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#CellVoyager is published in @NatureMethods! 🔬 Autonomous comp bio agent 🧬 Analyzes bio data to make new discoveries 💻 Runs in Jupyter notebooks → interactive & reproducible 📊 Generated expert-validated insights on COVID-19, cell–cell communication and aging

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James Zou

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14,080 görüntüleme • 5 ay önce •via X (Twitter)

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1/ The biggest blocker to effective AI-in-bio is lack of quality data, but Vevo Therapeutics is open-sourcing their gigantic #Tahoe100M single cell dataset (on Arc Institute's new Virtual Cell Atlas)! I talk w/Vevo's founders and Arc CEO/CTO about "Virtual Cell Models" and significance
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1/ The biggest blocker to effective AI-in-bio is lack of quality data, but Vevo Therapeutics is open-sourcing their gigantic #Tahoe100M single cell dataset (on Arc Institute's new Virtual Cell Atlas)! I talk w/Vevo's founders and Arc CEO/CTO about "Virtual Cell Models" and significance

sarah guo

44,512 görüntüleme • 1 yıl önce

We are thrilled to share that our first paper from my new lab, Spateo ( for spatiotemporal modeling of molecular holograms, is now online in Cell: Spateo is a comprehensive analytical framework for 3D whole-embryo spatiotemporal modeling. Its advanced features include: • 3D alignment and reconstruction at the whole-mouse-embryo scale (see the animation). • 3D spatial domain digitization and cell-cell communication analysis to understand spatial gene expression gradients and both inter- and intracellular communication. • 3D morphometric and volumetric analyses along with 3D morphogenesis vector field modeling to quantify dynamics such as surface area, volume, and cell density across organs, and to dissect the interplay between morphogenesis factors and cell migration. • A “Google Earth”-like browser, Spateo-viewer ( and for interactive and intuitive exploration of 3D spatial data. • Additional features, such as RNA signal-based single-cell segmentation. We are also honored that Nature “News and Views” has highlighted this work as well: This is really an amazing outcome after two years' heroic revision process that rewrite the entire paper using a new data ( for whole mouse embryos.
1:24

We are thrilled to share that our first paper from my new lab, Spateo ( for spatiotemporal modeling of molecular holograms, is now online in Cell: Spateo is a comprehensive analytical framework for 3D whole-embryo spatiotemporal modeling. Its advanced features include: • 3D alignment and reconstruction at the whole-mouse-embryo scale (see the animation). • 3D spatial domain digitization and cell-cell communication analysis to understand spatial gene expression gradients and both inter- and intracellular communication. • 3D morphometric and volumetric analyses along with 3D morphogenesis vector field modeling to quantify dynamics such as surface area, volume, and cell density across organs, and to dissect the interplay between morphogenesis factors and cell migration. • A “Google Earth”-like browser, Spateo-viewer ( and for interactive and intuitive exploration of 3D spatial data. • Additional features, such as RNA signal-based single-cell segmentation. We are also honored that Nature “News and Views” has highlighted this work as well: This is really an amazing outcome after two years' heroic revision process that rewrite the entire paper using a new data ( for whole mouse embryos.

evo-devo

58,470 görüntüleme • 1 yıl önce

GeoLibre v1.4.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings field data collection, image georeferencing, and a full Jupyter notebook experience right next to the map. What's new in v1.4.0 - Jupyter beside the map: Run notebooks alongside your map (JupyterLite in the browser, JupyterLab on the desktop). - Field data collection: Capture point observations in the field with custom, configurable forms. - Georeferencer: Pin a scanned map or image to the map using ground control points. - Spectral Index toolbox: Compute NDVI, NDWI, EVI, and more directly from your rasters. - Better internationalization: Correct rendering of right-to-left basemap labels (Arabic, Hebrew). - More reliable data handling: Render GeoPackages without GDAL's WASM thread crash. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python
1:12

