Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Today we release Rhaister, an elegant statistical model that predicts drug phenotypes in new contexts w/ accuracies comparable to experimental assays. And dropping Emerald Bay, a 2M cell dataset measuring long time-course phenotypes across 1000s of drug-cell line interactions.

Nima Alidoust

3,890 subscribers

63,283 görüntüleme • 2 ay önce •via X (Twitter)

Eğitim Bilim & Teknoloji

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

To replace animal testing with AI, we need MASSIVE human datasets. Today, we're thrilled to share Axiom's new data exploration tool, providing the ability to visually explore the world's largest primary human liver toxicity dataset. Built with Axiom's proprietary wetlab protocols, our dataset includes detailed liver toxicity profiles for over 100,000 distinct molecules. The key to this dataset is our ability to do high-throughput, multiplexed high-content screening with primary human liver cells. Traditionally, toxicity assays either sacrifice throughput or sacrifice biological relevance (using easy-to-grow immortalized cell lines instead of real human cells). We managed to combine throughput, physiological relevance, and multiplexing in one platform. The assays run in a high throughput format using automation, meaning thousands of compound-dose conditions can be tested in one experiment. We achieved this using pooled primary human hepatocytes, which are often fragile and expensive. By systemizing our automation and quality control processes, we were able to run over 120+ batches on the same donor pool with incredible reproducibility and consistency. We did this while integrating many readouts per well, whereas many existing toxicity assays only do a single readout. Our multiplexed approach provides far more data per experiment enabling us to measure 10-20 different toxicity phenotypes such as apoptosis, necrosis, mitochondrial fission, endoplasmic reticulum stress, stress granule formation, microtubules, and more all from a single well on a 384-well plate! The combination of scale, high content information, and data quality is exactly what is needed to train highly accurate AI models in biology. If you're interested, please explore the dataset in the comments below and let me know if you want to chat about the details!

To replace animal testing with AI, we need MASSIVE human datasets. Today, we're thrilled to share Axiom's new data exploration tool, providing the ability to visually explore the world's largest primary human liver toxicity dataset. Built with Axiom's proprietary wetlab protocols, our dataset includes detailed liver toxicity profiles for over 100,000 distinct molecules. The key to this dataset is our ability to do high-throughput, multiplexed high-content screening with primary human liver cells. Traditionally, toxicity assays either sacrifice throughput or sacrifice biological relevance (using easy-to-grow immortalized cell lines instead of real human cells). We managed to combine throughput, physiological relevance, and multiplexing in one platform. The assays run in a high throughput format using automation, meaning thousands of compound-dose conditions can be tested in one experiment. We achieved this using pooled primary human hepatocytes, which are often fragile and expensive. By systemizing our automation and quality control processes, we were able to run over 120+ batches on the same donor pool with incredible reproducibility and consistency. We did this while integrating many readouts per well, whereas many existing toxicity assays only do a single readout. Our multiplexed approach provides far more data per experiment enabling us to measure 10-20 different toxicity phenotypes such as apoptosis, necrosis, mitochondrial fission, endoplasmic reticulum stress, stress granule formation, microtubules, and more all from a single well on a 384-well plate! The combination of scale, high content information, and data quality is exactly what is needed to train highly accurate AI models in biology. If you're interested, please explore the dataset in the comments below and let me know if you want to chat about the details!

Brandon White

25,117 görüntüleme • 1 yıl önce

AI is not accelerating nearly fast enough to cure cancer. That is the problem. And it literally cannot without actually seeing what’s happening inside the cell. There’s this new project claiming their models will “solve” cancer by predicting drug responses at scale. Cool story. Except their entire approach still treats the cell like a black box. They’re optimizing for binding affinity while having zero idea what the drug actually does once it’s inside a real, living cell. Meanwhile we’re over here building the tools to watch the cell in real time. No staining or sequencing. Just direct observation of mechanism. You can simulate all you want. But if you can’t see the actual cellular response, you’re just making very confident guesses about something you’ve never actually looked at. We’ve spent two years proving this. The data is there. The difference is night and day. AI is a tool. It’s not the microscope. If your model can’t tell you why a drug is killing the wrong cells or why resistance is forming in real time… it’s not ready to cure cancer. It’s ready to waste another billion dollars. We’re not competing with AI. We’re giving it eyes. Goodbye.

AI is not accelerating nearly fast enough to cure cancer. That is the problem. And it literally cannot without actually seeing what’s happening inside the cell. There’s this new project claiming their models will “solve” cancer by predicting drug responses at scale. Cool story. Except their entire approach still treats the cell like a black box. They’re optimizing for binding affinity while having zero idea what the drug actually does once it’s inside a real, living cell. Meanwhile we’re over here building the tools to watch the cell in real time. No staining or sequencing. Just direct observation of mechanism. You can simulate all you want. But if you can’t see the actual cellular response, you’re just making very confident guesses about something you’ve never actually looked at. We’ve spent two years proving this. The data is there. The difference is night and day. AI is a tool. It’s not the microscope. If your model can’t tell you why a drug is killing the wrong cells or why resistance is forming in real time… it’s not ready to cure cancer. It’s ready to waste another billion dollars. We’re not competing with AI. We’re giving it eyes. Goodbye.

