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Showing papers by "State University of New York System published in 2021"


Journal ArticleDOI
TL;DR: In patients with diabetes and recent worsening heart failure, sotagliflozin therapy, initiated before or shortly after discharge, resulted in a significantly lower total number of deaths from cardiovascular causes and hospitalizations and urgent visits for heart failure than placebo.
Abstract: Background Sodium–glucose cotransporter 2 (SGLT2) inhibitors reduce the risk of hospitalization for heart failure or death from cardiovascular causes among patients with stable heart failu...

913 citations


Journal ArticleDOI
TL;DR: In patients with diabetes and chronic kidney disease, with or without albuminuria, sotagliflozin resulted in a lower risk of the composite of deaths from cardiovascular causes, hospitalizations for heart failure, and urgent visits for heart Failure than placebo but was associated with adverse events.
Abstract: Background The efficacy and safety of sodium–glucose cotransporter 2 inhibitors such as sotagliflozin in preventing cardiovascular events in patients with diabetes with chronic kidney dise...

541 citations


Journal ArticleDOI
TL;DR: The Q-Chem quantum chemistry program package as discussed by the authors provides a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, and methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques.
Abstract: This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange-correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an "open teamware" model and an increasingly modular design.

360 citations


Journal ArticleDOI
D. Adhikari1, H. Albataineh2, Darko Androić3, K. A. Aniol4, D. S. Armstrong5, T. Averett5, C. Ayerbe Gayoso5, S. Barcus6, V. Bellini7, R. S. Beminiwattha8, Jay Benesch6, H. Bhatt9, D. Bhatta Pathak8, D. Bhetuwal9, B. Blaikie10, Q. Campagna5, A. Camsonne6, G. D. Cates11, Y. Chen8, C. Clarke12, J. C. Cornejo13, S. Covrig Dusa6, P. Datta14, A. Deshpande12, Dipangkar Dutta9, C. Feldman12, E. Fuchey14, C. Gal12, C. Gal11, D. Gaskell6, T. Gautam15, Michael Gericke10, C. Ghosh12, C. Ghosh16, I. Halilovic10, J. O. Hansen6, F. Hauenstein17, W. Henry18, Charles Horowitz19, C. Jantzi11, Siyu Jian11, S. Johnston16, D. C. Jones18, B. Karki20, S. Katugampola11, Cynthia Keppel6, P. M. King20, D. King21, M. Knauss22, K. S. Kumar16, T. Kutz12, N. Lashley-Colthirst15, G. Leverick10, H. Liu16, N. Liyange11, S. Malace6, R. Mammei23, Juliette Mammei10, M. McCaughan6, D. McNulty1, D. G. Meekins6, C. Metts5, R. Michaels6, M. M. Mondal12, Jim Napolitano18, A. Narayan24, D. Nikolaev18, M. N. H. Rashad17, V. Owen5, C. Palatchi11, J. Pan10, B. Pandey15, S. Park12, Kent Paschke11, M. Petrusky12, Michael Pitt25, S. Premathilake11, Andrew Puckett14, B. P. Quinn13, R. W. Radloff20, S. Rahman10, A. Rathnayake11, Brendan Reed19, P. E. Reimer26, R. Richards12, S. Riordan26, Y. Roblin6, S. Seeds14, A. Shahinyan27, Paul Souder21, L. G. Tang15, L. G. Tang6, Michaela Thiel28, Y. Tian21, G. M. Urciuoli, E. W. Wertz5, Bogdan Wojtsekhowski6, B. Yale5, T. Ye12, A. Yoon29, A. Zec11, W. Zhang12, Jiawen Zhang30, Jiawen Zhang12, X. Zheng11 
TL;DR: In this paper, the parity-violating asymmetry in the elastic scattering of longitudinally polarized electrons from 208 Pb was measured, leading to an extraction of the neutral weak form factor F = 0.0036(exp)±0.0013(theo)
Abstract: We report a precision measurement of the parity-violating asymmetry A_{PV} in the elastic scattering of longitudinally polarized electrons from ^{208}Pb. We measure A_{PV}=550±16(stat)±8(syst) parts per billion, leading to an extraction of the neutral weak form factor F_{W}(Q^{2}=0.00616 GeV^{2})=0.368±0.013. Combined with our previous measurement, the extracted neutron skin thickness is R_{n}-R_{p}=0.283±0.071 fm. The result also yields the first significant direct measurement of the interior weak density of ^{208}Pb: ρ_{W}^{0}=-0.0796±0.0036(exp)±0.0013(theo) fm^{-3} leading to the interior baryon density ρ_{b}^{0}=0.1480±0.0036(exp)±0.0013(theo) fm^{-3}. The measurement accurately constrains the density dependence of the symmetry energy of nuclear matter near saturation density, with implications for the size and composition of neutron stars.

