The response of plants to reduced water availability is controlled by a complex osmotic stress and abscisic acid (ABA)-dependent signal transduction network. The core ABA signaling components are snf1-related protein kinase2s (SnRK2s) which are activated by ABA-dependent inhibition of type 2C protein phosphatases and by an unknown ABA-independent osmotic stress signaling pathway. Limited water availability is one of the key factors that negatively impacts crop yields. The plant hormone abscisic acid (ABA) and the signal transduction network it activates, enhance plant drought tolerance through stomatal closure, and inhibition of seed germination and growth. As plants are constantly exposed to changing water conditions, reversibility and robustness of the ABA signal transduction cascade is important for plants to balance growth and drought stress resistance. Core ABA signaling components have been established the ABA receptors PYRABACTIN RESISTANCE (PYR/PYL) or REGULATORY COMPONENT OF ABA RECEPTOR (RCAR) inhibit type 2C protein phosphatases (PP2Cs) resulting in the activation of the SnRK2 protein kinases SnRK2.2, 2.3 and OST1/SnRK2.6 . However, it has remained unclear whether direct autophosphorylation or trans-phosphorylation by unknown protein kinases re-activates these SnRK2 protein kinases in response to stress. The osmotic stress sensing mechanism and upstream signal transduction mechanisms leading to SnRK2 activation remain largely unknown in plants.

Researchers at the University of California, Davis have developed a method to produce cellulase enzymes by utilizing a simple agroinfiltration process to transiently express full length cellulases in plant tissue.

UCLA researchers in the Department of Electrical and Computer Engineering have developed an image analysis platform to measure the viscosity of nanoliter volume liquids.

Researchers at the University of California, Davis have developed a one-step spray dry calcium cross-linked alginate encapsulation process where the calcium is released from a chelating agent.

Although the plastic waste crisis has reached a breaking point, current recycling approaches are unable to remediate microplastic pollution. Biodegradable and renewable plastics have shown promise but impact neither microplastic elimination nor complete plastic recycling due to diffusion-limited enzymatic surface erosion and random chain scission. Here it is shown that nanoscopic dispersion of trace enzyme (e.g. lipase) in plastics (e.g. polycaprolactone [PCL]) leads to fully functional plastics with eco-friendly microplastic elimination and programmable degradation. Nanoscopic enzyme encapsulation leads to:continuous degradation to achieve 95% microplastic eliminationa single chain-based degradation mechanism with repolymerizable small molecule by-products via selective chain end scission rather than random chain scissionspatially- and temporally-programmable degradation of melt-processed host matrix due to the dependence of single chain degradation on local lamellae thickness regardless of bulk percent crystallinity formulation of conductive ink for 3-D printing with full recovery of the precious metal filler With recent developments in synthetic biology and genome information, nanoscopically embedding catalytically active enzymes in plastics may lead to an immediate, environmentally friendly and technologically viable solution toward microplastic elimination and material recycling.

Despite decades of effort, it remains challenging, if not impossible, to achieve similar transport performance similar to natural channels. Inspired by the known crystal structures of transmembrane channel proteins, protein sequence-structure-transport relationships have been applied to guide material design. However, producing both molecularly defined channel sizes and channel lumen surfaces that are chemically diverse and spatially heterogeneous have been out of reach. We show that a 4-monomer-based random heteropolymer (RHP) exhibits selective proton transport at a rate similar to those of natural proton channels. Statistical control over the monomer distribution in the RHP leads to well-modulated segmental heterogeneity in hydrophobicity, which facilitates the single RHP chains to insert into lipid bilayers. This in turn produces rapid and selective proton transport, despite the sequence variability among RHP chains. We have demonstrated the importance of:the adaptability enabled by the statistical similaritythe modularity afforded by monomer chemical diversity to achieve uniform behavior in heterogeneous systems. 

