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here comes WebAtlas: our "Google Maps" for tissue atlases with integrated single cell and spatial transcriptomics so anyone & anywhere can access/explore atlas datasets on a web browser. Fantastic collab with Muzlifah Haniffa, led by Tong LI 李彤 Dave Horsfall & Daniela. Links 👇

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Omer Ali Bayraktar

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53,035 Aufrufe • vor 3 Jahren •via X (Twitter)

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Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

Paper: Portal where you can access datasets:

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

The integration of single cell + spatial transcriptomics can build awesome tissue atlases but it is hard to share these datasets in a globally accessible & usable format without technical burden on the users. We aimed to solve this problem with WebAtlas

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

WebAtlas addresses 2 key challenges in this area: 1) how to deal with myriad technologies and data types in sc/spatial omics? and 2) how to freely browse integrated datasets?

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

The core of WebAtlas is a data ingestion pipeline that unifies data types from nearly all commonly used atlassing technologies (works on sc/snRNAseq, Visium, Xenium, MERSCOPE, In Situ Sequencing, seqFISH) into the cloud-optimised & scalable Zarr format @zarr_dev

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

We put special care into image components of spatial data i.e. raw images and cell segmentation masks are handled as OME-Zarr for multi-scale visualisation. This was a parallel effort with SpatialData & we are keen to support their format in the future

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

We then re-used the awesome Vitessce web visualisation framework from @ngehlenborg that allows fully interactive exploration of sc + ST data. We customised it for coordinated browsing of cell types & gene expression in integrated sc and ST datasets

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

We showcased WebAtlas on a "triple" modality atlas of the developing human hindlimb from @teichlab that combines scRNAseq, Visium spatial RNA-Seq and In Situ Sequencing (ISS done by @krobertssci in my lab). You can explore it on & few more tweets below.

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

We harmonised cell types & gene expression across 3X modalities with computational integration: our Cell2location tool to integrate scRNAseq + Visium and StabMap from @shazanfar to transfer cell types & impute gene expression from scRNA-seq to ISS (

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

The result is stunning WebAtlas (the video on top) where you can cross-query cell types & genes across all modalities in one place, where you can browse the ISS spatial cell map to explore the beautiful structure of the developing bone, muscle and skin across the embryonic limb.

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

Many fun things to do on the integrated atlas. For example, the scRNAseq -> ISS imputation allowed us to identify a novel spatial gene expression gradient in chondroprogenitors that give rise to the cartilage anlage! On WebAtlas, you can cross-validate this on Visium data!

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

WebAtlas is tech agnostic and supports many technologies as listed above e.g. see a Xenium dataset of a human breast tumour on WebAtlas here

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

...and an integrated scRNA-seq + seqFISH atlas of mouse embryogenesis from @MarioniLab ( - webAtlas link here:

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

Finally, WebAtlas is scalable: to date it has handled scRNAseq with 900k cells and MERSCOPE with 700k cells Currently works best with <10 Million RNA molecules

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

Try WebAtlas! Our landing page with all datasets tweeted above & shown in paper: Repo: Tutorials:

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

I am excited about sharing our upcoming @humancellatlas datasets with the community on WebAtlas and a future where we can build "Next-Gen" multi-modal tissue atlases on the cloud. If you are interested, please reach out!

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

This was a wonderful collab with @Muzz_Haniffa @BioinfoTongLI @Dave_Horsfall @krobertssci @PengHeCam @shazanfar @teichlab. Special thx to @notjustmoore who aligned us with SpatialData from @fabian_theis and @OliverStegle...

Profilbild von Omer Ali Bayraktar
Omer Ali Bayraktarvor 3 Jahren

..., @zarr_dev & Vitessce @ngehlenborg & @openmicroscopy teams for our technical foundations, and @wellcometrust @sangerinstitute for supporting our work!

