Researchers at the University of California Davis have developed a technology that enables the provable editing of DNNs (deep neural networks) to meet specified safety criteria without altering their architecture.

Researchers at the University of California, Davis have developed a biologic dressing derived from fish skin to enhance wound healing.

Researchers at the University of California, Davis have developed a haptic interface designed to aid visually impaired individuals in navigating their environment using their portable electronic devices.

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.

Researchers at the University of California, Davis have developed a technology that provides a novel method for decoding biosignals into speech, enhancing communication for individuals with speech impairments.

Researchers at the University of California, Davis have identified an orally administered molecule from microbial origin, capable of repairing damaged gut epithelial barriers and restoring energy metabolism.

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.

This technology represents a breakthrough in non-invasive hemodynamic monitoring by utilizing coherent light to assess physiological parameters with high accuracy

Researchers at the University of California, Davis, have developed a device to monitor the heart using radiofrequency signals to improve the detection and diagnosis of various cardiovascular conditions. The device can integrate with existing mobile products, which is particularly helpful for older adults and those with limited access to adequate medical facilities.

Researchers at the University of California, Davis have developed advanced compounds targeting neuraminidase activity to combat viral infections and understand cellular mechanisms.

Researchers at the University of California, Davis have developed a unique approach to target solid tumors using novel Fas-targeting antibodies designed for improved selectivity and efficacy in immunotherapy.

Researchers at the University of California, Davis, have developed new synthesis methods for the rapid and highly pure production of glycosphingolipids. The prototyped process can produce pure glycosphingolipids that can be used within basic disease research and drug and diagnostic development.

      While robots have proven effective in enhancing the precision and time efficiency of MRI-guided interventions across various medical applications, safety remains a formidable challenge for robots operating within MRI environments. As the robots assume full control of medical procedures, the reliability of their operation becomes paramount. Precise control over robot forces is particularly crucial to ensure safe interaction within the MRI environment. Furthermore, the confined space in the MRI bore complicates the safe operation of human-robot interaction, presenting challenges to maneuverability. However, there exists a notable scarcity of force-controlled robot actuators specifically tailored for MRI applications.       To overcome these challenges, UC Berkeley researchers have developed a novel MRI-compatible rotary series elastic actuator module utilizing velocity-sourced ultrasonic motors for force-controlled robots operating within MRI scanners. Unlike previous MRI-compatible SEA designs, the module incorporates a transmission force sensing series elastic actuator structure, while remaining compact in size. The actuator is cylindrical in shape with a length shorter than its diameter and integrates seamlessly with a disk-shaped motor. A precision torque controller enhances the robustness of the invention’s torque control even in the presence of varying external impedance; the torque control performance has been experimentally validated in both 3 Tesla MRI and non-MRI environments, achieving a settling time of 0.1 seconds and a steady-state error within 2% of its maximum output torque. It exhibits consistent performance across low and high external impedance scenarios, compared to conventional controllers for velocity-sourced SEAs that struggle with steady-state performance under low external impedance conditions.

Researchers at the University of California, Davis have developed a system that significantly improves the accuracy and efficiency of stress detection through electrodermal activity monitoring.

Researchers at the University of California, Davis, have developed a new optoretinography) imaging and analysis system for diagnosing and monitoring retinal health and diseases.

Researchers at the University of California, Davis have developed a novel 3D printing approach to culture and construct epithelial tubular mini-tissues.

A novel method for producing dissolvable alginate microfibers critical for advanced tissue engineering and microfluidic network fabrication.

Researchers at the University of California, Davis have developed a semiautomated solution for identifying differences in tissue architectures or cell types as well as visualizing and analyzing cell densities and cell-cell associations in a tissue sample.

Researchers at the University of California, Davis have developed an operant behavioral assay to study thermosensation, pain, or avoidance and tolerance of an animal to noxious environments.