239 citations


Journal ArticleDOI
01 Feb 2021
TL;DR: In a cohort study of linked statewide HIV diagnosis, COVID-19 laboratory diagnosis, and hospitalization databases, persons living with an HIV diagnosis were more likely to receive a diagnosis of, be hospitalized with, and die in-hospital with CO VID-19 compared with those not living with a HIV diagnosis.
Abstract: Importance New York State has been an epicenter for both the US coronavirus disease 2019 (COVID-19) and HIV/AIDS epidemics. Persons living with diagnosed HIV may be more prone to COVID-19 infection and severe outcomes, yet few studies have assessed this possibility at a population level. Objective To evaluate the association between HIV diagnosis and COVID-19 diagnosis, hospitalization, and in-hospital death in New York State. Design, Setting, and Participants This cohort study, conducted in New York State, including New York City, between March 1 and June 15, 2020, matched data from HIV surveillance, COVID-19 laboratory-confirmed diagnoses, and hospitalization databases to provide a full population-level comparison of COVID-19 outcomes between persons living with diagnosed HIV and persons living without diagnosed HIV. Exposures Diagnosis of HIV infection through December 31, 2019. Main Outcomes and Measures The main outcomes were COVID-19 diagnosis, hospitalization, and in-hospital death. COVID-19 diagnoses, hospitalizations, and in-hospital death rates comparing persons living with diagnosed HIV with persons living without dianosed HIV were computed, with unadjusted rate ratios and indirect standardized rate ratios (sRR), adjusting for sex, age, and region. Adjusted rate ratios (aRRs) for outcomes specific to persons living with diagnosed HIV were assessed by age, sex, region, race/ethnicity, transmission risk, and CD4+T-cell count–defined HIV disease stage, using Poisson regression models. Results A total of 2988 persons living with diagnosed HIV (2109 men [70.6%]; 2409 living in New York City [80.6%]; mean [SD] age, 54.0 [13.3] years) received a diagnosis of COVID-19. Of these persons living with diagnosed HIV, 896 were hospitalized and 207 died in the hospital through June 15, 2020. After standardization, persons living with diagnosed HIV and persons living without diagnosed HIV had similar diagnosis rates (sRR, 0.94 [95% CI, 0.91-0.97]), but persons living with diagnosed HIV were hospitalized more than persons living without diagnosed HIV, per population (sRR, 1.38 [95% CI, 1.29-1.47]) and among those diagnosed (sRR, 1.47 [95% CI, 1.37-1.56]). Elevated mortality among persons living with diagnosed HIV was observed per population (sRR, 1.23 [95% CI, 1.07-1.40]) and among those diagnosed (sRR, 1.30 [95% CI, 1.13-1.48]) but not among those hospitalized (sRR, 0.96 [95% CI, 0.83-1.09]). Among persons living with diagnosed HIV, non-Hispanic Black individuals (aRR, 1.59 [95% CI, 1.40-1.81]) and Hispanic individuals (aRR, 2.08 [95% CI, 1.83-2.37]) were more likely to receive a diagnosis of COVID-19 than White individuals, but they were not more likely to be hospitalized once they received a diagnosis or to die once hospitalized. Hospitalization risk increased with disease progression to HIV stage 2 (aRR, 1.29 [95% CI, 1.11-1.49]) and stage 3 (aRR, 1.69 [95% CI, 1.38-2.07]) relative to stage 1. Conclusions and Relevance In this cohort study, persons living with diagnosed HIV experienced poorer COVID-related outcomes relative to persons living without diagnosed HIV; Previous HIV diagnosis was associated with higher rates of severe disease requiring hospitalization, and hospitalization risk increased with progression of HIV disease stage.

219 citations


Journal ArticleDOI
01 Jan 2021
TL;DR: An expansive, multilevel model of the current knowledge of how humans are using technology during the COVID-19 pandemic is sketched and various effects have been observed, such as improved patient outcomes, continued education, and decreased outbreak impact.
Abstract: The relationship between humans and digital technologies has been documented extensively in the past decades, but has yet to be reviewed through the lens of the current global pandemic crisis This review synthesizes the rapidly growing literature on digital technology use during the current COVID-19 pandemic It addresses the following four topics: (1) the specific digital technologies that have been used, (2) the specific populations who have used these digital technologies, (3) the specific activities that individuals and groups have used these digital technologies, and (4) the specific effects of using these digital technologies on humans during the pandemic The 281 empirical articles we have identified suggest that (1) 28 various forms of technologies have been used, ranging from computers to artificial intelligence, (2) 8 different populations of users are using these technologies, primarily medical professionals, (3) 32 generalized types of activities are involved, including providing health services remotely, analyzing data, and communicating, and (4) 35 various effects have been observed, such as improved patient outcomes, continued education, and decreased outbreak impact Through this rapid review, we sketched an expansive, multilevel model of the current knowledge of how humans are using technology during the COVID-19 pandemic Major findings and future directions are discussed