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Satoshi’s Mom

10,798 Aufrufe • vor 5 Monaten

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:
2:06

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

Andrew Ng

186,182 Aufrufe • vor 1 Jahr

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:
1:07

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

SpAItial AI

176,524 Aufrufe • vor 8 Monaten

✨Star Atlas - Our First Ever Naabathon Winners 🏆 📢Message From The Team "We are building a platform & we want to enable guilds, engineers, entities to build on top of [Star Atlas] similar to Shopify" Michael Kane, Executive Producer Learn more about each winning project :👇 "Hey everyone, thank you so much for your participation in the first ever Star Atlas Naabathon (hackathon)! We love supporting and enabling co-creation around Star Atlas and we hope you enjoyed participating. Here are the winners:👇 Best Project Award: Atom by meta_arion who won $10,000 USDC, 3,000,000 $ATLAS and an Ogrika Sunpaa! ▪️ Atom has drag and drop user interfaces, and the automations you can do in SAGE are on par, and in some parts better than those built by Star Atlas. Atom won by a unanimous vote. 🔸Galactic Marketplace Award: Atlas Link by harshh.16 and sidh1999 who won $2,500 USDC, 1,500,000 ATLAS and an Ogrika Tursic! ▪️ Atlas Link is a AI Chat Bot built on a large language model, indexing all of Star Atlas off chain metadata, on-chain program state, marketplace orders, you can talk to it, communicate to it & strategize and execute on your strategies with it 🔸Defi Award: - StarAtlasLocker by .solwalker who won $2,500 USDC, 1,500,000 ATLAS and an Ogrika Tursic! ▪️ Uses Squads multisig to interact with our $ATLAS and $POLIS lockers 🔸Data Award: Star Atlas Crew Dashboard by Aephia Viktor who won $2,500 USDC, 1,500,000 ATLAS and an Ogrika Tursic ▪️ 🔸Resources Award: Star Atlas Dispatcher by Teykarat who won $2,500 USDC, 1,500,000 ATLAS and an Ogrika Tursic! ▪️ An automation tool for SAGE. Highly complex, highly detailed and tremendous for SAGE. Automate almost everything in SAGE, set it and forget it. 🔸Game Award: Warfront Invasion by visualjc who won $2,500 USDC, 1,500,000 ATLAS and an Ogrika Tursic! 🔸Star Atlas: Golden Era Award: Eveeye Ship Comparison and Viewer who won $2,500 USDC, 1,500,000 ATLAS and an Ogrika Tursic! ▪️ Compare any two assets visually and statistically. The user interface is very impressive, the user experience is very impressive. If you're interested in learning more about these hackathon submissions, be sure to join us for next week's Atlas Brew, where we’ll showcase and discuss the entries!" ⚠️ The Atlas Brew is an official weekly Star Atlas event hosted by Santi, Dom and José in Discord every Wednesday at 10am PT. Topics of the week can range from talking about new releases, team member interviews, theorycrafting and more! :Come join the conversation: #WeAreStarAtlas #Solana #Gamefi #P2E #NFTs #Crypto #StarCitizen #Starfield #Web3gaming
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✨Star Atlas - Our First Ever Naabathon Winners 🏆 📢Message From The Team "We are building a platform & we want to enable guilds, engineers, entities to build on top of [Star Atlas] similar to Shopify" Michael Kane, Executive Producer Learn more about each winning project :👇 "Hey everyone, thank you so much for your participation in the first ever Star Atlas Naabathon (hackathon)! We love supporting and enabling co-creation around Star Atlas and we hope you enjoyed participating. Here are the winners:👇 Best Project Award: Atom by meta_arion who won $10,000 USDC, 3,000,000 $ATLAS and an Ogrika Sunpaa! ▪️ Atom has drag and drop user interfaces, and the automations you can do in SAGE are on par, and in some parts better than those built by Star Atlas. Atom won by a unanimous vote. 🔸Galactic Marketplace Award: Atlas Link by harshh.16 and sidh1999 who won $2,500 USDC, 1,500,000 ATLAS and an Ogrika Tursic! ▪️ Atlas Link is a AI Chat Bot built on a large language model, indexing all of Star Atlas off chain metadata, on-chain program state, marketplace orders, you can talk to it, communicate to it & strategize and execute on your strategies with it 🔸Defi Award: - StarAtlasLocker by .solwalker who won $2,500 USDC, 1,500,000 ATLAS and an Ogrika Tursic! ▪️ Uses Squads multisig to interact with our $ATLAS and $POLIS lockers 🔸Data Award: Star Atlas Crew Dashboard by Aephia Viktor who won $2,500 USDC, 1,500,000 ATLAS and an Ogrika Tursic ▪️ 🔸Resources Award: Star Atlas Dispatcher by Teykarat who won $2,500 USDC, 1,500,000 ATLAS and an Ogrika Tursic! ▪️ An automation tool for SAGE. Highly complex, highly detailed and tremendous for SAGE. Automate almost everything in SAGE, set it and forget it. 🔸Game Award: Warfront Invasion by visualjc who won $2,500 USDC, 1,500,000 ATLAS and an Ogrika Tursic! 🔸Star Atlas: Golden Era Award: Eveeye Ship Comparison and Viewer who won $2,500 USDC, 1,500,000 ATLAS and an Ogrika Tursic! ▪️ Compare any two assets visually and statistically. The user interface is very impressive, the user experience is very impressive. If you're interested in learning more about these hackathon submissions, be sure to join us for next week's Atlas Brew, where we’ll showcase and discuss the entries!" ⚠️ The Atlas Brew is an official weekly Star Atlas event hosted by Santi, Dom and José in Discord every Wednesday at 10am PT. Topics of the week can range from talking about new releases, team member interviews, theorycrafting and more! :Come join the conversation: #WeAreStarAtlas #Solana #Gamefi #P2E #NFTs #Crypto #StarCitizen #Starfield #Web3gaming

