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(SD2022-270) Algorithm for de novo drug discovery

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. In drug discovery, a common paradigm today involves performing an initial high-throughput experimental screening of available compounds to identify hit compounds, which is both resource-intensive and can only identify which existing compounds are promising, relying on the further work of medicinal chemists to optimize these hit compounds into lead compounds. As an alternative, many computational methods have been proposed for de novo drug design, including genetic algorithms and other deep learning-based approaches. Methods outside of deep learning are generally not flexible enough to be entirely useful in drug discovery, while current state-of-the-art deep learning methods are either prohibitively slow or cannot generate molecules with an adequate level of desirability.

Method to Identify Vomocytosis Events via Time-Lapse Fluorescence Microscopy

Researchers at the University of California, Davis have developed a method to identify vomocytosis events in time-lapse microscopy videos.

(SD2020-421) Virtual Electrodes for Imaging of Cortex-Wide Brain Activity: Decoding of cortex-wide brain activity from local recordings of neural potentials

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;}

(SD2019-220) Spatiotemporal resolution enhancement of biomedical images

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.

Neural Network Machine Learning Applied to Diagnose Acute Kidney Injury

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

Advanced Imaging By LASER-Trained Algorithms Used To Process Broad-Field Light Photography and Videography

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.

Covidseeker. Digital Contact Tracing And Hotspotting In Real-Time

UCSF PIs developed a novel software platform for COVID-19 contact tracing and hotspotting called COVIDseeker. Covidseeker looks back in time and may be able to recreate people’s movements when infection rates were rising and falling in the spring and summer of 2020, giving epidemiologists an invaluable source of data as they try to predict what is going to happen in the fall and winter.This digital health invention has applications broader than COVID-19. The software can potentially be leveraged for other infectious diseases, treating obesity, and controlling smoking or alcohol addiction by showing where and when people are when they smoke, what are the triggers and how their location contributes to the risk of developing a particular disease.

Deep Learning Techniques For In Vivo Elasticity Imaging

Imaging the material property distribution of solids has a broad range of applications in materials science, biomechanical engineering, and clinical diagnosis. For example, as various diseases progress, the elasticity of human cells, tissues, and organs can change significantly. If these changes in elasticity can be measured accurately over time, early detection and diagnosis of different disease states can be achieved. Elasticity imaging is an emerging method to qualitatively image the elasticity distribution of an inhomogeneous body. A long-standing goal of this imaging is to provide alternative methods of clinical palpation (e.g. manual breast examination) for reliable tumor diagnosis. The displacement distribution of a body under externally applied forces (or displacements) can be acquired by a variety of imaging techniques such as ultrasound, magnetic resonance, and digital image correlation. A strain distribution, determined by the gradient of a displacement distribution, can be computed (or approximated) from measured displacements. If the strain and stress distributions of a body are both known, the elasticity distribution can be computed using the constitutive elasticity equations. However, there is currently no technique that can measure the stress distribution of a body in vivo. Therefore, in elastography, the stress distribution of a body is commonly assumed to be uniform and a measured strain distribution can be interpreted as a relative elasticity distribution. This approach has the advantage of being easy to implement. The uniform stress assumption in this approach, however, is inaccurate for an inhomogeneous body. The stress field of a body can be distorted significantly near a hole, inclusion, or wherever the elasticity varies. Though strain-based elastography has been deployed on many commercial ultrasound diagnostic-imaging devices, the elasticity distribution predicted based on this method is prone to inaccuracies.To address these inaccuracies, researchers at UC Berkeley have developed a de novo imaging method to learn the elasticity of solids from measured strains. Our approach involves using deep neural networks supervised by the theory of elasticity and does not require labeled data for the training process. Results show that the Berkeley method can learn the hidden elasticity of solids accurately and is robust when it comes to noisy and missing measurements.

Automated Histological Image Processing tool for Identifying and Quantifying Tissue Calcification

Researchers at UCI have developed a method of identifying, quantifying, and visualizing tissue with calcification. The image processing tool can automatically characterize calcium deposits in CT images histological tissue, especially when it has accumulated in unusual places in the body.

Virtual Reality For Anhedonia Program

UCLA researchers in the Department of Psychology have developed a behavioral training program for the improvement of anhedonia.

Artificial Intelligence Enabled Control of Hemodynamics and Anesthesia in Surgery Patients

UCLAresearchers from the Department of Mechanical and Aerospace Engineering and the Department of Anesthesiology have developed a method for artificial intelligence guided control of anesthetics and other medications during surgery and in the Intensive Care Unit (ICU).

Automated Biomarker Prediction Using Optical Coherence Tomography

UCLA researchers in the Department of Computational Medicine have developed a computer program capable of automatically and accurately diagnosing optical diseases using OCT.

