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Can weight loss leave a lasting imprint on our fat cells?

Losing weight is often touted as a cornerstone of better health, particularly for people dealing with obesity and its associated health risks.

Anyone who has ever tried to get rid of a few extra kilos knows the frustration: the weight drops initially, only to be back within a matter of weeks—the yo-yo effect has struck. Researchers at ETH Zurich have now been able to show that this is all down to epigenetics.

Epigenetics is the part of genetics that’s based not on the sequence of genetic , but on small yet characteristic chemical markers on these building blocks. The sequence of building blocks has evolved over a long period of time; we all inherit them from our parents.

Epigenetic markers, on the other hand, are more dynamic: , our and the condition of our body—such as obesity—can change them over the course of a lifetime. But they can remain stable for many years, sometimes decades, and during this time, they play a key role in determining which genes are active in our cells and which are not.

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Using data and samples from volunteers, including Kaiser Permanente Washington members participating in the Adult Changes in Thought Study (ACT Study), the researchers used advanced genomic technologies and machine learning models to create a timeline of the cellular and molecular changes caused by…

Mapping the disease at the cellular level identifies possible new treatment targets.

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Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have used machine learning and supercomputer simulations to investigate how tiny gold nanoparticles bind to blood proteins. The studies discovered that favorable nanoparticle-protein interactions can be predicted from machine learning models that are trained from atom-scale molecular dynamics simulations. The new methodology opens ways to simulate the efficacy of gold nanoparticles as targeted drug delivery systems in precision nanomedicine.

Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a largely unexplored field of research, with the potential for novel applications in bioimaging, biosensing, and nanomedicine. Developing such applications relies critically on understanding the dynamical properties of the nano–bio interface.

Modeling the properties of the nano-bio interface is demanding since the important processes such as electronic charge transfer, or restructuring of the biomolecule surface can take place in a wide range of length and time scales, and the atomistic simulations need to be run in the appropriate aqueous environment.

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We are now more connected than ever, but also more lonely. Could AI companionship be the cure? In this episode, Emily Chang explores the future tech behind a growing market of relationships-on-demand.

Technology that once seemed like science fiction is rapidly becoming reality, transforming the very essence of our existence. In this four-part series, Emily Chang unravels the future of being human in an age of unprecedented innovation.

Watch more of Posthuman with Emily Chang: • Posthuman with Emily Chang.

#Tech #AI
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#AI #ExponentialTechnologies

The book “Intelciety. Intelligent Society. Are We Ready for the Challenge?” explores the profound changes that artificial intelligence (AI) and other emerging technologies are causing in modern society. Vicente Ferreira da Silva addresses how these technologies are transforming various fields, from medicine and biotechnology to robotics and nanotechnology, and questions whether we are truly prepared to deal with these advances.

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Called Evo, the AI was inspired by the large language models, or LLMs, underlying popular chatbots such as OpenAI’s ChatGPT and Anthropic’s Claude. These models have taken the world by storm for their prowess at generating human-like responses. From simple tasks, such as defining an obtuse word, to summarizing scientific papers or spewing verses fit for a rap battle, LLMs have entered our everyday lives.

If LLMs can master written languages—could they do the same for the language of life?

This month, a team from Stanford University and the Arc Institute put the theory to the test. Rather than training Evo on content scraped from the internet, they trained the AI on nearly three million genomes—amounting to billions of lines of genetic code—from various microbes and bacteria-infecting viruses.

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