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.

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.  

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. 

A flexible reflectance oximeter array (ROA) composed of printed organic light-emitting diodes (OLEDs) and organic photodiodes (OPDs), which senses reflected light from tissue to determine the oxygen saturation. Since reflected light is used as the signal, the sensor array can be used beyond the conventional sensing locations. We implemented the ROA to measure SpO2 on the forehead with 1.1% mean error and to create two-dimensional (2D) oxygenation maps of the adult forearm under pressure cuff-induced ischemia. Due to the mechanical flexibility, 2D oxygenation mapping capability, and the ability to place the sensor in diverse places, the ROA is promising for novel medical sensing applications such as mapping oxygenation in tissues, wounds, or transplanted organs.

Methods for the simultaneous printing via doctor blading of at least two different colored emissive layers for organic light emitting diodes (OLEDs) on a single substrate.

Sequence census experiments utilize next-generation sequence data to estimate the relative abundance of target sequences.  Since the samples are often short DNA fragments, they must first be assigned to the correct transcripts and genes that produced them, and this alignment or mapping step currently takes up the majority of computing power and time in most expression analyses. To avoid this costly process, UC Berkeley researchers have developed a software program (kallisto) for quantifying abundances of transcripts from RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads based on pseudo-alignment for rapidly determining the compatibility of reads with targets, without the need for alignment. Pseudo-alignment of reads preserves the key information needed for quantification. 

DNA sequence assembly software is used for designing the construction of longer DNA molecules from fragments of shorter DNA molecules. Current methods of sequence assembly are expensive, slow and prone to failure. Investigators at UC Berkeley have developed a sequence assembly tool, Design Evolver, which is cheaper, faster, easier to implement and less prone to failure than alternate tools. The software works in conjunction with the j5 DNA assembly tool developed and licensed by the Joint BioEnergy Institute (JBEI). Design Evolver’s algorithm can further optimize designs that have been processed by j5.

Optical trapping systems are commercially available through several companies. In these systems, the optical trap precision relies on the passive stability of the instrument itself, and therefore demands costly engineering solutions to limit environmental noise that can be coupled into the optomechanical components. Consequently, high-resolution measurements are not possible in common biological laboratory settings that typically lack appropriate vibration isolation and temperature stability.  Researchers at the University of California, Berkeley have developed an invention that addresses a critical problem currently limiting the performance of high-resolution optical traps: that the mechanical drift of optical components often results in physical drift in the location of an optical trap that obscures the displacement-of-interest. The motion of biological motor proteins that are specific to interacting with DNA often take steps along the double helix that is on the order of 0.3 nanometers in size. Accurate measurement of displacements on this scale requires that drift of the trap positions be limited to no more than a few angstroms. However, the current best-performing optical traps suffer from instrumental drift that is almost twice what can be tolerated. Owing to the critical role of these components in all optical trapping systems, and the previously undetectable levels of mechanical drift they undergo, we sought to measure the trap drift with angstrom-level precision using a new approach. This new approach has successfully measured for and corrected for the mechanical drift of these components and demonstrated that this novel invention is capable of consistently reducing the noise floor to levels that have not previously been accomplished.