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Showing papers by "Lawrence Berkeley National Laboratory published in 2022"


Journal ArticleDOI
TL;DR: The novel abstractions that have been added to Kokkos version 3 such as hierarchical parallelism, containers, task graphs, and arbitrary-sized atomic operations to prepare for exascale era architectures are described.
Abstract: As the push towards exascale hardware has increased the diversity of system architectures, performance portability has become a critical aspect for scientific software. We describe the Kokkos Performance Portable Programming Model that allows developers to write single source applications for diverse high-performance computing architectures. Kokkos provides key abstractions for both the compute and memory hierarchy of modern hardware. We describe the novel abstractions that have been added to Kokkos version 3 such as hierarchical parallelism, containers, task graphs, and arbitrary-sized atomic operations to prepare for exascale era architectures. We demonstrate the performance of these new features with reproducible benchmarks on CPUs and GPUs.

117 citations


Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors evaluated the carbon reduction changes of commercial building operations in China's 30 provinces during the period 2001-2016, and built a framework of the reduction intensity, amount, and efficiency.

88 citations


Journal ArticleDOI
TL;DR: In this article , a low-crosslink-density polyacrylamide hydrogel at 96% water content having hyperbranched silica nanoparticles (HBSPs) as the major junction points is realized.
Abstract: The elastic storage and release of mechanical energy has been key to many developments throughout the history of mankind. Resilience, absent hysteresis, has been an elusive goal to achieve, particularly at large deformations. Using a low-crosslink-density polyacrylamide hydrogel at 96% water content having hyperbranched silica nanoparticles (HBSPs) as the major junction points, a hysteresis-free material is realized. The fatigue-free characteristic of these composite hydrogels is evidenced by the invariance of the stress-strain curves at strain ratios of 4, even after 5000 cycles. At a strain ratio of 7, only a 1.3% hysteresis is observed. A markedly increased strain-ratio-at-break of 11.5 is observed. The unique attributes of these resilient hydrogels are manifested in the high-fidelity detection of dynamic deformations under cyclic loading over a broad range of frequencies, difficult to achieve with other materials.

49 citations


Journal ArticleDOI
TL;DR: In this paper , Lagrangian perturbation theory is used to self-consistently work at the level of two-point functions, i.e. directly with the measured data, without approximating the constraints with summary statistics normalized by the drag scale.
Abstract: We present a new method for consistent, joint analysis of the pre- and post-reconstruction two-point functions of the BOSS survey. The post-reconstruction correlation function is used to accurately measure the distance-redshift relation and expansion history, while the pre-reconstruction power spectrum multipoles constrain the broad-band shape and the rate-of-growth of large-scale structure. Our technique uses Lagrangian perturbation theory to self-consistently work at the level of two-point functions, i.e.\ directly with the measured data, without approximating the constraints with summary statistics normalized by the drag scale. Combining galaxies across the full redshift range and both hemispheres we constrain $\Omega_m=0.303 \pm 0.0082$, $H_0=69.23 \pm 0.77$ and $\sigma_8=0.733 \pm 0.047$ within the context of $\Lambda$CDM. These constraints are in good agreement both with the Planck primary CMB anisotropy data and recent cosmic shear surveys.

43 citations


Journal ArticleDOI
TL;DR: The Beam Energy Scan Theory (BEST) Collaboration was formed with the goal of providing a theoretical framework for analyzing data from the BES program at the relativistic heavy ion collider (RHIC) at Brookhaven National Laboratory as mentioned in this paper.