GeoLibre v1.4.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings field data collection, image georeferencing, and a full Jupyter notebook experience right next to the map. What's new in v1.4.0 - Jupyter beside the map: Run notebooks alongside your map (JupyterLite in the browser, JupyterLab on the desktop). - Field data collection: Capture point observations in the field with custom, configurable forms. - Georeferencer: Pin a scanned map or image to the map using ground control points. - Spectral Index toolbox: Compute NDVI, NDWI, EVI, and more directly from your rasters. - Better internationalization: Correct rendering of right-to-left basemap labels (Arabic, Hebrew). - More reliable data handling: Render GeoPackages without GDAL's WASM thread crash. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python

Qiusheng Wu

30,046 görüntüleme • 2 ay önce

Massive breakthrough here! Someone fixed every major flaw in Jupyter Notebooks. The .ipynb format is stuck in 2014. It was built for a different era - no cloud collaboration, no AI agents, no team workflows. Change one cell, and you get 50+ lines of JSON metadata in your git diff. Code reviews become a nightmare. Want to share a database connection across notebooks? Configure it separately in each one. Need comments or permissions? Too bad. Jupyter works for solo analysis but breaks for teams building production AI systems. Deepnote just open-sourced the solution (Apache 2.0 license) They've built a new notebook standard that actually fits modern workflows: ↳ Human-readable YAML - Git diffs show actual code changes, not JSON noise. Code reviews finally work. ↳ Project-based structure - Multiple notebooks share integrations, secrets, and environment settings. Configure once, use everywhere. ↳ 23 new block - SQL, interactive inputs, charts, and KPIs as first-class citizens. Build data apps, not just analytics notebooks. ↳ Multi-language support - Python and SQL in one notebook. Modern data work isn't single-language anymore. ↳ Full backward and forward compatibility: convert any Jupyter notebook to Deepnote and vice versa with one command. npx @ deepnote/convert notebook.ipynb Then open it in VS Code, Cursor, WindSurf, or Antigravity. Your existing notebooks migrate instantly. Their cloud version adds real-time collaboration with comments, permissions, and live editing. I've shared the GitHub repo link in the replies! It's 100% open-source.
0:34

Massive breakthrough here! Someone fixed every major flaw in Jupyter Notebooks. The .ipynb format is stuck in 2014. It was built for a different era - no cloud collaboration, no AI agents, no team workflows. Change one cell, and you get 50+ lines of JSON metadata in your git diff. Code reviews become a nightmare. Want to share a database connection across notebooks? Configure it separately in each one. Need comments or permissions? Too bad. Jupyter works for solo analysis but breaks for teams building production AI systems. Deepnote just open-sourced the solution (Apache 2.0 license) They've built a new notebook standard that actually fits modern workflows: ↳ Human-readable YAML - Git diffs show actual code changes, not JSON noise. Code reviews finally work. ↳ Project-based structure - Multiple notebooks share integrations, secrets, and environment settings. Configure once, use everywhere. ↳ 23 new block - SQL, interactive inputs, charts, and KPIs as first-class citizens. Build data apps, not just analytics notebooks. ↳ Multi-language support - Python and SQL in one notebook. Modern data work isn't single-language anymore. ↳ Full backward and forward compatibility: convert any Jupyter notebook to Deepnote and vice versa with one command. npx @ deepnote/convert notebook.ipynb Then open it in VS Code, Cursor, WindSurf, or Antigravity. Your existing notebooks migrate instantly. Their cloud version adds real-time collaboration with comments, permissions, and live editing. I've shared the GitHub repo link in the replies! It's 100% open-source.

Akshay 🚀

33,358 görüntüleme • 9 ay önce

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource
0:52

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource

Bo Wang

199,747 görüntüleme • 2 yıl önce

Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34.2% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on OpenAI's MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:
3:51

Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34.2% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on OpenAI's MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Neo AI