213 citations


Journal ArticleDOI
TL;DR: The primary emphasis is device performance of OER-related proton exchange membrane (PEM) electrolyzers, ORR-related PEM fuel cells, NRR-driven ammonia electrosynthesis from water and nitrogen, and AOR-related direct ammonia fuel cells.
Abstract: Clean and efficient energy storage and conversion via sustainable water and nitrogen reactions have attracted substantial attention to address the energy and environmental issues due to the overwhelming use of fossil fuels. These electrochemical reactions are crucial for desirable clean energy technologies, including advanced water electrolyzers, hydrogen fuel cells, and ammonia electrosynthesis and utilization. Their sluggish reaction kinetics lead to inefficient energy conversion. Innovative electrocatalysis, i.e., catalysis at the interface between the electrode and electrolyte to facilitate charge transfer and mass transport, plays a vital role in boosting energy conversion efficiency and providing sufficient performance and durability for these energy technologies. Herein, a comprehensive review on recent progress, achievements, and remaining challenges for these electrocatalysis processes related to water (i.e., oxygen evolution reaction, OER, and oxygen reduction reaction, ORR) and nitrogen (i.e., nitrogen reduction reaction, NRR, for ammonia synthesis and ammonia oxidation reaction, AOR, for energy utilization) is provided. Catalysts, electrolytes, and interfaces between the two within electrodes for these electrocatalysis processes are discussed. The primary emphasis is device performance of OER-related proton exchange membrane (PEM) electrolyzers, ORR-related PEM fuel cells, NRR-driven ammonia electrosynthesis from water and nitrogen, and AOR-related direct ammonia fuel cells.

199 citations


Journal ArticleDOI
TL;DR: This survey article proposes to answer the question: how to train distributed machine learning models for resource-constrained IoT devices, and highlights an overview of FL and provides a comprehensive survey of the problem statements and emerging challenges.
Abstract: Federated learning (FL) is a distributed machine learning strategy that generates a global model by learning from multiple decentralized edge clients. FL enables on-device training, keeping the client’s local data private, and further, updating the global model based on the local model updates. While FL methods offer several advantages, including scalability and data privacy, they assume there are available computational resources at each edge-device/client. However, the Internet-of-Things (IoTs) enabled devices, e.g., robots, drone swarms, and low-cost computing devices (e.g., Raspberry Pi), may have limited processing ability, low bandwidth and power, or limited storage capacity. In this survey paper, we propose to answer this question: how to train distributed machine learning models for resource-constrained IoT devices? To this end, we first explore the existing studies on FL, relative assumptions for distributed implementation using IoT devices, and explore their drawbacks. We then discuss the implementation challenges and issues when applying FL to an IoT environment. We highlight an overview of FL and provide a comprehensive survey of the problem statements and emerging challenges, particularly during applying FL within heterogeneous IoT environments. Finally, we point out the future research directions for scientists and researchers who are interested in working at the intersection of FL and resource-constrained IoT environments.

197 citations


Journal ArticleDOI
TL;DR: This paper used data from 989 partnered, US parents to empirically examine whether the loss of childcare and new homeschooling demands are associated with employment outcomes early in the COVID-19 pandemic and also highlighted the role fathers can play in buffering against reduced labor force participation among mothers.
Abstract: The COVID-19 pandemic has dramatically affected employment, particularly for mothers Many believe that the loss of childcare and homeschooling requirements are key contributors to this trend, but previous work has been unable to test these hypotheses due to data limitations This study uses novel data from 989 partnered, US parents to empirically examine whether the loss of childcare and new homeschooling demands are associated with employment outcomes early in the pandemic We also consider whether the division of childcare prior to the pandemic is associated with parents? employment For parents with young children, the loss of full-time childcare was associated with an increased risk of unemployment for mothers but not fathers Yet, father involvement in childcare substantially buffered against negative employment outcomes for mothers of young children For parents with school-age children, participation in homeschooling was associated with adverse employment outcomes for mothers but not fathers Overall, this study provides empirical support for the current discourse on gender differences in employment during the pandemic and also highlights the role fathers can play in buffering against reduced labor force participation among mothers This article is protected by copyright All rights reserved

188 citations


Journal ArticleDOI
TL;DR: In this paper, the authors summarized the progress of supramolecular cancer nanotheranostics and provided guidance for designing new targeted theranostic agents based on extensive state-of-the-art research.
Abstract: Among the many challenges in medicine, the treatment and cure of cancer remains an outstanding goal given the complexity and diversity of the disease. Nanotheranostics, the integration of therapy and diagnosis in nanoformulations, is the next generation of personalized medicine to meet the challenges in precise cancer diagnosis, rational management and effective therapy, aiming to significantly increase the survival rate and improve the life quality of cancer patients. Different from most conventional platforms with unsatisfactory theranostic capabilities, supramolecular cancer nanotheranostics have unparalleled advantages in early-stage diagnosis and personal therapy, showing promising potential in clinical translations and applications. In this review, we summarize the progress of supramolecular cancer nanotheranostics and provide guidance for designing new targeted supramolecular theranostic agents. Based on extensive state-of-the-art research, our review will provide the existing and new researchers a foundation from which to advance supramolecular cancer nanotheranostics and promote translationally clinical applications.