36 citations


Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a metaverse of medical technology and artificial intelligence (MeTAI) to facilitate the development, prototyping, evaluation, regulation, translation and refinement of AI-based medical practice, especially medical imaging-guided diagnosis and therapy.
Abstract: The metaverse integrates physical and virtual realities, enabling humans and their avatars to interact in an environment supported by technologies such as high-speed internet, virtual reality, augmented reality, mixed and extended reality, blockchain, digital twins and artificial intelligence (AI), all enriched by effectively unlimited data. The metaverse recently emerged as social media and entertainment platforms, but extension to healthcare could have a profound impact on clinical practice and human health. As a group of academic, industrial, clinical and regulatory researchers, we identify unique opportunities for metaverse approaches in the healthcare domain. A metaverse of ‘medical technology and AI’ (MeTAI) can facilitate the development, prototyping, evaluation, regulation, translation and refinement of AI-based medical practice, especially medical imaging-guided diagnosis and therapy. Here, we present metaverse use cases, including virtual comparative scanning, raw data sharing, augmented regulatory science and metaversed medical intervention. We discuss relevant issues on the ecosystem of the MeTAI metaverse including privacy, security and disparity. We also identify specific action items for coordinated efforts to build the MeTAI metaverse for improved healthcare quality, accessibility, cost-effectiveness and patient satisfaction. The metaverse is gaining prominence in industry, academia and social media. Wang and colleagues envision a medical technology and AI ecosystem, and present this perspective on the future of healthcare in the metaverse.

28 citations


Journal ArticleDOI
TL;DR: In this article, artificial mediators, such as neutral red (NR) and 2-hydroxy-1,4-naphthoquinone (HNQ), increased acetate synthesis significantly, suggesting that these mediators improve the electron transport capability between the suspended cells and electrode.

28 citations


Journal ArticleDOI
TL;DR: In this paper , a Néel-type skyrmion lattice was observed at room temperature in a single-phase, layered 2D magnet, specifically a 50% Co-doped Fe5GeTe2 (FCGT) system.
Abstract: Novel magnetic ground states have been stabilized in two-dimensional (2D) magnets such as skyrmions, with the potential next-generation information technology. Here, we report the experimental observation of a Néel-type skyrmion lattice at room temperature in a single-phase, layered 2D magnet, specifically a 50% Co-doped Fe5GeTe2 (FCGT) system. The thickness-dependent magnetic domain size follows Kittel's law. The static spin textures and spin dynamics in FCGT nanoflakes were studied by Lorentz electron microscopy, variable-temperature magnetic force microscopy, micromagnetic simulations, and magnetotransport measurements. Current-induced skyrmion lattice motion was observed at room temperature, with a threshold current density, jth = 1 × 106 A/cm2. This discovery of a skyrmion lattice at room temperature in a noncentrosymmetric material opens the way for layered device applications and provides an ideal platform for studies of topological and quantum effects in 2D.

26 citations


Journal ArticleDOI
TL;DR: In this paper , the authors used luminous red galaxies selected from the imaging surveys that are being used for targeting by the DESI in combination with CMB lensing maps from the Planck collaboration to probe the amplitude of large-scale structure over 18,000 sq.deg.
Abstract: We use luminous red galaxies selected from the imaging surveys that are being used for targeting by the Dark Energy Spectroscopic Instrument (DESI) in combination with CMB lensing maps from the Planck collaboration to probe the amplitude of large-scale structure over $0.4\le z\le 1$. Our galaxy sample, with an angular number density of approximately $500\,\mathrm{deg}^{-2}$ over 18,000 sq.deg., is divided into 4 tomographic bins by photometric redshift and the redshift distributions are calibrated using spectroscopy from DESI. We fit the galaxy autospectra and galaxy-convergence cross-spectra using models based on cosmological perturbation theory, restricting to large scales that are expected to be well described by such models. Within the context of $\Lambda$CDM, combining all 4 samples and using priors on the background cosmology from supernova and baryon acoustic oscillation measurements, we find $S_8=\sigma_8(\Omega_m/0.3)^{0.5}=0.73\pm 0.03$. This result is lower than the prediction of the $\Lambda$CDM model conditioned on the Planck data. Our data prefer a slower growth of structure at low redshift than the model predictions, though at only modest significance.

26 citations


Journal ArticleDOI
D. Q. Adams1, C. Alduino1, F. Alessandria, K. Alfonso2  +243 moreInstitutions (22)
TL;DR: The CUORE experiment was the first to reach a tonne-scale, mK-cooled, experimental mass as mentioned in this paper, which has been operational since 2017 at a temperature of about 10 mK.