Siggy.ink 🧐

11,810 Aufrufe • vor 1 Jahr

GeoLibre v1.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 50+ pull requests of new capabilities. A few highlights: - GIS in your pocket. A native Android build with offline tile caching and download-a-region support, so you can take your maps into the field with no signal. - AI, built in. A natural-language GIS assistant that turns plain-English requests into real geoprocessing, plus an AI segmentation toolbox powered by SamGeo and SAM 3 for extracting features from imagery. - Automate everything with Python. A full scripting API and an in-app Python Console, with new helpers for local rasters, choropleths, marker clusters, split-map comparisons, legends, and colorbars. - Map together, live. Real-time multi-user collaboration so you can open a project and edit the map with others at the same time. - Tell stories with maps. A scroll-driven story map builder and presenter that exports interactive narrative maps to standalone HTML. - A much bigger analysis toolbox. Reproject, explode, and aggregate tools, IDW and kriging interpolation, zonal statistics, a raster calculator, a Spatial Statistics toolbox, and network analysis with isochrones, service areas, and OD cost matrices, plus batch runs and model/pipeline chaining. - Smarter raster and SQL. Single-band pseudocolor classification, RGB band combinations, a no-backend client-side raster fallback, Apache Sedona as a SQL Workspace engine, and transparent S3, GCS, and Azure URL support in queries. - More ways to add, view, and share. New Shapefile and GeoPackage export, glTF/GLB 3D model layers, multi-provider batch and reverse geocoding, collapsible layer groups, and a macOS Homebrew cask. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #Android #GeoLibre
1:45

GeoLibre v1.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 50+ pull requests of new capabilities. A few highlights: - GIS in your pocket. A native Android build with offline tile caching and download-a-region support, so you can take your maps into the field with no signal. - AI, built in. A natural-language GIS assistant that turns plain-English requests into real geoprocessing, plus an AI segmentation toolbox powered by SamGeo and SAM 3 for extracting features from imagery. - Automate everything with Python. A full scripting API and an in-app Python Console, with new helpers for local rasters, choropleths, marker clusters, split-map comparisons, legends, and colorbars. - Map together, live. Real-time multi-user collaboration so you can open a project and edit the map with others at the same time. - Tell stories with maps. A scroll-driven story map builder and presenter that exports interactive narrative maps to standalone HTML. - A much bigger analysis toolbox. Reproject, explode, and aggregate tools, IDW and kriging interpolation, zonal statistics, a raster calculator, a Spatial Statistics toolbox, and network analysis with isochrones, service areas, and OD cost matrices, plus batch runs and model/pipeline chaining. - Smarter raster and SQL. Single-band pseudocolor classification, RGB band combinations, a no-backend client-side raster fallback, Apache Sedona as a SQL Workspace engine, and transparent S3, GCS, and Azure URL support in queries. - More ways to add, view, and share. New Shapefile and GeoPackage export, glTF/GLB 3D model layers, multi-provider batch and reverse geocoding, collapsible layer groups, and a macOS Homebrew cask. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #Android #GeoLibre

Qiusheng Wu

18,075 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 5 Monaten

⚡️📣👇Tremendously excited to share our new Cell article, where we develop TriPath, a method for analyzing 3D pathology samples using weakly supervised AI. Article: TriPath enables 3D computational pathology via 3D multiple instance learning allowing AI models to capture intricate morphological details from pathology volumes. Code: Blog post: Tested on two different imaging modalities, and patient cohorts from two institutions. Our superstar Andrew H. Song put in a monumental effort of leading the study, in a fantastic collaboration with Jonathan Liu at University of Washington . Interesting aspects: - Utilizing the whole tissue volume and leveraging 3D deep learning enable superior risk prediction performance compared to 2D deep learning baselines based on a few sampled tissue sections that emulate standard clinical practice. This indicates TriPath can harness additional information provided by 3D tissue morphology. - The performance is also superior to clinical baselines from a reader study that involved six expert pathologists. - The morphologically heterogeneous tissue volume could lead to opposing patient-level outcome predictions, dependent on which portion of the tissue volume is used. This concurs with current clinical literature warning that tissue sampling bias can lead to misdiagnosis. Some limitations: - While the 3D pathology cohort size is unprecedented, it is smaller than typical 2D pathology cohorts. Further large-scale studies will be required for validation. Nevertheless, we believe that this study will initiate a positive cycle, encouraging academic institutions and pharmaceutical companies to contribute large banks of human tissue blocks with paired clinical outcomes, thus speeding up advancements in 3D computational pathology. Concluding insights: We believe that 3D pathology is just around the corner - It has the huge potential to not only augment/improve the current clinical practice centered around 2D examination of human tissue, but also help reveal novel biomarkers for prognosis and therapeutic response.. Harvard Medical School Harvard Data Science Initiative Mass General Brigham Broad Institute
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⚡️📣👇Tremendously excited to share our new Cell article, where we develop TriPath, a method for analyzing 3D pathology samples using weakly supervised AI. Article: TriPath enables 3D computational pathology via 3D multiple instance learning allowing AI models to capture intricate morphological details from pathology volumes. Code: Blog post: Tested on two different imaging modalities, and patient cohorts from two institutions. Our superstar Andrew H. Song put in a monumental effort of leading the study, in a fantastic collaboration with Jonathan Liu at University of Washington . Interesting aspects: - Utilizing the whole tissue volume and leveraging 3D deep learning enable superior risk prediction performance compared to 2D deep learning baselines based on a few sampled tissue sections that emulate standard clinical practice. This indicates TriPath can harness additional information provided by 3D tissue morphology. - The performance is also superior to clinical baselines from a reader study that involved six expert pathologists. - The morphologically heterogeneous tissue volume could lead to opposing patient-level outcome predictions, dependent on which portion of the tissue volume is used. This concurs with current clinical literature warning that tissue sampling bias can lead to misdiagnosis. Some limitations: - While the 3D pathology cohort size is unprecedented, it is smaller than typical 2D pathology cohorts. Further large-scale studies will be required for validation. Nevertheless, we believe that this study will initiate a positive cycle, encouraging academic institutions and pharmaceutical companies to contribute large banks of human tissue blocks with paired clinical outcomes, thus speeding up advancements in 3D computational pathology. Concluding insights: We believe that 3D pathology is just around the corner - It has the huge potential to not only augment/improve the current clinical practice centered around 2D examination of human tissue, but also help reveal novel biomarkers for prognosis and therapeutic response.. Harvard Medical School Harvard Data Science Initiative Mass General Brigham Broad Institute

