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Chronic Wound Mouse Model

Prof. Manuela Martins-Green and colleagues from the University of California, Riverside have developed a mouse model for chronic wounds in db/db-/- diabetic mice. Wounds are considered chronic when the body is unable to properly facilitate every stage of the healing process due to Oxidative Stress (OS), which occurs when an imbalance of redox chemicals exists in the damaged tissues. To create the chronic  wounds the mice were treated with inhibitors of antioxidant enzymes (IAE) at the time of wounding only. The wounds were then covered with tegaderm membranes and allowed to become chronic. Control wounds were treated with placebo. Fig 1: Percent open wound area over time in wounds of C57BL/6 and db/db-/- mice.  

High Resolution Laser Speckle Imaging of Blood Flow

Prof. Guillermo Aguilar and his colleagues from the University of California, Riverside have developed a new approach to laser speckle imaging, called Laser Speckle Optical Flow Imaging (LSOFI) to be used for autonomous blood vessel detection and as a qualitative tool for blood flow visualization. LSOFI works by capturing the speckle displacement caused by different physical behavior and use the data to create a mapped image. It has been shown that LSOFI has many advantages over LSCI methods both in temporal and spatial resolution. Namely, LSOFI can be used to produce higher resolution images compared with the LSCI method using less frames. Combining this technology with Graphics Processing Unit (GPU) computation increases the speed of LSOFI, so GPU enabled LSOFI shows potential to create a fast and fully functional quasi-real time blood flow imaging system.  Fig 1: Comparison of blood flow imaging techniques applied to the raw image. The shown results are for Laser Speckle Optical Flow Imaging (LSOFI) using the Farneback Optical Flow algorithm, traditional Laser Speckle Imaging (LSI), and Temporal Frame Averaging (sLASCA).  

Vehicle Make and Model Identification

Prof. Bir Bhanu and his colleagues from the University of California, Riverside have developed a method for  analyzing real-time video feed of vehicles from a rear  view  perspective to identify the make and model of a vehicle. This method works by using a software system for detecting the Regions-of-Interest (ROIs) of moving vehicles and moving shadows, computing structural and other features and using a vehicle make and model database for vehicle identification. The system performs calculations based on factors found in all vehicles, so it is reliable regardless of vehicle color and type. The system is compatible with low resolution video feed, so it is able to analyze video feed in real-time. Thus, this technology holds potential for innovating fields like vehicle surveillance, vehicle security, class-based vehicle tolling, and traffic monitoring where reliable real-time video analysis is needed.  Figure 1: Example of the direct rear view of moving vehicles.  

Multi-Omics CoAnalysis (MOCA) Software

Researchers at the University of California, Riverside have developed a software program named Multi-Omics CoAnalysis (MOCA), which is an integrative, interactive, and informative (i3) workbench. Using MOCA, researchers will be able to statistically analyze and interactively visualize the experimental data and generate the corresponding correlative omics data. Data can be presented in various formats including box plots, line plots, heat maps, volcano plots, principal component analysis, coefficient distribution plot, and network plot with an adjacency matrix. The graphical user-interface (GUI) of MOCA delivers intuitive and interactive data visualizations, and enables access to many types of metadata and experimental data in a user-friendly manner.  Fig 1: MOCA-generated image of a metabolic network in MEP pathway Fig 2: MOCA-generated pattern plot by using machine learning

Vehicle Logo Identification in Real-Time

Brief description not available

Generating Visual Analytics and Player Statistics for Soccer

Prof. Bhanu and his colleagues from the University of California, Riverside have developed a system to automate the process of player talent identification by performing visual analytics and generating statistics at the match, team and player level for soccer from a video using computer vision and machine learning techniques. This work uses a database of 49,952 images which are annotated into two classes namely: players with the ball and players without the ball. The system can identify which players are controlling the ball. Compared to other state-of-the-art approaches, this technology has demonstrated an accuracy of 86.59% on identifying players controlling the ball and an accuracy of 84.73% in generating the match analytics and player statistics. Figure 1: Visualization of features learned by the system Figure 2: Visualization of gray scale features learned by the system  

Low Cost and Scalable Sap Feeding Insect Rearing and Gene Editing System

Profs. Peter Atkinson and Linda Walling at UCR have developed an in vitro rearing system on 3.5-cm and 6-cm  leaf disc plates that support egg to adult development in as little as 19 days. This system translates to a small-footprint, cost-effective rearing process, which can be industrialized, automated  and applied to other sap-feeding insects. Each plate may be used as an independent experiment or a mini-colony of a new whitefly genetic strain. Creating genetically modified whiteflies and other sap-feeding insects for genetic manipulation involves microinjecting embryos (eggs), which remain attached to excised leaf discs, which have been pretreated to remain viable throughout the whitefly life cycle.  This technology can be used to maintain colonies of whitefly in a more cost-effective way than existing approaches. In addition, this technology has been used to generate the first genetic mutants in the glassy-winged sharpshooter, Homalodisca vitripennis, a significant pest of Californian viticulture and thus opening the possibility of developing new strategies for its control and elimination. Fig. 1A shows a wild-type male whitefly and a mutant white male whitefly, which was generated by CRISPR/Cas9 mutagenesis using the leaf-disc injection and rearing protocols. Fig. 1 B shows a mosaic-eyed glassy-winged sharpshooter that was generated using the same technology.     Fig. 2 Each incubator (left) can hold up to 700 experiments/mini-whitefly colonies compared to the bugdorm (right), which houses one colony/experiment per tent. One incubator would replace ~11 biosafety level 2 (BSL2) greenhouses.    

Plants with Enhanced Immunity to Root Knot Nematodes

Prof. Kaloshian and her colleagues from the University of California, Riverside, have developed plants with enhanced immunity resulting in enhanced resistance to RKNs. The methods comprise introducing into a plant a gene editing construct that specifically inhibits activity of G-LecRK-VI.13 gene, a negative regulator of plant immunity. Additionally, the descendant of this plant also carry the enhanced resistance to RKNs. The invention could be used in a broad range of important agricultural crops including rice, lettuce, and tomatoes. This approach holds potential for increasing crop quality and yield, considering that plant damage from RKNs result in poor growth, a decline in quality and yield of the crop, and reduced resistance to environmental stresses. By triggering an enhanced immune response, by eliminating a negative regulator of immunity, the opportunity exists to develop more durable plant resistance towards RKNs and other types of nematodes.  Fig 1: Tomato plants, grown in a plastic house, infected with the root-knot nematode Meloidogyne incognita.