Showing papers by "Washington State University published in 2019"

Properties of the Binary Neutron Star Merger GW170817

Abstract: On August 17, 2017, the Advanced LIGO and Advanced Virgo gravitational-wave detectors observed a low-mass compact binary inspiral. The initial sky localization of the source of the gravitational-wave signal, GW170817, allowed electromagnetic observatories to identify NGC 4993 as the host galaxy. In this work, we improve initial estimates of the binary's properties, including component masses, spins, and tidal parameters, using the known source location, improved modeling, and recalibrated Virgo data. We extend the range of gravitational-wave frequencies considered down to 23 Hz, compared to 30 Hz in the initial analysis. We also compare results inferred using several signal models, which are more accurate and incorporate additional physical effects as compared to the initial analysis. We improve the localization of the gravitational-wave source to a 90% credible region of 16 deg2. We find tighter constraints on the masses, spins, and tidal parameters, and continue to find no evidence for nonzero component spins. The component masses are inferred to lie between 1.00 and 1.89 M when allowing for large component spins, and to lie between 1.16 and 1.60 M (with a total mass 2.73-0.01+0.04 M) when the spins are restricted to be within the range observed in Galactic binary neutron stars. Using a precessing model and allowing for large component spins, we constrain the dimensionless spins of the components to be less than 0.50 for the primary and 0.61 for the secondary. Under minimal assumptions about the nature of the compact objects, our constraints for the tidal deformability parameter Λ are (0,630) when we allow for large component spins, and 300-230+420 (using a 90% highest posterior density interval) when restricting the magnitude of the component spins, ruling out several equation-of-state models at the 90% credible level. Finally, with LIGO and GEO600 data, we use a Bayesian analysis to place upper limits on the amplitude and spectral energy density of a possible postmerger signal.

Robust noble metal-based electrocatalysts for oxygen evolution reaction

Abstract: The oxygen evolution reaction (OER) is a kinetically sluggish anodic reaction and requires a large overpotential to deliver appreciable current. Despite the fact that non-precious metal-based alkaline water electrocatalysts are receiving increased attention, noble metal-based electrocatalysts (NMEs) applied in proton exchange membrane water electrolyzers still have advantageous features of larger current and power densities with lower stack cost. Engineering NMEs for OER catalysis with high efficiency, durability and utilization rate is of vital importance in promoting the development of cost-effective renewable energy production and conversion devices. In this tutorial review, we covered the recent progress in the composition and structure optimization of NMEs for OER including Ir- and Ru-based oxides and alloys, and noble-metals beyond Ir and Ru with a variety of morphologies. To shed light on the fundamental science and mechanisms behind composition/structure–performance relationships and activity–stability relationships, integrated experimental and theoretical studies were pursued for illuminating the metal–support interaction, size effect, heteroatom doping effect, phase transformation, degradation processes and single-atom catalysis. Finally, the challenges and outlook are provided for guiding the rational engineering of OER electrocatalysts for applications in renewable energy-related devices.

Binary Black Hole Population Properties Inferred from the First and Second Observing Runs of Advanced LIGO and Advanced Virgo

Abstract: We present results on the mass, spin, and redshift distributions with phenomenological population models using the 10 binary black hole (BBH) mergers detected in the first and second observing runs completed by Advanced LIGO and Advanced Virgo. We constrain properties of the BBH mass spectrum using models with a range of parameterizations of the BBH mass and spin distributions. We find that the mass distribution of the more massive BH in such binaries is well approximated by models with no more than 1% of BHs more massive than 45 M and a power-law index of (90% credibility). We also show that BBHs are unlikely to be composed of BHs with large spins aligned to the orbital angular momentum. Modeling the evolution of the BBH merger rate with redshift, we show that it is flat or increasing with redshift with 93% probability. Marginalizing over uncertainties in the BBH population, we find robust estimates of the BBH merger rate density of R= (90% credibility). As the BBH catalog grows in future observing runs, we expect that uncertainties in the population model parameters will shrink, potentially providing insights into the formation of BHs via supernovae, binary interactions of massive stars, stellar cluster dynamics, and the formation history of BHs across cosmic time.