Parmita Mishra

48,203 görüntüleme • 2 ay önce

HOLY CRAP! I can't tell you how big this is for the medical community and drug discovery: Google Announces AlphaFold 3 AI. Details: Enhanced Molecular Prediction: AlphaFold 3 predicts the structure and interactions of all life's molecules, including proteins, DNA, RNA, ligands, and more, with unprecedented accuracy. Improved Interaction Accuracy: For protein interactions with other molecule types, AlphaFold 3 offers at least a 50% improvement over existing methods, and doubles the accuracy for some critical interactions. Transformative Potential for Science and Medicine: The model aims to deepen our understanding of biological processes and significantly advance drug discovery efforts. Accessibility for Researchers: AlphaFold 3's capabilities are largely accessible for free via the AlphaFold Server, providing an essential tool for scientific research. Drug Design Innovation: AlphaFold 3 is utilized by Isomorphic Labs in collaboration with pharmaceutical companies to accelerate drug design, potentially leading to new treatments for various diseases. Foundation in AlphaFold 2: Building on the breakthroughs of AlphaFold 2, this version extends its scope beyond proteins to a wide range of biomolecules, enhancing its utility in scientific research and application. Global Accessibility and Educational Support: The AlphaFold Server is a free platform for non-commercial research worldwide, supported by educational resources to foster wider adoption and innovation. Empowering Rapid Scientific Advancements: By making detailed molecular interactions easily accessible, AlphaFold 3 enables faster hypothesis testing and could reduce the time and cost typically associated with experimental protein-structure prediction. Responsible Development and Deployment: DeepMind has engaged with domain experts to assess the impacts and potential risks of AlphaFold, ensuring its responsible use in the scientific community. Broad Implications for Biology:AlphaFold 3 helps reveal complex cellular mechanisms and interactions, offering insights that could lead to improved agricultural crops, enhanced understanding of diseases, and novel therapeutic strategies.

HOLY CRAP! I can't tell you how big this is for the medical community and drug discovery: Google Announces AlphaFold 3 AI. Details: Enhanced Molecular Prediction: AlphaFold 3 predicts the structure and interactions of all life's molecules, including proteins, DNA, RNA, ligands, and more, with unprecedented accuracy. Improved Interaction Accuracy: For protein interactions with other molecule types, AlphaFold 3 offers at least a 50% improvement over existing methods, and doubles the accuracy for some critical interactions. Transformative Potential for Science and Medicine: The model aims to deepen our understanding of biological processes and significantly advance drug discovery efforts. Accessibility for Researchers: AlphaFold 3's capabilities are largely accessible for free via the AlphaFold Server, providing an essential tool for scientific research. Drug Design Innovation: AlphaFold 3 is utilized by Isomorphic Labs in collaboration with pharmaceutical companies to accelerate drug design, potentially leading to new treatments for various diseases. Foundation in AlphaFold 2: Building on the breakthroughs of AlphaFold 2, this version extends its scope beyond proteins to a wide range of biomolecules, enhancing its utility in scientific research and application. Global Accessibility and Educational Support: The AlphaFold Server is a free platform for non-commercial research worldwide, supported by educational resources to foster wider adoption and innovation. Empowering Rapid Scientific Advancements: By making detailed molecular interactions easily accessible, AlphaFold 3 enables faster hypothesis testing and could reduce the time and cost typically associated with experimental protein-structure prediction. Responsible Development and Deployment: DeepMind has engaged with domain experts to assess the impacts and potential risks of AlphaFold, ensuring its responsible use in the scientific community. Broad Implications for Biology:AlphaFold 3 helps reveal complex cellular mechanisms and interactions, offering insights that could lead to improved agricultural crops, enhanced understanding of diseases, and novel therapeutic strategies.

Brian Krassenstein

258,615 görüntüleme • 2 yıl önce

The most detailed 3D reconstruction of a cell ever created. Blows my mind every time. But what exactly are we looking at here? The average human cell contains: ~ 15-20 total distinct organelle types, totalling between ~1-10 million working together per cell. All these nano-machines in the cell are made up of proteins. ~ 8,000-10,000 distinct types of unique proteins, adding up to between 40 million - 10 trillion total proteins making up all those cellular systems. ~ 10,000 - 15,000 distinct types of RNA shuttling information around the cell, totalling up to ~10 million RNA molecules moving around the cell simultaneously. ~ Billions of Lipid molecules packed together into the cell membrane, which is also packed tightly with millions more protein-based nano-machines. And let's not forget billions of lines of DNA information to build and run it all. That's TRILLIONS of of individual molecular pieces working together to make a single cell function. That means there is more complexity in a single cell than humanity's largest cities. And people still believe this wasn't Divinely Designed. This is God's Glory on Display. But to make the point. A cell couldn't have evolved from some nebulous simpler "protocell" because even the simplest cells still require massive complexity. The "simplest" cell ever created was engineered by scientists knocking out pieces of a functional cell until it stopped functioning. Here is what they found is the absolute necessary minimal requirements of a cell to function: - Over ~531,000 lines of coded DNA information - 473 total genes to create hundreds of unique protein products (they later added 19 genes back in because the cell was so weak) - Hundreds of thousands of total proteins all working together - Extensive regulatory networks guiding all these interactions If the cell doesn't have all these systems in place, from the start... it doesn't live. Cell rely on an intricate network of complex systems, which are themselves built from complex interconnected pieces woven together into an incomprehensibly complex web of functionilty. Only intelligence has ever been observed creation vast interconnected systems like this. Life was clearly Created. It couldn't happen any other way.

The most detailed 3D reconstruction of a cell ever created. Blows my mind every time. But what exactly are we looking at here? The average human cell contains: ~ 15-20 total distinct organelle types, totalling between ~1-10 million working together per cell. All these nano-machines in the cell are made up of proteins. ~ 8,000-10,000 distinct types of unique proteins, adding up to between 40 million - 10 trillion total proteins making up all those cellular systems. ~ 10,000 - 15,000 distinct types of RNA shuttling information around the cell, totalling up to ~10 million RNA molecules moving around the cell simultaneously. ~ Billions of Lipid molecules packed together into the cell membrane, which is also packed tightly with millions more protein-based nano-machines. And let's not forget billions of lines of DNA information to build and run it all. That's TRILLIONS of of individual molecular pieces working together to make a single cell function. That means there is more complexity in a single cell than humanity's largest cities. And people still believe this wasn't Divinely Designed. This is God's Glory on Display. But to make the point. A cell couldn't have evolved from some nebulous simpler "protocell" because even the simplest cells still require massive complexity. The "simplest" cell ever created was engineered by scientists knocking out pieces of a functional cell until it stopped functioning. Here is what they found is the absolute necessary minimal requirements of a cell to function: - Over ~531,000 lines of coded DNA information - 473 total genes to create hundreds of unique protein products (they later added 19 genes back in because the cell was so weak) - Hundreds of thousands of total proteins all working together - Extensive regulatory networks guiding all these interactions If the cell doesn't have all these systems in place, from the start... it doesn't live. Cell rely on an intricate network of complex systems, which are themselves built from complex interconnected pieces woven together into an incomprehensibly complex web of functionilty. Only intelligence has ever been observed creation vast interconnected systems like this. Life was clearly Created. It couldn't happen any other way.