25 citations


Journal ArticleDOI
15 Jan 2022-Energy
TL;DR: In this paper, a dynamic optimization problem was formulated to explore prosumers' economic potentials, and the size parameter of WTTESs was swept in prosumers to obtain the optimal storage size considering the trade-off between the payback period and the heating cost saving.

Journal ArticleDOI
TL;DR: Results indicate that conditional variational autoencoders are capable of generating high-quality synthetic data samples, which in turns helps to enhance the accuracy in short-term building energy predictions.
Abstract: Short-term building energy predictions serve as one of the fundamental tasks in building operation management. While large numbers of studies have explored the value of various supervised machine learning techniques in energy predictions, few studies have addressed the potential data shortage problem in developing data-driven models. One promising solution is data augmentation, which aims to enrich existing building data resources for reliable predictive modeling. This study proposes a deep generative modeling-based data augmentation strategy for improving short-term building energy predictions. Two types of conditional variational autoencoders have been designed for synthetic energy data generation using fully connected and one-dimensional convolutional layers respectively. Data experiments have been designed to evaluate the value of data augmentation using actual measurements from 52 buildings. The results indicate that conditional variational autoencoders are capable of generating high-quality synthetic data samples, which in turns helps to enhance the accuracy in short-term building energy predictions. The average performance enhancement ratios in terms of CV-RMSE range between 12% and 18%. Practical guidelines have been obtained to ensure the validity and quality of synthetic building energy data. The research outcomes are valuable for enhancing the robustness and reliability of data-driven models for smart building operation management.

Journal ArticleDOI
TL;DR: In this article , the authors demonstrate deterministic and reversible control of chirality over mesoscale regions in ferroelectric vortices using an applied electric field using optical second-harmonic generation-based circular dichroism.
Abstract: Polar textures have attracted substantial attention in recent years as a promising analog to spin-based textures in ferromagnets. Here, using optical second-harmonic generation–based circular dichroism, we demonstrate deterministic and reversible control of chirality over mesoscale regions in ferroelectric vortices using an applied electric field. The microscopic origins of the chirality, the pathway during the switching, and the mechanism for electric field control are described theoretically via phase-field modeling and second-principles simulations, and experimentally by examination of the microscopic response of the vortices under an applied field. The emergence of chirality from the combination of nonchiral materials and subsequent control of the handedness with an electric field has far-reaching implications for new electronics based on chirality as a field-controllable order parameter.

Journal ArticleDOI
TL;DR: In this paper , the authors present a dataset of predicted electronic structure properties for thousands of metal-organic frameworks (MOFs) carried out using multiple density functional approximations and show that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semiconductors and insulators without magnetic character.
Abstract: Abstract With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3 d transition metal cations. With regards to partial atomic charges, we find that different density functional approximations predict similar charges overall, although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference. Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes. We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work, paving the way for future high-throughput screening studies. To encourage exploration and reuse of the theoretical calculations presented in this work, the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project.

Journal ArticleDOI
TL;DR: Test results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature simultaneously and could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.
Abstract: Demand response (DR) of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power grids. In this special fast DR event, effective control needs to guarantee expected power reduction and ensure an acceptable indoor environment. This study, therefore, developed a data-driven model predictive control (MPC) using support vector regression (SVR) for fast DR events. According to the characteristics of fast DR events, the optimized hyperparameters of SVR and shortened searching range of genetic algorithm are used to improve the control performance. Meanwhile, a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand controls. Test results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature simultaneously. Compared with RC-based MPC, the SVR-based MPC could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.

Journal ArticleDOI
TL;DR: In this article , the fundamental role of lattice dynamics in ferroelectric switching was elucidated by studying both freestanding bismuth ferrite (BiFeO3) membranes and films clamped to a substrate.
Abstract: Reducing the switching energy of ferroelectric thin films remains an important goal in the pursuit of ultralow-power ferroelectric memory and logic devices. Here, we elucidate the fundamental role of lattice dynamics in ferroelectric switching by studying both freestanding bismuth ferrite (BiFeO3) membranes and films clamped to a substrate. We observe a distinct evolution of the ferroelectric domain pattern, from striped, 71° ferroelastic domains (spacing of ~100 nm) in clamped BiFeO3 films, to large (10's of micrometers) 180° domains in freestanding films. By removing the constraints imposed by mechanical clamping from the substrate, we can realize a ~40% reduction of the switching voltage and a consequent ~60% improvement in the switching speed. Our findings highlight the importance of a dynamic clamping process occurring during switching, which impacts strain, ferroelectric, and ferrodistortive order parameters and plays a critical role in setting the energetics and dynamics of ferroelectric switching.

