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

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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 novel mass spectrometry database cataloging >2,000 biomarker compounds in exhaled breath condensate (EBC) for breath metabolomics research.

Progressive Multifocal Leukoencephalopathy (PML) is a devastating and often fatal demyelinating disease of the central nervous system caused by the reactivation of the JC virus (JCV). In immunocompromised patients, the absence of effective T cell surveillance allows the virus to infect and lyse oligodendrocytes, leading to irreversible neurological damage. UC Berkeley researchers have developed a method for discovering and engineering antigen-specific T cell receptors (TCRs) that specifically target JCV.

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.

Clustered regularly interspaced short palindromic repeats (CRISPR) screening is a cornerstone of functional genomics, enabling genome-wide knockout studies to identify genes involved in specific cellular processes or disease pathways. The success of CRISPR screens depends critically on the design of effective guide RNA (gRNA) libraries that maximize on-target activity while minimizing off-target effects. Current CRISPR screening lacks tools that can natively integrate next-generation sequencing (NGS) data for context-specific gRNA design, despite the wealth of genomic and transcriptomic information available from modern sequencing approaches. Traditional gRNA design tools have relied on static libraries with limited genome annotations and outdated scoring methods, lacking the flexibility to incorporate context-specific genomic information. Off-target effects are also a concern, with CRISPR-Cas9 systems tolerating up to three mismatches between single guide RNA (sgRNA) and genomic DNA, potentially leading to unintended mutations that could disrupt essential genes and compromise genomic integrity. Additionally, standard CRISPR library preparation methods can introduce bias through PCR amplification and cloning steps, resulting in non-uniform gRNA representation.

Manual interpretation of real-time quantitative PCR (RT-qPCR) data is prone to human error, noise, and variability, leading to potential misdiagnosis or test redundancies. UC Berkeley researchers have developed a novel deep learning framework that significantly improves diagnostic accuracy by fusing Long Short-Term Memory (LSTM) networks with Vision Transformers (ViT). This hybrid architecture captures both sequential fluorescence patterns and structural amplification dynamics from raw time-series data and image-based renderings. By leveraging a uniquely curated dataset of over 24,000 verified samples, the system accurately discriminates between true-positive and true-negative samples, predicts viral dilutions, and forecasts patient re-test outcomes, providing an objective tool for early triage and increased laboratory throughput.

A groundbreaking algorithm that significantly reduces communication time and message size in distributed machine learning, ensuring fast and reliable model convergence.

Scalable assessments of mental illness, the leading driver of disability worldwide, remain a critical roadblock toward accessible and equitable care. Researchers at UC Berkeley have introduced MAILA (MAchine-learning framework for Inferring Latent mental states from digital Activity), an innovation demonstrating that everyday human-computer interactions encode multiple dimensions of self-reported mental health and their changes over time. MAILA was trained to predict 1.3 million mental-health self-reports from 20,000 cursor and touchscreen recordings, identifying cognitive signatures of psychological function that go beyond what is conveyed by language. Key features and benefits include the ability to track dynamic mental states along three orthogonal dimensions, achieve near-ceiling accuracy in group-level predictions, and translate insights from general to clinical populations to identify individuals with self-reported mental illness.

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.

An innovative system for biometric identification that utilizes intra-body communication for secure authentication.

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.

A cornerstone of bacterial molecular biology is the ability to genetically manipulate the microbe under study. Manipulating the genomes of bacteria is critical to many fields. Such manipulations are made by genetic engineering, which often requires new pieces of DNA to be added to the genome. It is often difficult to move genes into a recalcitrant destination organism due to surveillance systems (CRISPR, Restriction Modification) of the destination/host which degrade invading DNA . It may be commercially desirable to evade these systems in the destination organism. However, evading these systems may require significant experimental effort to design and implement.

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 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.

      Integrating microelectronics with microfluidics, especially those implemented in silicon-based CMOS technology, has driven the next generation of in vitro diagnostics. CMOS/microfluidics platforms offer (1) close interfaces between electronics and biological samples, and (2) tight integration of readout circuits with multi-channel microfluidics, both of which are crucial factors in achieving enhanced sensitivity and detection throughput. Conventionally bulky benchtop instruments are now being transformed into millimeter-sized form factors at low cost, making the deployment for Point-of-Care (PoC) applications feasible. However, conventional CMOS/microfluidics integration suffers from significant misalignment between the microfluidics and the sensing transducers on the chip, especially when the transducer sizes are reduced or the microfluidic channel width shrinks, due to limitations of current fabrication methods.       UC Berkeley researchers have developed a novel methodology for fabricating microfluidics platforms closely embedded within a silicon chip implemented in CMOS technology. The process utilizes a one-step approach to create fluidic channels directly within the CMOS technology and avoids the previously cited misalignment. Three types of structures are presented in a TSMC 180-nm CMOS chip: (1) passive microfluidics in the form of a micro-mixer and a 1:64 splitter, (2) fluidic channels with embedded ion-sensitive field-effect transistors (ISFETs) and Hall sensors, and (3) integrated on-chip impedance-sensing readout circuits including voltage drivers and a fully differential transimpedance amplifier (TIA). Sensors and transistors are functional pre- and post-etching with minimal changes in performance. Tight integration of fluidics and electronics is achieved, paving the way for future small-size, high-throughput lab-on-chip (LOC) devices.