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An artificial pancreas originally developed at the University of Virginia Center for Diabetes Technology improves blood sugar control in children ages 2 to 6 with type 1 diabetes, according to a new study. Details of the clinical study and its findings have been published in the New England Journal of Medicine.

Trial participants using the artificial pancreas spent approximately three more hours per day in their target blood sugar range compared with participants in a who continued relying on the methods they were already using to manage their .

The Control-IQ system, manufactured by Tandem Diabetes Care, is a diabetes management device that automatically monitors and regulates . The artificial pancreas has an insulin pump that uses advanced control algorithms based on the person’s glucose monitoring information to adjust the insulin dose as needed.

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In a universe with more than a hundred billion billion planets, why have we only found life on one? DEAD SPACE offers a terrifying reason why: gigantic “Brethren Moons” made of meat with an unrelenting hunger for biomass.

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Generative artificial intelligence (AI) systems will inspire an explosion of creativity in the music industry and beyond, according to the University of Surrey researchers who are inviting the public to test out their new text-to-audio model.

AudioLDM is a new AI-based system from Surrey that allows users to submit a text prompt, which is then used to generate a corresponding audio clip. The system can process prompts and deliver clips using less than current AI systems without compromising or the users’ ability to manipulate clips.

The is able to try out AudioLDM by visiting its Hugging Face space. Their code is also open-sourced on GitHub with 1000+ stars.

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A multi-state study from the U.S. Center for Disease Control and Prevention’s (CDC) VISION Network has found that first-generation COVID-19 mRNA vaccines were associated with protection against COVID-19 during periods of omicron BA.4/BA.5 predominance.

The new analysis found that mRNA vaccines were protective against COVID-19-associated hospitalization and ICU admission or in-hospital death and noted less during BA.4/BA.5 predominance compared to earlier omicron variants.

During BA.4/BA.5 predominance, estimated 3-dose vaccine effectiveness against hospitalization was 68 percent between 7-and 119-days post-vaccination. Vaccine effectiveness against hospitalization decreased to 36 percent by 120 days or more post-vaccination.

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A new study published in Nature reports that a technology known as spatial omics can be used to map simultaneously how genes are switched on and off and how they are expressed in different areas of tissues and organs. This improved technology, developed by researchers at Yale University and Karolinska Institutet, could shed light on the development of tissues, as well as on certain diseases and how to treat them.

Almost all cells in the body have the same set of genes and can in principle become any kind of cell. What distinguishes the cells is how the genes in our DNA are used. In recent years, spatial omics have given us a deeper understanding of how cells read the genome in precise locations in tissues. Now, researchers have further evolved this technology to increase knowledge of how tissues develop and how different diseases arise.

A key part of the study is the researchers’ ability to spatially map simultaneously two crucial components of our genetic makeup, the epigenome and the . The epigenome controls the switching mechanisms that turn genes on and off in individual , whereas the transcriptome is the result of those gene expressions and what makes each cell unique.

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The Higgs boson doesn’t stick around for long. Once it is created in particle collisions, the famed particle lives for a mere less than a trillionth of a billionth of a second or, more precisely, 1.6 × 10-22 seconds. According to theory, that is, for so far experiments have only been able to set bounds on the value of the particle’s lifetime or to determine this property with a large uncertainty. Until now. In a new study, the CMS collaboration reports a value for the particle’s lifetime that has a small enough uncertainty to confirm that the Higgs boson does have such a short lifetime.

Measuring the Higgs boson’s lifetime is high on the wish list of particle physicists, because an experimental value of the lifetime would allow them not only to better understand the nature of the particle but also to find out whether or not the value matches the value predicted by the Standard Model of particle physics. A deviation from the prediction could point to new particles or forces not predicted by the Model, including new particles into which the Higgs boson would decay.

But it isn’t easy to measure the Higgs boson’s lifetime. For one, the predicted lifetime is too short to be measured directly. A possible solution entails measuring a related property called the mass width, which is inversely proportional to the lifetime and represents the small range of possible masses around the particle’s nominal mass of 125 GeV. But this isn’t easy either, as the predicted mass width of the Higgs boson is too small to be easily measured by experiments.

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“It is very difficult to change the properties of a medium quick enough, uniformly, and with enough contrast to time reflect electromagnetic signals because they oscillate very fast,” Gengyu Xu, a co-author and post-doc student at CUNY ASRC, said in a press statement. “Our idea was to avoid changing the properties of the host material, and instead create a metamaterial in which additional elements can be abruptly added or subtracted through fast switches.”

This time reflection also behaves differently than spatial reflections. Because this time echo reflects that last part of the signal first, the researchers say that if you looked in a time mirror, you would see your back instead of your face. To translate the experience acoustically, it’d be like listening to a tape on rewind—which is to say fast and high-pitched.

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The artificial intelligence (AI) language model ChatGPT has captured the world’s attention in recent months. This trained computer chatbot can generate text, answer questions, provide translations, and learn based on the user’s feedback. Large language models like ChatGPT may have many applications in science and business, but how much do these tools understand what we say to them, and how do they decide what to say back?

In new paper published in Neural Computation on February 17, 2023, Salk Professor Terrence Sejnowski, author of “The Deep Learning Revolution,” explores the relationship between the human and models to uncover why chatbots respond in particular ways, why those responses vary, and how to improve them in the future.

According to Sejnowski, language models reflect the intelligence and diversity of their interviewer.

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Reconstructing visual experiences from human brain activity offers a unique way to understand how the brain represents the world, and to interpret the connection between computer vision models and our visual system. While deep generative models have recently been employed for this task, reconstructing realistic images with high semantic fidelity is still a challenging problem. Here, we propose a new method based on a diffusion model (DM) to reconstruct images from human brain activity obtained via functional magnetic resonance imaging (fMRI). More specifically, we rely on a latent diffusion model (LDM) termed Stable Diffusion. This model reduces the computational cost of DMs, while preserving their high generative performance. We also characterize the inner mechanisms of the LDM by studying how its different components (such as the latent vector of image Z, conditioning inputs C, and different elements of the denoising U-Net) relate to distinct brain functions. We show that our proposed method can reconstruct high-resolution images with high fidelity in straight-forward fashion, without the need for any additional training and fine-tuning of complex deep-learning models. We also provide a quantitative interpretation of different LDM components from a neuroscientific perspective. Overall, our study proposes a promising method for reconstructing images from human brain activity, and provides a new framework for understanding DMs. Please check out our webpage at this https URL.

The authors have declared no competing interest.

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