Journal ArticleDOI
TL;DR: The first results of the Noble and Alkali Spin Detectors for Ultralight Coherent darK matter (NASDUCK) collaboration were reported in this article , where the authors derived new constraints on ALP-proton and ALP-neutron interactions in the $4\times 10-15}-4\ts 10-12}{~\rm eV/c^2}$ mass range.
Abstract: We report on the first results of the Noble and Alkali Spin Detectors for Ultralight Coherent darK matter (NASDUCK) collaboration. We search for the interactions of Axion-Like Particles (ALPs) with atomic spins using an earth-based precision quantum detector as it traverses through the galactic dark matter halo. The detector is composed of spin-polarized xenon gas which can coherently interact with a background ALP dark matter field and an in-situ rubidium Floquet optical-magnetometer. Conducting a five months-long search, we derive new constraints on ALP-proton and ALP-neutron interactions in the $4\times 10^{-15}-4\times 10^{-12}{~\rm eV/c^2}$ mass range. Our limits on the ALP-proton (ALP-neutron) couplings improve upon previous terrestrial bounds by up to 3 orders of magnitude for masses above $4\times 10^{-14}{~\rm eV/c^2}$ ($4\times 10^{-13}{~\rm eV/c^2}$). Moreover, barring the uncertain supernova constraints, the ALP-proton bound improves on all existing terrestrial and astrophysical limits, partially closing the unexplored region for couplings in the range $10^{-6}~{\rm GeV^{-1}}$ to $2\times 10^{-5}~{\rm GeV^{-1}}$. Finally, we also cast bounds on pseudo-scalar dark matter models in which dark matter is quadratically-coupled to the nucleons.

Journal ArticleDOI
TL;DR: In this paper, an interatomic potential for modeling monolayer and multilayered Molybdenum ditelluride (MoTe2) films is presented.
Abstract: Transition metal dichalcogenides (TMDs) offer superior properties over conventional materials in many areas such as in electronic devices. In recent years, TMDs have been shown to display a phase switching mechanism under the application of external mechanical strain, making them exciting candidates for phase change transistors. Molybdenum ditelluride (MoTe2) is one such material that has been engineered as a strain-based phase change transistor. In this work, we explore various aspects of the mechanical properties of this material by a suite of computational and experimental approaches. First, we present parameterization of an interatomic potential for modeling monolayer as well as multilayered MoTe2 films. For generating the empirical potential parameter set, we fit results from density functional theory calculations using a random search algorithm known as particle swarm optimization. The potential closely predicts structural properties, elastic constants, and vibrational frequencies of MoTe2 indicating a reliable fit. Our simulated mechanical response matches earlier larger scale experimental nanoindentation results with excellent prediction of fracture points. Simulation of uniaxial tensile deformation by molecular dynamics shows the complete non-linear stress-strain response up to failure. Mechanical behavior, including failure properties, exhibits directional anisotropy due to the variation of bond alignments with crystal orientation. Furthermore, we show the deterioration of mechanical properties with increasing temperature. Finally, we present computational and experimental evidence of an extended c-axis strain transfer length in MoTe2 compared to TMDs with smaller chalcogen atoms.

