Cardiovascular disease is the leading cause of death both worldwide and in the United States, with associated costs in the U.S. reaching approximately $229 billion, each, in 2017 and 2018. Early detection, which can drastically reduce both rates of death and treatment costs, requires access to facilities and highly-trained physicians that can be difficult to access in rural areas and developing countries—despite their prevalence of cardiovascular disease. Computer-based models that use, e.g., PCG (phonocardiogram), EKG (electrocardiogram), or other cardiac data, are a promising route to bridge the gap in standard-of-care for these underserved areas. However, current algorithms are unable to account for demographic features, such as race, sex, or other characteristics, which are known to affect both the structure of the heart and presentation of heart disease. To address this problem, UC Berkeley researchers have developed a new, cloud-based system for collecting a patient's continuous cardiovascular data, monitoring for and detecting disease, and keeping a doctor informed about the cardiac health of the patient. The system sends an alarm when disease or heart attack are detected. To generate the most accurate diagnoses by taking into account demographic information, the system includes private and ethical dataset collection and model-training techniques.

Plastics are essential for the modern world but are also non-sustainable products of the petrochemical industry that negatively impact our health, environment, and food chain. Natural biogenic plastics, such as polyhydroxyalkanoates (PHA), are readily biodegradable, can be produced more sustainably, and offer an attractive alternative. The global demand for bioplastics is increasing with the 2019 market value of $8.3B expected to reach a compound annual growth rate of 16.1% from 2020-2027 (https://www.grandviewresearch.com/industry-analysis/bioplastics-industry). However, current PHA production is constrained by the underlying physiology of the microorganisms which produce them, meaning bioplastic production is currently limited to inefficient, batch fermentation processes that are difficult to scale.To address this problem, UC Berkeley researchers have developed a new system for PHA production wherein the PHA are generated continuously throughout microorganism growth lifecycles. The invention allows these sustainable bioplastics to be produced via precision continuous fermentation technology, a scalable and efficient approach.

Rhamnolipids (RLs) are a class of bacterially produced biosurfactants that possess antimicrobial as well as surface-active properties. While RLs have broad utility in industry as antimicrobial biosurfactants, their anionic nature limits the efficacy of these molecules in certain applications. Alternatively, rhamnolipid esters (RLEs) exhibit improved properties as nonionic surfactants. However, a major challenge in RLE application in the commercial arena is that, to date, they are only reliably accessed via chemical synthesis, a costly and unsustainable approach.To address this problem, UC Berkeley researchers have developed a novel, reliable microbial source for biosynthesized RLEs enabling their production in an efficient, sustainable, and renewable manner. Additionally, three novel rhamnolipid methyl ester (RLME) congeners have been produced and a new enzyme for RLE production identified. The produced RLEs are expected to be more effective than RLs in many ways, including antifungal activity and hydrocarbon solubilization.

Existing approaches to visualizing 3D models are capable of producing highly detailed representations of 3D scenes with precision and significant compositional control, but they also require a significant amount of time and expertise by the user to create and configure. Recent developments in generative machine learning (GML) have brought about systems that are capable of quickly producing convincing synthetic images of objects, people, landscapes, and environments, without the need for a 3D model, but these are difficult to precisely control and compose. Therefore, current methods cannot directly relate to detailed 3D models with the fidelity required for many applications, including architecture, product/industrial design, and experience design. To address this opportunity, UC Berkeley researchers have developed a new, GML-integrated 3D modeling and visualization workflow. The workflow streamlines the visualization process by eliminating arduous and time-consuming aspects while maintaining important points of user control. The invention is tailored for the production of “semantically-guided” visualizations of 3D models by coupling the detailed compositional control offered by 3D models with the unique facility of defining visual properties of geometry using natural language. The invention allows designers to more rapidly, efficiently, and intuitively iterate on designs.

