A novel method using Diffusion Tensor Imaging (DTI) combined with Statistical Parametric Mapping (SPM) as an effective diagnostic tool for Traumatic Brain Injury.

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

An AI-powered platform for the generation of automated electronic patient anatomy education models, providing surgeons with clinically relevant patient anatomy data.

An innovative online method for personalized tinnitus treatment through customized therapeutic sounds and habituation exercises.

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.

An innovative algorithm linking electroencephalogram (EEG) neural data with cognitive model parameters to predict brain signals from behavioral data.

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.

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

Brief description not available

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. 

Deoxyribonucleic acid- (DNA-) based identification in forensics is typically accomplished via genotyping allele length at a defined set of short tandem repeat (STR) loci via polymerase chain reaction (PCR). These PCR assays are robust, reliable, and inexpensive. Given the multiallelic nature of each of these loci, a small panel of STR markers can provide suitable discriminatory power for personal identification. Massively parallel sequencing (MPS) technologies and genotype array technologies invite new approaches for DNA-based identification. Application of these technologies has provided catalogs of global human genetic variation at single-nucleotide polymorphic (SNP) sites and short insertion-deletion (INDEL) sites. For example, from the 1000 Genomes Project, there is now a catalog of nearly all human SNP and INDEL variation down to 1% worldwide frequency. Genotype files, generated via MPS or genotype array, can be compared between individuals to find regions that are co-inherited or identical-by-descent (IBD). These comparisons are the basis of the relative finder functions in many direct-to-consumer genetic testing products. A special case of relative-finding is self-identification. This is a trivial comparison of genotype files as self-comparisons will be identical across all sites, minus the error rate of the assay. For many forensic samples, however, the available DNA may not be suitable for PCR-based STR amplification, genotype array analysis, or MPS to the depth required for comprehensive, accurate genotype calling. In the case of PCR, one of the most common failure modes occurs when DNA is too fragmented for amplification. For these samples, it may be possible to directly observe the degree of DNA fragmentation from the decreased amplification efficiency of larger STR amplicons from a multiplex STR amplification. In the case of severely fragmented samples, where all DNA fragments are shorter than the shortest STR amplicon length, PCR simply fails with no product.

Diagnosing retinal disease, which affects over 200 million people worldwide, requires expensive and complicated analysis of the structure and function of retinal tissue. Recently, UCI developed a training algorithm which, for the first time, is able to assess tissue health from images collected using more common and less expensive optics.

Dysfluent speech modeling requires time-accurate and silence-aware transcription at both the word-level and phonetic-level. However, current research in dysfluency modeling primarily focuses on either transcription or detection, and the performance of each aspect remains limited.To address this problem, UC Berkeley researchers have developed a new unconstrained dysfluency modeling (UDM) approach that addresses both transcription and detection in an automatic and hierarchical manner. Furthermore, a simulated dysfluent dataset called VCTK++ enhances the capabilities of UDM in phonetic transcription. The effectiveness and robustness of UDM in both transcription and detection tasks has been demonstrated experimentally.UDM eliminates the need for extensive manual annotation by providing a comprehensive solution.

Electrical properties (EP), such as permittivity and conductivity, dictate the interactions between electromagnetic waves and biological tissue. EP are biomarkers for pathology characterization, such as cancer. Imaging of EP helps monitor the health of the tissue and can provide important information in therapeutic procedures. Magnetic resonance (MR)-based electrical properties tomography (MR-EPT) uses MR measurements, such as the magnetic transmit field B1+, to reconstruct EP. These reconstructions rely on the calculations of spatial derivatives of the measured B1+. However, the numerical approximation of derivatives leads to noise amplifications introducing errors and artifacts in the reconstructions. Recently, a supervised learning-based method (DL-EPT) has been introduced to reconstruct robust EP maps from noisy measurements. Still, the pattern-matching nature of this method does not allow it to generalize for new samples since the network’s training is done on a limited number of simulated data pairs, which makes it unrealistic in clinical applications. Thus, there is a need for a robust and realistic method for EP map construction.

Researchers at UC Irvine have developed a novel algorithm that more accurately filters raw blood pressure (BP) data collected from continuous non-invasive blood pressure sensors. The algorithm features improvements in eliminating baseline signal drift while maintaining signal integrity and BP estimation accuracy across significant hemodynamic changes.

Common nuclear imaging techniques include computed tomography (CT), single photon emission CT (SPECT), and positron emission tomography (PET). PET differs from other nuclear imaging techniques in that it can visualize both functional and biological activities, including detection of metabolism within human tissues. PET is especially good for imaging patients with cancer, or brain or heart conditions. At low energies, when positrons collide with electrons near the radionuclide decay, Gamma rays (annihilation photons) are created. Gammas originating from the same electron-positron annihilation are generated exclusively in an entangled Bell state. Gammas which do not share an annihilation origin event, such as randoms, are not entangled. Additionally, a gamma which undergoes an internal scatter becomes decoherent (unentangled) from its pair, such as the gammas found in the scattered coincidence pairs. Scattered and random events degrade the image quality. Recently, quantum-based techniques utilizing entanglement of annihilation photons has been recognized as one approach to address scatter and random and to optimize the signal to noise (SNR) ratio.