Mediator-Free Electroenzymatic Sensing with Enhanced Sensitivity and Selectivity for Wearable Metabolite and Nutrient Monitoring Applications

UCLA researchers in the Department of Electrical and Computer Engineering have developed a wearable electroenzymatic sensor for non-invasive monitoring of metabolites and nutrients. The sensor has been successfully tested in human subjects to be highly sensitive and selective, making it ideal for monitoring and improving individual well-being.

Wearable Voltammetric Monitoring of Electroactive Drugs

UCLA researchers in the Department of Electrical and Computer Engineering have developed a voltammetric wearable device capable of monitoring electroactive drug circulation and abundance in biofluids. This non-invasive monitoring system can be used for electroactive drug therapy management, drug compliance/abuse monitoring, drug-drug interaction studies, and personalized dosing.

IgEvolution: A Novel Tool for Clonal Analysis of Antibody Repertoires

Constructing antibody repertoires is an important error-correcting step in analyzing immunosequencing datasets that is important for reconstructing evolutionary (clonal) development of antibodies. However, the state-of-the-art repertoire construction tools typically miss low-abundance antibodies that often represent internal nodes in clonal trees and are crucially important for clonal tree reconstruction. Thus, although repertoire construction is a prerequisite for follow up clonal tree reconstruction, the existing repertoire reconstruction algorithms are not well suited for this task because they typically miss low-abundance antibodies that often represent internal nodes in clonal trees and are crucially important for clonal tree reconstruction.

A Fully‐automated Deep Learning System (software code) for the Detection, Prognosis, and Visualization of Pulmonary Disease.

The majority of state‐of‐the‐art lung segmentation algorithms in the literature do not simultaneously segment lungs, lung lobes, and airway in a single algorithm. Additionally, automated algorithms typically perform the segmentation task on a series of 2D slices, which can reduce segmentation accuracy of anatomical structures (i.e. lung lobes) that may require contextual information across all three spatial dimensions. Many existing algorithms also have not been validated on chest CTs across a wide variety of conditions to evaluate algorithm generalizability. Currently, quantification of respiratory measurements requires a radiologist, trained analyst, or technician to recognize, identify, and manually annotate anatomical landmarks such as the lung lobes or airway in the chest. A fully‐automated deep learning system may eliminate the need for manual analysis, thereby improving efficiency and expanding applicability to a large number of CTs.

Computational Cytometer Based On Magnetically-Modulated Coherent Imaging And Deep Learning

UCLA researchers in the Department of Electrical & Computer Engineering have designed and built a computational cytometer capable of detecting rare cells at low concentration in whole blood samples. This technique and instrumentation can be used for cancer metastasis detection, immune response characterization and many other biomedical applications.

Software - Unified algorithm for data cleaning, source separation, and imaging of electroencephalographic signals: Recursive Sparse Bayesian Learning (RSBL)

Electroencephalographic source imaging (a.k.a. magnetic/electric or M/EEG source imaging, ESI, or brain electrical tomography) usually depends upon sophisticated signal processing algorithms for data cleaning, source separation and imaging. Typically, these problems are addressed separately using a variety of heuristics, making it difficult to systematize a methodology for extracting robust brain source images on a wide range of applications.

Automatic Identification of Ophthalmic Medication for The Visually Impaired

Researchers at UCI are developing technology that allows visually impaired patients to use their smartphones to take pictures of their eye medication/eye drop bottles. The technology will recognize the eye medication and verbally communicate the medication and will audibly confirm the medication along with the instructions on use.

cBCI: Method and System for Diagnosing and Training Cognitive Fitness and Targeted Neural Network Function Underlying Cognitive Fitness in an Integrated Digital Approach

The inventors have created a brain computer interface (BCI) that serves as a diagnostic and training tool of cognitive abilities and neural network function.

Method and Apparatus for Movement Therapy Gaming System

Rehabilitation therapy, while an important tool for the long term recovery of patients affected by brain injury or disease, is expensive and requires one-on-one attention from a certified healthcare professional. UCI researchers have developed a computer-based system that provides arm movement therapy for patients. The system allows patients to independently practice hand and arm movements, improving therapeutic outcomes, while reducing hospital visits and cost for both patients and healthcare providers.

Financial Model for Informing Value-Based Payment Decisions

Researchers led by David Johnson from the Department of Urologic Oncology and the West Los Angeles Veteran’s Affairs Medical Center have developed an interactive web platform that predicts the financial outcomes for various stakeholders (physicians, hospitals, and payers) of transitioning from fee-for-service to bundled payments for robotic radical prostatectomy.

DARTS: Deep Learning Augmented RNA-seq Analysis of Transcript Splicing

Researchers led by Yi Xing have developed a novel deep learning algorithm to detect alternative splicing patterns in RNA-seq data

Combination of a drug with low level light therapy (LLT) for treatment of wounds

This is a combination of a drug and light technology for the purpose of accelerating the healing of wounds on the skin, ulcers, and elsewhere in the body. Both methods have been shown to accelerate wound healing, and combining the two will potentially result in more rapid healing than either would alone.  

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