Biological fluids are complex, with compositions that vary constantly and evade molecular definition. Nevertheless, within these fluids proteins fluctuate, fold, function, and evolve as programmed. Synthetic heteropolymers capable of emulating such interactions would replicate how proteins behave in biological fluids, individually and collectively, leading the way toward synthetic biological fluids. However, while there exist known monomeric sequence requirements, the chemical and sequence characteristics of proteins at the segmental level, rather than the monomeric level, may be the key factor governing how proteins transiently interact with neighboring molecules (and how biological fluids collectively behave). To address this opportunity, UC Berkeley researchers have developed a new process of heteropolymer design for protein stabilization and synthetic mimics of biological fluids. The process leverages chemical characteristics and sequential arrangements along protein chains at the segmental level to design heteropolymer ensembles as mixtures of disordered, partially folded, and folded proteins. In studies, for each heteropolymer ensemble, the level of segmental similarity to that of natural proteins determines its ability to replicate many functions of biological fluids, including: assisting protein folding during translation; preserving the viability of fetal bovine serum without refrigeration; enhancing the thermal stability of proteins; and, behaving like synthetic cytosol under biologically relevant conditions. Molecular studies further translated protein sequence information at the segmental level into intermolecular interactions with a defined range, degree of diversity and temporal and spatial availability.

Plasma medicine has emerged as a promising approach for treatment of biofilm-related and virus infections, assistance in cancer treatment, and treatment of wounds and skin diseases. However, an important challenge arises with the need to adapt control policies, often only determined after each treatment and using limited observations of therapeutic effects. Control policy adaptation that accounts for the variable characteristics of plasma and of target surfaces across different subjects and treatment scenarios is needed. Personalized, point-of-care plasma medicine can only advance efficaciously with new control policy strategies.To address this opportunity, UC Berkeley researchers have developed a novel control scheme for tailored and personalized plasma treatment of surfaces. The approach draws from concepts in deep learning, Bayesian optimization and embedded control. The approach has been demonstrated in experiments on a cold atmospheric plasma jet, with prototypical applications in plasma medicine.

For decades, researchers have been developing “cool roof” materials to cool buildings and save on energy usage from air conditioning. Cool roof materials are engineered to maximize infrared thermal emission, allowing heat to be effectively radiated into outer space and the building to cool down. Conventional cool roof materials emit heat even when it is cold outside, which exacerbates space heating costs and can outweigh energy-saving benefits. A temperature adaptive radiative coating (TARC) material was developed in 2021 that adapts its thermal emittance to ambient temperatures using metal-insulator transitions in vanadium oxide. TARC is projected to outperform existing roof materials in most climate areas, but the complicated structure required high-cost fabrication techniques such as photolithography, pulsed laser deposition, and XeF2 etching, which are not scalable.To address this problem, UC Berkeley researchers have developed a new scalable temperature-adaptive radiative coating (STARC). STARC has the same thermal emittance switching capability as TARC, allowing the thermal emittance to be switched between high- and low- emittance states at a preset temperature. However, STARC can be produced using high-throughput, roll-to-roll methods and low-cost materials. The STARC material also has an improved lifetime. As an added benefit, while cool roof materials are often engineered with uniformly low solar-absorption, the color and solar absorption of STARC can be tuned for aesthetic purposes or to meet local climate-specific needs.

 The dynamic patterning of 3D optical point clouds has emerged as a key enabling technology in volumetric processing across a number of applications. In the context of biological microscopy, 3D point cloud patterning is employed for non-invasive all-optical interfacing with cell ensembles. In augmented and virtual reality (AR/VR), near-eye display systems can incorporate virtual 3D point cloud-based objects into real-world scenes, and in the realm of material processing, point cloud patterning can be mobilized for 3D nanofabrication via multiphoton or ultraviolet lithography. Volumetric point cloud patterning with spatial light modulators (SLMs) is therefore widely employed across these and other fields. However, existing hologram computation methods, such as iterative, look-up table-based and deep learning approaches, remain exceedingly slow and/or burdensome. Many require hardware-intensive resources and sacrifices to volume quality.To address this problem, UC Berkeley researchers have developed a new, non-iterative point cloud holography algorithm that employs fast deterministic calculations. Compared against existing iterative approaches, the algorithm’s relative speed advantage increases with SLM format, reaching >100,000´ for formats as low as 512x512, and optimally mobilizes time multiplexing to increase targeting throughput.