188 citations


Journal ArticleDOI
TL;DR: This review identifies areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another and identifies applications and opportunities, raise open questions, and address potential challenges and limitations.
Abstract: Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

Journal ArticleDOI
TL;DR: The Hierarchical Taxonomy of Psychopathology (HiTOP) consortium proposed a model based on structural evidence to address problems of diagnostic heterogeneity, comorbidity, and unreliability.
Abstract: Traditional diagnostic systems went beyond empirical evidence on the structure of mental health Consequently, these diagnoses do not depict psychopathology accurately, and their validity in research and utility in clinicalpractice are therefore limited The Hierarchical Taxonomy of Psychopathology (HiTOP) consortium proposed a model based on structural evidence It addresses problems of diagnostic heterogeneity, comorbidity, and unreliability We review the HiTOP model, supporting evidence, and conceptualization of psychopathology in this hierarchical dimensional framework The system is not yet comprehensive, and we describe the processes for improving and expanding it We summarize data on the ability of HiTOP to predict and explain etiology (genetic, environmental, and neurobiological), risk factors, outcomes, and treatment response We describe progress in the development of HiTOP-based measures and in clinical implementation of the system Finally, we review outstanding challenges and the research agenda HiTOP is of practical utility already, and its ongoing development will produce a transformative map of psychopathology

Journal ArticleDOI
TL;DR: Theoretical calculations elucidate that the introduction of axial oxygen atom could optimize surface states of Ni-N4 moieties and enhance the charge polarization effect, therefore decreasing the potential barrier of intermediate COOH* formation, a key factor to accelerate the reaction kinetics and boost the CO2RR performance.
Abstract: Regulating the local environment and structure of metal center coordinated by nitrogen ligands (M-N4 ) to accelerate overall reaction dynamics of the electrochemical CO2 reduction reaction (CO2 RR) has attracted extensive attention. Herein, we develop an axial traction strategy to optimize the electronic structure of the M-N4 moiety and construct atomically dispersed nickel sites coordinated with four nitrogen atoms and one axial oxygen atom, which are embedded within the carbon matrix (Ni-N4 -O/C). The Ni-N4 -O/C electrocatalyst exhibited excellent CO2 RR performance with a maximum CO Faradic efficiency (FE) close to 100 % at -0.9 V. The CO FE could be maintained above 90 % in a wide range of potential window from -0.5 to -1.1 V. The superior CO2 RR activity is due to the Ni-N4 -O active moiety composed of a Ni-N4 site with an additional oxygen atom that induces an axial traction effect.

Journal ArticleDOI
TL;DR: Overall, these analyses indicate that the DASS-21 would best be used as a general score of distress rather than three separate factors of depression, anxiety, and stress, in the countries studied.
Abstract: This study evaluated the dimensionality, invariance, and reliability of the Depression, Anxiety, and Stress Scale-21 (DASS-21) within and across Brazil, Canada, Hong Kong, Romania, Taiwan, Turkey, United Arab Emirates, and the United States (N = 2,580) in college student samples. We used confirmatory factor analyses to compare the fit of four different factor structures of the DASS-21: a unidimensional model, a three-correlated-factors model, a higher order model, and a bifactor model. The bifactor model, with three specific factors (depression, anxiety, and stress) and one general factor (general distress), presented the best fit within each country. We also calculated ancillary bifactor indices of model-based dimensionality of the DASS-21 and model-based reliability to further examine the validity of the composite total and subscale scores and the use of unidimensional modeling. Results suggested the DASS-21 can be used as a unidimensional scale. Finally, measurement invariance of the best fitting model was tested across countries indicating configural invariance. The traditional three-correlated-factors model presented scalar invariance across Canada, Hong Kong, Romania, Taiwan, and the United States. Overall, these analyses indicate that the DASS-21 would best be used as a general score of distress rather than three separate factors of depression, anxiety, and stress, in the countries studied.

