Kainate receptors, also known as kainic acid receptors are ionotropic receptors that bind to and are responsive to glutamate in neurons. These were originally identified as being activated by the compund kainic acid, orignally isolated from algae. Postsynaptic kainate receptors are involved in excitatory neurotransmission while presynaptic kainate receptors are involved in inhibitory neurotransmission. Kainic acid is a potentially very useful compound but very difficult to synthesize. As a result, there are very few pharmacological tool compounds to study kainate receptors and none that are readily tunable to install labeling compounds. 

This invention introduces a novel approach to cardiac tissue stimulation and maturation through the use of photoactive organic and biological material blends.

Production of chiral chemicals through biotransformation requires an oxidoreductase enzyme and an efficient redox cofactor system comprising electron donors coupled to a dehydrogenase enzyme to regenerate the reduced cofactors.The researchers at the University of California, Irvine (UCI), provide a way to computationally design and optimize hydrogenase enzyme interaction with biomimetic cofactor analogs to improve increase enzymatic efficiency. The group has produced the modified enzyme and show that it is capable of a diverse range of chemical biotransformation.

This technology revolutionizes hydrogen production by using induction heating for catalytic methane decomposition, significantly increasing hydrogen yield.

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A revolutionary approach to treating stone disease and improving ureteral distensibility through pharmacological means.

Revolutionizing the upcycling of carboxylic acid-based chemical waste products to aldehyde derivatives using engineered biological systems.

A revolutionary drug modality for the selective modification and degradation of intracellular proteins in lysosomes.

Site-selective covalent modification of proteins is key to the development of new biomaterials, therapeutics, and other biological tools. As examples in the biomedical field, these techniques have been applied to the construction of antibody-drug conjugates, bispecific cell engagers, and targeted protein therapies, among other applications. While many bioconjugation strategies, such as azide-alkyne cycloaddition or thiol-maleimide coupling, have become widely adopted, the improvement of existing techniques is a highly active area of chemical biology research, as is the development of new synthetic applications of these methods. Key focuses of such efforts include increasing reaction efficiency and ease, balancing selectivity with tag size, and expanding the modification options beyond traditional cysteine and lysine residues. UC Berkeley researchers have developed compounds and methods using tyrosinase to couple small-molecule dithiols to tyrosine-tagged proteins, which effectively introduces a free thiol handle and provides a convenient method to bypass genetic incorporation of cysteine residues for bioconjugation. These newly thiolated proteins were then coupled to maleimide probes as well as other tyrosine-tagged proteins. The researchers were also able to conjugate targeting proteins to drugs, fluorescent probes, and therapeutic enzymes. This easy method to convert accessible tyrosine residues on proteins to thiol tags extends the use of tyrosinase-mediated oxidative coupling to a broader range of protein substrates. 

A novel method for selectively targeting and modulating brain border-associated myeloid cells for the treatment of neurological disorders.

Introducing a groundbreaking orthogonal redox cofactor, NMN+, to revolutionize redox reaction control in biomanufacturing.

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Researchers at the University of California, Davis have developed a technology that introduces new electrolyte compositions that significantly enhance the stability and efficiency of non-aqueous flow batteries.

       Spectral machine vision collects both the spectral and spatial dependence (x,y,λ) of incident light, containing potentially useful information such as chemical composition or micro/nanoscale structure.  However, analyzing the dense 3D hypercubes of information produced by hyperspectral and multispectral imaging causes a data bottleneck and demands tradeoffs in spatial/spectral information, frame rate, and power efficiency. Furthermore, real-time applications like precision agriculture, rescue operations, and battlefields have shifting, unpredictable environments that are challenging for spectroscopy. A spectral imaging detector that can analyze raw data and learn tasks in-situ, rather than sending data out for post-processing, would overcome challenges. No intelligent device that can automatically learn complex spectral recognition tasks has been realized.       UC Berkeley researchers have met this opportunity by developing a novel photodetector capable of learning to perform machine learning analysis and provide ultimate answers in the readout photocurrent. The photodetector automatically learns from example objects to identify new samples. Devices have been experimentally built in both visible and mid-infrared (MIR) bands to perform intelligent tasks from semiconductor wafer metrology to chemometrics. Further calculations indicate 1,000x lower power consumption and 100x higher speed than existing solutions when implemented for hyperspectral imaging analysis, defining a new intelligent photodetection paradigm with intriguing possibilities.

This invention introduces a groundbreaking liquid electrolyte for fluoride-ion batteries, offering high electrochemical stability, superior ionic conductivity, and excellent thermal stability.

