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Brief description not available

UC Berkeley researchers have developed a sophisticated computer-implemented framework that leverages transformer architectures to model the evolution of biological sequences over time. Unlike traditional phylogenetic models that often assume sites evolve independently, this framework utilizes a coupled encoder-decoder transformer to parameterize the conditional probability of a target sequence given multiple unaligned sequences. By capturing complex interactions and dependencies across different sites within a protein or genomic sequence, the model estimates the transition likelihood for each position. This estimation allows for a high-fidelity simulation of evolutionary trajectories. This approach enables a deeper understanding of how proteins change across different timescales and environmental pressures.

Researchers at the University of California, Davis have developed a computer-implemented method for accurately classifying congenital heart defects in newborns using pulse oximetry and machine learning.

Researchers at the University of California, Davis have developed an atmospheric pressure ionization source that employs an ordered array of micro-needles designed to ionize sample components.

SHIELD leverages a customized large language model pipeline to detect and investigate sophisticated cyber threats with high accuracy and interpretability.

A novel transparent contact lens device enabling real-time monitoring of corneal curvature during electrochemical vision therapy.

Efficiently isolating specific biological components from complex mixtures is a cornerstone of modern biotechnology. UC Berkeley researchers have developed a robust method for the selective magnetic separation of target cells and molecules contained within microcompartments, allowing for the rapid isolation and recovery of high-purity biological samples. This approach is particularly effective for high-throughput screening and the analysis of rare cellular populations.