Durum wheat genome highlights past domestication signatures and future improvement targets

Abstract: The domestication of wild emmer wheat led to the selection of modern durum wheat, grown mainly for pasta production. We describe the 10.45 gigabase (Gb) assembly of the genome of durum wheat cultivar Svevo. The assembly enabled genome-wide genetic diversity analyses revealing the changes imposed by thousands of years of empirical selection and breeding. Regions exhibiting strong signatures of genetic divergence associated with domestication and breeding were widespread in the genome with several major diversity losses in the pericentromeric regions. A locus on chromosome 5B carries a gene encoding a metal transporter (TdHMA3-B1) with a non-functional variant causing high accumulation of cadmium in grain. The high-cadmium allele, widespread among durum cultivars but undetected in wild emmer accessions, increased in frequency from domesticated emmer to modern durum wheat. The rapid cloning of TdHMA3-B1 rescues a wild beneficial allele and demonstrates the practical use of the Svevo genome for wheat improvement. Genome assembly of durum wheat cultivar Svevo enables genome-wide genetic diversity analyses highlighting modifications imposed by thousands of years of empirical selection and breeding.

Tests of General Relativity with GW170817

Abstract: The recent discovery by Advanced LIGO and Advanced Virgo of a gravitational wave signal from a binary neutron star inspiral has enabled tests of general relativity (GR) with this new type of source. This source, for the first time, permits tests of strong-field dynamics of compact binaries in the presence of matter. In this Letter, we place constraints on the dipole radiation and possible deviations from GR in the post-Newtonian coefficients that govern the inspiral regime. Bounds on modified dispersion of gravitational waves are obtained; in combination with information from the observed electromagnetic counterpart we can also constrain effects due to large extra dimensions. Finally, the polarization content of the gravitational wave signal is studied. The results of all tests performed here show good agreement with GR.

Improved Knowledge Distillation via Teacher Assistant

Abstract: Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too large to be deployed on edge devices like smartphones or embedded sensor nodes. There have been efforts to compress these networks, and a popular method is knowledge distillation, where a large (teacher) pre-trained network is used to train a smaller (student) network. However, in this paper, we show that the student network performance degrades when the gap between student and teacher is large. Given a fixed student network, one cannot employ an arbitrarily large teacher, or in other words, a teacher can effectively transfer its knowledge to students up to a certain size, not smaller. To alleviate this shortcoming, we introduce multi-step knowledge distillation, which employs an intermediate-sized network (teacher assistant) to bridge the gap between the student and the teacher. Moreover, we study the effect of teacher assistant size and extend the framework to multi-step distillation. Theoretical analysis and extensive experiments on CIFAR-10,100 and ImageNet datasets and on CNN and ResNet architectures substantiate the effectiveness of our proposed approach.

NAS-Bench-101: Towards Reproducible Neural Architecture Search

Abstract: Recent advances in neural architecture search (NAS) demand tremendous computational resources, which makes it difficult to reproduce experiments and imposes a barrier-to-entry to researchers without access to large-scale computation. We aim to ameliorate these problems by introducing NAS-Bench-101, the first public architecture dataset for NAS research. To build NAS-Bench-101, we carefully constructed a compact, yet expressive, search space, exploiting graph isomorphisms to identify 423k unique convolutional architectures. We trained and evaluated all of these architectures multiple times on CIFAR-10 and compiled the results into a large dataset of over 5 million trained models. This allows researchers to evaluate the quality of a diverse range of models in milliseconds by querying the pre-computed dataset. We demonstrate its utility by analyzing the dataset as a whole and by benchmarking a range of architecture optimization algorithms.

A reference genome for pea provides insight into legume genome evolution

Abstract: We report the first annotated chromosome-level reference genome assembly for pea, Gregor Mendel’s original genetic model. Phylogenetics and paleogenomics show genomic rearrangements across legumes and suggest a major role for repetitive elements in pea genome evolution. Compared to other sequenced Leguminosae genomes, the pea genome shows intense gene dynamics, most likely associated with genome size expansion when the Fabeae diverged from its sister tribes. During Pisum evolution, translocation and transposition differentially occurred across lineages. This reference sequence will accelerate our understanding of the molecular basis of agronomically important traits and support crop improvement.

Climate change impact and adaptation for wheat protein

Abstract: Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Recommendations for reporting ion mobility Mass Spectrometry measurements

Abstract: Here we present a guide to ion mobility mass spectrometry experiments, which covers both linear and nonlinear methods: what is measured, how the measurements are done, and how to report the results, including the uncertainties of mobility and collision cross section values. The guide aims to clarify some possibly confusing concepts, and the reporting recommendations should help researchers, authors and reviewers to contribute comprehensive reports, so that the ion mobility data can be reused more confidently. Starting from the concept of the definition of the measurand, we emphasize that (i) mobility values (K0) depend intrinsically on ion structure, the nature of the bath gas, temperature, and E/N; (ii) ion mobility does not measure molecular surfaces directly, but collision cross section (CCS) values are derived from mobility values using a physical model; (iii) methods relying on calibration are empirical (and thus may provide method‐dependent results) only if the gas nature, temperature or E/N cannot match those of the primary method. Our analysis highlights the urgency of a community effort toward establishing primary standards and reference materials for ion mobility, and provides recommendations to do so.

Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids

Abstract: The power grid is rapidly transforming, and while recent grid innovations increased the utilization of advanced control methods, the next-generation grid demands technologies that enable the integration of distributed energy resources (DERs)—and consumers that both seamlessly buy and sell electricity. This paper develops an optimization model and blockchain-based architecture to manage the operation of crowdsourced energy systems (CESs), with peer-to-peer (P2P) energy trading transactions (ETTs). An operational model of CESs in distribution networks is presented considering various types of ETT and crowdsourcees. Then, a two-phase operation algorithm is presented: Phase I focuses on the day-ahead scheduling of generation and controllable DERs, whereas Phase II is developed for hour-ahead or real-time operation of distribution networks. The developed approach supports seamless P2P energy trading between individual prosumers and/or the utility. The presented operational model can also be used to operate islanded microgrids. The CES framework and the operation algorithm are then prototyped through an efficient blockchain implementation, namely, the IBM Hyperledger Fabric. This implementation allows the system operator to manage the network users to seamlessly trade energy. Case studies and prototype illustration are provided.

Tuning Pt-CeO2 interactions by high-temperature vapor-phase synthesis for improved reducibility of lattice oxygen.

Abstract: In this work, we compare the CO oxidation performance of Pt single atom catalysts (SACs) prepared via two methods: (1) conventional wet chemical synthesis (strong electrostatic adsorption–SEA) with calcination at 350 °C in air; and (2) high temperature vapor phase synthesis (atom trapping–AT) with calcination in air at 800 °C leading to ionic Pt being trapped on the CeO2 in a thermally stable form. As-synthesized, both SACs are inactive for low temperature (<150 °C) CO oxidation. After treatment in CO at 275 °C, both catalysts show enhanced reactivity. Despite similar Pt metal particle size, the AT catalyst is significantly more active, with onset of CO oxidation near room temperature. A combination of near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) and CO temperature-programmed reduction (CO-TPR) shows that the high reactivity at low temperatures can be related to the improved reducibility of lattice oxygen on the CeO2 support. While single-atom catalysts (SACs) have attracted a lot of interest, the nature of the active sites in SACs remains elusive. Here the authors elucidate that depositing single atoms via high temperature synthesis leads to improved reducibility of lattice oxygen on CeO2 yielding low temperature reactivity of Pt catalysts in CO oxidation.

Limbic Motor Circuits and Neuropsychiatry

Abstract: Limbic Motor Circuits and Neuropsychiatry explores the neural circuitry employed by mammals to interpret environmental stimuli that provoke adaptive behavioral responses. Internationally recognized biomedical scientists have contributed chapters that describe and evaluate the anatomy, physiology, pharmacology, and pathophysiology of how motivationally relevant environmental or interoceptive stimuli are translated into adaptive or maladaptive behavioral responses. The book also examines how classic limbic nuclei communicate with classic motor systems and the implications in neuropsychiatric disorders. This reference presents exciting new information that will interest neuroscientists, psychiatrists, neuropsychopharmacologists, and behavioral pharmacologists.

Taxonomy of the order Bunyavirales : update 2019

Abstract: In February 2019, following the annual taxon ratification vote, the order Bunyavirales was amended by creation of two new families, four new subfamilies, 11 new genera and 77 new species, merging of two species, and deletion of one species. This article presents the updated taxonomy of the order Bunyavirales now accepted by the International Committee on Taxonomy of Viruses (ICTV).

15 years of GDR: New data and functionality in the Genome Database for Rosaceae.

Abstract: The Genome Database for Rosaceae (GDR, https://www.rosaceae.org) is an integrated web-based community database resource providing access to publicly available genomics, genetics and breeding data and data-mining tools to facilitate basic, translational and applied research in Rosaceae. The volume of data in GDR has increased greatly over the last 5 years. The GDR now houses multiple versions of whole genome assembly and annotation data from 14 species, made available by recent advances in sequencing technology. Annotated and searchable reference transcriptomes, RefTrans, combining peer-reviewed published RNA-Seq as well as EST datasets, are newly available for major crop species. Significantly more quantitative trait loci, genetic maps and markers are available in MapViewer, a new visualization tool that better integrates with other pages in GDR. Pathways can be accessed through the new GDR Cyc Pathways databases, and synteny among the newest genome assemblies from eight species can be viewed through the new synteny browser, SynView. Collated single-nucleotide polymorphism diversity data and phenotypic data from publicly available breeding datasets are integrated with other relevant data. Also, the new Breeding Information Management System allows breeders to upload, manage and analyze their private breeding data within the secure GDR server with an option to release data publicly.

BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions.

Abstract: Big datasets, accumulated from biomedical and agronomic studies, provide the potential to identify genes that control complex human diseases and agriculturally important traits through genome-wide association studies (GWAS). However, big datasets also lead to extreme computational challenges, especially when sophisticated statistical models are employed to simultaneously reduce false positives and false negatives. The newly developed fixed and random model circulating probability unification (FarmCPU) method uses a bin method under the assumption that quantitative trait nucleotides (QTNs) are evenly distributed throughout the genome. The estimated QTNs are used to separate a mixed linear model into a computationally efficient fixed effect model (FEM) and a computationally expensive random effect model (REM), which are then used iteratively. To completely eliminate the computationally expensive REM, we replaced REM with FEM by using Bayesian information criteria. To eliminate the requirement that QTNs be evenly distributed throughout the genome, we replaced the bin method with linkage disequilibrium information. The new method is called Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). Both real and simulated data analyses demonstrated that BLINK improves statistical power compared to FarmCPU, in addition to remarkably reducing computing time. Now, a dataset with one million individuals and one-half million markers can be analyzed within three hours, instead of one week using FarmCPU.

Glucose Oxidase-Integrated Metal-Organic Framework Hybrids as Biomimetic Cascade Nanozymes for Ultrasensitive Glucose Biosensing.

Abstract: Nanozyme/natural enzyme hybrid plays a vital role in biosensing, therapy, and catalysis owing to the integrated advantages in the selectivity of natural enzymes and controllable catalytic activity of nanozymes. Herein, Fe-MIL-88B-NH2 [(Fe-metal-organic framework (MOF)] with remarkable peroxidase-like activity, ultrahigh stability, and high biocompatibility was utilized for immobilization of glucose oxidase (GOx) via an amidation coupling reaction. On the basis of the excellent selectivity and catalytic activity of Fe-MOF-GOx, a cascade catalysis was performed for the colorimetric detection of glucose. The integrated Fe-MOF-GOx not only exhibited higher stability and reusability than their mixtures including Fe-MOF and free GOx system but also possessed a wide linear range (1-500 μM), with a low detection limit of 0.487 μM for glucose detection.

Stabilizing High Metal Loadings of Thermally Stable Platinum Single Atoms on an Industrial Catalyst Support

Abstract: Single-atom catalysts have attracted attention because of improved atom efficiency, higher reactivity, and better selectivity. A major challenge is to achieve high surface concentrations while prev...

Fe–N–C Single-Atom Nanozymes for the Intracellular Hydrogen Peroxide Detection

Abstract: Recently, in situ detection of hydrogen peroxide (H2O2) generated from live cells have caused tremendous attention, because it is of great significance in the control of multiple biological processes. Herein, Fe-N-C single-atom nanozymes (Fe-N-C SAzymes) with intrinsic peroxidase-like activity were successfully prepared via high-temperature calcination using FeCl2, glucose, and dicyandiamide as precursors. The Fe-N-C SAzymes with FeNx as active sites were similar to natural metalloproteases, which can specifically enhance the peroxidase-like activity rather than oxidase-like activity. Accordingly, owing to the excellent catalytic efficiency of the Fe-N-C SAzymes, colorimetric biosensing of H2O2 in vitro was performed via a typical 3,3',5,5'-tetramethylbenzidine induced an allochroic reaction, demonstrating the satisfactory specificity and sensitivity. With regard to the practical application, in situ detection of H2O2 generated from the Hela cells by the Fe-N-C SAzymes was also performed, which can expand the applications of the newborn SAzymes.

Cannabidiol modulates serotonergic transmission and reverses both allodynia and anxiety-like behavior in a model of neuropathic pain