Journal ArticleDOI
TL;DR: In this article , climate models simulate heatwaves and the increased intensity and probability of extreme heat reasonably well on large scales, but changes in annual daily maximum temperatures do not follow global warming over some regions including the Eastern United States and parts of Asia, reflecting the influence of local drivers as well as natural variability.
Abstract: It sounds straightforward. As the Earth warms due to the increased concentration of greenhouse gases in the atmosphere, global temperatures rise and so heatwaves become warmer as well. This means that a fixed temperature threshold is passed more often: the probability of extreme heat increases. However, land use changes, vegetation change, irrigation, air pollution, and other changes also drive local and regional trends in heatwaves. Sometimes they enhance heatwave intensity, but they can also counteract the effects of climate change, and in some regions, the mechanisms that impact on trends in heatwaves have not yet been fully identified. Climate models simulate heatwaves and the increased intensity and probability of extreme heat reasonably well on large scales. However, changes in annual daily maximum temperatures do not follow global warming over some regions, including the Eastern United States and parts of Asia, reflecting the influence of local drivers as well as natural variability. Also, temperature variability is unrealistic in many models, and can fail standard quality checks. Therefore, reliable attribution and projection of change in heatwaves remain a major scientific challenge in many regions, particularly where the moisture budget is not well simulated, and where land surface changes, changes in short-lived forcers, and soil moisture interactions are important.

Journal ArticleDOI
TL;DR: In this article , the effects of ART treatment on neurogenesis and proliferation of NSPCs using a rodent MCAO model were investigated, and the results of diffusion tensor imaging, electron microscopic, and immunofluorescence of Tuj-1 also revealed that ischemia-induced white matter lesion was alleviated by ART treatment.
Abstract: Promoting neurogenesis and proliferation of endogenous neural stem/progenitor cells (NSPCs) is considered a promising strategy for neurorehabilitation after stroke. Our previous study revealed that a moderate dose of artesunate (ART, 150 mg/kg) could enhance functional recovery in middle cerebral artery occlusion (MCAO) mice. This study aimed to investigate the effects of ART treatment on neurogenesis and proliferation of NSPCs using a rodent MCAO model. MRI results indicated that the ischemic brain volume of MCAO mice was reduced by ART treatment. The results of diffusion tensor imaging, electron microscopic, and immunofluorescence of Tuj-1 also revealed that ischemia-induced white matter lesion was alleviated by ART treatment. After ischemia/reperfusion, the proportion of Brdu + endogenous NSPCs in the ipsilateral subventricular zone and peri-infarct cortex was increased by ART treatment. Furthermore, the neuro-restorative effects of ART were abolished by the overexpression of FOXO3a. These findings suggested that ART could rescue ischemia/reperfusion damage and alleviate white matter injury, subsequently contributing to post-stroke functional recovery by promoting neurogenesis and proliferation of endogenous NSPCs via the FOXO3a/p27Kip1 pathway.

Journal ArticleDOI
10 Jan 2022
TL;DR: In this paper , the authors consider the extent to which these experiments can test if the graviton exists and demonstrate that this "Newtonian entanglement" requires the existence of massless bosons, universally coupled to mass, in the Hilbert space of low-energy scattering states.
Abstract: Many experiments have recently been proposed to test whether non-relativistic gravitational interactions can generate entanglement. In this note, I consider the extent to which these experiments can test if the graviton exists. Assuming unitarity and Lorentz invariance of the $S$-matrix, I demonstrate that this "Newtonian entanglement" requires the existence of massless bosons, universally coupled to mass, in the Hilbert space of low-energy scattering states. These bosons could be the usual spin-2 gravitons, but in principle there are other possibilities like spin-0 scalar gravitons. I suggest a concept for a more refined experiment to rule these out. The special role of $d=3+1$ spacetime dimensions and the possibility that unitarity is violated by gravity are highlighted.