861,393 görüntüleme • 1 yıl önce

GeoLibre v1.6.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings multi-map layouts, advanced cartographic symbology and labeling, and a one-click way to install external plugins from a zip. What's new in v1.6.0 - Multi-map grid: Split the workspace into a grid of synchronized map views to compare basemaps, layers, or time steps side by side. - Advanced symbology: Style features with a rule-based renderer, proportional symbols, fill patterns, and a built-in marker library. - Label engine: Label vector features by any attribute, with full placement and styling control. - Install plugins from a zip: Add external plugins from an uploaded zip on both desktop and web. - New vector analysis: Movement, space-time, and cell-coverage tools under Processing. - Faster sample data: Ready-to-load example datasets from a dropdown in every Add Data panel. - Place search in the layer panel: Geocode and fly to a location without leaving the Layers panel. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python #Cartography
0:38

GeoLibre v1.6.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings multi-map layouts, advanced cartographic symbology and labeling, and a one-click way to install external plugins from a zip. What's new in v1.6.0 - Multi-map grid: Split the workspace into a grid of synchronized map views to compare basemaps, layers, or time steps side by side. - Advanced symbology: Style features with a rule-based renderer, proportional symbols, fill patterns, and a built-in marker library. - Label engine: Label vector features by any attribute, with full placement and styling control. - Install plugins from a zip: Add external plugins from an uploaded zip on both desktop and web. - New vector analysis: Movement, space-time, and cell-coverage tools under Processing. - Faster sample data: Ready-to-load example datasets from a dropdown in every Add Data panel. - Place search in the layer panel: Geocode and fly to a location without leaving the Layers panel. Try it out - Live demo: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #Python #Cartography

Qiusheng Wu

15,473 görüntüleme • 2 ay önce

📣 Introducing AVA AI Agent on Telegram – AvaChat is Now Live! 📲 Start your conversation with AvaChat today: The next generation of AI-powered assistance has arrived. AvaChat, one of the fastest AI Agent in Web3, is now available as a Telegram MiniApp, designed to streamline communication, enhance productivity, and provide real-time insights—all within a secure and private environment. Key Capabilities: ➜ Agentic AI Capabilities ➜ AI-Assisted Communication ➜ Private & Secure Conversations ➜ Advanced Knowledge & Insights – Whether you need in-depth explanations or market analysis. ➜ Financial & Market Data – Stay informed with real-time cryptocurrency updates at your request. AvaChat provides instant access to powerful tools and resources, helping you make informed decisions effortlessly. ✨ The video guide on how to add the Ava Mini App to your smartphone's home screen is coming soon.
1:31

📣 Introducing AVA AI Agent on Telegram – AvaChat is Now Live! 📲 Start your conversation with AvaChat today: The next generation of AI-powered assistance has arrived. AvaChat, one of the fastest AI Agent in Web3, is now available as a Telegram MiniApp, designed to streamline communication, enhance productivity, and provide real-time insights—all within a secure and private environment. Key Capabilities: ➜ Agentic AI Capabilities ➜ AI-Assisted Communication ➜ Private & Secure Conversations ➜ Advanced Knowledge & Insights – Whether you need in-depth explanations or market analysis. ➜ Financial & Market Data – Stay informed with real-time cryptocurrency updates at your request. AvaChat provides instant access to powerful tools and resources, helping you make informed decisions effortlessly. ✨ The video guide on how to add the Ava Mini App to your smartphone's home screen is coming soon.

AvaChat

33,865 görüntüleme • 1 yıl önce

📣 Introducing AVA AI Agent on Telegram – AvaChat is Now Live! The next generation of AI-powered assistance has arrived. AvaChat, one of the fastest AI Agent in Web3, is now available as a Telegram MiniApp, designed to streamline communication, enhance productivity, and provide real-time insights—all within a secure and private environment. 📲 Start your conversation with AvaChat today: Key Capabilities: ➜ Agentic AI Capabilities ➜ AI-Assisted Communication ➜ Private & Secure Conversations ➜ Advanced Knowledge & Insights – Whether you need in-depth explanations or market analysis. ➜ Financial & Market Data – Stay informed with real-time cryptocurrency updates at your request. AvaChat provides instant access to powerful tools and resources, helping you make informed decisions effortlessly. ✨ The video guide on how to add the Ava Mini App to your smartphone's home screen is coming soon.
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📣 Introducing AVA AI Agent on Telegram – AvaChat is Now Live! The next generation of AI-powered assistance has arrived. AvaChat, one of the fastest AI Agent in Web3, is now available as a Telegram MiniApp, designed to streamline communication, enhance productivity, and provide real-time insights—all within a secure and private environment. 📲 Start your conversation with AvaChat today: Key Capabilities: ➜ Agentic AI Capabilities ➜ AI-Assisted Communication ➜ Private & Secure Conversations ➜ Advanced Knowledge & Insights – Whether you need in-depth explanations or market analysis. ➜ Financial & Market Data – Stay informed with real-time cryptocurrency updates at your request. AvaChat provides instant access to powerful tools and resources, helping you make informed decisions effortlessly. ✨ The video guide on how to add the Ava Mini App to your smartphone's home screen is coming soon.

