National Academy of Sciences

Antarctic Krill Fishery Management Under Threat from Climate Change

Researchers at the Alfred Wegener Institute have emphasized the critical need for improved management of the Antarctic krill fishery to prevent harm to the Southern Ocean ecosystem. Krill populations are being threatened by climate change, while current catch limits are not taking into account climate variability or krill population dynamics.

Escherichia coli Study Reveals Significant Effects on Bacillus Strains' Protease Activity

Research conducted at the National Academy of Sciences of Ukraine has shown that isolated intracellular and extracellular lipopolysaccharides (LPS) of Escherichia coli can significantly stimulate the protease activity of various Bacillus strains. The study found that both LPS1 and LPS2 of E. coli 23 increased the elastase activity of Bacillus

National Academy of Sciences

Discovery of Artificial Interleukin Receptors Uncovers Methods to Design and Engineer Immune Responses

Scientists at Colorado State University have unveiled a breakthrough in the field of Immunology, detailing the creation of artificial interleukin receptors that can be designed and engineered to elicit specific immune responses. This research, published in the Proceedings of the National Academy of Sciences, marks a significant step forward in

National Academy of Sciences

Temperature Adaptation in Lactate Dehydrogenase-A Reveals Convergent Evolution in Key Protein Regions

Research conducted at the Ocean University of China has shed light on the mechanisms underlying temperature adaptation in lactate dehydrogenase-A (LDH-A) enzymes. The study identified specific amino acid substitutions that contribute to the thermal stability of LDH-A, revealing striking convergence in the sites of temperature-adaptive evolution across diverse species. This

Artificial neural networks

Equivariant Neural Networks for Physics and Chemistry

Researchers from the University of Chicago have developed a new class of artificial neural networks called equivariant neural nets, which can efficiently incorporate symmetries into the network structure. This innovation has significant implications for applications in physics and chemistry, where symmetries play a crucial role in understanding complex phenomena. The

Artificial neural networks

Breakthrough in Transition Metal Complexes: Researchers Train Graph Neural Networks for Predictive Screening

Scientists at the Massachusetts Institute of Technology have developed a novel dataset of 70,069 unique ligands of known coordination from experimental structures of transition metal complexes (TMCs). By training graph neural network models on this dataset, researchers have achieved high accuracy and precision in predicting the total number and

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