Faisal Mahmood

65,577 Aufrufe • vor 2 Jahren

I’m thrilled to announce the launch of Spatial Film—an immersive 180° and 360° film app we’ve built: After months of self-teaching Swift and app development, I finally turned my dream of having a platform dedicated to showcasing the highest-quality immersive films into reality. I’m not a developer by trade—I’m an immersive filmmaker—but I wanted a space where our original 3D 180 and 360 films could shine, free from the heavy compression of typical platforms. So, four months ago, I dove into coding, and here we are with version 1! This journey has been both challenging and incredibly rewarding, and I couldn’t have done it alone. My gratitude goes out to the immersive and VR community, our loyal Patreon members, and developers around the world who generously offered advice and encouragement along the way. What makes Spatial Film unique? It’s 100% original content, from immersive films to 360° environments, all crafted to provide an unparalleled experience. Some of you might recognize us as immersive filmmakers and educators on YouTube, where we’ve shared tutorials and inspired countless creators on Meta, Apple, Pico, HTC, and even back in the early YouTube VR days. Now, we’re taking advantage of Vision Pro’s stunning display to bring our work to you in the best quality possible. Our journey with immersive content goes way back—we’ve worked with 12K camera rigs like Jaunt, Google Jump, and Yi Halo, creating wild experiences for brands. But despite our efforts, our films were often stuck on platforms with heavy compression, which limits the quality. Vision Pro changed that for us, but it’s taken a lot of work to adapt. We’re still learning, refining, and working hard to optimize this new pipeline. Our hope with Spatial Film is to champion immersive storytelling on our own terms, without big corporations that might restrict artistic freedom. We’re here to bring you genuine, immersive stories—not influenced by financial projections. This platform is small, just me and a Shiba Inu sidekick named Kimchi, and we plan to keep it that way. No outside investment, no mission drift—just pure, immersive storytelling. And the best part? We’re releasing free films every other week, so you can explore and download our latest releases without a paywall. Every film also comes with its own immersive environment, free of charge. If you’d like to support us, you can subscribe to access the full library for a super affordable price, helping us keep this going as a true “mom-and-pop” shop on VisionOS. Feedback is more than welcome as we continue learning, optimizing, and improving the experience for all of you. Thank you for being part of this journey with us!
0:27

