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Biosensor - Comprised of “Turn-on” Probes - with the Ability to Detect DNA Sequences in Living Cells

Researchers have developed a split-enzyme system that can detect genetic information in living cells by using luciferase linked to programmable DNA-binding domains.

(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;}

Deep Learning-Based Approach to Accelerate T cell Receptor Design

Researchers at the University of California, Davis have developed a deep learning simulation model to predict mutated T-cell receptor affinity and avidity for immunotherapy applications.

Workflow to Computationally Optimize Upcycling of Critical Metals from Spent Lit

This technology computationally optimizes the upcycling of critical metals in deep eutectic solvents with molecular dynamics, artificial intelligence, and experimental approaches.

Reinforcement Learning with Real-time Docking of 3D Structures for SARS-COV-2

The inventors propose a novel framework generating new molecules that potentially inhibit the Mpro protein, the main protease of SARS-COV-2. The technology combines deep reinforcement learning (RL) with real-time molecular docking on the 3d structure of Mpro using AutoDock Vina, an open-source program for doing molecular docking. A second second docking software, Glide, was used to validate the generated molecules. The AutoDock and Glide docking softwares showed consensus on 41 molecules as potential potent Mpro inhibitors that were sufficiently easy to synthesize. The inventors show that this method samples the drug chemical space efficiently, covering a much broader space than molecules submitted to the COVID moonshot project, and the molecules have the correct shape and non-bonded interactions to fit into the binding pocket. Moreover, this approach only relies on the structure of the target protein, which means it can be easily adapted for future development of other inhibitors.

Programmable System that Mixes Large Numbers of Small Volume, High-Viscosity, Fluid Samples Simultaneously

Researchers at the University of California, Davis have developed a programmable machine that shakes and repeatedly inverts large numbers of small containers - such as vials and flasks – in order to mix high-viscosity fluids.

Aluminum Microchips for Biosensing and Pathogen Identification

Prof. Quan Cheng and colleagues from the University of California, Riverside have developed aluminum (Al) microchips for highly sensitive SPR detection of bioanalytical targets. This technology allows for determination of binding kinetics of drug targets and disease marker detection. In addition to applications for SPR, these Al microchips enable other surface-based techniques such as enhanced Raman spectroscopy and MALDI-MS for direct pathogen identification. Compared to traditional gold substrates, Al has a broad range of advantages. It is more plasmonically active, leading to high optical sensitivity, and it is chemically flexible for design of various analytical platforms. Al also has several manufacturing benefits that make it commercially appealing when compared to gold, such as higher abundance, lower cost, and simple integration into existing manufacturing processes such as CMOS. Fig 1: (Top) Fabrication of aluminum microchips. (Bottom) Aluminum demonstrates a high theoretical and practical plasmonic activity correlating to a higher detection sensitivity for biological targets.  

Plasmonic Gold Microchips for Swift Microbial Identification with MALDI-MS

Prof. Quan Cheng and colleagues from the University of California, Riverside have developed a gold microchip consisting of a nanoscale film fabricated on a gold substrate for highly effective, matrix-free laser desorption ionization mass spectrometry (LDI-MS) analysis of lipids. This technology allows for effective analysis of low mass metabolites without the need for time consuming extraction methods. The microchip also enhances fluorescent signal through metal enhanced fluorescence (MEF) allowing single cells to be located easily and improves ionization of lipids. Fig 1: Gold microchips enable localization of cells with MEF and efficient ionization of lipid species. The lipid fingerprint can then be used to trace changes caused by toxicants or identify microbial species present.  

Stable N-acetylated analogs of Sialic Acids and Sialosides

Researchers at the University of California, Davis have constructed a library of glycans containing N-acetyl sialic acids to mimic those containing naturally occurring O-acetyl sialic acids.

2-D Polymer-Based Device for Serial X-Ray Crystallography

Researchers at the University of California, Davis have developed a single-use chip for the identification of protein crystals using X-ray based instruments.

One-Pot Multienzyme Synthesis of Sialidase Reagents, Probes and Inhibitors

Researchers at the University of California, Davis, have developed an environmentally friendly one-pot multienzyme (OPME) method for synthesizing sialidase reagents, probes, and inhibitors.

Nanopore Sensor to Characterize Nano and Microscale Particles and Cells

Researchers at the University of California, Riverside can discriminate between mixed populations of cells and particles in solution using pressure to displace objects across a nanopore multiple times.  Ionic current flow through the nanopore indicates the pressure required to translocate the object in the pore, which correlates to the object’s mass and volume.  Key to these results is that a nanopore sensor allows pressure oscillations to capture and release repeatedly the same object to learn about its inertia and morphology.  Such data can provide details about the size and shape of analytes, their morphologies and structural constraints, or even pathological conditions of living cells. Fig. 1 Nanopore sensing of differently sized cells in a mixed bacterial culture.  