Abstract: Clinical studies indicate that cannabidiol (CBD), the primary nonaddictive component of cannabis that interacts with the serotonin (5-HT)1A receptor, may possess analgesic and anxiolytic effects However, its effects on 5-HT neuronal activity, as well as its impact on models of neuropathic pain are unknown First, using in vivo single-unit extracellular recordings in rats, we demonstrated that acute intravenous (iv) increasing doses of CBD (01-10 mg/kg) decreased the firing rate of 5-HT neurons in the dorsal raphe nucleus, which was prevented by administration of the 5-HT1A antagonist WAY 100635 (03 mg/kg, iv) and the TRPV1 antagonist capsazepine (1 mg/kg, iv) but not by the CB1 receptor antagonist AM 251 (1 mg/kg, iv) Repeated treatment with CBD (5 mg/kg/day, subcutaneously [sc], for 7 days) increased 5-HT firing through desensitization of 5-HT1A receptors Rats subjected to the spared nerve injury model for 24 days showed decreased 5-HT firing activity, mechanical allodynia, and increased anxiety-like behavior in the elevated plus maze test, open-field test, and novelty-suppressed feeding test Seven days of treatment with CBD reduced mechanical allodynia, decreased anxiety-like behavior, and normalized 5-HT activity Antiallodynic effects of CBD were fully prevented by capsazepine (10 mg/kg/day, sc, for 7 days) and partially prevented by WAY 100635 (2 mg/kg/day, sc, for 7 days), whereas the anxiolytic effect was blocked only by WAY Overall, repeated treatment with low-dose CBD induces analgesia predominantly through TRPV1 activation, reduces anxiety through 5-HT1A receptor activation, and rescues impaired 5-HT neurotransmission under neuropathic pain conditions

Challenges and Opportunities for Bio-oil Refining: A Review

Abstract: Bio-oil derived from fast pyrolysis of lignocellulosic materials is among the most complex and inexpensive raw oils that can be produced today. Although commercial or demonstration scale fast pyrol...

DroidCat: Effective Android Malware Detection and Categorization via App-Level Profiling

Abstract: Most existing Android malware detection and categorization techniques are static approaches, which suffer from evasion attacks, such as obfuscation. By analyzing program behaviors, dynamic approaches are potentially more resilient against these attacks. Yet existing dynamic approaches mostly rely on characterizing system calls which are subject to system-call obfuscation. This paper presents DroidCat, a novel dynamic app classification technique, to complement existing approaches. By using a diverse set of dynamic features based on method calls and inter-component communication (ICC) Intents without involving permission, app resources, or system calls while fully handling reflection, DroidCat achieves superior robustness than static approaches as well as dynamic approaches relying on system calls. The features were distilled from a behavioral characterization study of benign versus malicious apps. Through three complementary evaluation studies with 34 343 apps from various sources and spanning the past nine years, we demonstrated the stability of DroidCat in achieving high classification performance and superior accuracy compared with the two state-of-the-art peer techniques that represent both static and dynamic approaches. Overall, DroidCat achieved 97% F1-measure accuracy consistently for classifying apps evolving over the nine years, detecting or categorizing malware, 16%–27% higher than any of the two baselines compared. Furthermore, our experiments with obfuscated benchmarks confirmed higher robustness of DroidCat over these baseline techniques. We also investigated the effects of various design decisions on DroidCat’s effectiveness and the most important features for our dynamic classification. We found that features capturing app execution structure such as the distribution of method calls over user code and libraries are much more important than typical security features such as sensitive flows.

A review of the electrical and mechanical properties of carbon nanofiller-reinforced polymer composites

Abstract: Within decades of development, carbon nanomaterials such as carbon black, fullerene, carbon nanotube, carbon nanofiber, graphene and their combined nanofillers have been tremendously applied in polymer material industries, generating a series of fascinating multifunctional composites in the fields from portable electronic devices, sports, entertainments to automobile, aerospace and military. Among the various material properties of the composites, electrical conductivity and mechanical performance are the two most important parameters for evaluating the effectiveness of nanofillers in the polymer matrices. In this review, we focus on the electrical and mechanical properties of diverse dimensional carbon nanofillers (e.g., zero-, one-, two-, three-dimensional nanofillers or their combinations)-reinforced polymer composites to seek the most efficient and effective approach to obtain high-performance polymeric nanocomposites.

Oxidase-Like Fe-N-C Single-Atom Nanozymes for the Detection of Acetylcholinesterase Activity.

Abstract: Single-atom catalysts (SACs) have attracted extensive attention in the catalysis field because of their remarkable catalytic activity, gratifying stability, excellent selectivity, and 100% atom utilization. With atomically dispersed metal active sites, Fe-N-C SACs can mimic oxidase by activating O2 into reactive oxygen species, O2 - • radicals. Taking advantages of this property, single-atom nanozymes (SAzymes) can become a great impetus to develop novel biosensors. Herein, the performance of Fe-N-C SACs as oxidase-like nanozymes is explored. Besides, the Fe-N-C SAzymes are applied in biosensor areas to evaluate the activity of acetylcholinesterase based on the inhibition toward nanozyme activity by thiols. Moreover, this SAzymes-based biosensor is further used for monitoring the amounts of organophosphorus compounds.