The easy answer is that truly accurate calorie expenditure can't be obtained from your wrist. As smartwatches are unable to measure your metabolism, they have to analyze data such as heart rate measurements or the distance you've covered during a run before they can make a ballpark guess at how many calories you've burned. Whereas fitness wearables can directly measure your heart rate from your wrist, energy expenditure has to be calculated through proxy methods, such as machine learning algorithms. Anna Shcherbina, an assistant professor who also worked on the Stanford University study mentioned above, partially blames the algorithms wearables are using to assess calorie output. "My take on this is that it's very hard to train an algorithm that would be accurate across a wide variety of people because energy expenditure is variable based on someone's fitness level, height and weight, etc," stated Shcherbina.

What happens if you eat too many gummy vitamins? More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. They're awfully yummy, but are they good for you? Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Feeding skunk kits is a careful art involving 15 cups of fruit and veggies, a dozen eggs, and 15 cups of dog food, and more food in a single day. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Please sir, may I have some more? Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Can you eat too much sugar free candy? But it'll probably just send you running to the bathroom. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Sugar-free candy generally replaces sugar with either artificial sweeteners or sugar alcohols. Breakthroughs, discoveries, and DIY tips sent six days a week.

Children under 16 will be banned from buying high-caffeine energy drinks in England from April, the government has said. Drinks containing more than 150mg of caffeine per litre will be illegal to sell to children and younger teenagers in shops, restaurants, cafes, vending machines and online. Lower-caffeine soft drinks, such Diet Coke, are not affected and neither are tea and coffee. However, drinks including Red Bull, Monster, Relentless and Prime would all breach the limit. The new rules aim to reduce obesity levels in children and prevent issues such as disrupted sleep, increased anxiety and lack of concentration, as well as poor school results.

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Plus: The US House has passed new youth online safety legislation. AI agents are not your "coworkers" Imagine coming in to work to learn that a new underling will report to you. The worker is not a person but an AI tool--one that your company nonetheless calls Alex, an "employee" with a title and defined responsibilities. How well do you think you would work with Alex? If you're anything like the managers studied by Boston University professor Emma Wiles, treating that AI as a coworker would lead you to do a worse job. They caught 18% fewer errors when the work was attributed to an agentic AI employee rather than a chatbot. This is an alarming glimpse of the future Silicon Valley is hurling us toward.

Although Large Language Models (LLMs) have demonstrated remarkable progress, their proficiency in graph-related tasks remains notably limited, hindering the development of truly general-purpose models. Previous attempts, including pretraining graph foundation models or employing supervised fine-tuning, often face challenges such as the scarcity of large-scale, universally represented graph data. We introduce G1, a simple yet effective approach demonstrating that Reinforcement Learning (RL) on synthetic graph-theoretic tasks can significantly scale LLMs' graph reasoning abilities. To enable RL training, we curate Erdős, the largest graph reasoning dataset to date, comprising 50 diverse graph-theoretic tasks of varying difficulty levels, 100k training data and 5k test data, all drived from real-world graphs.

Protein-protein interactions (PPIs) are governed by surface complementarity and hydrophobic interactions at protein interfaces. However, designing diverse and physically realistic protein structure and surfaces that precisely complement target receptors remains a significant challenge in computational protein design. In this work, we introduce PepBridge, a novel framework for the joint design of protein surface and structure that seamlessly integrates receptor surface geometry and biochemical properties. Starting with a receptor surface represented as a 3D point cloud, PepBridge generates complete protein structures through a multi-step process. First, it employs denoising diffusion bridge models (DDBMs) to map receptor surfaces to ligand surfaces. Next, a multi-model diffusion model predicts the corresponding structure, while Shape-Frame Matching Networks ensure alignment between surface geometry and backbone architecture. This integrated approach facilitates surface complementarity, conformational stability, and chemical feasibility. Extensive validation across diverse protein design scenarios demonstrates PepBridge's efficacy in generating structurally viable proteins, representing a significant advancement in the joint design of top-down protein structure.