Researchers at the University of California, Davis (“UC Davis”) have developed a unisex swimwear garment designed to prevent swimming-related injuries and to assist in injury recovery during training.

Professor Kendrick Davis and colleagues from the University of California, Riverside have developed measurement and mapping survey technology that is easy to use, pictorial based, and written by a design that ensures present and ongoing scale validation with Unicode for standardization across virtually all electronic platforms. The emoji-based measurement system (Eb-MS) consists of a linked/connected set of tables organized by three master sets, and sets of linked tables as domain families (i.e., Medicine, Education, etc.) This technology is advantageous because it may facilitate effective communication with individuals with certain health situations, such as stroke, brain injury, or vocal impairments, or with language barriers. 

         While nuclear magnetic resonance (NMR) is one of the most universal synthetic chemistry tools for its ability to measure highly specific kinetic and structural information nondestructively/noninvasively, it is costly and low-throughput primarily due to the small sample-size volumes and expensive equipment needed for stringent magnetic field homogeneity. Conversely, zero-to-ultralow field (ZULF) NMR is an emerging alternative offering similar chemical information but relaxing field homogeneity requirements during detection. ZULF NMR has been further propelled by recent advancements in key componentry, optically pumped magnetometers (OPMs), but suffers in scope due to its low sensitivity and its susceptibility to noise. It has not been possible to detect most organic molecules without resorting to hyperpolarization or 13C enrichment using ZULF NMR.         To overcome these challenges, UC Berkeley researchers have developed a multi-channel ZULF spectrometer that greatly improves on both the sensitivity and throughput abilities of state-of-the art ZULF NMR devices. The novel spectrometer was used in the first reported detection of organic molecules in natural isotopic abundance by ZULF NMR, with sensitivity comparable to current commercial benchtop NMR spectrometers. A proof-of-concept multichannel version of the ZULF spectrometer was capable of measuring three distinct chemical samples simultaneously. The combined sensitivity and throughput distinguish the present ZULF NMR spectrometer as a novel chemical analysis tool at unprecedented scales, potentially enabling emerging fields such as robotic chemistry, as well as meeting the demands of existing fields such as chemical manufacturing, agriculture, and pharmaceutical industries.

         Recent advancements in nuclear magnetic resonance (NMR) spectroscopy have underscored the need for novel instrumentation, but current commercial instrumentation performs well primarily for pre-existing, mainstream applications. Modalities involving, in particular, integrated electron-nuclear spin control, dynamic nuclear polarization (DNP), and non-traditional NMR pulse sequences would benefit greatly from more flexible and capable hardware and software. Advances in these areas would allow many innovative NMR methodologies to reach the market in the coming years.          To address this opportunity, UC Berkeley researchers have developed a novel high-speed, high-memory NMR spectrometer and hyperpolarizer. The device is compact, rack-mountable and cost-effective compared to existing spectrometers. Furthermore, the spectrometer features robust, high-speed NMR transmit and receive functions, synthesizing and receiving signals at the Larmor frequency and up to 2.7GHz. The spectrometer features on-board, phase-sensitive detection and windowed acquisition that can be carried out over extended periods and across millions of pulses. These and additional features are tailored for integrated electron-nuclear spin control and DNP. The invented spectrometer/hyperpolarizer opens up new avenues for NMR pulse control and DNP, including closed-loop feedback control, electron decoupling, 3D spin tracking, and potential applications in quantum sensing.

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Rapid antimicrobial susceptibility testing (AST) is a method for quickly determining the most effective antibiotic therapy for patients with bacterial infections. These techniques enable the detection and quantification of antibiotic-resistant and susceptible bacteria metabolites at concentrations near or below ng/mL in complex media. Employing bacterial metabolites as a sensing platform, the system integrates machine learning data analysis processes to differentiate between antibiotic susceptibility and resistance in clinical infections within an hour. With the results, a clinician can prescribe appropriate medicine for the patient's bacterial infection.