Divinely Designed

166,325 görüntüleme • 3 ay önce

>>To be ER-100 >>The first epigenetic reprogramming remedy to enter human tests >>Developed by Life Biosciences >>Co-founded by Harvard scientists, including David Sinclair >>Uses OSK factors (Oct4, Sox2, Klf4) to reset the cell phone clock >>Turns off the genes linked to aging >>FDA releases clinical trials in January 2026 >>First cell rejuvenation therapy to reach the human phase >>Focused on optic neuropathies, glaucoma and vision lost by age >>Trump shocks the world at the press conference: "We have an experimental drug that brings the dead back" >>"We caught people who were dead, received extreme unction, the children crying... and put them in the medicine. The person has improved. It works!" >>The seventh great advance in longevity that Hollywood and science dreamed of In the same Boston where Harvard dreamed of overcoming aging, now science begins to rewrite what "to die" means - and Trump only threw gasoline on the fire. 🔥

>>To be ER-100 >>The first epigenetic reprogramming remedy to enter human tests >>Developed by Life Biosciences >>Co-founded by Harvard scientists, including David Sinclair >>Uses OSK factors (Oct4, Sox2, Klf4) to reset the cell phone clock >>Turns off the genes linked to aging >>FDA releases clinical trials in January 2026 >>First cell rejuvenation therapy to reach the human phase >>Focused on optic neuropathies, glaucoma and vision lost by age >>Trump shocks the world at the press conference: "We have an experimental drug that brings the dead back" >>"We caught people who were dead, received extreme unction, the children crying... and put them in the medicine. The person has improved. It works!" >>The seventh great advance in longevity that Hollywood and science dreamed of In the same Boston where Harvard dreamed of overcoming aging, now science begins to rewrite what "to die" means - and Trump only threw gasoline on the fire. 🔥

Treta Real news

207,963 görüntüleme • 3 ay önce

Today we’re thrilled to announce JAM-2 — the first AI model capable of generating drug-quality antibodies straight from the computer, with industry-leading success rates. > Drug-like affinities: Picomolar to single-digit nanomolar antibody binders for half of 26 targets while testing Unlocking hard targets: Up to 11% success rate for direct on-cell GPCR binders; top antibody hits in the single-digit nanomolar range. > Unprecedented epitope breadth: JAM-2 routinely designed antibodies that hit 30–70% of user-defined epitopes, now enabling intentional design of biology — not chance discovery. > Drug-like developability: Over 50% of antibody designs passed core industry developability criteria with zero optimization. > Massive leverage: A four-person team prosecuted 16 targets in parallel in < 1 month. JAM-2 is the first de novo antibody design capability ready for front-line use in drug discovery, matching or surpassing traditional discovery approaches. We’re already deploying JAM-2 with multiple large pharma partners and seeing excellent results. If you’re interested in partnering on molecule development or accessing JAM-2, contact bd@nabla.bio. Read more in our whitepaper (link below)

Today we’re thrilled to announce JAM-2 — the first AI model capable of generating drug-quality antibodies straight from the computer, with industry-leading success rates. > Drug-like affinities: Picomolar to single-digit nanomolar antibody binders for half of 26 targets while testing Unlocking hard targets: Up to 11% success rate for direct on-cell GPCR binders; top antibody hits in the single-digit nanomolar range. > Unprecedented epitope breadth: JAM-2 routinely designed antibodies that hit 30–70% of user-defined epitopes, now enabling intentional design of biology — not chance discovery. > Drug-like developability: Over 50% of antibody designs passed core industry developability criteria with zero optimization. > Massive leverage: A four-person team prosecuted 16 targets in parallel in < 1 month. JAM-2 is the first de novo antibody design capability ready for front-line use in drug discovery, matching or surpassing traditional discovery approaches. We’re already deploying JAM-2 with multiple large pharma partners and seeing excellent results. If you’re interested in partnering on molecule development or accessing JAM-2, contact [email protected]. Read more in our whitepaper (link below)

Nabla Bio

201,419 görüntüleme • 9 ay önce

$I’m starting a company. An internet-scale dataset is created every day that can be used to cure diseases. ㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤ Yet decisions for developing new drugs use a tiny fraction of it. ㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤ After years of building and investing in therapeutics companies, I'm convinced that this is about to change. · There are two kinds of knowledge. Facts, and connections. We have an abundance of known facts about biology, but we have barely begun to make all the connections. ㅤ Our company is connecting the world's pharmaceutical data, drawing a coherent understanding from an archipelago of information. Thousands of decisions behind breakthrough drugs—from experimental design to new product planning—can be augmented by this scientific intelligence. · We are partnering with drug developers who’ve dedicated their careers to their craft. We know that engineering tools for those at the pinnacle of human expertise is not going to be easy. But it will change how life-saving discoveries are brought to patients. If this sounds like fun, reach out. We're recruiting founding engineers.$

I’m starting a company. An internet-scale dataset is created every day that can be used to cure diseases. ㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤ Yet decisions for developing new drugs use a tiny fraction of it. ㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤㅤ After years of building and investing in therapeutics companies, I'm convinced that this is about to change. · There are two kinds of knowledge. Facts, and connections. We have an abundance of known facts about biology, but we have barely begun to make all the connections. ㅤ Our company is connecting the world's pharmaceutical data, drawing a coherent understanding from an archipelago of information. Thousands of decisions behind breakthrough drugs—from experimental design to new product planning—can be augmented by this scientific intelligence. · We are partnering with drug developers who’ve dedicated their careers to their craft. We know that engineering tools for those at the pinnacle of human expertise is not going to be easy. But it will change how life-saving discoveries are brought to patients. If this sounds like fun, reach out. We're recruiting founding engineers.

