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A novel simplicity-biased molecular structure generation method and Bayesian optimization based active learning have been used to discover a new class of heterocyclic molecules for organic non-aqueous redox flow batteries.
The large polarization barriers between the quantum wells and quantum barriers in long-wavelength GaN-based light-emitting diodes (LEDs) inhibit their performance by requiring excess driving voltages to reach standard operating current densities. Lateral injection of carriers directly into quantum wells is required to circumvent this issue. V-defects are naturally occurring inverted hexagonal defects with semipolar {$10\bar{1}1$}-plane sidewalls generated on surface depressions from threading dislocations. LEDs engineered to intentionally generate V-defects below the active region of the LED can achieve lateral carrier injection through the V-defect sidewalls and have already been able to demonstrate world record wall-plug efficiencies for LEDs in the green-red wavelengths. V-defects can be enlarged during kinetically limited growth where the growth rate of the c-plane GaN is faster than that of their sidewalls, leaving them unfilled. We report on the metal organic chemical vapor deposition growth conditions required to fill in V-defects with p-GaN during epitaxial growth of the LED post the active region. Circular transmission length measurements of Pd/Au contacts processed on p-GaN surfaces with various amounts of unfilled V-defects showed no significant difference in their sheet resistance and specific contact resistance. J–V measurements of LEDs grown with varying unfilled V-defect densities showed no significant difference in the forward bias regime. However, in the reverse bias regime, catastrophic breakdown occurred at markedly lower voltages for samples with larger unfilled V-defect densities. This suggests that unfilled V-defects may act as hotspots for device failure, and planarizing LED surfaces may help prevent early degradation of LED devices.
SUMMARY Quito, the capital of Ecuador, with more than 2.5 M inhabitants, is exposed to a high seismic hazard due to its proximity to the Pacific subduction zone and active crustal faults, both capable of generating significant earthquakes. Furthermore, the city is located in an intermontane piggy-back basin prone to seismic wave amplification. To understand the basin’s seismic response and characterize its geological structure, 20 broad and medium frequency band seismic stations were deployed in Quito’s urban area between May 2016 and July 2018 that continuously recorded ambient seismic noise. We first compute horizontal-to-vertical spectral ratios to determine the resonant frequency distribution in the entire basin. Secondly, we cross-correlate seismic stations operating simultaneously to retrieve interstations surface-wave Green’s functions in the frequency range of 0.1–2 Hz. We find that Love waves travelling in the basin’s longitudinal direction (NNE–SSW) show much clearer correlograms than those from Rayleigh waves. We then compute Love wave phase-velocity dispersion curves and invert them in conjunction with the HVSR curves to obtain shear-wave velocity profiles throughout the city. The inversions highlight a clear difference in the basin’s structure between its northern and southern parts. In the centre and northern areas, the estimated basin depth and mean shear-wave velocity are about 200 m and 1800 ms−1, respectively, showing resonance frequency values between 0.6 and 0.7 Hz. On the contrary, the basement’s depth and shear-wave velocity in the southern part are about 900 m and 2500 ms−1, having a low resonance frequency value of around 0.3 Hz. This difference in structure between the centre-north and the south of the basin explains the spatial distribution of low-frequency seismic amplifications observed during the Mw 7.8 Pedernales earthquake in April 2016 in Quito.
Abstract In planta, H2O2 is produced as a by-product of enzymatic reactions and during defense responses. Ascorbate peroxidase (APX) is a key enzyme involved in scavenging cytotoxic H2O2. Here, we report the crystal structure of cytosolic APX from sorghum (Sorghum bicolor) (Sobic.001G410200). While the overall structure of SbAPX was similar to that of other APXs, SbAPX uniquely displayed four bound ascorbates rather than one. In addition to the ɣ-heme pocket identified in other APXs, ascorbates were bound at the δ-meso and two solvent-exposed pockets. Consistent with the presence of multiple binding sites, our results indicated that the H2O2-dependent oxidation of ascorbate displayed positive cooperativity. Bound ascorbate at two surface sites established an intricate proton network with ascorbate at the ɣ-heme edge and δ-meso sites. Based on crystal structures, steady-state kinetics, and site-directed mutagenesis results, both ascorbate molecules at the ɣ-heme edge and the one at the surface are expected to participate in the oxidation reaction. We provide evidence that the H2O2-dependent oxidation of ascorbate by APX produces a C2-hydrated bicyclic hemiketal form of dehydroascorbic acid at the ɣ-heme edge, indicating two successive electron transfers from a single-bound ascorbate. In addition, the δ-meso site was shared with several organic compounds, including p-coumaric acid and other phenylpropanoids, for the potential radicalization reaction. Site-directed mutagenesis of the critical residue at the ɣ-heme edge (R172A) only partially reduced polymerization activity. Thus, APX removes stress-generated H2O2 with ascorbates, and also uses this same H2O2 to potentially fortify cell walls via oxidative polymerization of phenylpropanoids in response to stress.