Journal ArticleDOI
Roger C. Wiens1, Sylvestre Maurice, S. Robinson1, Anthony Nelson1, P. Cais, P. Bernardi, Raymond Newell1, Samuel M. Clegg1, Shiv K. Sharma, S. A. Storms1, Jonathan Deming1, D. T. Beckman1, Ann Ollila1, Olivier Gasnault, Ryan B. Anderson, Y. André2, S. Michael Angel3, Gorka Arana4, Elizabeth C. Auden1, Pierre Beck, Joseph Becker1, Karim Benzerara, Sylvain Bernard, Olivier Beyssac, Louis Borges1, Bruno Bousquet, Kerry Boyd1, M. Caffrey1, Jeffrey Carlson5, Kepa Castro4, Jorden Celis1, B. Chide6, Kevin Clark5, Edward A. Cloutis7, Elizabeth C. Cordoba5, Agnes Cousin, Magdalena Dale1, Lauren DeFlores5, Dorothea Delapp1, M. Deleuze2, Matthew R. Dirmyer1, C. Donny2, Gilles Dromart8, M. George Duran1, Miles Egan, Joan Ervin5, Cécile Fabre, Amaury Fau, Woodward W. Fischer9, Olivier Forni, Thierry Fouchet, Reuben Fresquez1, Jens Frydenvang10, Denine Gasway1, Ivair Gontijo5, John P. Grotzinger9, Xavier Jacob, Sophie Jacquinod, Jeffrey R. Johnson11, Roberta A. Klisiewicz1, James Lake1, Nina Lanza1, J. Javier Laserna12, Jérémie Lasue, Stéphane Le Mouélic, C. Legett1, Richard Leveille13, Eric Lewin, Guillermo Lopez-Reyes14, Ralph D. Lorenz11, Eric Lorigny2, Steven P. Love1, Briana Lucero1, Juan Manuel Madariaga4, Morten Madsen5, Soren N. Madsen5, Nicolas Mangold, Jose Antonio Manrique14, J. P. Martinez1, Jesús Martínez-Frías, K. McCabe1, Timothy H. McConnochie15, Justin McGlown1, Scott M. McLennan16, Noureddine Melikechi17, Pierre-Yves Meslin, John Michel1, David Mimoun6, Anupam K. Misra, Gilles Montagnac8, Franck Montmessin, Valerie Mousset2, Naomi Murdoch6, Horton E. Newsom18, Logan Ott1, Zachary R. Ousnamer5, L. Parès, Yann Parot, Rafal Pawluczyk, C. Glen Peterson1, Paolo Pilleri, Patrick Pinet, Gabriel Pont2, Francois Poulet, Cheryl Provost, Benjamin Quertier, Heather Quinn1, William Rapin, Jean-Michel Reess, A. Regan1, A. Reyes-Newell1, Philip J. Romano5, Clement Royer, Fernando Rull14, Benigno Sandoval1, Joseph H. Sarrao1, Violaine Sautter, Marcel J. Schoppers5, Susanne Schröder, Daniel Seitz1, Terra Shepherd1, Pablo Sobron19, Bruno Dubois, Vishnu Sridhar5, M. Toplis, I. Torre-Fdez4, Ian A. Trettel5, M. L. Underwood5, Andres Valdez1, Jacob Valdez1, D. Venhaus1, Peter Willis5 
TL;DR: The SuperCam body unit (BU) of the Mars 2020 rover as mentioned in this paper was designed to receive light from the mast unit via a 5.8 m opti-cal fiber and the light is split into three wavelength bands by a demultiplexer, and routed via fiber bundles to three optical spectrometers, two of which (UV and violet; 245-340 and 385-465 nm) are crossed Czerny-Turner reflection spectrometer, nearly identical to their counterparts on ChemCam.
Abstract: TheSuperCaminstrumentsuiteprovidestheMars2020rover,Perseverance,with a number of versatile remote-sensing techniques that can be used at long distance as well as within the robotic-arm workspace. These include laser-induced breakdown spectroscopy (LIBS), remote time-resolved Raman and luminescence spectroscopies, and visible and in- frared (VISIR; separately referred to as VIS and IR) reflectance spectroscopy. A remote micro-imager (RMI) provides high-resolution color context imaging, and a microphone can be used as a stand-alone tool for environmental studies or to determine physical properties of rocks and soils from shock waves of laser-produced plasmas. SuperCam is built in three parts: The mast unit (MU), consisting of the laser, telescope, RMI, IR spectrometer, and associated electronics, is described in a companion paper. The on-board calibration targets are described in another companion paper. Here we describe SuperCam’s body unit (BU) and testing of the integrated instrument. The BU, mounted inside the rover body, receives light from the MU via a 5.8 m opti- cal fiber. The light is split into three wavelength bands by a demultiplexer, and is routed via fiber bundles to three optical spectrometers, two of which (UV and violet; 245–340 and 385–465 nm) are crossed Czerny-Turner reflection spectrometers, nearly identical to their counterparts on ChemCam. The third is a high-efficiency transmission spectrometer contain- ing an optical intensifier capable of gating exposures to 100 ns or longer, with variable delay times relative to the laser pulse. This spectrometer covers 535–853 nm (105–7070 cm−1 Ra- man shift relative to the 532 nm green laser beam) with 12 cm−1 full-width at half-maximum peak resolution in the Raman fingerprint region. The BU electronics boards interface with the rover and control the instrument, returning data to the rover. Thermal systems maintain a warm temperature during cruise to Mars to avoid contamination on the optics, and cool the detectors during operations on Mars. Results obtained with the integrated instrument demonstrate its capabilities for LIBS, for which a library of 332 standards was developed. Examples of Raman and VISIR spec- troscopy are shown, demonstrating clear mineral identification with both techniques. Lumi- nescence spectra demonstrate the utility of having both spectral and temporal dimensions. Finally, RMI and microphone tests on the rover demonstrate the capabilities of these sub- systems as well.