Michael Retchin

181,956 görüntüleme • 2 yıl önce

A visit to my favorite place ever, the butcher shop, before a trip to get a needle. I've been on light duties for a long time due to a touch of arthritis. We've been waiting for a new drug called Librela to be available in Canada. It's finally here & today I get my 1st shot. It's had great success in the UK but I'm hoping it'll have great success in my shoulder. Video description: The camera pans from a rottweiler laying on a floor across to a meat counter at a butcher shop and back to the dog

A visit to my favorite place ever, the butcher shop, before a trip to get a needle. I've been on light duties for a long time due to a touch of arthritis. We've been waiting for a new drug called Librela to be available in Canada. It's finally here & today I get my 1st shot. It's had great success in the UK but I'm hoping it'll have great success in my shoulder. Video description: The camera pans from a rottweiler laying on a floor across to a meat counter at a butcher shop and back to the dog

Team Servicerottie🇨🇦🐕‍🦺🦽

29,102 görüntüleme • 3 yıl önce

Your every thought, memory, and movement begins here: an electric spark turned chemical message, as one neuron whispers to the next. What you’re seeing This animation captures neurotransmission, the process that allows nerve cells to communicate across microscopic gaps called synapses. When an electrical impulse reaches the end of Cell A (the presynaptic neuron), it triggers the release of neurotransmitters—tiny signaling molecules that cross the synaptic cleft and bind to receptors on Cell B (the postsynaptic neuron). 🟡 Opens ion channels that generate a new electrical signal in the next neuron 🟡 Coordinates how the brain encodes movement, emotion, and thought 🟡 Operates on millisecond precision using hundreds of molecular components 💡 The bigger picture Neurotransmission is the foundation of consciousness, a seamless conversation of electricity and chemistry happening trillions of times each second. Every sensation, decision, and memory depends on this invisible dialogue between your brain’s 86 billion neurons.

Your every thought, memory, and movement begins here: an electric spark turned chemical message, as one neuron whispers to the next. What you’re seeing This animation captures neurotransmission, the process that allows nerve cells to communicate across microscopic gaps called synapses. When an electrical impulse reaches the end of Cell A (the presynaptic neuron), it triggers the release of neurotransmitters—tiny signaling molecules that cross the synaptic cleft and bind to receptors on Cell B (the postsynaptic neuron). 🟡 Opens ion channels that generate a new electrical signal in the next neuron 🟡 Coordinates how the brain encodes movement, emotion, and thought 🟡 Operates on millisecond precision using hundreds of molecular components 💡 The bigger picture Neurotransmission is the foundation of consciousness, a seamless conversation of electricity and chemistry happening trillions of times each second. Every sensation, decision, and memory depends on this invisible dialogue between your brain’s 86 billion neurons.

William A. Wallace, Ph.D.

125,496 görüntüleme • 9 ay önce

#OlaElectric — 2024 vs 2026 In 2024 At the Time of the IPO - • Only #scooters were available — plagued with numerous technical issues and poor reliability • Very limited scooter range with few model options • No #cell manufacturing business whatsoever • No #Roadster Motorcycle in the pipeline • No #Shakti Inverters — zero presence in the #energy segment • No service centers — after-sales support was virtually non-existent • No #HyperService network of any kind • No spare parts inventory — customers struggled to get repairs done • Outdated operating system powering the vehicles. Where #OlaElectric Stands Today(2026) - • #Scooters are technically refined, tested, and vastly improved • Expanded scooter range catering to multiple customer segments • 3 GWh #Cell Manufacturing facility ready for commercial production - with #Bharet cell 4680 already running in Ola vehicles and 6 GWh cell Manufacturering will be ready for commercial production by july End • 46100 NMC/LFP cell manufacturing ready by September end • #Roadster plus Motorcycle launched and commanding with 50% market share in Northern India • #Shakti Inverters added as a strong new revenue stream • Nationwide service center network established and operational • #HyperService rolled out for faster, more efficient customer support • Spare parts inventory maintained at every service center across the country • Advanced, updated operating system delivering a superior user experience. Bhavish Aggarwal Ola Electric #EV #MadeInIndia #Electricvehicle

#OlaElectric — 2024 vs 2026 In 2024 At the Time of the IPO - • Only #scooters were available — plagued with numerous technical issues and poor reliability • Very limited scooter range with few model options • No #cell manufacturing business whatsoever • No #Roadster Motorcycle in the pipeline • No #Shakti Inverters — zero presence in the #energy segment • No service centers — after-sales support was virtually non-existent • No #HyperService network of any kind • No spare parts inventory — customers struggled to get repairs done • Outdated operating system powering the vehicles. Where #OlaElectric Stands Today(2026) - • #Scooters are technically refined, tested, and vastly improved • Expanded scooter range catering to multiple customer segments • 3 GWh #Cell Manufacturing facility ready for commercial production - with #Bharet cell 4680 already running in Ola vehicles and 6 GWh cell Manufacturering will be ready for commercial production by july End • 46100 NMC/LFP cell manufacturing ready by September end • #Roadster plus Motorcycle launched and commanding with 50% market share in Northern India • #Shakti Inverters added as a strong new revenue stream • Nationwide service center network established and operational • #HyperService rolled out for faster, more efficient customer support • Spare parts inventory maintained at every service center across the country • Advanced, updated operating system delivering a superior user experience. Bhavish Aggarwal Ola Electric #EV #MadeInIndia #Electricvehicle

Anjaan_Musafir05

14,357 görüntüleme • 2 ay önce

E11 Bio is excited to unveil PRISM technology for mapping brain wiring with simple light microscopes. Today, brain mapping in humans and other mammals is bottlenecked by accurate neuron tracing. PRISM uses molecular ID codes and AI to help neurons trace themselves. We discovered a new cell barcoding approach exceeding comparable methods by more than 750x. This is the heart of PRISM. We integrated this capability with microscopy and AI image analysis to automatically trace neurons at high resolution and annotate them with molecular features. This is a key advance towards economically viable brain mapping - 95% of costs stem from neuron tracing. It is also an important step towards democratizing neuron tracing for everyday neuroscience. Solving these problems is critical for curing brain disorders, building safer and human-like AI, and even simulating brain function. In our first pilot study, we acquired a unique dataset in mouse hippocampus. Barcodes improved the accuracy of tracing genetically labelled neurons by 8x – with a clear path to 100x or more. They also permit tracing across spatial gaps – essential for mitigating tissue section loss in whole-brain scaling. Using molecular annotation, we uncover an intriguing feature of synaptic organization, demonstrating how PRISM can be used for systematic discovery 🧵