Second-order optical effects are essential to the active control of light and the generation of new spectral components. The inversion symmetry, however, prevents achieving a bulk $χ^{(2)}$ response, limiting the portfolio of the second-order nonlinear materials. Here, we demonstrate subpicosecond conversion of a statically passive dielectric to a transient second-order nonlinear medium upon the ultrafast transfer of hot electrons. Induced by an optical switching signal, the amorphous dielectric with vanishing intrinsic $χ^{(2)}$ develops dynamically tunable second-order nonlinear responses. By taking the second-harmonic generation as an example, we show that breaking the inversion symmetry through hot-electron dynamics can be leveraged to address the critical need for all-optical control of second-order nonlinearities in nanophotonics. We report our approach can be generically adopted in a variety of material and device platforms, offering a new class of complex nonlinear media with promising potentials for all-optical information processing.
Most of the current antibody-drug conjugates (ADCs) in clinic are heterogeneous mixtures. To produce homogeneous ADCs, established procedures often require multiple steps or long reaction times. The introduced mutations or foreign sequences may cause high immunogenicity. Here, we explore a new concept of transforming CD38 enzymatic activity into a facile approach for generating site-specific ADCs. This was achieved through coupling bifunctional antibody-CD38 fusion proteins with designer dinucleotide-based covalent inhibitors with stably attached payloads. The resulting adenosine diphosphate–ribosyl cyclase–enabled ADC (ARC-ADC) with a drug-to-antibody ratio of 2 could be rapidly generated through single-step conjugation. The generated ARC-ADC targeting human epidermal growth factor receptor 2 (HER2) displays excellent stability and potency against HER2-positive breast cancer both in vitro and in vivo. This proof-of-concept study demonstrates a new strategy for production of site-specific ADCs. It may provide a general approach for the development of a novel class of ADCs with potentially enhanced properties.
A complex role for chlorine radicals Radicals are atoms or molecules that are highly reactive because they have an unpaired electron. A common means of investigating whether they are involved in a particular reaction is to try to trap them with an acceptor compound. Yang et al. reinvestigated a photoinduced alkane oxidation reaction for which a trapping study had previously implicated alkoxy radicals. Their spectroscopic, kinetic, and isotopic labeling studies revealed that chlorine, rather than alkoxy, was the key radical intermediate; the prior trapping results had stemmed from its complexation with alcohols. Science , abd8408, this issue p. 847
Hydrogenotrophic methanogens produce CH 4 using H 2 as an electron donor to reduce CO 2 . In the absence of H 2 , many are able to use formate or alcohols as alternate electron donors. Methanogens from the order Methanomicrobiales are capable of growth with H 2 , but many lack genes encoding hydrogenases that are typically found in other hydrogenotrophic methanogens. In an effort to better understand electron flow in methanogens from the Methanomicrobiales, we undertook a genetic and biochemical study of heterodisulfide reductase (Hdr) in Methanoculleus thermophilus. Hdr catalyzes an essential reaction by coupling the first and last steps of methanogenesis through flavin-based electron bifurcation. Hdr from M. thermophilus co-purified with formate dehydrogenase (Fdh) and only displayed activity when formate was supplied as an electron donor. We found no evidence of an Hdr associated hydrogenase, and H 2 could not function as an electron donor, even with Hdr purified from cells grown on H 2 . Here, we found that cells catalyze a formate hydrogenlyase activity that is likely essential for generating the formate needed for the Hdr reaction. Together, these results highlight the importance of formate as an electron donor for methanogenesis and suggest the ability to use formate is closely integrated into the methanogenic pathway in organisms from the order Methanomicrobiales.
