By combining bioorthogonal metabolic labelling and resolution enhancement through sequential imaging of DNA barcodes, the molecular organization of individual sugars in the native glycocalyx has been resolved at a spatial resolution of 9 ångström.
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Tesla AI5 & AI6 Chips “Compressing Reality”?! What Did Elon See?!
Elon Musk has revealed Tesla’s new AI chips, AI5 and AI6, which will drive the company’s shift towards AI-powered services, enabling significant advancements in Full Self-Driving capabilities and potentially revolutionizing the self-driving car industry and beyond.
## Questions to inspire discussion.
Tesla’s AI Chip Advancements.
🚀 Q: What are the key features of Tesla’s AI5 and AI6 chips? A: Tesla’s AI5 and AI6 chips are inference-first, designed for high-throughput and efficient processing of AI models on devices like autos, Optimus, and Grok voice agents, being 40x faster than previous models.
💻 Q: How do Tesla’s AI5 and AI6 chips compare to previous models? A: Tesla’s AI5 chip is a 40x improvement over AI4, with 500 TOPS expanding to 5,000 TOPS, enabling excellent performance in full self-driving and Optimus humanoid robots.
🧠 Q: What is the significance of softmax in Tesla’s AI5 chip? A: AI5 is designed to run softmax natively in a few steps, unlike AI4 which relies on CPU and runs softmax in 40 steps in emulation mode.
AI to integrate bulk multi-omics data for precision oncology
“Cancer and other complex diseases arise from the interplay of various biological factors, for example, at the DNA, RNA, and protein levels,” explains the author. Characteristic changes at these levels — such as the amount of HER2 protein produced in breast or stomach cancer — are often recorded, but typically not yet analyzed in conjunction with all other therapy-relevant factors.
This is where Flexynesis comes in. “Comparable tools so far have often been either difficult to use, or only useful for answering certain questions,” says the author. “Flexynesis, by contrast, can answer various medical questions at the same time: for example, what type of cancer is involved, what drugs are particularly effective in this case, and how these will affect the patient’s chances of survival.” The tool also helps identify suitable biomarkers for diagnosis and prognosis, or — if metastases of unknown origin are discovered — to identify the primary tumor. “This makes it easier to develop comprehensive and personalized treatment strategies for all kinds of cancer patients,” says the author.
Nearly 50 new cancer therapies are approved every year. This is good news. “But for patients and their treating physicians, it is becoming increasingly difficult to keep track and to select the treatment methods from which the people affected — each with their very individual tumor characteristics — will benefit the most,” says the senior author. The researcher has been working for some time on developing tools that use artificial intelligence to make more precise diagnoses and that also determine the best form of therapy tailored to individual patients.
The team has now developed a toolkit called Flexynesis, which does not rely solely on classical machine learning but also uses deep learning to evaluate very different types of data simultaneously — for example, multi-omics data as well as specially processed texts and images, such as CT or MRI scans. “In this way, it enables doctors to make better diagnoses, prognoses, and develop more precise treatment strategies for their patients,” says the author. Flexynesis is described in detail in a paper published in “Nature Communications.”
“We are running multiple translational projects with medical doctors who want to identify biomarkers from multi-omics data that align with disease outcomes,” says the first and co-corresponding author of the publication. “Although many deep-learning based methods have been published for this purpose, most have turned out to be inflexible, tied to specific modeling tasks, or difficult to install and reuse. That gap motivated us to build Flexynesis as a proper toolkit, which is flexible for different modeling tasks and packaged on PyPI, Guix, Docker, Bioconda, and Galaxy, so others can readily apply it in their own pipelines.”
Higher HRV, Lower RHR: 2,577 Days Of Tracking
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NASA finds Titan’s alien lakes may be creating primitive cells
Saturn’s moon Titan may be more alive with possibilities than we thought. New NASA research suggests that in Titan’s freezing methane and ethane lakes, simple molecules could naturally arrange themselves into vesicles—tiny bubble-like structures that mimic the first steps toward life. These compartments, born from splashing droplets and complex chemistry in Titan’s atmosphere, could act like primitive cell walls.
NASA research has shown that cell-like compartments called vesicles could form naturally in the lakes of Saturn’s moon Titan.
Titan is the only world apart from Earth that is known to have liquid on its surface. However, Titan’s lakes and seas are not filled with water. Instead, they contain liquid hydrocarbons like ethane and methane.
Machine learning for materials discovery and optimization
This Collection supports and amplifies research related to SDG 9 — Industry, Innovation & Infrastructure.
Discovering new materials with customizable and optimized properties, driven either by specific application needs or by fundamental scientific interest, is a primary goal of materials science. Conventionally, the search for new materials is a lengthy and expensive manual process, frequently based on trial and error, requiring the synthesis and characterization of many compositions before a desired material can be found. In recent years this process has been greatly improved by a combination of artificial intelligence and high-throughput approaches. Advances in machine learning for materials science, data-driven materials prediction, autonomous synthesis and characterization, and data-guided high-throughput exploration, can now significantly accelerate materials discovery.
This Collection brings together the latest computational and experimental advances in artificial intelligence, machine learning and data-driven approaches to accelerate high-throughput prediction, synthesis, characterization, optimization, discovery, and understanding of new materials.
Cannabis use associated with quadrupled risk of developing type 2 diabetes, finds study of over 4 million adults
Cannabis use is linked to an almost quadrupling in the risk of developing diabetes, according to an analysis of real-world data from over 4 million adults, being presented at the Annual Meeting of the European Association for the Study of Diabetes (EASD) held in Vienna, Austria (15–19 Sept).
Cannabis use is increasing globally with an estimated 219 million users (4.3% of the global adult population) in 2021, but its long-term metabolic effects remain unknown. While some studies have suggested potential anti-inflammatory or weight management properties, others have raised concerns regarding glucose metabolism and insulin resistance, and the magnitude of the risk of developing diabetes hasn’t been clear.
To strengthen the evidence base, Dr. Ibrahim Kamel from the Boston Medical Center, Massachusetts, U.S. and colleagues analyzed electronic health records from 54 health care organizations (TriNetX Research Network, with centers from across U.S. and Europe) to identify 96,795 outpatients (aged between 18 and 50 years, 52.5% female) with cannabis-related diagnoses (ranging from occasional use to dependence, including cases of intoxication and withdrawal) between 2010 and 2018.
