s reform agenda.

Badar Shahbaz 🇵🇰

16,231 Aufrufe • vor 6 Monaten

Welcome to the Lab of the Future! 🧬🤖 Excited to share LUMI-lab, out today in Cell — a self-driving platform that pairs an AI foundation model with a robotic lab to autonomously discover ionizable lipids (LNPs) for mRNA delivery. The core problem: Designing lipid nanoparticles (LNPs) is hard. The chemical space of ionizable lipids is vast, experimental cycles are slow, and — critically — historical LNP datasets are far too small to train a predictive model from scratch. Most AI approaches in this space hit a wall immediately: not enough data to learn from. Our solution: lab-in-the-loop foundation model learning. Instead of training on LNP data alone, LUMI starts as a transformer-based foundation model pretrained across broad chemical space, building rich molecular representations before it ever sees a single LNP experiment. Then it enters a closed loop with a robotic synthesis platform: predict → synthesize → assay → update. Each round of real wet-lab experiments fine-tunes the model, which then proposes smarter candidates for the next round. The lab isn't just validating AI predictions — it's actively teaching the model, continuously. What happened when we let it run: LUMI-lab autonomously synthesized and screened 1,700+ ionizable lipids in human bronchial epithelial cells. The top candidate — LUMI-6 — features a brominated lipid tail, a structural motif that had been largely overlooked in LNP design. LUMI found it without being told where to look. When formulated into LNPs and delivered intratracheally to mice, LUMI-6 achieved 20.3% gene editing efficiency in lung epithelial cells — a compelling result for one of the hardest-to-reach therapeutic targets, directly relevant to diseases like cystic fibrosis and alpha-1 antitrypsin deficiency. Why this matters beyond LNPs: This is a proof of concept for a broader thesis — that foundation model pretraining + active learning + robotic experimentation can overcome the data scarcity bottleneck that plagues AI-driven discovery in biology. You don't need a massive domain-specific dataset to start. You need a model that can generalize, a lab that can generate the right data, and a loop that connects them. Huge congratulations to first authors Yue Xu, Haotian Cui, and Kuan Pang, and to the entire Bowen LI team. Grateful to our collaborators at University Health Network and Leslie Dan Faculty of Pharmacy, and to Princess Margaret Cancer Centre Research Princess Margaret Cancer Centre Research. 📄 Paper:

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