Journal ArticleDOI
TL;DR: In this paper , the authors show that the anomalies coincide with a deep minimum in domain wall (DW) mobility, indicating a crossover between two regimes of DW propagation, and demonstrate that this crossover is a manifestation of a 2D phase transition that occurs within the DW, in which the magnetization texture changes from continuous rotation to unidirectional variation.
Abstract: The ferromagnetic phase of Co3Sn2S2 is widely considered to be a topological Weyl semimetal, with evidence for momentum-space monopoles of Berry curvature from transport and spectroscopic probes. As the bandstructure is highly sensitive to the magnetic order, attention has focused on anomalies in magnetization, susceptibility and transport measurements that are seen well below the Curie temperature, leading to speculation that a "hidden" phase coexists with ferromagnetism. Here we report spatially-resolved measurements by Kerr effect microscopy that identify this phase. We find that the anomalies coincide with a deep minimum in domain wall (DW) mobility, indicating a crossover between two regimes of DW propagation. We demonstrate that this crossover is a manifestation of a 2D phase transition that occurs within the DW, in which the magnetization texture changes from continuous rotation to unidirectional variation. We propose that the existence of this 2D transition deep within the ferromagnetic state of the bulk is a consequence of a giant quality factor for magnetocrystalline anisotropy unique to this compound. This work broadens the horizon of the conventional binary classification of DWs into Bloch and Néel walls, and suggests new strategies for manipulation of domain walls and their role in electron and spin transport.

Journal ArticleDOI
TL;DR: In this paper , a multi-model ensemble analysis based on the volc-pinatubo-full experiment performed within the Model Intercomparison Project on the climatic response to Volcanic forcing is presented.
Abstract: Abstract. This paper provides initial results from a multi-model ensemble analysis based on the volc-pinatubo-full experiment performed within the Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP) as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The volc-pinatubo-full experiment is based on an ensemble of volcanic forcing-only climate simulations with the same volcanic aerosol dataset across the participating models (the 1991–1993 Pinatubo period from the CMIP6-GloSSAC dataset). The simulations are conducted within an idealized experimental design where initial states are sampled consistently across models from the CMIP6-piControl simulation providing unperturbed preindustrial background conditions. The multi-model ensemble includes output from an initial set of six participating Earth system models (CanESM5, GISS-E2.1-G, IPSL-CM6A-LR, MIROC-E2SL, MPI-ESM1.2-LR and UKESM1). The results show overall good agreement between the different models on the global and hemispheric scales concerning the surface climate responses, thus demonstrating the overall effectiveness of VolMIP's experimental design. However, small yet significant inter-model discrepancies are found in radiative fluxes, especially in the tropics, that preliminary analyses link with minor differences in forcing implementation; model physics, notably aerosol–radiation interactions; the simulation and sampling of El Niño–Southern Oscillation (ENSO); and, possibly, the simulation of climate feedbacks operating in the tropics. We discuss the volc-pinatubo-full protocol and highlight the advantages of volcanic forcing experiments defined within a carefully designed protocol with respect to emerging modelling approaches based on large ensemble transient simulations. We identify how the VolMIP strategy could be improved in future phases of the initiative to ensure a cleaner sampling protocol with greater focus on the evolving state of ENSO in the pre-eruption period.

Journal ArticleDOI
TL;DR: In this article , a 3-year-old previously healthy female developed acute liver failure secondary to type 2 autoimmune hepatitis preceded by mild infection with SARS-CoV-2.
Abstract: Although elevated liver enzymes are common in hospitalized children with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, pediatric acute liver failure is an uncommon manifestation of COVID-19 disease. We describe the case of a 3-year-old previously healthy female who developed acute liver failure secondary to type 2 autoimmune hepatitis preceded by mild infection with SARS-CoV-2. Testing for viral hepatitis was negative, and the patient did not meet diagnostic criteria for multisystem inflammatory disease in children (MIS-C). A liver biopsy showed acute submassive hepatocyte necrosis with brisk CD3+ T lymphocyte infiltration and no evidence of fibrosis or chronic liver disease. Treatment with high-dose methylprednisolone resulted in rapid normalization of alanine aminotransferase (ALT), aspartate aminotransferase (AST), international normalized ratio (INR), and ammonia levels, and liver transplantation was avoided. This case highlights a possible association between SARS-CoV-2 infection and subsequent development of autoimmune liver disease presenting with acute liver failure.