E11 Bio is excited to unveil PRISM technology for mapping brain wiring with simple light microscopes. Today, brain mapping in humans and other mammals is bottlenecked by accurate neuron tracing. PRISM uses molecular ID codes and AI to help neurons trace themselves. We discovered a new cell barcoding approach exceeding comparable methods by more than 750x. This is the heart of PRISM. We integrated this capability with microscopy and AI image analysis to automatically trace neurons at high resolution and annotate them with molecular features. This is a key advance towards economically viable brain mapping - 95% of costs stem from neuron tracing. It is also an important step towards democratizing neuron tracing for everyday neuroscience. Solving these problems is critical for curing brain disorders, building safer and human-like AI, and even simulating brain function. In our first pilot study, we acquired a unique dataset in mouse hippocampus. Barcodes improved the accuracy of tracing genetically labelled neurons by 8x – with a clear path to 100x or more. They also permit tracing across spatial gaps – essential for mitigating tissue section loss in whole-brain scaling. Using molecular annotation, we uncover an intriguing feature of synaptic organization, demonstrating how PRISM can be used for systematic discovery 🧵

Andrew Payne

112,544 görüntüleme • 11 ay önce

The term "continual learning" has become overloaded if you see it as an ML problem. One classic thread is about memorization: regularization-based continual learning methods, such as EWC, MAS, and SI, estimate which parameters mattered for previous tasks and resist changing them too much. One modern thread is about adaptation: test-time training and inference-time learning methods, such as TTT, adapt part of the model on the incoming test stream before making predictions. These are sometimes discussed as separate threads. But in modern scalable architectures, I think they are better seen as complementary constraints: a model that learns quickly at test time also benefits from a mechanism for deciding what not to forget. In our #ECCV2026 paper, we study this in large-scale 4D reconstruction: how to build fast spatial memory that can adapt over long observation streams while reducing collapse and forgetting. Instead of using fully plastic test-time updates, we stabilize fast-weight adaptation with an elastic prior that balances adaptation and memory. Key ideas: - Elastic Test-Time Training: Fisher-weighted consolidation for fast-weight updates - EMA anchor weights that provide a moving reference for stability - Chunk-by-chunk inference for long 3D/4D observation streams We show that this scales across large 3D/4D pretraining settings, including both LRM-style and LVSM-style models, and improves reconstruction across benchmarks including Stereo4D, NVIDIA, and DL3DV-140. We release model checkpoints across different design choices: resolution, post-training curriculum, and whether the model uses an explicit 4DGS intermediate representation. - Homepage: - Paper: - Code: - Models: This work is co-led with Xueyang Yu, contributed by Haoyu Zhen Yuncong Yang, and advised by Michigan SLED Lab Chuang Gan.

The term "continual learning" has become overloaded if you see it as an ML problem. One classic thread is about memorization: regularization-based continual learning methods, such as EWC, MAS, and SI, estimate which parameters mattered for previous tasks and resist changing them too much. One modern thread is about adaptation: test-time training and inference-time learning methods, such as TTT, adapt part of the model on the incoming test stream before making predictions. These are sometimes discussed as separate threads. But in modern scalable architectures, I think they are better seen as complementary constraints: a model that learns quickly at test time also benefits from a mechanism for deciding what not to forget. In our #ECCV2026 paper, we study this in large-scale 4D reconstruction: how to build fast spatial memory that can adapt over long observation streams while reducing collapse and forgetting. Instead of using fully plastic test-time updates, we stabilize fast-weight adaptation with an elastic prior that balances adaptation and memory. Key ideas: - Elastic Test-Time Training: Fisher-weighted consolidation for fast-weight updates - EMA anchor weights that provide a moving reference for stability - Chunk-by-chunk inference for long 3D/4D observation streams We show that this scales across large 3D/4D pretraining settings, including both LRM-style and LVSM-style models, and improves reconstruction across benchmarks including Stereo4D, NVIDIA, and DL3DV-140. We release model checkpoints across different design choices: resolution, post-training curriculum, and whether the model uses an explicit 4DGS intermediate representation. - Homepage: - Paper: - Code: - Models: This work is co-led with Xueyang Yu, contributed by Haoyu Zhen Yuncong Yang, and advised by Michigan SLED Lab Chuang Gan.

Martin Ziqiao Ma

33,847 görüntüleme • 2 ay önce

Folks, right now, 25 of the largest pharmaceutical companies in America control 70% of the market. This lack of competition drives up prices – making it harder for hardworking American families to access the health care they need. Today, my Administration is doing something about it. Let me explain: Hundreds of billions of taxpayer dollars are spent on research, discovery, and development of new prescription drugs. And while I firmly believe that the strength of a nation can be measured by the boldness of its science, the quality of its research, and the progress it helps bring forth, I also believe that the folks who paid for the research – you – ought to be able to access and afford the final product. That's why my Administration is proposing that if a drug made using taxpayer funds is not reasonably available to Americans, the government reserves the right to "march in" and license that drug to another manufacturer who could sell it for less. This is an important step toward ending Big Pharma price gouging. It's good for competition. It's good for our economy. And it's good for the millions of Americans who can't afford their medications – who know all too well that fine line between dignity and dependence that the price of a prescription drug can draw.