Cardiovascular disease is the leading cause of death both worldwide and in the United States, with associated costs in the U.S. reaching approximately $229 billion, each, in 2017 and 2018. Early detection, which can drastically reduce both rates of death and treatment costs, requires access to facilities and highly-trained physicians that can be difficult to access in rural areas and developing countries—despite their prevalence of cardiovascular disease. Computer-based models that use, e.g., PCG (phonocardiogram), EKG (electrocardiogram), or other cardiac data, are a promising route to bridge the gap in standard-of-care for these underserved areas. However, current algorithms are unable to account for demographic features, such as race, sex, or other characteristics, which are known to affect both the structure of the heart and presentation of heart disease. To address this problem, UC Berkeley researchers have developed a new, cloud-based system for collecting a patient's continuous cardiovascular data, monitoring for and detecting disease, and keeping a doctor informed about the cardiac health of the patient. The system sends an alarm when disease or heart attack are detected. To generate the most accurate diagnoses by taking into account demographic information, the system includes private and ethical dataset collection and model-training techniques.

Measurement of electrical activity in nervous tissue has many applications in medicine, but the implantation of a large number of sensors is traditionally very risky and costly. Devices must be large due to their necessary complexity and power requirements, driving up the risk further and discouraging adoption. To address these problems, researchers at UC Berkeley have developed devices and methods to allow small, very simple and power-efficient sensors to transmit information by backscatter feedback. That is, a much more complex and powerful external interrogator sends an electromagnetic or ultrasound signal, which is modulated by the sensor nodes and reflected back to the interrogator. Machine learning algorithms are then able to map the reflected signals to nervous activity. The asymmetric nature of this process allows most of the complexity to be offloaded to the external interrogator, which is not subject to the same constraints as implanted devices. This allows for larger networks of nodes which can generate higher resolution data at lower risks and costs than existing devices.

Almost 800,000 people suffer a stroke each year in the U.S. and approximately two-thirds survive and require rehabilitation. Stroke is a leading cause of serious long-term disability. Between 2017 and 2018, stroke-related costs in the U.S. were about $53B. This total includes the cost of health care services, medicines to treat stroke, and missed days of work. According to the U.S. National Institute of Neurological Disorders and Stroke, research shows the most important element in any neurorehabilitation program is carefully directed, well-focused, repetitive practice, which is the same kind of practice used by all people when they learn a new skill, such as playing the piano or pitching a baseball. The emergence of gaming technologies, such as videogames and virtual reality (VR), opens the door to a variety of possibilities for neurorehabilitation activities.

Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily employ reinforcement learning, Markov sampling, or deep generative models guided by Gaussian processes, which can be prohibitively slow when generating molecules with high binding affinity calculated by computationally-expensive physicsbased methods. Researchers a UC San Diego have developed a new approach, named Latent Inceptionism on Molecules (LIMO), which significantly accelerates molecule generation with an inceptionism-like technique. LIMO employs a variational autoencoder-generated latent space and property prediction by two neural networks in sequence to enable faster gradient-based reverse-optimization of molecular properties.

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As an important tool for electrophysiological recordings, neural electrodes implanted on the brain surface have been instrumental in basic neuroscience research to study large-scale neural dynamics in various cognitive processes, such as sensorimotor processing as well as learning and memory. In clinical settings, neural recordings have been adopted as a standard tool to monitor the brain activity in epilepsy patients before surgery for detection and localization of epileptogenic zones initiating seizures and functional cortical mapping. Neural activity recorded from the brain surface exhibits rich information content about the collective neural activities reflecting the cognitive states and brain functions. For the interpretation of surface potentials in terms of their neural correlates, most research has focused on local neural activities.   From basic neuroscience research to clinical treatments and neural engineering, electrocorticography (ECoG) has been widely used to record surface potentials to evaluate brain function and develop neuroprosthetic devices. However, the requirement of invasive surgeries for implanting ECoG arrays significantly limits the coverage of different cortical regions, preventing simultaneous recordings from spatially distributed cortical networks. However, this rich information content of surface potentials encoded for the large-scale cortical activity remains unexploited and little is known on how local surface potentials are correlated with the spontaneous neural activities of distributed large-scale cortical networks. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

Cardiac MRI is the clinical reference standard for visual and quantitative assessment of heart function. Specifically, cine balanced steady-state free precession (SSFP) can yield cardiac images with high myocardium–blood pool contrast for evaluation of left ventricular (LV) function. However, MRI suffers from long acquisition times, often requiring averaging across multiple heartbeats, and necessitates a trade-off among spatial resolution, temporal resolution, and scan time. Clinically, radiologists are forced to balance acquisition time with resolution to fit clinical needs, and certain applications such as real-time imaging may require small acquisition matrices. Image scaling is typically performed by using conventional upscaling methods, such as Fourier domain zero padding and bicubic interpolation. These methods, however, do not readily recover spatial detail, such as the myocardium–blood pool interface or delineation of papillary muscles.

Researchers at the University of California, Davis have developed machine learning models to enhance the accessibility and accuracy of acute kidney injury (AKI) testing.

Diagnosing retinal disease, which affects over 200 million people worldwide, requires expensive and complicated analysis of the structure and function of retinal tissue. Recently, UCI developed a training algorithm which, for the first time, is able to assess tissue health from images collected using more common and less expensive optics.