Cyber operations is drowning in diverse, high-volume, multi-source data. To get a full picture of current operations and identify malicious events and actors, analysts must see through data generated by a mix of human activity and benign automated processes. Although many monitoring and alert systems exist, they typically use signature-based detection methods. We introduce a general method rooted in spectral graph theory to discover patterns and anomalies without a priori knowledge of signatures. We derive and propose a new graph-theoretic centrality measure based on the derivative of the graph Laplacian matrix in the direction of a vertex. To build intuition about our measure, we show how it identifies the most central vertices in standard network datasets and compare to other graph centrality measures. Finally, we focus our attention on studying its effectiveness in identifying important IP addresses in network flow data. Using both real and synthetic network flow data, we conduct several experiments to test our measure’s sensitivity to two types of injected attack profiles and show that vertices participating in injected attack profiles exhibit noticeable changes in our centrality measures, even when the injected anomalies are relatively small, and in the presence of simulated network dynamics.
This software contains a routine for active sampling of a DFT-generated dataset for training a neural network potential (SchNet). Major updates to the base SchNet code are also included to accommodate large systems (100s of atoms). We also provide code to apply the neural network potential in dynamic shear simulations.
This tool takes a genome assembly as input and predicts per-site nucleosome occupancy as output. Trained on physical maps of nucleosome binding preferences across the fungal kingdom, NuclPred can be applied broadly across fungi (and other eukaryotes). This breadth, combined with its accuracy, means it could have both basic and applied biological implications, for example in understanding eukaryotic gene regulation and genetic engineering. Almost universally across eukaryotes, nucleosomes - each wrapping ~150 base pairs of DNA - serve to package DNA inside the nucleus, with major consequences on DNA access, gene activity and DNA integration. NuclPred was generated using a supervised deep learning approach combining convolutional and recurrent neural networks to take DNA features (nucleotides, GC content and structural information) as input, then use that information to predict the physical attractiveness DNA sequences might have for forming nucleosomes. With this information at hand, researchers can design more efficient CRISPR constructs, explore the interplay between DNA signatures and other regulators impact nucleosome locations, predict expression patterns, etc. This tool will be published as part of a manuscript currently under revision at iScience (draft attached).
Advanced nuclear reactors are a promising option for aiding the world in achieving its net-zero carbon emission goals, however, there are significant challenges to attaining and maintaining economic competitiveness with other sources of electricity. To improve the economic competitiveness of advanced reactor designs, a project was initiated to explore the use of Markov Decision Processes (MDPs) to guide asset-management decision-making during advanced reactor operation. MDPs are a powerful tool for optimizing decision-making in complex environments and their application to advanced reactors can aid in planning maintenance and repair activities to minimize downtime and maximize generation. The described approach expands on previous work regarding the use of MDPs for operational decision-making through the direct incorporation of real-time plant information. The integral MDP analysis includes information from online component diagnostic tools and the plant’s real-time generation risk assessment (GRA) and probabilistic risk assessment (PRA), which evaluate plant risk from both an economic and safety perspective. The result is an asset-management optimization framework that is based on real-time data regarding plant component status and the current best-estimate of plant risk. The paper presents an overview of the theoretical framework to incorporate the different information pathways into an integral MDP analysis, along with example analyses.
Protein posttranslational modifications (PTMs) by O-GlcNAc globally rise during pressure-overload hypertrophy (POH). However, a major knowledge gap exists on the specific proteins undergoing changes in O-GlcNAc levels during POH primarily because this PTM is low abundance and easily lost during standard mass spectrometry (MS) conditions used for protein identification. Methodologies have emerged to enrich samples for O-GlcNAcylated proteins prior to MS analysis. Accordingly, our goal was to identify the specific proteins undergoing changes in O-GlcNAc levels during POH. We used C57/Bl6 mice subjected to Sham or transverse aortic constriction (TAC) to create POH. From the hearts, we labelled the O-GlcNAc moiety with tetramethylrhodamine azide (TAMRA) before sample enrichment by TAMRA immunoprecipitation (IP). We used LC-MS/MS to identify and quantify the captured putative O-GlcNAcylated proteins. We identified a total of 700 putative O-GlcNAcylated proteins in Sham and POH. Two hundred thirty-three of these proteins had significantly increased enrichment in POH over Sham suggesting higher O-GlcNAc levels whereas no proteins were significantly decreased by POH. We examined two MS identified metabolic enzymes, CPT1B and the PDH complex, to validate by immunoprecipitation. We corroborated increased O-GlcNAc levels during POH for CPT1B and the PDH complex. Enzyme activity assays suggests higher O-GlcNAcylation increases CPT1 activity and decreases PDH activity during POH. In summary, we generated the first comprehensive list of proteins with putative changes in O-GlcNAc levels during POH. Our results demonstrate the large number of potential proteins and cellular processes affected by O-GlcNAc and serve as a guide for testing specific O-GlcNAc-regulated mechanisms during POH.