Folks, right now, 25 of the largest pharmaceutical companies in America control 70% of the market. This lack of competition drives up prices – making it harder for hardworking American families to access the health care they need. Today, my Administration is doing something about it. Let me explain: Hundreds of billions of taxpayer dollars are spent on research, discovery, and development of new prescription drugs. And while I firmly believe that the strength of a nation can be measured by the boldness of its science, the quality of its research, and the progress it helps bring forth, I also believe that the folks who paid for the research – you – ought to be able to access and afford the final product. That's why my Administration is proposing that if a drug made using taxpayer funds is not reasonably available to Americans, the government reserves the right to "march in" and license that drug to another manufacturer who could sell it for less. This is an important step toward ending Big Pharma price gouging. It's good for competition. It's good for our economy. And it's good for the millions of Americans who can't afford their medications – who know all too well that fine line between dignity and dependence that the price of a prescription drug can draw.

President Biden Archived

1,851,715 görüntüleme • 2 yıl önce

BREAKING: President Donald J. Trump just signed an executive order implementing Most Favored Nation prescription drug pricing — which will deliver dramatically lower drug prices for the American people. Here's what it does: REDUCING DRUG PRICES FOR AMERICANS AND TAXPAYERS: Today, President Donald J. Trump signed an Executive Order to bring the prices Americans and taxpayers pay for prescription drugs in line with those paid by similar nations. — The Order directs the U.S. Trade Representative and Secretary of Commerce to take action to ensure foreign countries are not engaged in practices that purposefully and unfairly undercut market prices and drive price hikes in the United States. — The Order instructs the Administration to communicate price targets to pharmaceutical manufacturers to establish that America, the largest purchaser and funder of prescription drugs in the world, gets the best deal. — The Secretary of Health and Human Services will establish a mechanism through which American patients can buy their drugs directly from manufacturers who sell to Americans at a “Most-Favored-Nation” price, bypassing middlemen. — If drug manufacturers fail to offer most-favored-nation pricing, the Order directs the Secretary of Health and Human Services to: (1) propose rules that impose most-favored-nation pricing; and (2) take other aggressive measures to significantly reduce the cost of prescription drugs to the American consumer and end anticompetitive practices. GETTING A BETTER DEAL FOR AMERICANS: President Trump is once again taking action to keep pharmaceutical manufacturers from charging Americans high drug prices while giving steep discounts to other wealthy nations. — According to recent data, the prices Americans pay for brand-name drugs are more than three times the price other OECD nations pay, even after accounting for discounts manufacturers provide in the U.S. — The United States has less than five percent of the world’s population, yet funds roughly 75% of global pharmaceutical profits. — Drug manufacturers discount their products to gain access to foreign markets and then subsidize those discounts through high prices charged in America—in essence, Americans are subsidizing drug-manufacturer profits and foreign health systems, despite drug manufacturers benefiting from generous research subsidies and enormous healthcare spending by the U.S. Government. — In his first term, President Trump took historic action to keep Medicare and seniors from paying more for drugs than economically comparable countries, which the Biden Administration rescinded before it could take effect. — Instead of fixing this problem, the Biden Administration’s greatest achievement was to negotiate prices that were, on average, 78 percent higher than in 11 comparable countries as part of Biden’s effort to “beat Medicare.” DELIVERING ON PROMISES TO PUT AMERICAN PATIENTS FIRST: President Trump is delivering on his promise to once again put America first by furthering efforts to get American patients and taxpayers a fair deal for prescription drugs. — This Order builds on actions from President Trump’s first term to make progress on reducing price disparities at home and expands those efforts by including Medicaid in addition to Medicare. — President Trump recently signed an Executive Order to take additional action to lower drug prices, including by providing massive discounts to low-income patients for lifesaving medicines, facilitating importation programs, and increasing the availability of generic and biosimilar medicines. — President Trump is also working to make drug prices radically transparent, as he recently signed an Executive Order to build on his historic price transparency efforts undertaken during his first term. — President Trump has been relentless in his effort to address the unfair and outrageous prices Americans pay for prescription drugs: — President Trump: “In case after case, our citizens pay massively higher prices than other nations pay for the same exact pill, from the same factory, effectively subsidizing socialism aboard [abroad] with skyrocketing prices at home. So we would spend tremendous amounts of money in order to provide inexpensive drugs to another country. And when I say the price is different, you can see some examples where the price is beyond anything — four times, five times different.”

Rapid Response 47

611,256 görüntüleme • 1 yıl önce

$🚀 A better, faster co-folding-based binding affinity model. Predicting how tightly a drug candidate binds to its target is critical in drug discovery. It also requires massive computational resources. State-of-the-art models can take 20 seconds to a minute per prediction, impractical for the demands of large scale early-stage programs . 💠 Today, Recursion’s Valence Labs is releasing Nesso-1: the fastest open-source co-folding-based binding affinity model available. At 1 second per prediction, it’s roughly 20x faster than our previous collaboration on Boltz-2 while matching or surpassing its accuracy across public and internal benchmarks. By leveraging NVIDIA Healthcare cuEquivariance, we’ve been able to further accelerate both training and inference by an additional 2-3x. We look forward to continuing to improve Nesso-1 in collaboration with NVIDIA. Weights and code are fully open-sourced. The core architectural ideas behind Nesso-1 build on the insight that coarse-grained co-folding representations can match full-atom models for affinity prediction at a fraction of the cost. Nesso-1 is the first open implementation of this approach with no proprietary dependencies, trained entirely on public data, built to be reproducible and extensible. We’re already using Nesso-1 internally in active drug discovery programs. Fast, reliable affinity prediction at scale is foundational to the kind of autonomous design loops that define our vision for Autonomous Precision Design and Nesso-1 is a meaningful step toward that. 👉 Report: 👉 Github: 👉 HF:$