AITECH CLOUD NETWORK

54,027 görüntüleme • 1 yıl önce

Single-cell technologies now let us profile entire transcriptomes in individual cells. But how do we make sense of this complexity in a biologically meaningful way? Many methods summarise cells into a single embedding, but this often comes at the cost of interpretability, especially when multiple gene programs are active at once. We developed Tripso, a self-supervised transformer model that represents cells through multiple gene program-specific embeddings, while also uncovering new programs directly from the data. Instead of collapsing biology into a single vector, Tripso decomposes cell state into multiple representations, each reflecting a different gene program. We explored this across multiple systems. In human hematopoiesis, spanning development to aging, Tripso identified distinct age-associated program activity, including stronger JAK-STAT signalling in early life and dynamic IKZF1-related changes during B cell maturation. By comparing in vitro culture conditions with in vivo hematopoietic stem cell states, Tripso suggested that targeting the SEC61 translocon could enhance stem cell maintenance ex vivo, a prediction that we subsequently validated experimentally. In parallel, we identified a previously uncharacterised tissue-resident memory T-cell program associated with atopic dermatitis and mapped it to distinct spatial immune niches Together, these results show how modelling cells through gene programs can lead to interpretable and experimentally testable insights. More broadly, this work points toward a more interpretable and biologically grounded models of cell state. As single-cell datasets continue to grow, we hope approaches like Tripso will help bridge the gap between data-driven representations and biological insight. This work wouldn’t have been possible without the contributions of an amazing team. Thank you to co-first authors Marie, Tomoya Isobe, Amirhosein Vahidi, Carlo Leonardi, and everyone from roser's Lab, Haniffa Lab, Nicola Wilson and Bertie Gottgens's Lab, bringing together expertise across Cambridge Stem Cell Institute, Open Targets, Wellcome Sanger Institute and Cambridge University. Marie is one of the very best PhD students I have ever supervised. She is truly a force of nature, exceptionally resourceful, deeply innovative, and one of the most impressive scientists I have worked with. I am immensely proud of her and all that she has accomplished. As she begins her internship at Genentech , I have no doubt she will do amazing work there and continue to make her mark. paper: code:
1:25

Single-cell technologies now let us profile entire transcriptomes in individual cells. But how do we make sense of this complexity in a biologically meaningful way? Many methods summarise cells into a single embedding, but this often comes at the cost of interpretability, especially when multiple gene programs are active at once. We developed Tripso, a self-supervised transformer model that represents cells through multiple gene program-specific embeddings, while also uncovering new programs directly from the data. Instead of collapsing biology into a single vector, Tripso decomposes cell state into multiple representations, each reflecting a different gene program. We explored this across multiple systems. In human hematopoiesis, spanning development to aging, Tripso identified distinct age-associated program activity, including stronger JAK-STAT signalling in early life and dynamic IKZF1-related changes during B cell maturation. By comparing in vitro culture conditions with in vivo hematopoietic stem cell states, Tripso suggested that targeting the SEC61 translocon could enhance stem cell maintenance ex vivo, a prediction that we subsequently validated experimentally. In parallel, we identified a previously uncharacterised tissue-resident memory T-cell program associated with atopic dermatitis and mapped it to distinct spatial immune niches Together, these results show how modelling cells through gene programs can lead to interpretable and experimentally testable insights. More broadly, this work points toward a more interpretable and biologically grounded models of cell state. As single-cell datasets continue to grow, we hope approaches like Tripso will help bridge the gap between data-driven representations and biological insight. This work wouldn’t have been possible without the contributions of an amazing team. Thank you to co-first authors Marie, Tomoya Isobe, Amirhosein Vahidi, Carlo Leonardi, and everyone from roser's Lab, Haniffa Lab, Nicola Wilson and Bertie Gottgens's Lab, bringing together expertise across Cambridge Stem Cell Institute, Open Targets, Wellcome Sanger Institute and Cambridge University. Marie is one of the very best PhD students I have ever supervised. She is truly a force of nature, exceptionally resourceful, deeply innovative, and one of the most impressive scientists I have worked with. I am immensely proud of her and all that she has accomplished. As she begins her internship at Genentech , I have no doubt she will do amazing work there and continue to make her mark. paper: code:

