Identifying divertor detachment using a machine learning model trained on divertor camera images from DIII-D
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Using the Giant Metrewave Radio Telescope, we performed deep observations to search for radio pulsations in the directions of unidentified Fermi-Large Area Telescope {gamma}-ray sources. We report the discovery of an eclipsing black-widow millisecond pulsar, PSR J1544+4937, identified with the uncataloged {gamma}-ray source FERMI J1544.2+4941. This 2.16 ms pulsar is in a 2.9 hr compact circular orbit with a very low mass companion (M{sub c} > 0.017M{sub Sun }). At 322 MHz this pulsar is found to be eclipsing for 13% of its orbit, whereas at 607 MHz the pulsar is detected throughout the low-frequency eclipse phase. Variations in the eclipse ingress phase are observed, indicating a clumpy and variable eclipsing medium. Moreover, additional short-duration absorption events are observed around the eclipse boundaries. Using the radio timing ephemeris we were able to detect {gamma}-ray pulsations from this pulsar, confirming it as the source powering the {gamma}-ray emission.
Motivation: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems such as protein complexes, metabolic reactions, and signal transduction pathways. Hypergraphs are generalizations of graphs that naturally model multi-way interactions in data, and we therefore seek to understand how they can more faithfully identify, and potentially predict, complex relationships in genomic expression data sets. Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges are differentially expressed genes and vertices represent conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Our results demonstrate the utility of using hypergraphs to represent complex biological systems, and highlight potentially interesting biological results about host response to highly pathogenic viruses.
Toxoplasma gondii, an Apicomplexan parasite, causes significant morbidity and mortality, including severe disease in immunocompromised hosts and devastating congenital disease, with no effective treatment for the bradyzoite stage. To address this, we used the Tropical Disease Research database, crystallography, molecular modeling, and antisense to identify and characterize a range of potential therapeutic targets for toxoplasmosis. Phosphoglycerate mutase II (PGMII), nucleoside diphosphate kinase (NDK), ribulose phosphate 3-epimerase (RPE), ribose-5-phosphate isomerase (RPI), and ornithine aminotransferase (OAT) were structurally characterized. Crystallography revealed insights into the overall structure, protein oligomeric states and molecular details of active sites important for ligand recognition. Literature and molecular modeling suggested potential inhibitors and druggability. The targets were further studied with vivoPMO to interrupt enzyme synthesis, identifying the targets as potentially important to parasitic replication and, therefore, of therapeutic interest. Targeted vivoPMO resulted in statistically significant perturbation of parasite replication without concomitant host cell toxicity, consistent with a previous CRISPR/Cas9 screen showing PGM, RPE, and RPI contribute to parasite fitness. PGM, RPE, and RPI have the greatest promise for affecting replication in tachyzoites. These targets are shared between other medically important parasites and may have wider therapeutic potential.
Six blendstocks identified by the Co-Optimization of Fuels & Engines Program were used to prepare fuel blends using a fixed blendstock for oxygenate blending and a target RON of 97. The blendstocks included ethanol, n-propanol, isopropanol, isobutanol, diisobutylene, and a bioreformate surrogate. The blends were analyzed and used to establish interaction factors for a non-linear molar blending model that was used to predict RON and MON of volumetric blends of the blendstocks up to 35 vol%. Projections of efficiency increase, volumetric fuel economy increase, and tailpipe CO2 emissions decrease were produced using two different estimation techniques to evaluate the potential benefits of the blendstocks. Ethanol was projected to provide the greatest benefits in efficiency and tailpipe CO2 emissions, but at intermediate levels of volumetric fuel economy increase over a smaller range of blends than other blendstocks. A bioreformate surrogate blendstock was projected to provide the greatest increase in volumetric fuel economy and the lowest increase in efficiency. Tailpipe CO2 emissions for blends of the bioreformate surrogate were higher at all blend levels compared to the baseline E10 fuel.
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We introduce a new structure finding algorithm that self-consistently parses large scale cosmological structure into clusters, filaments and voids. This structure finding algorithm probes the structure at multiple scales and classifies the appropriate regions with the most probable structure type and size. We show that it can identify the baryon fraction of intercluster medium and cosmological voids.
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In the iron-based superconductors (FeSCs), orbital differentiation is an important phenomenon, whereby correlations stronger on the d xy orbital than on the d xz /d yz orbital yield quasi-particles with d xy orbital character having larger mass renormalization and abnormal temperature evolution. However, the physical origin of this orbital di erentiation is debated between the Hund's coupling induced unbinding of spin and orbital degrees of freedom and the Hubbard interaction instigated orbital selective Mott transition. Here we use angle-resolved photoemission spectroscopy to identify an orbital-dependent correlation-induced quasi-particle (QP) anomaly in LiFeAs. Lastly, the excellent agreement between our photoemission measurements and first-principles many-body theory calculations shows that the orbital-differentiated QP lifetime anomalies in LiFeAs are controlled by the Hund's coupling.
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Leakage through abandoned wells and geologic faults poses the greatest risk to CO 2 storage permanence. For shallow aquifers, secondary CO 2 plumes emanating from the leak zones may go undetected for a sustained period of time and has the greatest potential to cause large-scale and long-term environmental impacts. Identification of the attributes of leak zones, including their shape, location, and strength, is required for environmental risk assessment. This study applies a parametric level set (PaLS) method to characterize leak zones. Level set methods are appealing for tracking topological changes and recovering unknown shapes of objects. However, level set evolution using the conventional level set methods is computationally challenging. In PaLS, the level set function is approximated by using a weighted sum of basis functions and the level set evolution problem is replaced by an optimization problem. The efficacy of PaLS is demonstrated by reconstructing attributes of a CO 2 leak zone in a carbonate aquifer. Our results show that PaLS is a robust source identification method that can recover the approximate leak zone locations in the presence of measurement errors, model parameter uncertainty, and inaccurate guesses of source flux strengths. Accurate delineation of source geometry is achievable when a relatively dense observation network is used. The PaLS-based, source recovery framework introduced in this work is generic and can be adapted for any reactive transport model by simply switching the pre- and post-processing routines.