🚀 A better, faster co-folding-based binding affinity model. Predicting how tightly a drug candidate binds to its target is critical in drug discovery. It also requires massive computational resources. State-of-the-art models can take 20 seconds to a minute per prediction, impractical for the demands of large scale early-stage programs . 💠 Today, Recursion’s Valence Labs is releasing Nesso-1: the fastest open-source co-folding-based binding affinity model available. At 1 second per prediction, it’s roughly 20x faster than our previous collaboration on Boltz-2 while matching or surpassing its accuracy across public and internal benchmarks. By leveraging NVIDIA Healthcare cuEquivariance, we’ve been able to further accelerate both training and inference by an additional 2-3x. We look forward to continuing to improve Nesso-1 in collaboration with NVIDIA. Weights and code are fully open-sourced. The core architectural ideas behind Nesso-1 build on the insight that coarse-grained co-folding representations can match full-atom models for affinity prediction at a fraction of the cost. Nesso-1 is the first open implementation of this approach with no proprietary dependencies, trained entirely on public data, built to be reproducible and extensible. We’re already using Nesso-1 internally in active drug discovery programs. Fast, reliable affinity prediction at scale is foundational to the kind of autonomous design loops that define our vision for Autonomous Precision Design and Nesso-1 is a meaningful step toward that. 👉 Report: 👉 Github: 👉 HF:

Recursion

156,768 görüntüleme • 1 ay önce

Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

MrNeRF

27,428 görüntüleme • 1 yıl önce

Growing Sl8 Means Growing Its Community Our work on expanding Sl8 never stops. One of the key priorities for Cassator is continuously bringing new users and new creators into the ecosystem - not through isolated campaigns, but by building long-term relationships with influencers, communities, and partners across different markets. Today, Sl8 is already supported by a growing number of influencers from different countries and across multiple social platforms. This gives us a strong foundation to keep expanding our reach, strengthening awareness around the platform, and introducing Sl8 to new audiences. One of the markets currently in active development is the GCC region, where we are now significantly scaling our presence. We are preparing a large-scale creator campaign focused on bringing new audiences into Sl8, and 48 influencers have already agreed to work with us as part of this expansion. At the same time, negotiations continue with a much broader pool of creators across the region. Our approach is to enter each new market through relevant local creators and communities, building awareness around Sl8 and converting that attention into a growing, engaged user base. The expansion process continues to gain momentum, with new creators, partnerships, and markets joining the Sl8 ecosystem step by step.

Growing Sl8 Means Growing Its Community Our work on expanding Sl8 never stops. One of the key priorities for Cassator is continuously bringing new users and new creators into the ecosystem - not through isolated campaigns, but by building long-term relationships with influencers, communities, and partners across different markets. Today, Sl8 is already supported by a growing number of influencers from different countries and across multiple social platforms. This gives us a strong foundation to keep expanding our reach, strengthening awareness around the platform, and introducing Sl8 to new audiences. One of the markets currently in active development is the GCC region, where we are now significantly scaling our presence. We are preparing a large-scale creator campaign focused on bringing new audiences into Sl8, and 48 influencers have already agreed to work with us as part of this expansion. At the same time, negotiations continue with a much broader pool of creators across the region. Our approach is to enter each new market through relevant local creators and communities, building awareness around Sl8 and converting that attention into a growing, engaged user base. The expansion process continues to gain momentum, with new creators, partnerships, and markets joining the Sl8 ecosystem step by step.

Cassator Corp.

22,096 görüntüleme • 18 gün önce

over the weekend, i built an app that i sincerely hope you will never have a need for, but if you do happen to need a friendly, free, private mri viewer designed to make it easy for you to track tumor progression, you can try it here: here's the story: as some of you may know, last last september, my six year old daughter mira was diagnosed with an extremely rare brain tumor called an adamatinomatous craniopharyngioma, and since then our family has been doing everything we can possibly do to find a cure for her. we tortured chatgpt deep research, put together our own private research team, raised $1.4M and donated it all to Hankinson-Mitra Lab research thanks to $MIRA, explored every remotely applicable drug whether on the market or not, and even began working with md anderson to develop a personalized vaccine that we hope can lead to a more permanent cure unfortunately, we received the devastating news last march that the tumor has continued to grow since her initial surgery, and we had to start to consider more drastic options which would have seriously impacted mira’s quality of life. thankfully, with the help of dr. sabine mueller UCSF Benioff SF and the Hankinson-Mitra Lab at the university of colorado, in april, we started her on an alternative but extremely experimental treatment for this disease. to our unimaginable relief, her tumor has responded extraordinarily well to this treatment which combines tocilizumab (an arthritis drug that blocks IL6 receptors) with avastin (a colon cancer drug that inhibhits VEGF proteins). we know this, because mira gets an MRI scan of her brain every few months. and every time we get a new scan, the first thing we do is compare it against her last scan. so we have to find the matching weight of the scan, and then find the same plane, and then carefully find the slices of the scan where the tumor is visible, and then find the closest match to last month’s scan, then adjust the zoom, rotation, and brightness / contrast so they all look the same. we got pretty good at this. but it shouldn't be this hard. so i built last weekend using gemini 3 with some gpt 5.2 xhigh. you just import your DICOM MRI files (either zip, files or a folder), and you can align all of your scans across multiple dates instantly, just click and drag a rectangle around the tumor on any image, and it will use some very clever algorithms to automatically align up and find the closest matching slice from all your other scans, match the brightness / contrast, rotation, pan, zoom, and even shear to make sure the registration is as close as possible, and make it as easy to possible to compare tumor progression. it has a grid view so you can see all your scans for the same location all at once, and an overlay view so you can quickly compare two scans visually (by holding down the space bar to toggle quickly between two scans), along with tools to animate your scans both within the same sequence as well as over multiple scans to show progression. there is no server, it runs entirely locally on your browser - nothing ever gets uploaded and it's all open source: if you've found this useful, please consider a donation to the UCSF Benioff SF hospital foundation, who has given us extraordinary care over the past year or so:

