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A multimodal, wireless wearable device enabling continuous and detailed stress assessment and subclassification.

A novel method for detecting, diagnosing, monitoring, and treating gastrointestinal cancers by analyzing DNA methylation levels in patient samples.

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Researchers at the University of California, Davis have developed an AI-powered framework that bridges the gap between predictive feature analysis and actionable interventions by extracting evidence-based recommendations from scientific literature.

Researchers at the University of California, Davis have developed a novel mass spectrometry database cataloging >2,000 biomarker compounds in exhaled breath condensate (EBC) for breath metabolomics research.

Current methods for treating nervous system disorders often rely on generalized approaches that may not optimally address the individual patient's specific pathology, leading to suboptimal outcomes. This innovation, developed by UC Berkeley researchers, provides a method to identify the most critical, or "influential," nodes within a patient's functional connectivity network derived from time-series data of an organ or organ system. The method involves obtaining multiple time-series datasets from an affected organ/system, using them to map the functional connectivity network, and then determining the most influential nodes within that network. By providing this specific and personalized information to a healthcare provider, a treatment can be prescribed that precisely targets the respective organ corresponding to these influential nodes. This personalized, data-driven approach offers a significant advantage over conventional treatments by focusing intervention on the most impactful biological targets, potentially leading to more effective and efficient patient care.

Researchers at the University of California, Davis have developed a computer-implemented method to identify viral mutations that enhance transmission and predict their prevalence in populations over time.

Traditional environmental policymaking often struggles to efficiently target interventions to achieve multiple, complex air quality goals simultaneously across a geographic area. This innovation, developed by UC Berkeley researchers, addresses this challenge by providing a sophisticated, multi-objective optimization method for targeted reduction of air pollution. The method generates a comprehensive mitigation pathway by integrating several modules: a forward module to model pollutant concentrations, a target concentration surface that defines the policy goals, a prioritization module to assess uncertainty and importance via a prioritization covariance matrix, and a Bayesian inversion module to estimate optimum emissions required to meet the target. This systematic, data-driven approach culminates in a mitigation pathway that guides the performance of specific pollution control measures, offering a significant advantage over conventional, less targeted policy-making by ensuring resources are directed where they will have the maximum environmental impact.

A revolutionary drug modality for the selective modification and degradation of intracellular proteins in lysosomes.

The identification of chemical shifts is a foundational step in determining a protein's three-dimensional structure via Nuclear Magnetic Resonance (NMR) spectroscopy. Current computational methods often struggle with accuracy and efficiency, particularly in handling the complex influence of protein side chains on shift values. UCBShift 2.0, a technology by UC Berkeley researchers, addresses this critical bottleneck by providing a highly accurate chemical shifts identifier. This innovation is a computational tool that includes a sequence transfer predictor for initial protein analysis, a novel machine learning module  to predict side chain shifts, and a regressor that combines these outputs to produce a highly accurate predicted chemical shift for the entire protein. By specifically leveraging augmented feature extraction that includes side chain information, UCBShift 2.0 achieves greater predictive power and speed compared to existing methods, streamlining the time-consuming process of protein structure determination.

Achieving precise and tunable control over endogenous gene expression in eukaryotic cells remains a significant challenge, particularly for therapeutic applications or detailed biological studies where fine-tuning is required rather than complete on/off switching. This innovation, developed by UC Berkeley researchers, addresses this by providing a novel, programmable method for transcriptional tuning. The innovation is a two-domain fusion protein comprising the transcriptional repression domain (TRD) of the methyl-CpG-binding domain (MBD) protein MeCP2 linked to a dead Cas9 (dCas9) domain. When combined with a single guide RNA (sgRNA) that targets a specific endogenous gene, this fusion protein partially inhibits, or "tunes," the expression of that gene. Unlike traditional methods like RNAi or full CRISPR interference (CRISPRi), which often aim for complete knockdown, this system offers a highly specific and titratable way to dial down gene expression, providing a distinct advantage in studies requiring subtle modulation of gene dosage or for developing dose-dependent therapeutic strategies.

Advances in biological research have been greatly influenced by the development of organoids, a specialized form of 3D cell culture. Created from pluripotent stem cells, organoids are effective in vitro models in replicating the structure and progression of organ development, providing an exceptional tool for studying the complexities of biology. Among these, cerebral cortex organoids (hereafter "organoid") have become particularly instrumental in providing valuable insights into brain formation, function, and pathology. Modern methods of interfacing with organoids involve any combination of encoding information, decoding information, or perturbing the underlying dynamics through various timescales of plasticity. Our knowledge of biological learning rules has not yet translated to reliable methods for consistently training neural tissue in goal-directed ways. In vivo training methods commonly exploit principles of reinforcement learning and Hebbian learning to modify biological networks. However, in vitro training has not seen comparable success, and often cannot utilize the underlying, multi-regional circuits enabling dopaminergic learning. Successfully harnessing in vitro learning methods and systems could uniquely reveal fundamental mesoscale processing and learning principles. This may have profound implications, from developing targeted stimulation protocols for therapeutic interventions to creating energy-efficient bio-electronic systems.