I’m thrilled to announce the launch of Spatial Film—an immersive 180° and 360° film app we’ve built: After months of self-teaching Swift and app development, I finally turned my dream of having a platform dedicated to showcasing the highest-quality immersive films into reality. I’m not a developer by trade—I’m an immersive filmmaker—but I wanted a space where our original 3D 180 and 360 films could shine, free from the heavy compression of typical platforms. So, four months ago, I dove into coding, and here we are with version 1! This journey has been both challenging and incredibly rewarding, and I couldn’t have done it alone. My gratitude goes out to the immersive and VR community, our loyal Patreon members, and developers around the world who generously offered advice and encouragement along the way. What makes Spatial Film unique? It’s 100% original content, from immersive films to 360° environments, all crafted to provide an unparalleled experience. Some of you might recognize us as immersive filmmakers and educators on YouTube, where we’ve shared tutorials and inspired countless creators on Meta, Apple, Pico, HTC, and even back in the early YouTube VR days. Now, we’re taking advantage of Vision Pro’s stunning display to bring our work to you in the best quality possible. Our journey with immersive content goes way back—we’ve worked with 12K camera rigs like Jaunt, Google Jump, and Yi Halo, creating wild experiences for brands. But despite our efforts, our films were often stuck on platforms with heavy compression, which limits the quality. Vision Pro changed that for us, but it’s taken a lot of work to adapt. We’re still learning, refining, and working hard to optimize this new pipeline. Our hope with Spatial Film is to champion immersive storytelling on our own terms, without big corporations that might restrict artistic freedom. We’re here to bring you genuine, immersive stories—not influenced by financial projections. This platform is small, just me and a Shiba Inu sidekick named Kimchi, and we plan to keep it that way. No outside investment, no mission drift—just pure, immersive storytelling. And the best part? We’re releasing free films every other week, so you can explore and download our latest releases without a paywall. Every film also comes with its own immersive environment, free of charge. If you’d like to support us, you can subscribe to access the full library for a super affordable price, helping us keep this going as a true “mom-and-pop” shop on VisionOS. Feedback is more than welcome as we continue learning, optimizing, and improving the experience for all of you. Thank you for being part of this journey with us!

Hugh Hou

17,557 Aufrufe • vor 1 Jahr

GeoLibre v2.7.0 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 building analysis instead of typing it: a visual Model Builder for processing workflows, a STAC browser that can add every asset type the app can draw, and a Chrome extension that opens the data on any web page in one click. 97 pull requests merged in 9 days, from 11 contributors besides me, 9 of whom sent their first contribution to GeoLibre in this cycle. Thank you all. What's new in v2.7.0 - Model Builder, a visual canvas for processing: drop tools as nodes, wire one tool's output into the next tool's input, and save the graph as a model that re-runs as a single job. The whole graph is validated before anything executes. - Your AI assistant can build one for you: describe a workflow in plain English, and it authors a validated model and opens it for review before it runs. Any model copies out as a runnable Python script. - Open data in GeoLibre, a Chrome extension now on the Chrome Web Store: it finds the dataset links and map services on the page you are viewing, including services inside embedded maps, and opens the ones you pick together on one map. - The STAC browser adds everything: PMTiles, GeoParquet, and Zarr with a variable picker join the COGs it already loaded, Icechunk repositories are read through their own manifest, and private Planetary Computer assets are signed for you. A static catalog can now be walked as a tree, not only searched. - Select features by drawing on the map: click, rectangle, polygon, freehand, and radius gestures, with Shift and Alt combining into the selection you already have. No more describing features in an expression to pick a handful of them. - Apache Iceberg tables load as vector layers, read in the browser through DuckDB, from a metadata location or a REST catalog, with the row count reported before anything is scanned. - Encoded polylines are a first-class format, with a codec, an interactive preview, processing tools, layer export, and Python support. - New vector tools: merge layers, extract vertices, and generate points along lines and polygon boundaries, all running client-side. - An activity log for shared projects and collaboration sessions, so a project owner can see who opened and edited their work. - Plugins and processing tools are now translatable too, closing the last gap where a non-English interface still read half in English. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #MapLibre #GeoLibre
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GeoLibre v2.7.0 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 building analysis instead of typing it: a visual Model Builder for processing workflows, a STAC browser that can add every asset type the app can draw, and a Chrome extension that opens the data on any web page in one click. 97 pull requests merged in 9 days, from 11 contributors besides me, 9 of whom sent their first contribution to GeoLibre in this cycle. Thank you all. What's new in v2.7.0 - Model Builder, a visual canvas for processing: drop tools as nodes, wire one tool's output into the next tool's input, and save the graph as a model that re-runs as a single job. The whole graph is validated before anything executes. - Your AI assistant can build one for you: describe a workflow in plain English, and it authors a validated model and opens it for review before it runs. Any model copies out as a runnable Python script. - Open data in GeoLibre, a Chrome extension now on the Chrome Web Store: it finds the dataset links and map services on the page you are viewing, including services inside embedded maps, and opens the ones you pick together on one map. - The STAC browser adds everything: PMTiles, GeoParquet, and Zarr with a variable picker join the COGs it already loaded, Icechunk repositories are read through their own manifest, and private Planetary Computer assets are signed for you. A static catalog can now be walked as a tree, not only searched. - Select features by drawing on the map: click, rectangle, polygon, freehand, and radius gestures, with Shift and Alt combining into the selection you already have. No more describing features in an expression to pick a handful of them. - Apache Iceberg tables load as vector layers, read in the browser through DuckDB, from a metadata location or a REST catalog, with the row count reported before anything is scanned. - Encoded polylines are a first-class format, with a codec, an interactive preview, processing tools, layer export, and Python support. - New vector tools: merge layers, extract vertices, and generate points along lines and polygon boundaries, all running client-side. - An activity log for shared projects and collaboration sessions, so a project owner can see who opened and edited their work. - Plugins and processing tools are now translatable too, closing the last gap where a non-English interface still read half in English. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #MapLibre #GeoLibre

