This process of harvesting energy from rain is new.

Researchers in Italy have engineered an artificial leaf that can be embedded within plants to create electricity from raindrops or wind. It functions extremely well under rainy or windy conditions to light up LED lights and power itself, according to a report by IEEE Spectrum.

Fabian Meder, a researcher studying bioinspired soft robotics at the Italian Institute of Technology (IIT) in Genoa, Italy, told the science news outlet that the system could be practical for agricultural applications and remote environmental monitoring in order to observe plant health or monitor climate conditions.

Coldsnowstorm/iStock.

It functions extremely well under rainy or windy conditions to light up LED lights and power itself, according to a report by IEEE Spectrum published on Wednesday.

ST… PAUL, Minn. (AP) — Minnesota regulators said Thursday they’re monitoring the cleanup of a leak of 400,000 gallons of radioactive water from Xcel Energy’s Monticello nuclear power plant, and the company said there’s no danger to the public.

“Xcel Energy took swift action to contain the leak to the plant site, which poses no health and safety risk to the local community or the environment,” the Minneapolis-based utility said in a statement.

While Xcel reported the leak of water containing tritium to state and federal authorities in late November, the spill had not been made public before Thursday. State officials said they waited to get more information before going public with it.

(NewsNation) — Studies have shown that Alzheimer’s may become the defining disease of the baby boomer generation.

According to The Alzheimer’s Association, the number of people age 65 and over living with Alzheimer’s now is nearly 7 million. That number is expected to rise to over 13 million by 2050.

Physician and best-selling author Dr. Ian Smith says it’s not known exactly what causes Alzheimer’s.

Health — operanewsapp.

A new paper published in the Journal of Medical Internet Research describes how generative models such as DALL-E 2, a novel deep learning model for text-to-image generation, could represent a promising future tool for image generation, augmentation, and manipulation in health care. Do generative models have sufficient medical domain knowledge to provide accurate and useful results? Dr. Lisa C Adams and colleagues explore this topic in their latest viewpoint titled “What Does DALL-E 2 Know About Radiology?”

First introduced by OpenAI in April 2022, DALL-E 2 is an artificial intelligence (AI) tool that has gained popularity for generating novel photorealistic images or artwork based on textual input. DALL-E 2’s generative capabilities are powerful, as it has been trained on billions of existing text-image pairs off the internet.

To understand whether these capabilities can be transferred to the medical domain to create or augment data, researchers from Germany and the United States examined DALL-E 2’s radiological knowledge in creating and manipulating X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound images.

Northwestern Medicine scientists have uncovered a mechanism by which exercise activates metabolic benefits in the body, according to a new study published in Cell Metabolism.

It’s well known that exercise elicits many health benefits. However, how this is accomplished is not yet well understood. During exercise, autophagy, the body’s cellular recycling system that allows old or damaged cellular structures to be broken down, is activated in both contracting muscles and various non-contracting organs, such as the liver.

In the study, investigators performed proteomic analyses on the blood of mice before and after exercise. They identified a protein secreted from contracting muscle, FN1, which significantly increased in the plasma and serum of mice after exercise.

Summary: Text-to-image generation deep learning models like OpenAI’s DALL-E 2 can be a promising new tool for image augmentation, generation, and manipulation in a healthcare setting.

Source: JMIR Publications

A new paper published in the Journal of Medical Internet Research describes how generative models such as DALL-E 2, a novel deep learning model for text-to-image generation, could represent a promising future tool for image generation, augmentation, and manipulation in health care.

A study of the electron excitation response of DNA to proton radiation has elucidated mechanisms of damage incurred during proton radiotherapy.

Radiobiology studies on the effects of ionizing radiation on human health focus on the deoxyribonucleic acid (DNA) molecule as the primary target for deleterious outcomes. The interaction of ionizing radiation with tissue and organs can lead to localized energy deposition large enough to instigate double strand breaks in DNA, which can lead to mutations, chromosomal aberrations, and changes in gene expression. Understanding the mechanisms behind these interactions is critical for developing radiation therapies and improving radiation protection strategies. Christopher Shepard of the University of North Carolina at Chapel Hill and his colleagues now use powerful computer simulations to show exactly what part of the DNA molecule receives damaging levels of energy when exposed to charged-particle radiation (Fig. 1) [1]. Their findings could eventually help to minimize the long-term radiation effects from cancer treatments and human spaceflight.

The interaction of radiation with DNA’s electronic structure is a complex process [2, 3]. The numerical models currently used in radiobiology and clinical radiotherapy do not capture the detailed dynamics of these interactions at the atomic level. Rather, these models use geometric cross-sections to predict whether a particle of radiation, such as a photon or an ion, crossing the cell volume will transfer sufficient energy to cause a break in one or both of the DNA strands [4– 6]. The models do not describe the atomic-level interactions but simply provide the probability that some dose of radiation will cause a population of cells to lose their ability to reproduce.

Contribution of exercise to brain resilience.

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A new University of Illinois project is using advanced object recognition technology to keep toxin-contaminated wheat kernels out of the food supply and to help researchers make wheat more resistant to fusarium head blight, or scab disease, the crop’s top nemesis.

“Fusarium head blight causes a lot of economic losses in wheat, and the associated toxin, deoxynivalenol (DON), can cause issues for human and animal health. The disease has been a big deterrent for people growing wheat in the Eastern U.S. because they could grow a perfectly nice crop, and then take it to the elevator only to have it get docked or rejected. That’s been painful for people. So it’s a big priority to try to increase resistance and reduce DON risk as much as possible,” says Jessica Rutkoski, assistant professor in the Department of Crop Sciences, part of the College of Agricultural, Consumer and Environmental Sciences (ACES) at Illinois. Rutkoski is a co-author on the new paper in the Plant Phenome Journal.

Increasing resistance to any crop disease traditionally means growing a lot of genotypes of the crop, infecting them with the disease, and looking for symptoms. The process, known in plant breeding as phenotyping, is successful when it identifies resistant genotypes that don’t develop symptoms, or less severe symptoms. When that happens, researchers try to identify the genes related to disease resistance and then put those genes in high-performing hybrids of the crop.