Journal ArticleDOI
10 Feb 2021
TL;DR: This Review discusses the approaches used to modulate and evaluate the nuclease resistance of DNA nanostructures and provides an overview of the techniques employed to evaluate resistance to degradation and quantify stability.
Abstract: DNA nanotechnology has progressed from proof-of-concept demonstrations of structural design towards application-oriented research As a natural material with excellent self-assembling properties, DNA is an indomitable choice for various biological applications, including biosensing, cell modulation, bioimaging and drug delivery However, a major impediment to the use of DNA nanostructures in biological applications is their susceptibility to attack by nucleases present in the physiological environment Although several DNA nanostructures show enhanced resistance to nuclease attack compared with duplexes and plasmid DNA, this may be inadequate for practical application Recently, several strategies have been developed to increase the nuclease resistance of DNA nanostructures while retaining their functions, and the stability of various DNA nanostructures has been studied in biological fluids, such as serum, urine and cell lysates This Review discusses the approaches used to modulate nuclease resistance in DNA nanostructures and provides an overview of the techniques employed to evaluate resistance to degradation and quantify stability

Journal ArticleDOI
TL;DR: In this paper, an effective strategy to boost PGM catalysts through integrating PGM-free atomically-dispersed single metal active sites in the carbon support toward the cathode oxygen reduction reaction (ORR) was reported.
Abstract: Significantly reducing platinum group metal (PGM) loading while improving catalytic performance and durability is critical to accelerating proton-exchange membrane fuel cells (PEMFCs) for transportation. Here we report an effective strategy to boost PGM catalysts through integrating PGM-free atomically-dispersed single metal active sites in the carbon support toward the cathode oxygen reduction reaction (ORR). We achieved uniform and fine Pt nanoparticle (NP) (∼2 nm) dispersion on an already highly ORR-active FeN4 site-rich carbon (FeN4–C). Furthermore, we developed an effective approach to preparing a well-dispersed and highly ordered L12 Pt3Co intermetallic nanoparticle catalyst on the FeN4–C support. DFT calculations predicted a synergistic interaction between Pt clusters and surrounding FeN4 sites through weakening O2 adsorption by 0.15 eV on Pt sites and reducing activation energy to break O–O bonds, thereby enhancing the intrinsic activity of Pt. Experimentally, we verified the synergistic effect between Pt or Pt3Co NPs and FeN4 sites, leading to significantly enhanced ORR activity and stability. Especially in a membrane electrode assembly (MEA) with a low cathode Pt loading (0.1 mgPt cm−2), the Pt/FeN4–C catalyst achieved a mass activity of 0.451 A mgPt−1 and retained 80% of the initial values after 30 000 voltage cycles (0.60 to 0.95 V), exceeding DOE 2020 targets. Furthermore, the Pt3Co/FeN4 catalyst achieved significantly enhanced performance and durability concerning initial mass activity (0.72 A mgPt−1), power density (824 mW cm−2 at 0.67 V), and stability (23 mV loss at 1.0 A cm−2). The approach to exploring the synergy between PGM and PGM-free Fe–N–C catalysts provides a new direction to design advanced catalysts for hydrogen fuel cells and various electrocatalysis processes.

Journal ArticleDOI
J. Adam1, L. Adamczyk2, J. R. Adams3, J. K. Adkins4  +357 moreInstitutions (58)
TL;DR: In this paper, the first evidence of a non-monotonic variation in the kurtosis times variance of the net-proton number (proxy for net-baryon number) distribution as a function of collision energy was reported.
Abstract: Nonmonotonic variation with collision energy (sqrt[s_{NN}]) of the moments of the net-baryon number distribution in heavy-ion collisions, related to the correlation length and the susceptibilities of the system, is suggested as a signature for the quantum chromodynamics critical point. We report the first evidence of a nonmonotonic variation in the kurtosis times variance of the net-proton number (proxy for net-baryon number) distribution as a function of sqrt[s_{NN}] with 3.1 σ significance for head-on (central) gold-on-gold (Au+Au) collisions measured solenoidal tracker at Relativistic Heavy Ion Collider. Data in noncentral Au+Au collisions and models of heavy-ion collisions without a critical point show a monotonic variation as a function of sqrt[s_{NN}].

