Many genomes encode Restriction-Modification systems (RMs) that act to protect the host cell from invading DNA by cutting at specific sites (frequently short 4-6 base reverse complement palindromes). RMs also protect host DNA from unfavorably being cut by modifying sites within the host DNA that could be targets by the host’s own surveillance enzymes. It is also not unusual to find that these enzymes are adjacent to each other in the host genome. Traditional approaches to understanding these sites involve finding a methylase that is typically adjacent to a restriction enzyme, and then extracting DNA, expressing protein and then testing DNA sequence for evidence of cutting. In certain laboratory research (e.g., programs that involve transforming DNA/RNA) it may be desirable to more comprehensively understand the sequences being surveilled by the host. Moreover, it may be desirable in certain laboratory research to know/predict which surveillance enzymes are present in a genome in order to affect cell transformation efficiency through evasion of those sequences.

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Optical chromatography (OC) is an optofluidic technique enabling label-free fractionation of microscopic particles, e.g., bioparticles from heterogenous mixtures. This technique relies on a laser beam along a microfluidic channel to create opposing optical scattering and fluidic drag forces. Variable strength and balance of these forces may be harnessed for selective sorting of bioparticles based on their size, composition, and morphology. OC has been successfully applied to fractionation of blood components such as human erythrocytes, monocytes, granulocytes, and lymphocytes. OC offers unique capabilities as a modern separation technique, especially when combined with multi-stage sequential fractionation and microfluidic network-based purification approaches, and it particularly excels in distinguishing bioparticles with subtle differences. However, there are several key limitations with OC being widely adopted. In order to create strong optical scattering forces along the microfluidic channels, expensive and sophisticated laser sources must be precisely aligned along the fluidic channel with a well-controlled beam waist profile, requiring a complicated optical alignment procedure that employs multiple multi-axis positioners. While microfluidic approaches using OC hold promise for broader use, multiplexed and high throughput systems remain overly complicated and cost-prohibitive.

High-throughput next-generation sequencing (NGS) systems have allowed for large scale collection of transcriptomic data with single cell resolution. Within this data lies variability allowing researchers to characterize and/or infer certain morphological aspects of interest, such as single cell type, cell state, cell growth trajectories, and inter-cellular gene regulatory networks. All of these qualities are important parts of understanding how cells interact with one another, both for building better cellular models in vitro and for understanding biological processes in vivo. While the size of single cell data has increased massively, NGS techniques for key pieces of analysis have not kept pace, using slow, manual pipelines of domain experts for initial clustering. Attempts to improve NGS classification performance have fallen short as the numbers of cell types (often asymmetric) and cell subtypes have increased while the number of samples per label has become small. The technical variability between NGS experiments can make robust classification between multiple tissue samples difficult. Moreover, the high-dimensional nature of NGS transcriptomic data makes this type of analysis statistically and computationally intractable.

The function of living tissue relies not only on its structure, but crucially on its dynamics at an array of timescales. Structural imaging of biological molecules at very high resolution has become routine in recent years, but these static snapshots provide little insight into the structural changes crucial for biological function. It is well known that changes in the geometry of macromolecules induce fluctuations in the Raman spectrum, but measurements of these fluctuations inherently suffer from poor signal strengths, meaning that dynamics at many timescales are obscured by the time-averaging necessary to obtain sufficient sensitivity.To address these problems, researchers at UC Berkeley have developed a method for probing the Raman spectrum, and hence dynamics of biological molecules at very high sensitivity and across timescales inaccessible to extant techniques. This technique, in fact, can in principle obtain arbitrarily fine spectral and temporal resolution, opening the door to, for example, probe everything from the dynamics of side chain rotations (picoseconds) to protein folding and domain motion (milliseconds).

Long read sequencing (e.g. nanopore sequencing) involves a tradeoff between the length of the DNA fragment sequenced, which allows for greater ease of data assembly relative to massively parallel sequencing technologies (e.g. Illumina (R) sequencing) and accuracy of individual base calls.This technology takes advantage of the long read capabilities of nanopore sequencing to improve the accuracy of reads of highly variable nucleic acid species, including cDNAs, and which can be highly variable due to alternative RNA splicing. 