Mo Lotfollahi

22,525 görüntüleme • 5 ay önce

How to Create a Professional Data Analysis Report—Even If You’re Not a Data Specialist Today on Agent 101—MuleRun’s first review series where real users test AI agents in real work scenarios—we introduce “Smart Q,” a data analysis expert agent designed to turn anyone into a data-savvy reporter. 1. Team Expertise Smart Q was developed by a team with over 10 years of data analysis experience at a giant corporation. This background ensures that the agent delivers insights and reports that meet professional standards. 2. The Traditional Approach & Its Pain Points Traditionally, creating a data analysis report required deep expertise in tools like Excel, SQL, or Python. You’d need to clean the data, run calculations, generate visualizations, and summarize findings—all of which is time-consuming and prone to human error. For non-specialists, this process is often inaccessible and intimidating. 3. How Smart Q Uses AI—and What Problems It Solves With Smart Q, the entire reporting process is simplified into three steps: upload your raw data, ask a question, and receive charts, key insights, and a polished report—all generated by AI expert. Its key advantages include: ✅Accessibility:No technical background required. ✅Speed:Get a complete analysis in minutes, not hours. ✅Clarity:Receive expert-level conclusions presented in clear, actionable language. Want to become a tester for future AI agents? Engage with this video—comment, like, or share—and we’ll be sure to notice your support! 🎥 See Smart Q in action. #mulerun #mulerun4U #SmartQ
4:11

How to Create a Professional Data Analysis Report—Even If You’re Not a Data Specialist Today on Agent 101—MuleRun’s first review series where real users test AI agents in real work scenarios—we introduce “Smart Q,” a data analysis expert agent designed to turn anyone into a data-savvy reporter. 1. Team Expertise Smart Q was developed by a team with over 10 years of data analysis experience at a giant corporation. This background ensures that the agent delivers insights and reports that meet professional standards. 2. The Traditional Approach & Its Pain Points Traditionally, creating a data analysis report required deep expertise in tools like Excel, SQL, or Python. You’d need to clean the data, run calculations, generate visualizations, and summarize findings—all of which is time-consuming and prone to human error. For non-specialists, this process is often inaccessible and intimidating. 3. How Smart Q Uses AI—and What Problems It Solves With Smart Q, the entire reporting process is simplified into three steps: upload your raw data, ask a question, and receive charts, key insights, and a polished report—all generated by AI expert. Its key advantages include: ✅Accessibility:No technical background required. ✅Speed:Get a complete analysis in minutes, not hours. ✅Clarity:Receive expert-level conclusions presented in clear, actionable language. Want to become a tester for future AI agents? Engage with this video—comment, like, or share—and we’ll be sure to notice your support! 🎥 See Smart Q in action. #mulerun #mulerun4U #SmartQ

MuleRun

30,549 görüntüleme • 9 ay önce

🧬🔬 What exactly is this rejuvenation paradigm called “partial reprogramming” or “epigenetic rejuvenation” that has taken the longevity field by storm in the past few years with billion-dollar companies like Altos Labs dedicated to it, and visionary investors like Sam Altman, Brian Armstrong, Yuri Milner investing in it? This clip provides a quick overview of partial reprogramming, Yamanaka factors, the rejuvenation they produce during cellular reprogramming, and their potential connection to germline reset of aging — the active rejuvenation process triggered upon fertilization that enables new babies to be born young despite starting out as an egg cell of the same age as their mother.
4:04