The 2001 Atlanta Household Travel Survey collected demographic, socioeconomic, and travel information on work and non-work travel behavior for a 48-hour travel period. The study was conducted on behalf of the Atlanta Regional Commission, and it is an essential element in the transportation planning and modeling efforts for the 13-county Atlanta region. The main objective of the study was to produce data that could be used to develop and calibrate travel demand models for use in travel forecasting, land use planning, and air quality planning to improve the transportation system. The second component in the survey was the deployment of an electronic travel diary with person-based GPS and an accelerometer to collect health and activity information. Travel data includes trip generation, trip distribution, and modal choice. The survey recruited a total of 12,184 households to participate in the study. Of these, 8,069 households (66%) completed travel. The 8,069 households, when weighted, represent 21,323 persons, 14,449 vehicles, and 126,127 places visited from April 2001 through April 2002. This dataset is part of the Metropolitan Travel Survey Archive, which includes travel surveys from numerous public agencies across the United States and is archived by the Transportation Secure Data Center to ensure their continued public availability.
Most cities in the United States withdraw surface water to meet public water supply needs. The lands on which this water is generated are often developed for human activities -- such as agriculture, mining, and industry -- that may compete for water resources or contaminate water supplies. Cities are thereby connected to other sectors through their water supply catchments. This connection is an example of an multisectoral urban teleconnection. The Geospatial Analytics for Multisectoral Urban Teleconnections (``gamut``) package provides national-scale information on these teleconnections by combining land use data with hydrological analysis to characterize urban source watershed human interactions across the conterminous United States.
Pacific Northwest National Laboratory (PNNL) is providing baseline technical support to Washington River Protection Solutions (WRPS) for the One System River Protection Project (RPP) Integrated Flowsheet team. This report documents the evaluation of the technical bases available to support iodine speciation and distribution within Hanford wastes and subsequent waste streams generated during direct feed low-activity waste (DFLAW) pretreatment operations (specifically, waste retrievals and staging, and particle filtration and cesium decontamination using crystalline silicotitanate (CST) ion exchange [in the tank side cesium removal (TSCR) system]. The task performed a literature survey of information related to iodine species in environments analogous to Hanford tank waste and the subsequent waste streams to define a technical basis for the possible iodine speciation in Hanford waste. In doing so it can be determined how likely laboratory studies on iodine speciation in tank waste are to be universally relevant across the Hanford tanks. The task evaluated iodide and iodate as the primary species of interest with a focus on organo-iodine where appropriate.
This is the final technical report for the DOE-funded Panorama 360 project. Panorama 360 provided a resource for the collection, analysis, and sharing of performance data about end-to-end scientific workflows executing on DOE facilities. The work focused on workflows that include experimental data generation at DOE facilities. The main activities of Panorama 360 include the development of: 1. A distributed repository that stores different types of workflow execution data (e.g., point and time series performance traces at fine- and coarse-grained levels); 2. A set of open-source data capture, curation, and publishing tools fully integrated with a state-of-the-art workflow management system that automates data ingestion to the repository and enables users to discover, query, and process data from the repository; 3. A set of analysis algorithms and machine learning based tools to perform analysis and characterization of the gathered data, which can be used to detect anomalous performance or system faults; and 4. Best practices and recommendations for workflow evaluation, analysis, execution, and architectures.
This final technical report from the lead institution, USC grant #DE-SC0012636, serves as the final technical report for collaborative institution UNC-CH grant #DE-SC0012390. The goal was to develop a repository and associated capabilities for data collection, ingestion, and analysis for a broad class of DOE applications that span experimental and simulation science workflows. In particular, this work focuses on workflows that include experimental data generation at DOE facilities. The main activities of Panorama 360 include the development of: (1) A distributed repository that stores different types of workflow execution data (e.g., point and time series performance traces at fine- and coarse-grained levels); (2) A set of open-source data capture, curation, and publishing tools fully integrated with a state-of-the-art workflow management system that automates data ingestion to the repository and enables users to discover, query, and process data from the repository; (3) A set of analysis algorithms and machine learning based tools to perform analysis and characterization of the gathered data, which can be used to detect anomalous performance or system faults; and (4) Best practices and recommendations for workflow evaluation, analysis, execution, and architectures.