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Gregor Mendel, the Moravian monk, was indeed “decades ahead of his time and truly deserves the title of ‘founder of genetics.’” So concludes an international team of scientists as the 200th birthday of Mendel approaches on 20 July.

The team, from KeyGene in the Netherlands and the John Innes Centre in the UK, draw on newly-discovered to conclude that, when his proposals are viewed in the light of what was known of cells in the mid-19th century, Mendel was decades ahead of his time.

“Uncovering hidden details about Mendel has helped to build a picture of the scientific and intellectual environment in which he worked. At the outset Mendel knew nothing about genetics and had to deduce it all for himself. How he went about this is highly instructive,” said Dr. Noel Ellis from the John Innes Centre, one of the contributors to the study.

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Machine learning is transforming all areas of biological science and industry, but is typically limited to a few users and scenarios. A team of researchers at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has developed METIS, a modular software system for optimizing biological systems. The research team demonstrates its usability and versatility with a variety of biological examples.

Though engineering of biological systems is truly indispensable in biotechnology and , today machine learning has become useful in all fields of biology. However, it is obvious that application and improvement of algorithms, computational procedures made of lists of instructions, is not easily accessible. Not only are they limited by programming skills but often also insufficient experimentally-labeled data. At the intersection of computational and experimental works, there is a need for efficient approaches to bridge the gap between machine learning algorithms and their applications for biological systems.

Now a team at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has succeeded in democratizing machine learning. In their recent publication in Nature Communications, the team presented together with collaboration partners from the INRAe Institute in Paris, their tool METIS. The application is built in such a versatile and modular architecture that it does not require computational skills and can be applied on different biological systems and with different lab equipment. METIS is short from Machine-learning guided Experimental Trials for Improvement of Systems and also named after the ancient goddess of wisdom and crafts Μῆτις, or “wise counsel.”

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A team of researchers at DeepMind, London, working with colleagues from the University of Exeter, University College London and the University of Oxford, has trained an AI system to find a policy for equitably distributing public funds in an online game. In their paper published in the journal Nature Human Behavior, the group describes the approach they took to training their system and discuss issues that were raised in their endeavor.

How a society distributes wealth is an issue that humans have had to face for thousands of years. Nonetheless, most economists would agree that no system has yet been established in which all of its members are happy with the status quo. There have always been inequitable levels of income, with those on top the most satisfied and those on the bottom the least satisfied. In this latest effort, the researchers in England took a new approach to solving the problem—asking a computer to take a more logical approach.

The researchers began with the assumption that , despite their flaws, are thus far the most agreeable of those tried. They then enlisted the assistance of volunteers to play a simple resource allocation —the players of the game decided together the best ways to share their mutual resources. To make it more realistic, the players received different amounts of resources at the outset and there were different distribution schemes to choose from. The researchers ran the game multiple times with different groups of volunteers. They then used the data from all of the games played to train several AI systems on the ways that humans work together to find a solution to such a problem. Next, they had the AI systems play a similar game against one another, allowing for tweaking and learning over multiple iterations.

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When robots appear to engage with people and display human-like emotions, people may perceive them as capable of “thinking,” or acting on their own beliefs and desires rather than their programs, according to research published by the American Psychological Association.

“The relationship between anthropomorphic shape, human-like behavior and the tendency to attribute independent thought and intentional behavior to robots is yet to be understood,” said study author Agnieszka Wykowska, Ph.D., a principal investigator at the Italian Institute of Technology. “As increasingly becomes a part of our lives, it is important to understand how interacting with a that displays human-like behaviors might induce higher likelihood of attribution of intentional agency to the robot.”

The research was published in the journal Technology, Mind, and Behavior.

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It’s just a “speck of the universe.”

The first image from NASA’s James Webb Space Telescope offered humanity a stunning new view of the universe on Monday — a first-of-its-kind infrared image so distant in the cosmos that it shows stars and galaxies as they appeared 13 billion years ago.

President Joe Biden revealed the new image Monday at the White House alongside Vice President Kamala Harris and NASA officials. Dubbed “Webb’s First Deep Field,” it is the first full-color image from the $10 billion observatory that launched into space last year, and the highest-resolution infrared view of the universe yet captured.

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Remission of depression with new magnetic therapy:3.

Although she’d tried medications and therapy, Chase felt her symptoms get worse over the course of a few months. And she knew things were really getting serious when thoughts of suicide crept in.

That’s when her mother found research about a new type of treatment for depression called Stanford neuromodulation therapy, which uses magnetic fields to stimulate the brain. (It was previously referred to as Stanford accelerated intelligent neuromodulation therapy or SAINT.)

The treatment is similar to transcranial magnetic stimulation, a non-invasive therapy that’s been used to help treat depression for about 15 years.

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The BA.5 variant is now the most dominant strain of COVID-19 in the country, according to the Centers for Disease Control and Prevention. And while it’s hard to get an exact count — given how many people are taking rapid tests at home — there are indications that both reinfections and hospitalizations are increasing.

For example: Some 31,000 people across the U.S. are currently hospitalized with the virus, with admissions up 4.5% compared to a week ago. And data from New York state shows that reinfections started trending upwards again in late June.

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