Qiusheng Wu

14,793 Aufrufe • vor 18 Tagen

Excited to share our new work. Over the past decade, single-cell genomics has transformed our ability to map cellular systems. But a major question remains: Can we predict how perturbations reshape cellular trajectories over time? In 2018, we first showed that it is possible to predict cellular responses to perturbations — ranging from disease signals to chemical treatments — even in unseen contexts. In 2022, we introduced CPA (MSB 2022; NeurIPS 2022), extending this idea to predict responses to unseen chemical and genetic perturbations, including their combinations. Since then, the field of perturbation modeling has grown enormously. The community has pushed the space forward with many creative ideas and powerful models. It’s exciting to see how fast things are moving — even though many fundamental challenges remain. One of the biggest is that cells are not static. They move through trajectories during development, immune responses, and disease. Yet most current models still predict perturbation effects within a single state, rather than how early perturbations propagate across future states and reshape downstream outcomes. To address this, we developed PerturbGen, a trajectory-aware generative AI model that predicts how genetic perturbations reshape downstream cellular states. Huge credit to the people who made this work possible. Thanks to co-first authors Kevin Ly, Adib Miraki, Tomoya Isobe, AmirHoss3in Vahidi, Delshad Vaghari & Anthony Rostron. Special recognition to Kevin Ly and Adib Miraki for driving this work over the finish line. Grateful for our outstanding collaborators from Haniffa Lab, Bertie Gottgens lab Gosia Trynka and many others — a true cross-institute effort across Cambridge Stem Cell Institute, Open Targets ,Wellcome Sanger Institute and Cambridge University.🎉 PerturbGen learns transcriptional dynamics across cellular trajectories. By introducing perturbations at an early source state, it can simulate how these effects propagate into future states along differentiation trajectories. Scaling this across genes enables the creation of dynamic in silico perturbation atlases — maps of how perturbations reshape biological trajectories over time. We explored this idea across three biological questions. First, in a human in vivo LPS immune challenge, PerturbGen predicted that perturbing a transient IL1B signal dampens downstream inflammatory programs in myeloid cells, with pathway changes reversing signatures observed in an independent IL-1β stimulation experiment. Second, in human hematopoiesis, PerturbGen predicted transcriptional responses to CRISPR transcription factor knockouts and enabled construction of perturbation atlases revealing lineage- and age-specific regulatory programs. These programs could also be linked to human genetics and blood diseases, including recapitulation of signatures associated with ETV6-related thrombocytopenia. Finally, we asked whether perturbation modeling could help improve complex tissue models. We built a dynamic perturbation atlas of human skin organoids to identify perturbations that could guideorganoid cells towardhuman fetal skin states. PerturbGen prioritized activation of Wnt signaling via GSK3β inhibition. Experimental validation confirmed the prediction: treatment with CHIR99021 induced stromal gene programs and shifted organoid fibroblasts toward transcriptional states observed in fetal skin stroma. Together, these results show how trajectory-aware perturbation modeling can connect gene perturbations to developmental programs, human genetics, disease mechanisms, and experimental interventions. More broadly, we think these point toward a future where single-cell atlases become predictive systems. As atlases expand across tissues, developmental windows, and modalities, models like PerturbGen could enable dynamic, virtual perturbation atlases— allowing us to simulate interventions, generate hypotheses, and design experiments before stepping into the lab. Preprint Code Excited to see how the community builds on this work.
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Excited to share our new work. Over the past decade, single-cell genomics has transformed our ability to map cellular systems. But a major question remains: Can we predict how perturbations reshape cellular trajectories over time? In 2018, we first showed that it is possible to predict cellular responses to perturbations — ranging from disease signals to chemical treatments — even in unseen contexts. In 2022, we introduced CPA (MSB 2022; NeurIPS 2022), extending this idea to predict responses to unseen chemical and genetic perturbations, including their combinations. Since then, the field of perturbation modeling has grown enormously. The community has pushed the space forward with many creative ideas and powerful models. It’s exciting to see how fast things are moving — even though many fundamental challenges remain. One of the biggest is that cells are not static. They move through trajectories during development, immune responses, and disease. Yet most current models still predict perturbation effects within a single state, rather than how early perturbations propagate across future states and reshape downstream outcomes. To address this, we developed PerturbGen, a trajectory-aware generative AI model that predicts how genetic perturbations reshape downstream cellular states. Huge credit to the people who made this work possible. Thanks to co-first authors Kevin Ly, Adib Miraki, Tomoya Isobe, AmirHoss3in Vahidi, Delshad Vaghari & Anthony Rostron. Special recognition to Kevin Ly and Adib Miraki for driving this work over the finish line. Grateful for our outstanding collaborators from Haniffa Lab, Bertie Gottgens lab Gosia Trynka and many others — a true cross-institute effort across Cambridge Stem Cell Institute, Open Targets ,Wellcome Sanger Institute and Cambridge University.🎉 PerturbGen learns transcriptional dynamics across cellular trajectories. By introducing perturbations at an early source state, it can simulate how these effects propagate into future states along differentiation trajectories. Scaling this across genes enables the creation of dynamic in silico perturbation atlases — maps of how perturbations reshape biological trajectories over time. We explored this idea across three biological questions. First, in a human in vivo LPS immune challenge, PerturbGen predicted that perturbing a transient IL1B signal dampens downstream inflammatory programs in myeloid cells, with pathway changes reversing signatures observed in an independent IL-1β stimulation experiment. Second, in human hematopoiesis, PerturbGen predicted transcriptional responses to CRISPR transcription factor knockouts and enabled construction of perturbation atlases revealing lineage- and age-specific regulatory programs. These programs could also be linked to human genetics and blood diseases, including recapitulation of signatures associated with ETV6-related thrombocytopenia. Finally, we asked whether perturbation modeling could help improve complex tissue models. We built a dynamic perturbation atlas of human skin organoids to identify perturbations that could guideorganoid cells towardhuman fetal skin states. PerturbGen prioritized activation of Wnt signaling via GSK3β inhibition. Experimental validation confirmed the prediction: treatment with CHIR99021 induced stromal gene programs and shifted organoid fibroblasts toward transcriptional states observed in fetal skin stroma. Together, these results show how trajectory-aware perturbation modeling can connect gene perturbations to developmental programs, human genetics, disease mechanisms, and experimental interventions. More broadly, we think these point toward a future where single-cell atlases become predictive systems. As atlases expand across tissues, developmental windows, and modalities, models like PerturbGen could enable dynamic, virtual perturbation atlases— allowing us to simulate interventions, generate hypotheses, and design experiments before stepping into the lab. Preprint Code Excited to see how the community builds on this work.