BMSO represents a groundbreaking advancement in crosslinking mass spectrometry (XL-MS), enabling comprehensive mapping of protein-protein interactions.

Researchers at the University of California, Davis have developed an innovative technology that incorporates advanced microchannel architecture into scalable solar thermal receiver unit cells, enabling highly efficient solar energy conversion.

A novel approach to polymer compatibilization that enhances mechanical strength and compatibility across diverse polymer blends.

An innovative Palladium hydride catalyst that significantly enhances the electroreduction of carbon dioxide (CO2) to formate with exceptional tolerance for carbon monoxide (CO).

This technology capitalizes on azide-masked nitrene crosslinking chemistry to introduce a scalable and efficient method for the compatibilization and recycling of mixed plastics.

      Molecular dynamics (MD) is a computational materials science modality widely used in academic and industrial settings for materials discovery and more. A critical aspect of modern MD calculations are machine learning interatomic potentials (MLIPs), which learn from reference quantum mechanical calculations and predict the energy and forces of atomic configurations quickly. MLIPs allow for more accurate and comprehensive exploration of material/molecular properties at-scale. However, state-of-the-art MLIP methods mostly use a short-range approximation, which may be sufficient for describing properties of homogeneous bulk systems but fail for liquid-vapor interfaces, dielectric response, dilute ionic solutions with Debye-Huckel screening, and interactions between gas phase molecules. Short-range MLIPs neglect all long-range interactions, such as Coulomb and dispersion interactions.      To address the current shortcoming, UC Berkeley researchers have developed a straightforward and efficient algorithm to account for long-range interactions in MLIPs. The algorithm can predict system properties including those with charged, polar or apolar molecular dimers, bulk water, and water-vapor interfaces. In these cases standard short-range MLIPs lead to unphysical predictions, even when utilizing message passing algorithms. The present method eliminates artifacts while only about doubling the computational cost. Furthermore, it can be incorporated into most existing MLIP architectures, including potentials based on local atomic environments such as HDNPP, Gaussian Approximation Potentials (GAP), Moment Tensor Potentials (MTPs), atomic cluster expansion (ACE), and MPNN (e.g., NequIP, MACE).

Optical neural networks (ONNs) are a promising computational alternative for deep learning due to their inherent massive parallelism for linear operations. However, the development of energy-efficient and highly parallel optical nonlinearities, a critical component in ONNs, remains an outstanding challenge. To address this situation, researchers at UC Berkeley and Berkeley National Lab developed a nonlinear optical microdevice array (NOMA) compatible with incoherent illumination by integrating the liquid crystal cell with silicon photodiodes at the single-pixel level. The researchers fabricated NOMA with over half a million pixels, each functioning as an optical analog of the rectified linear unit at ultralow switching energy down to 100 femtojoules/pixel. The team demonstrated an optical multilayer neural network. This work holds promise for large-scale and low-power deep ONNs, computer vision, and real-time optical image processing.

A scalable and less toxic underwater adhesive developed from two small molecule precursors, providing fast and stable adhesion.

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         While nuclear magnetic resonance (NMR) is one of the most universal synthetic chemistry tools for its ability to measure highly specific kinetic and structural information nondestructively/noninvasively, it is costly and low-throughput primarily due to the small sample-size volumes and expensive equipment needed for stringent magnetic field homogeneity. Conversely, zero-to-ultralow field (ZULF) NMR is an emerging alternative offering similar chemical information but relaxing field homogeneity requirements during detection. ZULF NMR has been further propelled by recent advancements in key componentry, optically pumped magnetometers (OPMs), but suffers in scope due to its low sensitivity and its susceptibility to noise. It has not been possible to detect most organic molecules without resorting to hyperpolarization or 13C enrichment using ZULF NMR.         To overcome these challenges, UC Berkeley researchers have developed a multi-channel ZULF spectrometer that greatly improves on both the sensitivity and throughput abilities of state-of-the art ZULF NMR devices. The novel spectrometer was used in the first reported detection of organic molecules in natural isotopic abundance by ZULF NMR, with sensitivity comparable to current commercial benchtop NMR spectrometers. A proof-of-concept multichannel version of the ZULF spectrometer was capable of measuring three distinct chemical samples simultaneously. The combined sensitivity and throughput distinguish the present ZULF NMR spectrometer as a novel chemical analysis tool at unprecedented scales, potentially enabling emerging fields such as robotic chemistry, as well as meeting the demands of existing fields such as chemical manufacturing, agriculture, and pharmaceutical industries.