Protein Inhibitor of Type II-A CRISPR-Cas System

The inventors have discovered three protein inhibitors of the type II-A CRISPR-Cas system that specifically inhibit Cas9 from staphylococcus aureus. This finding is of potential importance to many companies in the CRISPR space. 

Novel Tunable Hydrogel for Biomedical Applications

Prof. Huinan Liu’s lab at the University of California, Riverside has developed a novel tunable hydrogel that achieves tunable crosslinking, reversible phase transition, and may be used as a 3DP scaffold. This new hydrogel utilizes dynamic coordination of its innate carboxylic groups and metal ions. Adding methylacrylate or other functional groups is not required for this technology and the resulting hydrogel is less toxic. Since the functionalization of this hydrogel is not required, it is less process-intensive and results in a more cost-effective hydrogel.  In addition, the UV curing is no longer needed since methylacrylate is no longer utilized to crosslink the hydrogel.   Fig 1: Optical micrographs of top view and cross-section of HyA hydrogels printed using cold-stage method and direct writing method. Hydrogels printed using direct writing method showed better structural integrity and stability.

Protein Inhibitor of Type VI-B CRISPR-Cas System

The inventors have discovered the first protein inhibitor of the type VI-B CRISPR-Cas system. By controlling this CRISPR system, one could possibly ameliorate the toxicity and off-target cleavage activity observed with the use of the type VI CRISPR system. Moreover, these proteins can also serve as an antidote for instances where the use of CRISPR-Cas technology poses a safety risk. Additionally, this technology can also be used for engineering genetic circuits in mammalian cells. This finding is of potential importance to many companies in the CRISPR space. 

Automated Tip Conditioning ML-Based Software For Scanning Tunneling Spectroscopy

Scanning tunneling microscopy (STM) techniques and associated spectroscopic (STS) methods, such as dI/dV point spectroscopy, have been widely used to measure electronic structures and local density of states of molecules and materials with unprecedented spatial and energy resolutions. However, the quality of dI/dV spectra highly depends on the shape of the probe tips, and atomically sharp tips with well-defined apex structures are required for obtaining reliable spectra. In most cases, STS measurements are performed in ultra-high vacuum  and low temperature (4 K) to minimize disturbances. Advance tip preparation and constant in situ tip conditioning are required before and during the characterization of target molecules and materials. A common way to prepare STM tips is to repetitively poke them on known and bare substrates (i.e. coinage metals or silicon) to remove contaminations and to potentially coat the tip with substrate atoms. The standard dI/dV spectra of the substrate is then used as a reference to determine whether the tip is available for further experiments. However, tip geometry changes during the poking process are unpredictable, and consequently tip conditioning is typically slow and needs to be constantly monitored. Therefore, it restricts the speed of high-quality STM spectroscopic studies. In order to make efficient use of instrument idle time and minimize the research time wasted on tip conditioning, UC Berkeley researchers developed software based on Python and machine learning that can automate the time-consuming tip conditioning processes. The program is designed to do tip conditioning on Au(111) surfaces that are clean or with low molecular coverage with little human intervention. By just one click, the program is capable of continued poking until the tip can generate near-publication quality spectroscopic data on gold surfaces. It can control the operation of a Scienta Omicron STM and automatically analyze the collected topographic images to find bare Au areas that are large enough for tip conditioning. It will then collect dI/dV spectra at selected positions and use machine learning models to determine their quality compared to standard dI/dV spectra for Au20 and determine if the tip is good enough for further STS measurements. If the tip condition is not ideal, the program will control the STM to poke at the identified positions until the machine learning model predicts the tip to be in good condition.

Improved guide RNA and Protein Design for CasX-based Gene Editing Platform

The inventors have developed two new CasX gene-editing platforms (DpbCasXv2 and PlmCasXv2) through rationale structural engineering of the CasX protein and gRNA, which yield improved in vitro and in vivo behaviors. These platforms dramatically increase DNA cleavage activity and can be used as the basis for further improving CasX tools.The RNA-guided CRISPR-associated (Cas) protein CasX has been reported as a fundamentally distinct, RNA-guided platform compared to Cas9 and Cpf1. Structural studies revealed structural differences within the nucleotide-binding loops of CasX, with a compact protein size less than 1,000 amino acids, and guide RNA (gRNA) scaffold stem. These structural differences affect the active ternary complex assembly, leading to different in vivo and in vitro behaviors of these two enzymes.