Mo Lotfollahi

17,165 Aufrufe • vor 6 Monaten

Spectre AI Soars and Secures Google Scale Tier Membership with $200,000 in Development Resources We're thrilled to announce a significant milestone for Spectre AI! After a lot of networking, and a rigorous selection process, we've been accepted into the prestigious Google Scale Tier program. We had Start Tier, now we have Scale Tier! This membership signifies Google's recognition of Spectre AI's potential to become a potential game-changer in the blockchain space, and it grants us access to a wealth of resources to fuel our growth – $200,000 in development funding from GoogleStartups to use their advanced tools. What is the Google Scale Tier? The Google Scale Tier is a highly selective program designed to nurture high-growth startups with exceptional potential. Going beyond simple funding, this program grants a comprehensive suite of benefits to empower us to scale our technology and achieve new heights. Unlocking Cutting-Edge Tech and Expertise Our Google Scale Tier membership unlocks a treasure trove of resources to accelerate our development journey: $200,000 in Google Development Resources: This crucial boost will allow us to leverage Google Cloud and cutting-edge tools, along with collaboration with top Google engineers. These experts will work closely with our team to integrate these powerful resources seamlessly into our entire suite of products, including AI Predictions, Sentiment Analysis, and Technical Analysis. Imagine the possibilities for enhanced accuracy, efficiency, and deeper market insights leveraged by Google's technology! Collaboration with Google Engineering Experts: As mentioned earlier, the $200,000 in development resources includes access to Google engineers – the masterminds behind cutting-edge technologies like Long Short-Term Memory (LSTM) models, Machine Learning (ML), and advanced graphing models. These experts will collaborate with our team to integrate these powerful tools into our products. Dedicated Google Representative: A dedicated Google representative from their Irish headquarters has become our go-to person, ensuring seamless collaboration and ongoing support throughout our journey. Thank you GoogleStartupUK The Future of Spectre AI: Enhanced All-in-One Products This partnership extends far beyond individual features. Here's what you can expect across our entire product suite: Next-Level Functionality: We'll leverage Google's advanced algorithms and massive datasets to refine all our tools, including AI Predictions, Sentiment Analysis, and Technical Analysis. This means more reliable and insightful information to guide your investment strategies. Expanded Capabilities: We're exploring groundbreaking new features for our entire product suite, like real-time analysis, multi-factor modeling, and even deeper market insights. Enhanced User Experience: Navigating through all our tools will be smoother than ever. We'll work with Google to refine the user interface across the board, making it easier to understand and leverage the power of AI in your crypto journey. The Data Visualization Revolution: Buckle up, X Bubblemaps users! Google's advanced graphing models are poised to transform how you visualize and explore data within Spectre AI. We can't wait to unveil a whole new level of visualization that will take your on-chain analysis to the next level. This is just the beginning! We're incredibly grateful for this opportunity to partner with Google and revolutionize the future of our all-in-one blockchain analysis suite. Stay tuned for exciting updates as we develop groundbreaking new features together. Thank you for being a part of the Spectre AI community! #google #googlecloud #spectre #ai #tech #innovation $spect
0:30

