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Unsupervised WiFi-Enabled Device-User Association for Personalized Location-Based Services

With the emergence of the Internet of Things in smart homes and buildings, determining the identity and mobility of people are key to realizing personalized, context-aware and location-based services - such as adjusting lights and temperature as well as setting preferences of electronic devices in the vicinity. Conventional electronic user identification approaches either require proactive cooperation by users or deployment of dedicated infrastructure. Consequently, existing approaches are intrusive, inconvenient, or expensive to ubiquitously implement. For example: biometric identification requires specific hardware and physical interaction; and vision-based (video) approaches need favorable lighting and introduce privacy issues. To address this situation, researchers at UC Berkeley developed an identification system that uses existing, pervasive WiFi infrastructure and users' WiFi-enabled devices. The innovative Berkeley technology cleverly leverages attributes such as the MAC address and RSS of users' WiFi-enabled devices. Furthermore, the Berkeley approach is facilitated by an unsupervised learning scheme that maps each user identification with associated WiFi-enabled devices. This technology could serve as a vital underpinning for practical personalized context-aware and location-based services in the era of the Internet of Things.

Catechol Functionalized Elastin-Like Polypeptide Adhesives

Reliably reconnecting severed tissues is a critical part of any invasive medical procedure. Although, sutures and staples are ideal choices due to their effectiveness, adhesives hold great promise as alternatives for bonding tissues. Although many bio-adhesives are commercially available, no product yet combines all desirable properties such as consistent adhesion to wet surfaces, mechanical durability, molecular-level customizability and biocompatibility. UC Berkeley research have developed a recombinant protein-based adhesive by chemically functionalizing elastin-like polypeptides (ELPs).  These ELPs form stable and flexible hydrogels de-swell in aqueous conditions. An additional strength of using recombinant proteins is exhibited by significantly enhancing cell binding to this ELP by a simple modular addition of an engineered protein with ‘RGD’ peptides. 

Simultaneous Detection Of Protein Isoforms And Nucleic Acids From Low Starting Cell Numbers

Embryo-specific nucleic acid modifications, including retrotransposon activity-derived genomic modifications and alternative splicing of mRNA, is crucial for the development of mammalian embryos. However, determining if all genomic modifications and mRNA isoforms translate to protein variations remain intriguing questions due to difficulty in measuring protein isoforms and nucleic acids from small starting cell numbers.    UC Researchers have developed a system for performing dual nucleic acid and protein isoform measurements on low starting cell numbers equivalent to the number of blastomeres composing early embryonic development stages (morula and blastocysts).  The system integrates fractionation polyacrylamide gel electrophoresis (fPAGE) with off-chip analysis of nucleic acids in the nuclei. An additional method can be used to remove nuclei for off-chip analysis. The system can measure expression of protein isoforms from the cytoplasmic fraction of 1-100 cells while achieving analysis of either DNA or mRNA retained in the nuclei. The researchers have demonstrated signal from immunoprobed protein correlates strongly with protein expression prior to lysis in TurboGFP-expressing cells and that mRNA levels correlate with protein abundance in TurboGFP-expressing cells.

Bioinspired Hydrogels for the Treatment of Volumetric Muscle Loss Injury

Injuries that involve a degree of muscle tissue loss that exceeds the endogenous regenerative capacity of muscle, resulting in permanent cosmetic and functional deficits of either the injured muscle or the muscle unit, are referred to as volumetric muscle loss (VML) injuries. Current treatment for VML injury involves surgical muscle transfer, although these procedures are often associated with poor engraftment and donor site morbidity.    UC Berkeley and U.Va researchers have developed a new technology for the treatment of VML injuries that overcomes the limitations associated with current treatments for VML injury.  The Matrix Assisted Cell Transplantation (MACT) technology developed by the researchers employs “bioinspired” materials designed to emulate regulatory processes that modulate cell function in the stem/progenitor cell microenvironment.  The technology includes: 1) peptide ligands to imitate the natural extracellular matrix (ECM); 2) proteolytic remodeling via matrix metalloproteinase (MMP) sensitive peptide crosslinks; and, 3) growth factors with engineered density and presentation.    The technology and the materials used have been shown to significantly improve donor survival after transplantation, promote angiogenesis, and encourage donor cell integration with the host tissue.

Device-Free Gesture Recognition System

The popularity of Internet of Things (IoT) devices (without tradition human-computer interfaces) has made gesture recognition an advantageous form of human-computer interaction - especially in smart home applications. However, conventional gesture recognition approaches have issues that limit their pervasive use. For example, wearable devices (e.g. watches and wristbands) with inertial sensors can be inconvenient to always wear; radio frequency systems are cost prohibitive for large-scale deployment; and vision-based systems require favorable lighting and introduce privacy concerns. Recently, WiFi infrastructure, and associated WiFi-enabled mobile and IoT devices have become ubiquitous, and correspondingly, have enabled many context-aware and location-based services. To address the opportunities for gesture recognition and take advantage of the popularity of WiFi, researchers at UC Berkeley developed a gesture recognition system based on analyzing signals from existing WiFi-enabled devices. This novel WiFi-enabled, device-free gesture recognition system can identify human gestures with consistent high accuracy and has robust environmental dynamics.

Device-Free Human Identification System

In our electronically connected society, human identification systems are critical to secure authentication, and also enabling for tailored services to individuals. Conventional human identification systems, such as biometric-based or vision-based approaches, require either the deployment of dedicated infrastructure, or the active cooperation of users to carry devices. Consequently, pervasive implementation of conventional human identification systems is expensive, inconvenient, or intrusive to privacy. Recently, WiFi infrastructure, and associated WiFi-enabled mobile and IoT devices have become ubiquitous, and correspondingly, have enabled many context-aware and location-based services. To address the challenges of human identification systems and take advantage of the popularity of WiFi, researchers at UC Berkeley developed a human identification system based on analyzing signals from existing WiFi-enabled devices. This novel device-free approach uses WiFi signal analysis to reveal the unique, fine-grained gait patterns of individuals as the "fingerprint" for human identification.

CRISPR-based Graphene Biosensor for Digital Detection of DNA Mutations

UC Berkeley and Keck Institute researchers have reported the development and testing of a graphene-based field-effect transistor that uses CRISPR technology to enable the digital detection of a target sequence within intact genomic material. Termed CRISPR–Chip, the biosensor uses the gene-targeting capacity of catalytically deactivated Cas9 complexed with a specific single-guide RNA and immobilized on the transistor to yield a label-free nucleic-acid-testing device whose output signal can be measured with a simple handheld reader.  

High Electromechanical Coupling Disk Resonators

Capacitive-gap transduced micromechanical resonators routinely post Q several times higher than piezoelectric counterparts, making them the preferred platform for HF and low-VHF (e.g. 60-MHz) timing oscillators, as well as very narrowband (e.g. channel-select) low-loss filters. However, the small electromechanical coupling (as gauged by the resonator's motion-to-static capacitance ratio, Cx/Co) of these resonators at higher frequency prevents sub-mW GSM reference oscillators and complicates the realization of wider bandwidth filters. To address this situation, researchers at UC Berkeley developed a capacitive-gap transduced radial mode disk resonator with reduced mass and stiffness. This novel Berkeley disk resonator has a measured electromechanical coupling strength (Cx/Co) of 0.56% at 123 MHz without electrode-to-resonator gap scaling. This is an electromechanical coupling strength improvement of more than 5x compared with a conventional radial contour-mode disk at the same frequency. This increase should help improve the passbands of channel-select filters targeted for low power wireless transceivers and lower the power of MEMS-based oscillators.