A groundbreaking method for the efficient isolation and preservation of high-purity small extracellular vesicles (sEVs - exosomes) from biofluids using a novel EXO-PEG-TR reagent.

BMSO represents a groundbreaking advancement in crosslinking mass spectrometry (XL-MS), enabling comprehensive mapping of protein-protein interactions.

Researchers at the University of California, Davis have developed advancements in understanding exodermal differentiation in plant roots highlighting the role of two transcription factors in plant adaptation and survival.

Researchers at the University of California, Davis have developed a novel cell segmentation technology for accurate analysis of non-spherical cells and that offers a comprehensive, high-throughput approach for analyzing the transcriptomic and metabolomic data to study complex biological processes at the single-cell level.

The University of California, Riverside has developed a new omics software named, ShowMEPATH, employing a faster and easier approach to compare changes in metabolites within multiple sample groups, along with an automated algorithm to facilitate the process. The software introduces a novel tool to visualize volcano plots, called Parallel Fold Change (PFC) plot. Unlike current software solutions, PFC enables researchers to easily process their large omics data sets to compare various biological networks. The PFC plot is an efficient tool for analyzing and interpreting complex biological comparisons and it helps researchers to efficiently map omics pathways.  Fig 1: This figure illustrates a Parallel Fold Change (PFC) plot and demonstrates the parallel comparison of multiple samples in metabolomics. The tool examines the fold-change patterns of 45 metabolites across 16 scenarios involving 8 genotypes and 3 treatments. Using ShowMEPATH, researchers can identify detailed patterns within biological experiments, with the ability to hover over lines in the PFC plots for seamless access to KEGG modules or pathways, thereby streamlining the exploration of related biological information 

Researchers at the University of California, Davis have developed a technology that offers a sophisticated solution for collecting and measuring gas emissions from surfaces, particularly skin, with high sensitivity and specificity.

A revolutionary method utilizing machine learning to derive systems biology models from experimental data to improve drug discovery and development.

      Raman spectroscopy, the inelastic scattering of light off molecular vibrations or solid- state phonons, is a critical method in chemical analytics, biological imaging, and materials or even art characterization. A common method for signal enhancement is surface enhanced Raman spectroscopy (SERS), where noble metal or dielectric nanostructures locally enhance the incoming and/or scattered field. SERS has found wide-spread applications in bio- analytics, fundamental science, viral and bacterial classification, and the study of tissue samples. Yet, obstacles towards more wide-spread adoption with wider scope are poor SERS substrate reproducibility and local hotspot fluctuations of metallic SERS substrates, and background emission from molecules, analytes, hot electrons, plasmons, or carriers in dielectrics that can significantly interfere with small signals of target analytes in SERS.       UC Berkeley researchers have developed an improved method for SERS that simultaneously minimizes spurious background emission, minimizes local heating even under high excitation powers, and maximizes the Raman signal enhancement of dielectric SERS substrates. Together these advantages render the method a powerful contender for sought after quantitative SERS and reliable analyte and single- molecule detection without fluctuations or other perturbations from SERS substrates. This enables commercially relevant usage, particularly in the biosciences and diagnostics, DNA/RNA sequencing, protein sequencing, determination of biomolecular binding constants, interconversion kinetics between biomolecular conformers, post-translational modifications, determination of molecular folding statuses, and classification of different proteoforms. It further has commercial potential in environmental monitoring, food safety, semiconductor inspection, polymer quality control and research, quality control in pharmaceuticals – including vesicles for drug delivery-, materials science, and physical science research.

An innovative mass spectrometry platform that utilizes sulfoxide-containing MS-cleavable heterobifunctional photoactivated cross-linkers to enhance protein structural elucidation.

Researchers at the University of California, Davis have developed improved variants of a Heparosan synthase supporting efficient synthesis of heparosan, heparin, and heparan sulfate analogs.

High-throughput next-generation sequencing (NGS) systems have allowed for large scale collection of transcriptomic data with single cell resolution. Within this data lies variability allowing researchers to characterize and/or infer certain morphological aspects of interest, such as single cell type, cell state, cell growth trajectories, and inter-cellular gene regulatory networks. All of these qualities are important parts of understanding how cells interact with one another, both for building better cellular models in vitro and for understanding biological processes in vivo. While the size of single cell data has increased massively, NGS techniques for key pieces of analysis have not kept pace, using slow, manual pipelines of domain experts for initial clustering. Attempts to improve NGS classification performance have fallen short as the numbers of cell types (often asymmetric) and cell subtypes have increased while the number of samples per label has become small. The technical variability between NGS experiments can make robust classification between multiple tissue samples difficult. Moreover, the high-dimensional nature of NGS transcriptomic data makes this type of analysis statistically and computationally intractable.