over the weekend, i built an app that i sincerely hope you will never have a need for, but if you do happen to need a friendly, free, private mri viewer designed to make it easy for you to track tumor progression, you can try it here: here's the story: as some of you may know, last last september, my six year old daughter mira was diagnosed with an extremely rare brain tumor called an adamatinomatous craniopharyngioma, and since then our family has been doing everything we can possibly do to find a cure for her. we tortured chatgpt deep research, put together our own private research team, raised $1.4M and donated it all to Hankinson-Mitra Lab research thanks to $MIRA, explored every remotely applicable drug whether on the market or not, and even began working with md anderson to develop a personalized vaccine that we hope can lead to a more permanent cure unfortunately, we received the devastating news last march that the tumor has continued to grow since her initial surgery, and we had to start to consider more drastic options which would have seriously impacted mira’s quality of life. thankfully, with the help of dr. sabine mueller UCSF Benioff SF and the Hankinson-Mitra Lab at the university of colorado, in april, we started her on an alternative but extremely experimental treatment for this disease. to our unimaginable relief, her tumor has responded extraordinarily well to this treatment which combines tocilizumab (an arthritis drug that blocks IL6 receptors) with avastin (a colon cancer drug that inhibhits VEGF proteins). we know this, because mira gets an MRI scan of her brain every few months. and every time we get a new scan, the first thing we do is compare it against her last scan. so we have to find the matching weight of the scan, and then find the same plane, and then carefully find the slices of the scan where the tumor is visible, and then find the closest match to last month’s scan, then adjust the zoom, rotation, and brightness / contrast so they all look the same. we got pretty good at this. but it shouldn't be this hard. so i built last weekend using gemini 3 with some gpt 5.2 xhigh. you just import your DICOM MRI files (either zip, files or a folder), and you can align all of your scans across multiple dates instantly, just click and drag a rectangle around the tumor on any image, and it will use some very clever algorithms to automatically align up and find the closest matching slice from all your other scans, match the brightness / contrast, rotation, pan, zoom, and even shear to make sure the registration is as close as possible, and make it as easy to possible to compare tumor progression. it has a grid view so you can see all your scans for the same location all at once, and an overlay view so you can quickly compare two scans visually (by holding down the space bar to toggle quickly between two scans), along with tools to animate your scans both within the same sequence as well as over multiple scans to show progression. there is no server, it runs entirely locally on your browser - nothing ever gets uploaded and it's all open source: if you've found this useful, please consider a donation to the UCSF Benioff SF hospital foundation, who has given us extraordinary care over the past year or so:

Siqi Chen

209,022 görüntüleme • 7 ay önce

Solidus Ai Tech Announcement As we approach the end of the year, we want to share a clear and considered update with our community. The final weeks of the year are shaped by holiday periods, Christmas breaks, and New Year’s downtime. Across the industry, attention drops, teams are offline, and meaningful engagement slows. Launching major products during this window would limit visibility, momentum, and adoption. After internal alignment across the entire team, we have collectively agreed to prioritize impact over timing. We are fully aligned on our goals, our roadmap, and the long-term direction of Solidus AI Tech. 2025 has been a challenging year for many. High volatility driven by geopolitical issues, increased institutional control over markets, and liquidity being pulled from altcoins have tested builders and communities alike. Through it all, our focus has remained unchanged: building real infrastructure, real products, and a sustainable ecosystem. For this reason, our upcoming products, the Compute Marketplace, Agent Forge 2.0, and Vision Makers, are now scheduled for release in early 2026, at a time when attention, participation, and momentum are back at full strength. This ensures each release receives the focus, usage, and traction it deserves. We would like to take this opportunity to wish everyone a Merry Christmas and a happy holiday season. Thank you for your continued support, patience, and belief in what we are building. Further updates will be shared in due course.

Solidus Ai Tech Announcement As we approach the end of the year, we want to share a clear and considered update with our community. The final weeks of the year are shaped by holiday periods, Christmas breaks, and New Year’s downtime. Across the industry, attention drops, teams are offline, and meaningful engagement slows. Launching major products during this window would limit visibility, momentum, and adoption. After internal alignment across the entire team, we have collectively agreed to prioritize impact over timing. We are fully aligned on our goals, our roadmap, and the long-term direction of Solidus AI Tech. 2025 has been a challenging year for many. High volatility driven by geopolitical issues, increased institutional control over markets, and liquidity being pulled from altcoins have tested builders and communities alike. Through it all, our focus has remained unchanged: building real infrastructure, real products, and a sustainable ecosystem. For this reason, our upcoming products, the Compute Marketplace, Agent Forge 2.0, and Vision Makers, are now scheduled for release in early 2026, at a time when attention, participation, and momentum are back at full strength. This ensures each release receives the focus, usage, and traction it deserves. We would like to take this opportunity to wish everyone a Merry Christmas and a happy holiday season. Thank you for your continued support, patience, and belief in what we are building. Further updates will be shared in due course.

AITECH CLOUD NETWORK

51,425 görüntüleme • 8 ay önce

I was eleven years old. My mother was taking me to an eye specialist when we were stopped by a patrol of the IRGC at this very spot. A few strands of my hair were visible, and I had grown too tall for my trousers. Again and again she pleaded with them not to take me, explaining that I was tall for my age, that she had just returned from the front lines and had not yet found the time to buy me new clothes. I was frightened, but my mother's fear was far greater than my own. Many years have passed since that afternoon. Yet when I see this place of cruelty and its power holders reduced to dust, I am overcome with emotion and an unexpected, trembling relief. To witness a regime of terror crumbling into ruin feels like learning to walk after long paralysis, like unfurling wings and discovering flight, like water offered at last to one who has wandered for hours through the Sahara. I feel a release, a memory long buried in the body beginning at last to loosen. In this moment, the child I once was seems to breathe again.

I was eleven years old. My mother was taking me to an eye specialist when we were stopped by a patrol of the IRGC at this very spot. A few strands of my hair were visible, and I had grown too tall for my trousers. Again and again she pleaded with them not to take me, explaining that I was tall for my age, that she had just returned from the front lines and had not yet found the time to buy me new clothes. I was frightened, but my mother's fear was far greater than my own. Many years have passed since that afternoon. Yet when I see this place of cruelty and its power holders reduced to dust, I am overcome with emotion and an unexpected, trembling relief. To witness a regime of terror crumbling into ruin feels like learning to walk after long paralysis, like unfurling wings and discovering flight, like water offered at last to one who has wandered for hours through the Sahara. I feel a release, a memory long buried in the body beginning at last to loosen. In this moment, the child I once was seems to breathe again.

Sheema Kalbasi شیما کلباسی

1,185,304 görüntüleme • 6 ay önce