🧬🔬 What exactly is this rejuvenation paradigm called “partial reprogramming” or “epigenetic rejuvenation” that has taken the longevity field by storm in the past few years with billion-dollar companies like Altos Labs dedicated to it, and visionary investors like Sam Altman, Brian Armstrong, Yuri Milner investing in it? This clip provides a quick overview of partial reprogramming, Yamanaka factors, the rejuvenation they produce during cellular reprogramming, and their potential connection to germline reset of aging — the active rejuvenation process triggered upon fertilization that enables new babies to be born young despite starting out as an egg cell of the same age as their mother.

Yuri Deigin

88,315 görüntüleme • 2 yıl önce

Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:
3:51

Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Neo AI

19,571,775 görüntüleme • 1 yıl önce

Heads must roll… 🚨 Sen. Ron Johnson says there is a total Media BLACKOUT on the new COVID Vax Child Death count study— 24% of 39,000 Victims Died the SAME DAY or Within 48 Hours! “They KNEW and Did NOTHING!” Sen. Johnson is discussing a new FDA analysis/report he released on pediatric deaths linked to the COVID-19 vaccines. The data shows overwhelming safety signals that were ignored by health authorities in every major category. Even worse! Dr. Peter Marks was made aware of these safety signals on March 26, 2021 that the inventors of the algorithm that analyzes the VAERS data was trying to (HIDE) the adverse events.
3:09

Heads must roll… 🚨 Sen. Ron Johnson says there is a total Media BLACKOUT on the new COVID Vax Child Death count study— 24% of 39,000 Victims Died the SAME DAY or Within 48 Hours! “They KNEW and Did NOTHING!” Sen. Johnson is discussing a new FDA analysis/report he released on pediatric deaths linked to the COVID-19 vaccines. The data shows overwhelming safety signals that were ignored by health authorities in every major category. Even worse! Dr. Peter Marks was made aware of these safety signals on March 26, 2021 that the inventors of the algorithm that analyzes the VAERS data was trying to (HIDE) the adverse events.

MJTruthUltra

424,509 görüntüleme • 3 ay önce

Today we are introducing Tara. Biological datasets are a source of insights and a means to train biological AI models. As the ability to reason at scale emerges, they take on a new role: the ground truth for testing what reasoning models produce, and the environment in which those models operate, get feedback, and improve. Tara, our autonomous research agent, is embedded in our ever-expanding datasets, lab-generated and synthetic, and built to test and evolve the hypotheses frontier models generate, matching the pace at which they produce new ideas. By keeping those models grounded in a vast space of high-precision biological data, we believe we can compound biological reasoning and close the impedance mismatch between hypothesis generation and validation.
4:03

Today we are introducing Tara. Biological datasets are a source of insights and a means to train biological AI models. As the ability to reason at scale emerges, they take on a new role: the ground truth for testing what reasoning models produce, and the environment in which those models operate, get feedback, and improve. Tara, our autonomous research agent, is embedded in our ever-expanding datasets, lab-generated and synthetic, and built to test and evolve the hypotheses frontier models generate, matching the pace at which they produce new ideas. By keeping those models grounded in a vast space of high-precision biological data, we believe we can compound biological reasoning and close the impedance mismatch between hypothesis generation and validation.