Journal ArticleDOI
TL;DR: Using a Fe-N-C model catalyst derived from the ZIF-8, three key morphological and structural elements of FeN 4 sites are deconvoluted, including particle sizes of catalysts, Fe content, andFe-N bond structures, which elucidated the origin of intrinsic activity improvement associated with the optimal local strain on the Fe- N bond.
Abstract: Atomically dispersed FeN4 active sites have exhibited exceptional catalytic activity and selectivity for the electrochemical CO2 reduction reaction (CO2RR) to CO. However, the understanding behind the intrinsic and morphological factors contributing to the catalytic properties of FeN4 sites is still lacking. By using a Fe-N-C model catalyst derived from the ZIF-8, we deconvoluted three key morphological and structural elements of FeN4 sites, including particle sizes of catalysts, Fe content, and Fe-N bond structures. Their respective impacts on the CO2RR were comprehensively elucidated. Engineering the particle size and Fe doping is critical to control extrinsic morphological factors of FeN4 sites for optimal porosity, electrochemically active surface areas, and the graphitization of the carbon support. In contrast, the intrinsic activity of FeN4 sites was only tunable by varying thermal activation temperatures during the formation of FeN4 sites, which impacted the length of the Fe-N bonds and the local strains. The structural evolution of Fe-N bonds was examined at the atomic level. First-principles calculations further elucidated the origin of intrinsic activity improvement associated with the optimal local strain of the Fe-N bond.

Journal ArticleDOI
TL;DR: Embedding two gate-tunable Al/InAs Josephson junctions in a loop geometry confirms that the signatures of a topological transition are compatible with the emergence of Majorana bound states.
Abstract: Topological superconductivity holds promise for fault-tolerant quantum computing. While planar Josephson junctions are attractive candidates to realize this exotic state, direct phase measurements as the fingerprint of the topological transition are missing. By embedding two gate-tunable $\mathrm{Al}/\mathrm{InAs}$ Josephson junctions in a loop geometry, we measure a $\ensuremath{\pi}$ jump in the junction phase with an increasing in-plane magnetic field ${\mathbit{B}}_{\ensuremath{\parallel}}$. This jump is accompanied by a minimum of the critical current, indicating a closing and reopening of the superconducting gap, strongly anisotropic in ${\mathbit{B}}_{\ensuremath{\parallel}}$. Our theory confirms that these signatures of a topological transition are compatible with the emergence of Majorana bound states.

Journal ArticleDOI
TL;DR: In this paper, the authors identified the bacterial species associated with each periodontal condition and prevalent species that do not change in abundance from one state to another (core species), and outlined species co-occurrence patterns via network analysis.
Abstract: The subgingival crevice harbors diverse microbial communities. Shifts in the composition of these communities occur with the development of gingivitis and periodontitis, which are considered as successive stages of periodontal health deterioration. It is not clear, however, to what extent health- and gingivitis-associated microbiota are protective, or whether these communities facilitate the successive growth of periodontitis-associated taxa. To further our understanding of the dynamics of the microbial stimuli that trigger disruptions in periodontal homeostasis, we reviewed the available literature with the aim of defining specific microbial signatures associated with different stages of periodontal dysbiosis. Although several studies have evaluated the subgingival communities present in different periodontal conditions, we found limited evidence for the direct comparison of communities in health, gingivitis, and periodontitis. Therefore, we aimed to better define subgingival microbiome shifts by merging and reanalyzing, using unified bioinformatic processing strategies, publicly available 16S ribosomal RNA gene amplicon datasets of periodontal health, gingivitis, and periodontitis. Despite inherent methodological differences across studies, distinct community structures were found for health, gingivitis, and periodontitis, demonstrating the specific associations between gingival tissue status and the subgingival microbiome. Consistent with the concept that periodontal dysbiosis is the result of a process of microbial succession without replacement, more species were detected in disease than in health. However, gingivitis-associated communities were more diverse than those from subjects with periodontitis, suggesting that certain species ultimately become dominant as dysbiosis progresses. We identified the bacterial species associated with each periodontal condition and prevalent species that do not change in abundance from one state to another (core species), and we also outlined species co-occurrence patterns via network analysis. Most periodontitis-associated species were rarely detected in health but were frequently detected, albeit in low abundance, in gingivitis, which suggests that gingivitis and periodontitis are a continuum. Overall, we provide a framework of subgingival microbiome shifts, which can be used to generate hypotheses with respect to community assembly processes and the emergence of periodontal dysbiosis.