Cardiovascular disease is among the leading causes of death for citizens in affluent nations, and the most significant cause of morbidity in those with cardiovascular disease is hypertension. Often called the “silent killer” because it has few clinical signs in its early stages, elevated blood pressure is often in an advanced stage before it is treated, leading to a substantially worse prognosis than if it had been detected earlier.In order to address this problem, researchers at UC Berkeley have developed a wearable device which continuously monitors diastolic blood pressure, transmitting data to a portable device such as a cell phone, where it can be stored and analyzed. The device utilizes piezoelectric transducers to perform the measurement, which allows the wearable device to remain small while containing a large number of sensors in order to reduce noise.

Nanopore sequencing requires a processive motor or other element to control the rate of RNA movement in single nucleotide steps through the nanopore sensor. The control element is typically situated several nucleotides from the sensor, therefore it necessarily releases before the end of the native RNA strand reaches the sensor. Thus, the bases along that terminal interval cannot be sequenced using conventional nanopore strategies. Furthermore the nucleotide sequence near that end in many eukaryotic RNAs is not typical. An important example is polyadenylated (poly A) RNA which often bears a 7 methylguanosine cap at the 5 prime end. The linkage between this modied cap and its neighbor is inverted, i.e. the two nucleotides are connected via the 5 prime carbons of their ribose sugars through triphosphate linker, rather than by a typical 5 prime to 3 prime linkage via a phosphodiester bond. 

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Although Next Generation Sequencing has vastly improved sequencing throughput while reducing sequencing costs, preparation of nucleic acid libraries for sequencing has become a bottleneck. In addition, it is difficult using short read next generation sequencing to assemble highly variable sequences that exceed 500 base pairs such as cDNAs derived from antibody heavy chain, antibody light chain, and T cell variable regions RNA.  

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Brief description not available

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.

This technology utilizes inter-subject measurement of brain activity for the purpose of matching individuals. In particular, the invention measures the similarity and differences in neural activity patterns between interacting individuals (either in person or online) as a signature measurement for their matching capabilities. Relevant applications can be in the world of human resources (e.g., building collaborative teams), patient-therapist matching and others. The application relies on the utilization of both custom and commercial devices for measuring brain activity.

Researchers at UC Berkeley have developed a machine learning model that can aide in the design of more efficient viral vector libraries.Directed evolution of biomolecules to generate large numbers of randomized variants is an important innovation in biochemistry. This methodology can be applied to myriad biomolecules of interest, including viruses. In the case of viral variants, this method may be used to select viral variants or viral vectors with specific properties such as tissue type specificity, increased replication capacity, or enhanced evasion of the immune system. However, testing large numbers of viral variants for specific properties is inherently time consuming and limits potential innovation.The inventors have devised a new method to optimize the functionality of viral libraries with many random variants. Specifically, this methodology comprises a machine learning model that systematically designs more effectively starting libraries by optimizing for a chosen factor. This method works by using a training set of viruses that can be evaluated experimentally for the chosen optimization factor (e.g., packaging efficiency, infectivity of a cell line, etc.). These experiments will then provide a fitness value for each viral variant, and the fitness value matched with viral variant sequences will in turn be used in a supervised machine learning model to select sequences for a larger library that is optimized for the chosen factor.

The inventors have developed a highly scalable multiplexed approach to increase the density of graphene nanoribbon- (GNR) based transistors. The technology forms a single device/chip (scale to 16,000 to >1,000,000 parallel transistors) on a single integrated circuit for single molecule biomolecular sensing, electrical switching, magnetic switching, and logic operations. This work relates to the synthesis and the manufacture of molecular electronic devices, more particularly sensors, switches, and complimentary metal-oxide semiconductor (CMOS) chip-based integrated circuits.Bottom-up synthesized graphene nanoribbons (GNRs) have emerged as one of the most promising materials for post-silicon integrated circuit architectures and have already demonstrated the ability to overcome many of the challenges encountered by devices based on carbon nanotubes or photolithographically patterned graphene. The new field of synthetic electronics borne out of GNRs electronic devices could enable the next generation of electronic circuits and sensors.  

Brief description not available

Brief description not available

Brief description not available

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. 

UCLA researchers in the Department of Molecular and Medical Pharmacology have developed a classifier for the identification and treatment of small cell neuroendocrine cancers and small-round-blue cell tumors not previously identified.

To develop a novel imaging-based single cell RNA-sequencing (scRNA-Seq) platform that allows capturing of spatiotemporal information and cellular behavior of the sequenced cells within tissue.

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

UCLA researchers in the Department of Electrical Engineering have developed an on-chip UV holographic imaging microscope that offers a low-cost, portable, and robust technique to image and distinguish protein crystals from salt crystals.

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.