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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.

Smart Dialysis Catheter

UCLA researchers in the Department of Cardiology at UCLA’s David Geffen School of Medicine have developed a smart dialysis catheter that can measure different patient vitals in real-time to prevent hospitalizations due to renal failure.

Deep Learning of Biomimetic Sensorimotor Control for Biomechanical Human Animation

UCLA researchers from the Department of Computer Science have developed a computer simulation model and associated software system for biomimetic human sensorimotor control.

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.

Polaris: Lifestyle Guide for Diabetes

Researchers at UCI have developed a comprehensive platform, Polaris, for personalized diabetes management. By combining standard blood glucose monitoring with activity tracking, Polaris provides users with real-time suggestions that encourage treatment adherence and promote healthy behaviors to better mitigate their symptoms.

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.

Learning Predictive Models Of Drug Response That Translate Across Biological Contexts

Translating biomarkers from basic research to clinical utility involves transfer of information across a series of contexts (from cells to disease animal models to humans) in which data are progressively harder to obtain. It is known that biomarkers identified in cell lines often do not translate to clinical settings and that is one of the main roadblocks in Translational Medicine. Presently, the state-of-the-art machine learning models require a number of training samples. The inventors show that conventional machine learning models, such as Random Forest, Linear Regression Model, Nearest Neighbors, cannot achieve accurate predictions and therefore there is a need for more accurate models.

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

A Method For Digital Pathology Using Augmented Reality

UCLA researchers in the Departments of Electrical Engineering and Computer Engineering have developed a novel method for automated image analysis of digital pathology slides.

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.  

Multimodal food journaling

Researchers at UCI have developed a hands-free, unobtrusive smartphone-based application for automatic food journaling. The app, which operates via voice command, is interactive and highly engaging thereby encouraging long-term user participation.  

A Method To Determine Cause Of A Cardiac Arrest And Provide Cause-Specific Decision Support In Real-Time Using Continuous Electrocardiography

Researchers led by Duc Hong Do from the Department of Cardiology at UCLA have developed an algorithm to detect the cause of cardiac arrest in a hospital setting.

A New Human-Monitor Interface For Interpreting Clinical Images

UCLA researchers in the Department of Radiological Sciences have invented a novel interactive tool that can rapidly focus and zoom on a large number of images using eye tracking technology.

A Method For Accurate Parametric Mapping Based On Characterization Of A Reference Tissue Or Region

UCLA researchers in the Department of Radiology have developed a novel method that addresses a common issue of MRI imaging misinterpretation due to the high field effects of B1+ inhomogeneity.

Dicom/Pacs Compression Techniques

Researchers led by Xiao Hu from the Department of Surgery at UCLA have created a novel and convenient way to compress and query medical images from a PACS system.

Deep-Learning-Based Computerized Prostate Cancer Classification Using A Hierarchical Classification Framework

UCLA researchers in the Department of Radiological Sciences have developed a deep-learning-based computerized algorithm for classification of prostate cancer using multi-parametric-MRI images.

Immersive Virtual Reality To Manage Pain

Researchers led by Mark Cohen from the Department of Psychiatry at UCLA have developed a virtual reality-based therapy to manage chronic pain.

Method To Determine Personalized Transcranial Magnetic Stimulation (Tms) Parameters To Enhance Clinical Treatment Outcomes In Major Depression And Neurological Disorders

Researchers led by Aimee Hunter from the Department of Psychaitry at UCLA have developed a methodology to determine parameters for personalized transcranial magnetic stimulation to treat depression.

A Device, Methodology And System For Monitoring, Classifying And Encouraging Activity

UCLA researchers in the Department of Computer Science have developed a new technology to fight the growing obesity epidemic by encouraging exercise.

A Non-Progressive Sampling Volumetric Modulated Arc Therapy (VMAT) Method

UCLA researchers in the Department of Radiation Oncology have developed a novel direct aperture optimization method for volumetric modulated arc therapy (VMAT) to solve the current arc optimization problem.

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