Nima Alidoust

30,365 görüntüleme • 2 ay önce

Ivermectin & the Microbiome: Dr. Sabine Hazan’s Groundbreaking Testimony on COVID-19 In a compelling testimony, Dr. Sabine Hazan, a leading gastroenterologist and microbiome expert, shared profound insights from her frontline COVID-19 research. By analyzing patient stool samples, Dr. Hazan discovered that severe COVID-19 patients lacked a critical gut bacterium—Bifidobacterium—while those heavily exposed but never infected had it in abundance. Bifidobacterium is a cornerstone of immunity, central to the trillion-dollar probiotic industry and abundant in newborns, explaining their early resilience to COVID-19. Aging, however, depletes this vital microbe, increasing vulnerability in older populations. Dr. Hazan’s team published these findings in their paper, The Lost Microbes of COVID-19, after an eight-month effort. Her research revealed that Vitamin C boosts Bifidobacterium levels, supporting its role in fighting viral infections like the common cold—a practice many rely on. More strikingly, Dr. Hazan observed that Ivermectin, a drug derived from a bacterium closely related to Bifidobacterium, rapidly increased this microbe in patients within 24 hours. Patients treated with Ivermectin showed improved oxygen saturation, prompting Dr. Hazan to hypothesize a link. Her Ivermectin hypothesis became the most-read of the pandemic, sparking global discussion. Yet, after eight months, it was controversially retracted. Dr. Hazan passionately argues that silencing hypotheses stifles science itself. “When we cannot make a hypothesis, this is not science,” she declared. Dr. Hazan’s work highlights the gut’s critical role in immunity and raises bold questions about health and disease prevention. Could nurturing our microbiome be key to resilience?
2:42

Ivermectin & the Microbiome: Dr. Sabine Hazan’s Groundbreaking Testimony on COVID-19 In a compelling testimony, Dr. Sabine Hazan, a leading gastroenterologist and microbiome expert, shared profound insights from her frontline COVID-19 research. By analyzing patient stool samples, Dr. Hazan discovered that severe COVID-19 patients lacked a critical gut bacterium—Bifidobacterium—while those heavily exposed but never infected had it in abundance. Bifidobacterium is a cornerstone of immunity, central to the trillion-dollar probiotic industry and abundant in newborns, explaining their early resilience to COVID-19. Aging, however, depletes this vital microbe, increasing vulnerability in older populations. Dr. Hazan’s team published these findings in their paper, The Lost Microbes of COVID-19, after an eight-month effort. Her research revealed that Vitamin C boosts Bifidobacterium levels, supporting its role in fighting viral infections like the common cold—a practice many rely on. More strikingly, Dr. Hazan observed that Ivermectin, a drug derived from a bacterium closely related to Bifidobacterium, rapidly increased this microbe in patients within 24 hours. Patients treated with Ivermectin showed improved oxygen saturation, prompting Dr. Hazan to hypothesize a link. Her Ivermectin hypothesis became the most-read of the pandemic, sparking global discussion. Yet, after eight months, it was controversially retracted. Dr. Hazan passionately argues that silencing hypotheses stifles science itself. “When we cannot make a hypothesis, this is not science,” she declared. Dr. Hazan’s work highlights the gut’s critical role in immunity and raises bold questions about health and disease prevention. Could nurturing our microbiome be key to resilience?

Camus

67,323 görüntüleme • 1 yıl önce

🚨 📣 MUST-WATCH: Exclusive interview with MIT Professor Retsef Levi Retsef Levi, PhD, leading expert in risk analysis & Chair of the CDC COVID-19 Immunization Workgroup on ACIP. We discuss hard data on vaccine safety: myocarditis risks, pregnancy effects, booster necessity, cardiac signals, research challenges, personalized assessments, and the urgent need for transparency. Our talk was all science, data, ethics, and rebuilding trust. NO politics involved. Here’s the full interview now live on X. Eager for your thoughts and takeaways! 🙏 PLEASE SUPPORT MY HARD WORK BY COMMENTING, SHARING, Subscribing to The Dana Parish Podcast! More links in bio. Reply below & 👀 chapter list in next tweet 👇🏼: #VaccineSafety #PublicHealth
1:11:07

🚨 📣 MUST-WATCH: Exclusive interview with MIT Professor Retsef Levi Retsef Levi, PhD, leading expert in risk analysis & Chair of the CDC COVID-19 Immunization Workgroup on ACIP. We discuss hard data on vaccine safety: myocarditis risks, pregnancy effects, booster necessity, cardiac signals, research challenges, personalized assessments, and the urgent need for transparency. Our talk was all science, data, ethics, and rebuilding trust. NO politics involved. Here’s the full interview now live on X. Eager for your thoughts and takeaways! 🙏 PLEASE SUPPORT MY HARD WORK BY COMMENTING, SHARING, Subscribing to The Dana Parish Podcast! More links in bio. Reply below & 👀 chapter list in next tweet 👇🏼: #VaccineSafety #PublicHealth

