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Scientists have long been fascinated with the physiological changes that birds undergo before and during migration. Some birds eat so much fat before their journeys that they double in body weight. In some species, their hearts are enlarged to pump more blood, or their digestive tracts grow and then shrink. But researchers have only recently started to explore at a fundamental level how migratory birds get the energy required to keep themselves aloft for days on end without eating.

Last year, two independent groups published research that explored migratory bird physiology in the lab and field to probe what happens at the subcellular level that allows birds to cover vast distances. They both found answers in biology’s most fundamental engine: mitochondria.

Their studies show how small changes in the number, shape, efficiency and interconnectedness of mitochondria can have huge physiological consequences that contribute to birds’ long-duration, continent-spanning flights.

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Can AI speed up aspects of the scientific process? Microsoft appears to think so.

At the company’s Build 2025 conference on Monday, Microsoft announced Microsoft Discovery, a platform that taps agentic AI to “transform the [scientific] discovery process,” according to a press release provided to TechCrunch. Microsoft Discovery is “extensible,” Microsoft says, and can handle certain science-related workloads “end-to-end.”

“Microsoft Discovery is an enterprise agentic platform that helps accelerate research and discovery by transforming the entire discovery process with agentic AI — from scientific knowledge reasoning to hypothesis formulation, candidate generation, and simulation and analysis,” explains Microsoft in its release. “The platform enables scientists and researchers to collaborate with a team of specialized AI agents to help drive scientific outcomes with speed, scale, and accuracy using the latest innovations in AI and supercomputing.”

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Using global land use and carbon storage data from the past 175 years, researchers at The University of Texas at Austin and Cognizant AI Labs have trained an artificial intelligence system to develop optimal environmental policy solutions that can advance global sustainability initiatives of the United Nations.

The AI tool effectively balances various complex trade-offs to recommend ways of maximizing carbon storage, minimizing economic disruptions and helping improve the environment and people’s everyday lives, according to a paper published today in the journal Environmental Data Science.

The project is among the first applications of the UN-backed Project Resilience, a team of scientists and experts working to tackle global decision-augmentation problems—including ambitious sustainable development goals this decade—through part of a broader effort called AI for Good.

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University of New Mexico researchers studying the health risks posed by gadolinium, a toxic rare earth metal used in MRI scans, have found that oxalic acid, a molecule found in many foods, can generate nanoparticles of the metal in human tissues.

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Diabetes has no well-established cure; thus, its management is critical for avoiding severe health complications involving multiple organs. This requires frequent glycaemia monitoring, and the gold standards for this are fingerstick tests. During the last decades, several blood-withdrawal-free platforms have been being studied to replace this test and to improve significantly the quality of life of people with diabetes (PWD). Devices estimating glycaemia level targeting blood or biofluids such as tears, saliva, breath and sweat, are gaining attention; however, most are not reliable, user-friendly and/or cheap. Given the complexity of the topic and the rise of diabetes, a careful analysis is essential to track scientific and industrial progresses in developing diabetes management systems. Here, we summarize the emerging blood glucose level (BGL) measurement methods and report some examples of devices which have been under development in the last decades, discussing the reasons for them not reaching the market or not being really non-invasive and continuous. After discussing more in depth the history of Raman spectroscopy-based researches and devices for BGL measurements, we will examine if this technique could have the potential for the development of a user-friendly, miniaturized, non-invasive and continuous blood glucose-monitoring device, which can operate reliably, without inter-patient variability, over sustained periods.

Diabetes is a lifelong disease that affects more than 400 millions of people worldwide (WHO. Diabetes, 2022). Emerging reports from the International Diabetes Federation state that diabetes is set to rise very fast, estimating 700 millions of cases in the next 25 years (IDF Diabetes Atlas, 2019). Among the various types of diabetes, all characterized by high blood glucose levels, the main two types are type 1 diabetes, an autoimmune condition where the pancreas produces little or no insulin, and type 2 diabetes, a metabolic disorder that results in hyperglycaemia due to insulin resistance. Diabetes, and related risk factors such as microvascular (retinopathy, nephropathy, and neuropathy) and macrovascular metabolic disorders, is so widespread that it has been defined “the epidemic of the century” (Kharroubi, 2015).

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Earlier this month, Aurora Innovation kicked off driverless truck operations in Texas, starting off with a freight route between Dallas and Houston for commercial customers. The SAE Level 4 trucks, operating without a safety driver in the cab, have been making the 250-mile route that has been the focus of quite a bit of testing by several autonomous truck developers, many of which have been getting driverless truck infrastructure ready.

Getting to this point took years of research and plenty of on-road testing, in environments open and closed to regular traffic, with Aurora Innovation achieving a successful round of validation testing. In fact, years of supervised testing by Aurora has already seen 10,000 customer loads delivered by its prototypes, spanning some 3 million miles.

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