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07 Jun 2021
TL;DR: For example, the authors found that between 2000 and 2019, most soybean expansion in South America was on pastures converted originally for cattle production, especially in the Brazilian Amazon, where 9% of forest loss was converted to soybeans by 2016.
Abstract: A prominent goal of policies mitigating climate change and biodiversity loss is to achieve zero deforestation in the global supply chain of key commodities, such as palm oil and soybean. However, the extent and dynamics of deforestation driven by commodity expansion are largely unknown. Here we mapped annual soybean expansion in South America between 2000 and 2019 by combining satellite observations and sample field data. From 2000 to 2019, the area cultivated with soybean more than doubled from 26.4 Mha to 55.1 Mha. Most soybean expansion occurred on pastures originally converted from natural vegetation for cattle production. The most rapid expansion occurred in the Brazilian Amazon, where soybean area increased more than tenfold, from 0.4 Mha to 4.6 Mha. Across the continent, 9% of forest loss was converted to soybean by 2016. Soybean-driven deforestation was concentrated at the active frontiers, nearly half located in the Brazilian Cerrado. Efforts to limit future deforestation must consider how soybean expansion may drive deforestation indirectly by displacing pasture or other land uses. Holistic approaches that track land use across all commodities coupled with vegetation monitoring are required to maintain critical ecosystem services. Deforestation is often driven by land conversion for growing commodity crops. This study finds that, between 2000 and 2019, most soybean expansion in South America was on pastures converted originally for cattle production, especially in the Brazilian Amazon. More soy-driven deforestation occurred in the Brazilian Cerrado.

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TL;DR: In this article, the structural evolution of CoN4 sites during thermal activation was investigated by developing a zeolitic imidazolate framework (ZIF)-8-derived carbon host as an ideal model for Co2+ ion adsorption.
Abstract: We elucidate the structural evolution of CoN4 sites during thermal activation by developing a zeolitic imidazolate framework (ZIF)-8-derived carbon host as an ideal model for Co2+ ion adsorption. Subsequent in situ X-ray absorption spectroscopy analysis can dynamically track the conversion from inactive Co-OH and Co-O species into active CoN4 sites. The critical transition occurs at 700 °C and becomes optimal at 900 °C, generating the highest intrinsic activity and four-electron selectivity for the oxygen reduction reaction (ORR). DFT calculations elucidate that the ORR is kinetically favored by the thermal-induced compressive strain of Co-N bonds in CoN4 active sites formed at 900 °C. Further, we developed a two-step (i.e., Co ion doping and adsorption) Co-N-C catalyst with increased CoN4 site density and optimized porosity for mass transport, and demonstrated its outstanding fuel cell performance and durability.


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TL;DR: In this paper, a dual-site electrocatalyst with atomically dispersed Fe/Mn-Nx-C dual metal sites embedded in N-doped carbon matrix is successfully designed and synthesized, which exhibits a state-of-the-art oxygen reduction reaction (ORR) activity with a half-wave potential (E1/2) of 0.88 V (vs. RHE) as well as a superior stability.
Abstract: Constructing and excavating single atom catalysts with high-density active sites and long-life durability for energy storage and conversion devices still remain bestially challenges. In this paper, a novel dual-site electrocatalyst with atomically dispersed Fe/Mn-Nx-C dual metal sites embedded in N-doped carbon matrix is successfully designed and synthesized, which exhibits a state-of-the-art oxygen reduction reaction (ORR) activity with a half-wave potential (E1/2) of 0.88 V (vs. RHE) as well as a superior stability. Besides, the Fe/Mn-Nx-C catalyst reaches a high power density of 208.6 mW cm−2 and a specific energy density of 825.5 W h kg-1 when this catalyst is employed in Zn-air battery, which is superior to most of the reported non-precious catalysts. Furthermore, theoretical DFT calculations reveal the excellent performance is induced through a synergic dual-site cascade mechanism, which overcomes the issue of low adsorption energy (Eads) of *OH on Fe-Nx site, followed by transfer of the *OH to adjacent Mn-Nx sites. As a result, the first three steps during ORR more favored occur on the Fe-Nx sites instead of the Mn sites to generate *OOH and *O intermediates due to the lower energy barriers. This mechanism is further approved by addition of methanol to verify the preferred adsorption of *OH on the Mn-Nx site.


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TL;DR: Bayesian inference is used for deep vision SHM models where uncertainty can be quantified using the Monte Carlo dropout sampling and the concept of surrogate models is proposed to develop the models for uncertainty‐assisted segmentation and prediction quality tagging.

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TL;DR: Elderly and people with comorbidities were susceptible to severe CO VID-19 infection and vitamin D supplementation may have prevention or treatment potential for COVID-19 disease.
Abstract: As effective medication to treat COVID-19 is currently unavailable, preventive remedies may be particularly important. To examine the relationship between serum 25-hydroxy vitamin D (25(OH)D) level...

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TL;DR: Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients’ outcomes.
Abstract: Background Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early. Methods Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients' outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models' performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model. Results The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at [https://phenomics.fudan.edu.cn/risk_scores/]. Conclusions Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients' outcomes.