Spectre AI Soars and Secures Google Scale Tier Membership with $200,000 in Development Resources We're thrilled to announce a significant milestone for Spectre AI! After a lot of networking, and a rigorous selection process, we've been accepted into the prestigious Google Scale Tier program. We had Start Tier, now we have Scale Tier! This membership signifies Google's recognition of Spectre AI's potential to become a potential game-changer in the blockchain space, and it grants us access to a wealth of resources to fuel our growth – $200,000 in development funding from GoogleStartups to use their advanced tools. What is the Google Scale Tier? The Google Scale Tier is a highly selective program designed to nurture high-growth startups with exceptional potential. Going beyond simple funding, this program grants a comprehensive suite of benefits to empower us to scale our technology and achieve new heights. Unlocking Cutting-Edge Tech and Expertise Our Google Scale Tier membership unlocks a treasure trove of resources to accelerate our development journey: $200,000 in Google Development Resources: This crucial boost will allow us to leverage Google Cloud and cutting-edge tools, along with collaboration with top Google engineers. These experts will work closely with our team to integrate these powerful resources seamlessly into our entire suite of products, including AI Predictions, Sentiment Analysis, and Technical Analysis. Imagine the possibilities for enhanced accuracy, efficiency, and deeper market insights leveraged by Google's technology! Collaboration with Google Engineering Experts: As mentioned earlier, the $200,000 in development resources includes access to Google engineers – the masterminds behind cutting-edge technologies like Long Short-Term Memory (LSTM) models, Machine Learning (ML), and advanced graphing models. These experts will collaborate with our team to integrate these powerful tools into our products. Dedicated Google Representative: A dedicated Google representative from their Irish headquarters has become our go-to person, ensuring seamless collaboration and ongoing support throughout our journey. Thank you GoogleStartupUK The Future of Spectre AI: Enhanced All-in-One Products This partnership extends far beyond individual features. Here's what you can expect across our entire product suite: Next-Level Functionality: We'll leverage Google's advanced algorithms and massive datasets to refine all our tools, including AI Predictions, Sentiment Analysis, and Technical Analysis. This means more reliable and insightful information to guide your investment strategies. Expanded Capabilities: We're exploring groundbreaking new features for our entire product suite, like real-time analysis, multi-factor modeling, and even deeper market insights. Enhanced User Experience: Navigating through all our tools will be smoother than ever. We'll work with Google to refine the user interface across the board, making it easier to understand and leverage the power of AI in your crypto journey. The Data Visualization Revolution: Buckle up, X Bubblemaps users! Google's advanced graphing models are poised to transform how you visualize and explore data within Spectre AI. We can't wait to unveil a whole new level of visualization that will take your on-chain analysis to the next level. This is just the beginning! We're incredibly grateful for this opportunity to partner with Google and revolutionize the future of our all-in-one blockchain analysis suite. Stay tuned for exciting updates as we develop groundbreaking new features together. Thank you for being a part of the Spectre AI community! #google #googlecloud #spectre #ai #tech #innovation $spect

SPECTRE AI

46,737 Aufrufe • vor 2 Jahren

We’re making our drug tapering training available for free! At Taper Clinic, we’ve developed a structured approach to safely help people reduce and discontinue psychiatric medications. Now, we’re sharing our step-by-step tapering process so anyone—whether you're tapering yourself or helping others—can access these tools. This guide is based on years of experience working with people facing complex medication withdrawals. We also provide resources on tapering hard-to-stop drugs, assessing patient history, and adjusting tapering speed. If you're a doctor, coach, pharmacist, or someone supporting a loved one, this mentorship program is for you. We hope this makes tapering safer and more accessible worldwide. 🔗 Get the free tapering guide here: 🔗 Apply for our free mentorship program: #MedicationTapering #TaperClinic #PsychiatricDrugWithdrawal
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We’re making our drug tapering training available for free! At Taper Clinic, we’ve developed a structured approach to safely help people reduce and discontinue psychiatric medications. Now, we’re sharing our step-by-step tapering process so anyone—whether you're tapering yourself or helping others—can access these tools. This guide is based on years of experience working with people facing complex medication withdrawals. We also provide resources on tapering hard-to-stop drugs, assessing patient history, and adjusting tapering speed. If you're a doctor, coach, pharmacist, or someone supporting a loved one, this mentorship program is for you. We hope this makes tapering safer and more accessible worldwide. 🔗 Get the free tapering guide here: 🔗 Apply for our free mentorship program: #MedicationTapering #TaperClinic #PsychiatricDrugWithdrawal

Dr. Josef 🇺🇸

38,407 Aufrufe • vor 1 Jahr