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Category: biological – Page 59

Toxins high-cite paperđŸ€©

Title: ☎Dr. Sara Ragucci and Dr. Antimo Di Maro.

Read this review to have an overview of Mushrooms:

Here, we report the current status of the bioactive peptides isolated and characterized from mushrooms during the last 20 years, considering ‘peptide’ a succession from to 2 to 100 amino acid residues. According to this accepted biochemical definition, we adopt ~10 kDa as the upper limit of molecular weight for a peptide. In light of this, a careful revision of data reported in the literature was carried out. The search revealed that in the works describing the characterization of bioactive peptides from mushrooms, not all the peptides have been correctly classified according to their molecular weight, considering that some fungal proteins (10 kDa MW) have been improperly classified as ‘peptides’. Moreover, the biological action of each of these peptides, the principles of their isolation as well as the source/mushroom species were summarized.

Continue reading “An Updated Review of Bioactive Peptides from Mushrooms in a Well-Defined Molecular Weight Range” | >

Progress update: Our latest AlphaFold model shows significantly improved accuracy and expands coverage beyond proteins to other biological molecules, including ligands.

Since its release in 2020, AlphaFold has revolutionized how proteins and their interactions are understood. Google DeepMind and Isomorphic Labs have been working together to build the foundations of a more powerful AI model that expands coverage beyond just proteins to the full range of biologically-relevant molecules.

Today we’re sharing an update on progress towards the next generation of AlphaFold. Our latest model can now generate predictions for nearly all molecules in the Protein Data Bank (PDB), frequently reaching atomic accuracy.

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In a new study, Deepmind and colleagues at Isomorphic Labs show early results from a new version of AlphaFold that brings fully automated structure prediction of biological molecules closer to reality.

The Google Deepmind AlphaFold and Isomorphic Labs team today unveiled the latest AlphaFold model. According to the companies, the updated model can now predict the structure of almost any molecule in the Protein Data Bank (PDB), often with atomic accuracy. This development, they say, is an important step towards a better understanding of the complex biological mechanisms within cells.

Since its launch in 2020, AlphaFold has influenced protein structure prediction worldwide. The latest version of the model goes beyond proteins to include a wide range of biologically relevant molecules such as ligands, nucleic acids and post-translational modifications. These structures are critical to understanding biological mechanisms in cells and have been difficult to predict with high accuracy, according to Deepmind.

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Research led by Peking University, China, has discovered a single type of retinal photoreceptor cell in Drosophila (fruit fly) is involved in both visual perception and circadian photoentrainment by co-releasing histamine and acetylcholine at the first visual synapse.

In a paper, “A single photoreceptor splits perception and entrainment by cotransmission,” published in Nature, the team details the discovery that the Drosophila visual system segregates and circadian photoentrainment by co-transmitting two neurotransmitters, histamine and acetylcholine, in the R8 cells.

Light detection involves capturing signals through photoreceptors in the eye, which are essential for image formation and subconscious visual functions, such as regulating biological rhythms according to the daily light-dark cycle (photoentrainment of the ). The optical system has distinct pathways for image formation (based on local contrast) and non-image-related tasks (based on global irradiance).

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OpenAI’s new preparedness team will address the potential dangers associated with AI, including nuclear threats.

OpenAI is forming a new team to mitigate the “catastrophic risks” associated with AI. In an update on Thursday.

The team will also work to mitigate “chemical, biological, and radiological threats,” as well as “autonomous replication,” or the act of an AI replicating itself. Some other risks that the preparedness team will address include AI’s ability to trick humans, as well as cybersecurity threats.

We believe that frontier AI
 More.

Continue reading “OpenAI forms new team to assess “catastrophic risks” of AI” | >

“We hope that this soft robotic arm exemplifies a future where machines assist, complement, and understand human needs more deeply than ever before.”

Drawing inspiration from the movements of elephant trunks and octopus tentacles, researchers at the CREATE lab of t. It ishe Swiss Federal Institute of Technology Lausanne (EPFL) has developed a revolutionary robotic structure, the “trimmed helicoid.”

Set to usher in greater compliance and control in robotic design, this structure ensures safer interactions between humans and robots and is a result of blending computational modeling with astute biological observations.

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Modern computer models—for example for complex, potent AI applications—push traditional digital computer processes to their limits. New types of computing architecture, which emulate the working principles of biological neural networks, hold the promise of faster, more energy-efficient data processing.

A team of researchers has now developed a so-called event-based architecture, using photonic processors with which data are transported and processed by means of light. In a similar way to the brain, this makes possible the continuous adaptation of the connections within the neural network. This changeable connections are the basis for learning processes.

For the purposes of the study, a team working at Collaborative Research Center 1,459 (Intelligent Matter)—headed by physicists Prof. Wolfram Pernice and Prof. Martin Salinga and computer specialist Prof. Benjamin Risse, all from the University of MĂŒnster—joined forces with researchers from the Universities of Exeter and Oxford in the UK. The study has been published in the journal Science Advances.

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Jailbroken large language models (LLMs) and generative AI chatbots — the kind any hacker can access on the open Web — are capable of providing in-depth, accurate instructions for carrying out large-scale acts of destruction, including bio-weapons attacks.

An alarming new study from RAND, the US nonprofit think tank, offers a canary in the coal mine for how bad actors might weaponize this technology in the (possibly near) future.

In an experiment, experts asked an uncensored LLM to plot out theoretical biological weapons attacks against large populations. The AI algorithm was detailed in its response and more than forthcoming in its advice on how to cause the most damage possible, and acquire relevant chemicals without raising suspicion.

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