Protein-based materials have the potential to change the current paradigm of materials science. However, it still remains a challenge to preserve protein hierarchical structure and function while making them readily processable. Protein structure is inherently fluid, and it is this property that contributes to their fragility outside of their native environment. Through the use of rationally designed statistically random heteropolymers, it is possible to stabilize proteins at each hierarchical level and process them in organic solvents, a common need for materials fabrication. The chemical and architectural complexities of statistically random heteropolymers provide a modular platform for tunable protein-polymer-solvent interactions. This provides opportunities not offered by small molecule surfactants or amphiphilic block copolymers. Through evaluation of horseradish peroxidase and green fluorescent protein structure, we show that statistically random heteropolymers can stabilize enzymes. Allowing for activity retention when stored in organic solvent, over 80% activity was observed after 24 hours. Furthermore, horseradish peroxidase and chymotrypsin proteins, when encapsulated in statistically random heteropolymers, are still accessible to their substrates while remaining inaccessible to the denaturing organic solvent. Statistically random heteropolymers have potential in creating stimuli-reponsive materials and nanoreactors composed of proteins and synthetic materials.

Researchers in the UCLA Department of Electrical and Computer Engineering have developed a Light Detection and Ranging (LiDAR) device capable of high resolution, high acquisition measurements with minimized walk error and adjustable detection quality.

UCLA researchers in the Department of Mechanical and Aerospace Engineering have developed a Compliant Membrane Acoustic Patterning (CAMP) technology capable of patterning cells in an arbitrary pattern at a high resolution over a large area.

UCLA researchers in the Department of Electrical and Computer Engineering have developed a Non-line-of-sight (NLOS) Imaging System using low cost thermal cameras that enable 3D recovery of NLOS heat source for imaging around corners.

UCR researchers have developed an inexpensive sensor to measure the energy content and fuel quality of gaseous combustible fuel. This sensor estimates the Wobbe Index in real time time and costs about $10. The sensor is confirmed to operate between -20°and 70°Celsius under pressures of -3600 Psi, with an accuracy of ±1%.  Fig. 1 shows the predicted Wobbe Index vs Actual Wobble Index, showing the accuracy of the sensor

UCLA researchers in the Department of Electrical and Computer Engineering have developed a method and apparatus to rapidly analyze optical and electrical signals at very high bandwidths while accommodating advanced signal modulation for lower cost and improved energy consumption.

Researchers at the University of California, Davis have developed voltage converters systems – with associated control schemes – that span a broad spectrum of potential applications.

UCLA researchers in the Department of Materials Science and Engineering have developed a novel lead halide perovskite solar cell based on a mixture of formamidinium perovskites and 2D perovskites.

UCLA researchers in the Department of Mechanical and Aerospace Engineering have developed a user-friendly analytical instrument that measures electrochemical impedance at rates many times faster than currently available devices and with comparable accuracy.

Researchers in UCLA's Department of Chemistry and Biochemistry have harnessed gel electrophoresis in order to direct and program controlled collisional reactions between pulse-like bands of molecules and/or colloidal reagent species.

UCLA researchers in the Department of Mechanical and Aerospace Engineering have developed flexible tactile sensors for curved surfaces that are robust against fatigue and suitable for robotic applications.

UCLA researchers in the Department of Electrical Engineering have developed a method of in-situ sweat rate monitoring, which can be integrated into wearable consumer electronics for physiological analyses.

Researchers led by Zhengrong Wang and Tony Nowatzki from the Computer Science Department at UCLA have created a way to improve computer processing power, speed, and efficiency by optimizing how processors access memory.

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UCLA researchers in the Department of Electrical and Computer Engineering have developed a novel method for real-time detection and characterization of pulsed THz waveforms that features differential detection of high sensitivity, and phase diversity to overcome the dispersion penalty for wideband operation.

Researchers at the University of California, Davis and Carnegie Mellon University have developed a new design and fabrication method for high pressure heat exchangers (HX) using additive manufacturing (AM). This method would allow for the creation of primary heat exchanger (PHX) systems with minimal energy loss.

Researchers in the UCLA Department of Civil and Environmental Engineering have developed a novel process cycle to separate and enrich divalent cations such Ca2+ and Mg2+ from high salinity brine solutions for CO2 mineralization.

Researchers in the UCLA Departments of Bioengineering and Medical and Molecular Pharmacology and the UCSF Department of Bioengineering and Therapeutic Sciences have developed a novel microvalve injector for capillary electrophoresis (CE) that improves injection repeatability and consistency.

In machine learning, a deep belief network is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables, with connections between the layers but not between units within each layer.