Software Defined Pulse Processing (SDPP) for Radiation Detection

Radiation detectors are typically instrumented with low noise preamplifiers that generate voltage pulses in response to energy deposits from particles (x-rays, gamma-rays, neutrons, protons, muons, etc.). This preamplifier signal must be further processed in order to improve the signal to noise ratio, and then subsequently estimate various properties of the pulse such as the pulse amplitude, timing, and shape. Historically, this “pulse processing” was carried out with complex, purpose-built analog electronics. With the advent of digital computing and fast analog to digital converters, this type of processing can be carried out in the digital domain.There are a number of commercial products that perform “hardware” digital pulse processing. The common element among these offerings is that the pulse processing algorithms are implemented in hardware (typically an FPGA or high performance DSP chip). However this hardware approach is expensive, and it's hard to tailor for a specific detector and application.To address these issues, researchers at UC Berkeley developed a solution that performs the pulse processing in software on a general purpose computer, using digital signal processing techniques. The only required hardware is a general purpose, high speed analog to digital converter that's capable of streaming the digitized detector preamplifier signal into computer memory without gaps. The Berkeley approach is agnostic to the hardware, and is implemented in such a way as to accommodate various hardware front-ends. For example, a Berkeley implementation uses the PicoScope 3000 and 5000 series USB3 oscilloscopes as the hardware front-end. That setup has been used to process the signal from a number of semiconductor and scintillator detectors, with results that are comparable to analog and hardware digital pulse processors.In comparison to current hardware solutions, this new software solution is much less expensive, and much more easily configurable. More specifically, the properties of the digital pulse shaping filter, trigger criteria, methods for estimating the pulse parameters, and formatting/filtering of the output data can be adjusted and tuned by writing simple C/C++ code.

Design For Nesting Height Adjustable Workbenches

Need to transport sturdy adjustable workbenches for use at sea or other temporary work spaces that need anchoring to walls or floors and you can't find a commercially available source?

Composition and Methods of a Nuclease Chain Reaction for Nucleic Acid Detection

This invention leverages the nuclease activity of CRISPR proteins for the direct, sensitive detection of specific nucleic acid sequences. This all-in-one detection modality includes an internal Nuclease Chain Reaction (NCR), which possesses an amplifying, feed-forward loop to generate an exponential signal upon detection of a target nucleic acid.Cas13 or Cas12 enzymes can be programmed with a guide RNA that recognizes a desired target sequence, activating a non-specific RNase or DNase activity. This can be used to release a detectable label. On its own, this approach is inherently limited in sensitivity and current methods require an amplification of genetic material before CRISPR-base detection. 

Mutation Organization Software for Adaptive Laboratory Evolution (ALE) Experimentation

Adaptive Laboratory Evolution (ALE) is a tool for the study of microbial adaptation. The typical execution of an ALE experiment involves cultivating a population of microorganisms in defined conditions (i.e., in a laboratory) for a period of time that enables the selection of improved phenotypes. Standard model organisms, such as Escherichia coli, have proven well suited for ALE studies due to their ease of cultivation and storage, fast reproduction, well known genomes, and clear traceability of mutational events. With the advent of accessible whole genome resequencing, associations can be made between selected phenotypes and genotypic mutations.   A review of ALE methods lists 34 separate ALE studies to date. Each study reports on novel combinations of selection conditions and the resulting microbial adaptive strategies. Large scale analysis of ALE results from such consolidation efforts could be a powerful tool for identifying and understanding novel adaptive mutations. 

Compositions And Methods For Allelic Gene Drive Systems And Lethal Mosaicism

Efficient super-Mendelian inheritance of transgenic insertional elements has been demonstrated in flies, mosquitoes, yeast, and mice. While numerous potentially impactful applications of such so-called gene-drive systems have been proposed they are currently limited to copying relatively large DNA cargo sequences (~1-10 Kb). Many desired genetic traits (e.g., drought tolerance in plants, crop yield, pest-resistance, or insecticide sensitivity), however, result from allelic variants altering only one or a few base pairs. An efficient system for super-Mendelian inheritance of such subtle genetic variants would accelerate a wide array of efforts to disseminate favorable traits throughout populations, or to assemble complex genotypes consisting of point-mutant alleles in combination with insertional transgenes for a multitude of research and applied purposes.

Microfluidic Dispenser for Automated, High-Precision, Liquids Handling

Researchers at the University of California, Davis have developed a robotic dispensing interface that uses a microfluidic-embedded container cap – often referred to as a microfluidic Cap-to-Dispense or μCD - to seamlessly integrate robotic operations into precision liquids handling.

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