A novel technology for real-time, non-invasive monitoring and adaptive control of electron radiotherapy treatments using acoustic signals.

This technology represents a pioneering approach to vaccine development, focusing on encapsulated adjuvants and antigens to enhance efficacy while minimizing side effects.

Researchers at the University of California, Davis have developed monoclonal antibodies engineered for the treatment and detection of autoimmune disorders and cancers in dogs.

A revolutionary software that integrates with surgical microscopes to accurately locate the trabecular meshwork (TM), enhancing the safety and efficiency of glaucoma surgeries.

A cutting-edge vaccine delivery platform that enhances tumor treatment by co-delivering MHC class I and II restricted antigens.

A groundbreaking non-pharmacological approach to controlling resistant hypertension through personalized, closed-loop neurostimulation.

Researchers at the University of California, Davis have developed method for separating quasi-periodic mixed-signals using a single data trace, enhancing wearable sensor applications.

       Spectral machine vision collects both the spectral and spatial dependence (x,y,λ) of incident light, containing potentially useful information such as chemical composition or micro/nanoscale structure.  However, analyzing the dense 3D hypercubes of information produced by hyperspectral and multispectral imaging causes a data bottleneck and demands tradeoffs in spatial/spectral information, frame rate, and power efficiency. Furthermore, real-time applications like precision agriculture, rescue operations, and battlefields have shifting, unpredictable environments that are challenging for spectroscopy. A spectral imaging detector that can analyze raw data and learn tasks in-situ, rather than sending data out for post-processing, would overcome challenges. No intelligent device that can automatically learn complex spectral recognition tasks has been realized.       UC Berkeley researchers have met this opportunity by developing a novel photodetector capable of learning to perform machine learning analysis and provide ultimate answers in the readout photocurrent. The photodetector automatically learns from example objects to identify new samples. Devices have been experimentally built in both visible and mid-infrared (MIR) bands to perform intelligent tasks from semiconductor wafer metrology to chemometrics. Further calculations indicate 1,000x lower power consumption and 100x higher speed than existing solutions when implemented for hyperspectral imaging analysis, defining a new intelligent photodetection paradigm with intriguing possibilities.

Researchers at the University of California, Davis have developed an approach for the rapid inactivation of bacteria and virus using photo-active matrices enhanced with bio-affinity ligands under daylight irradiation conditions.

Researchers at UCI have discovered the tertiary structure of the Chlamydia major outer membrane protein (MOMP). Despite historical challenges in formulating an effective vaccine, recent advancements in understanding MOMP's structure offer new pathways for vaccine development against urogenital and ocular infections caused by C. trachomatis.

Researchers at the University of California, Davis, have developed a modified, caninized monoclonal antibody that targets canine PD-L1, developed for use as dog cancer therapy.

A novel dendronized polypeptide architecture for efficient and safe mRNA delivery, suitable for anti-tumor immunotherapy.

An innovative system designed to enhance rehabilitation therapy for neurological conditions through comprehensive, computer-based solutions.

This device offers a non-invasive solution for treating nasal airway obstructions, significantly improving recovery time and patient outcomes.

A novel method for predicting the effective lens position (ELP) in cataract surgery through pre-operative measurements of natural lens curvatures.

A novel approach to treating tinnitus through electrical stimulation of the inner ear or auditory nerve.

Researchers at the University of California, Davis have developed an innovative technology designed to directly measure fetus blood oxygen saturation level through the maternal abdomen from the onset of labor until birth, thereby improving fetal health outcomes.

Researchers at the University of California, Davis, have developed a process for isolating anti-inflammatory lipids for treating autoimmune and inflammatory diseases.

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.

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

This unique device delivers electrical stimulation to the inner ear or cranial nerves to treat a panel of ear/brain disorders, including tinnitus.

Reseachers from UC San Diego demonstrated a proof of principle for a CAGEX RNA-targeting CRISPR–Cas13d system as a potential allele-sensitive therapeutic approach for HD, a strategy with broad implications for the treatment of other neurodegenerative disorders.

In magnetic resonance imaging (MRI) scanners, the detection of nuclear magnetic resonance (NMR) signals is achieved using radiofrequency, or RF, coils. RF coils are often equivalently called “resonance coils” due to their circuitry being engineered for resonance at a single frequency being received, for low-noise voltage gain and performance. However, such coils are therefore limited to a small bandwidth around the center frequency, restricting MRI systems from imaging more than one type of nucleus at a time (typically just hydrogen-1, or H1), at one magnetic field strength.To overcome the inherent restriction without sacrificing performance, UC Berkeley researchers have developed an MRI coil that can perform low-noise voltage gain at arbitrary relevant frequencies. These frequencies can be programmably chosen and can include magnetic resonance signals from any of various nuclei (e.g., 1H, 13C, 23Na, 31P, etc.), at any magnetic field strength (e.g., 50 mT, 1.5T, 3T, etc.). The multi-frequency resonance can be performed in a single system. The invention has further advantages in terms of resilience due to its decoupled response relative to other coils and system elements.

Inflatable pouches are attractive as actuators and structural links in soft robots due to their low deflated profile and high deformation ratio. Particularly compelling for minimally invasive surgery, deflated robots/actuators may be deployed in small form factors and maneuver delicately in tight spaces once inflated. However, current fabrication methods do not readily scale for production of actuators with less than 1 mm feature sizes; they often require precision handling of separator films; and/or there are limited multilayer integration capabilities. Fully miniaturized, high degree-of-freedom surgical pouch robots and actuators have not yet been realized.To overcome these challenges, UC Berkeley researchers have developed a rapid, monolithic, and scalable manufacturing method for fabricating thin-film-based pneumatic pouch soft robots. Small features (less than 0.3 mm) can be patterned at high speeds and using commercially available manufacturing tools while maintaining film planarity. Resulting robots can have complex, multilayer structures including single- and bi-directional joint actuators, structural links, integrated in-plane air channels, through-holes for interlayer connectivity, and air inlets to a supply manifold—from a single integrated processing step. Researchers have demonstrated a miniature four finger hand which can dexterously manipulate a cube (8 degrees of freedom), as well as an 10 degree-of-freedom planar arm with a gripper which can maneuver around obstacles. Entire pouch robot structures can have un-inflated thickness of less than 300 um and be inherently soft, allowing the robots to be used in tight spaces with fragile tissues for surgical applications.

      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.