Journal ArticleDOI
01 Feb 2022-CheM
TL;DR: In this article , an enantioselective, intermolecular hydroamination of unactivated terminal alkenes that occurs with equimolar amounts of alkene and amine is presented.
Abstract: Asymmetric alkene hydroamination could be a direct route to valuable chiral amines from abundant feedstocks. However, most asymmetric hydroaminations have limited synthetic value because they require a large excess of alkene, occur with modest enantioselectivity, and proceed with limited tolerance of functional groups. We report an enantioselective, intermolecular hydroamination of unactivated terminal alkenes that occurs with equimolar amounts of alkene and amine, tolerates many functional groups, and occurs in high yield, with high enantioselectivity and turnover numbers. Mechanistic studies revealed factors, including reversibility of the addition, reversible oxidation of the product amine, competing isomerization of the alkene reactant, and unfavorable replacement of sacrificial ligands in standard catalyst precursors by the chiral bisphosphine, that needed to be addressed to achieve enantioselective N-H additions to alkenes.

Journal ArticleDOI
TL;DR: A review of the state-of-the-art methods and applications for new physics searches in the context of terrestrial high-energy physics experiments, including the Large Hadron Collider, rare event searches and neutrino experiments, can be found in this paper .
Abstract: Compelling experimental evidence suggests the existence of new physics beyond the well-established and tested standard model of particle physics. Various current and upcoming experiments are searching for signatures of new physics. Despite the variety of approaches and theoretical models tested in these experiments, what they all have in common is the very large volume of complex data that they produce. This data challenge calls for powerful statistical methods. Machine learning has been in use in high-energy particle physics for well over a decade, but the rise of deep learning in the early 2010s has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present Review, which discusses methods and applications for new physics searches in the context of terrestrial high-energy physics experiments, including the Large Hadron Collider, rare event searches and neutrino experiments. Owing to the growing volumes of data from high-energy physics experiments, modern deep learning methods are playing an increasingly important role in all aspects of data taking and analysis. This Review provides an overview of key developments, with a focus on the search for physics beyond the standard model.

Journal ArticleDOI
TL;DR: In this article , the ensemble average of the most used Fourier space estimator in spectroscopic surveys, including all general relativistic (GR) effects, and allowing for an arbitrary choice of angular and radial selection functions, was computed.
Abstract: Abstract Measurements of the clustering of galaxies in Fourier space, and at low wavenumbers, offer a window into the early Universe via the possible presence of scale dependent bias generated by Primordial Non Gaussianites. On such large scales a Newtonian treatment of density perturbations might not be sufficient to describe the measurements, and a fully relativistic calculation should be employed. The interpretation of the data is thus further complicated by the fact that relativistic effects break statistical homogeneity and isotropy and are potentially divergent in the Infra-Red (IR). In this work we compute for the first time the ensemble average of the most used Fourier space estimator in spectroscopic surveys, including all general relativistic (GR) effects, and allowing for an arbitrary choice of angular and radial selection functions. We show that any observable is free of IR sensitivity once all the GR terms, individually divergent, are taken into account, and that this cancellation is a consequence of the presence of the Weinberg adiabatic mode as a solution to Einstein's equations. We then study the importance of GR effects, including lensing magnification, in the interpretation of the galaxy power spectrum multipoles, finding that they are in general a small, less than ten percent level, correction to the leading redshift space distortions term. This work represents the baseline for future investigations of the interplay between Primordial Non Gaussianities and GR effects on large scales and in Fourier space.

Journal ArticleDOI
TL;DR: The authors examined the characteristics of individuals with biomarker evidence of tauopathy but without β-amyloid (Aβ) (A-T+) in relation to individuals with (A+T+) and without (A-) evidence of Alzheimer's disease (AD).

Journal ArticleDOI
01 Aug 2022-Fuel
TL;DR: In this article , a method for systematically selecting molecular descriptor features and developing interpretable machine learning models without sacrificing accuracy is presented, which simplifies the process of selecting features by reducing feature multicollinearity and enables discoveries of new relationships between global properties and molecular descriptors.

Journal ArticleDOI
TL;DR: In this article, the role of rainfall seasonality on soil biodiversity and physicochemical properties was investigated in the Brazilian savanna, and the authors found that the abundance of the Formicidae family was the most abundant with 50% of all individuals in the dry season (April to September), while in the rainy season (October to March), the Isoptera order was the largest with approximately 39% of individuals.