Dana Parish

71,698 görüntüleme • 7 ay önce

GeoLibre v2.4.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release is about reaching more data and moving through it: new browsers for STAC, NASA Earthdata, and Hugging Face, a flight simulator, a Time Slider that animates tiled data and data cubes, and a live API for pages that embed the map. What's new in v2.4.0 - STAC catalogs browser: discover public catalogs from STAC Index, connect to static catalogs and STAC APIs, search a collection's items, and add any visualizable asset to the map. No more copying COG URLs by hand. - Earthdata GIS browser: search NASA's Earthdata GIS portal and add its imagery, map, and feature services, and its published web maps, as first-class layers. - Hugging Face on the map: search the Hub, walk a dataset repo's folders, and add its vector and raster files. You can create a dataset repo and upload layers to it too. - Flight Simulator: steer a continuous free-flight camera over terrain and 3D layers from the keyboard, instead of declaring a destination and watching a scripted flight. - Time Slider for tiled data and data cubes: bind vector tiles, PMTiles, and MBTiles to the timeline and animate them over their full extent, and let a Zarr store's own time dimension join the shared timeline. - Zarr gets a real Add Data path: open a remote store or a folder on disk, and pick variables and dimensions by their actual coordinate values rather than raw indices. - Live embed API: a versioned postMessage protocol so a host page can load a project, move the camera, highlight features, and open a tool at runtime, with events coming back out. - OGC API - Features: add collections as vector layers from whatever URL you have in hand, whether that is a landing page, /collections, or a full items URL. - H3 everywhere: a new hexagonal grid plugin that renders and inspects H3 cells and exports them as GeoJSON or CSV, plus typing an H3 index into the search box to fly straight to that cell. - Jupyter from outside the app: attach VS Code's Jupyter extension or jupyter console to the desktop app's server and your notebook cells drive the map. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #DataVisualization #GeoLibre
1:04

GeoLibre v2.4.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release is about reaching more data and moving through it: new browsers for STAC, NASA Earthdata, and Hugging Face, a flight simulator, a Time Slider that animates tiled data and data cubes, and a live API for pages that embed the map. What's new in v2.4.0 - STAC catalogs browser: discover public catalogs from STAC Index, connect to static catalogs and STAC APIs, search a collection's items, and add any visualizable asset to the map. No more copying COG URLs by hand. - Earthdata GIS browser: search NASA's Earthdata GIS portal and add its imagery, map, and feature services, and its published web maps, as first-class layers. - Hugging Face on the map: search the Hub, walk a dataset repo's folders, and add its vector and raster files. You can create a dataset repo and upload layers to it too. - Flight Simulator: steer a continuous free-flight camera over terrain and 3D layers from the keyboard, instead of declaring a destination and watching a scripted flight. - Time Slider for tiled data and data cubes: bind vector tiles, PMTiles, and MBTiles to the timeline and animate them over their full extent, and let a Zarr store's own time dimension join the shared timeline. - Zarr gets a real Add Data path: open a remote store or a folder on disk, and pick variables and dimensions by their actual coordinate values rather than raw indices. - Live embed API: a versioned postMessage protocol so a host page can load a project, move the camera, highlight features, and open a tool at runtime, with events coming back out. - OGC API - Features: add collections as vector layers from whatever URL you have in hand, whether that is a landing page, /collections, or a full items URL. - H3 everywhere: a new hexagonal grid plugin that renders and inspects H3 cells and exports them as GeoJSON or CSV, plus typing an H3 index into the search box to fly straight to that cell. - Jupyter from outside the app: attach VS Code's Jupyter extension or jupyter console to the desktop app's server and your notebook cells drive the map. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #DataVisualization #GeoLibre

Qiusheng Wu

14,268 görüntüleme • 1 ay önce