Showing papers by "Florida State University published in 2020"
University of Massachusetts Medical School1, Broad Institute2, Stanford University3, Cold Spring Harbor Laboratory4, University of Washington5, University of California, San Diego6, Massachusetts Institute of Technology7, Ludwig Institute for Cancer Research8, University of California, San Francisco9, Salk Institute for Biological Studies10, California Institute of Technology11, University of California, Irvine12, Pennsylvania State University13, Lawrence Berkeley National Laboratory14, University of Connecticut Health Center15, McGill University16, Université de Montréal17, University of Minnesota18, Florida State University19, Yale University20, University of Alabama in Huntsville21, University of Chicago22, University of California, Merced23, University of Colorado Boulder24, Icahn School of Medicine at Mount Sinai25, Pompeu Fabra University26, University of Southern California27, University of California, Berkeley28, Harvard University29, Boston University30, Tongji University31
TL;DR: The authors summarize the data produced by phase III of the Encyclopedia of DNA Elements (ENCODE) project, a resource for better understanding of the human and mouse genomes, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development.
Abstract: The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.
999 citations
TL;DR: Action could be taken to mitigate potential unintended consequences on suicide prevention efforts, which also represent a national public health priority, and to reduce the rate of new infections.
Abstract: Suicide rates have been rising in the US over the last 2 decades. The latest data available (2018) show the highest age-adjusted suicide rate in the US since 1941.1 It is within this context that coronavirus disease 2019 (COVID-19) struck the US. Concerning disease models have led to historic and unprecedented public health actions to curb the spread of the virus. Remarkable social distancing interventions have been implemented to fundamentally reduce human contact. While these steps are expected to reduce the rate of new infections, the potential for adverse outcomes on suicide risk is high. Actions could be taken to mitigate potential unintended consequences on suicide prevention efforts, which also represent a national public health priority.
679 citations
TL;DR: There was no large increase in loneliness but remarkable resilience in response to COVID-19, and individuals living alone and those with at least one chronic condition reported feeling lonelier at baseline but did not increase inoneliness during the implementation of social distancing measures.
Abstract: Social distancing and "stay-at-home" orders are essential to contain the coronavirus outbreak (COVID-19), but there is concern that these measures will increase feelings of loneliness, particularly in vulnerable groups The present study examined change in loneliness in response to the social restriction measures taken to control the coronavirus spread A nationwide sample of American adults (N = 1,545; 45% women; ages 18 to 98, M = 5368, SD = 1563) was assessed on three occasions: in late January/early February 2020 (before the outbreak), in late March (during the President's initial "15 Days to Slow the Spread" campaign), and in late April (during the "stay-at-home" policies of most states) Contrary to expectations, there were no significant mean-level changes in loneliness across the three assessments (d = 04, p > 05) In fact, respondents perceived increased support from others over the follow-up period (d = 19, p < 01) Older adults reported less loneliness overall compared to younger age groups but had an increase in loneliness during the acute phase of the outbreak (d = 14, p < 05) Their loneliness, however, leveled off after the issuance of stay-at-home orders Individuals living alone and those with at least one chronic condition reported feeling lonelier at baseline but did not increase in loneliness during the implementation of social distancing measures Despite some detrimental impact on vulnerable individuals, in the present sample, there was no large increase in loneliness but remarkable resilience in response to COVID-19 (PsycInfo Database Record (c) 2020 APA, all rights reserved)
589 citations
TL;DR: In this paper, a review compared trends and synthesized findings in vaccination receptivity over time across US and international polls, assessing survey design influences and evaluating context to inform policies and practices.
471 citations
TL;DR: This study suggests that a pronounced and prolonged deterioration in mental health occurred as the COVID-19 pandemic emerged in the UK between April and June 2020.
Abstract: Background The COVID-19 pandemic has had a range of negative social and economic effects that may contribute to a rise in mental health problems. In this observational population-based study, we examined longitudinal changes in the prevalence of mental health problems from before to during the COVID-19 crisis and identified subgroups that are psychologically vulnerable during the pandemic. Methods Participants (N = 14 393; observations = 48 486) were adults drawn from wave 9 (2017-2019) of the nationally representative United Kingdom Household Longitudinal Study (UKHLS) and followed-up across three waves of assessment in April, May, and June 2020. Mental health problems were assessed using the 12-item General Health Questionnaire (GHQ-12). Results The population prevalence of mental health problems (GHQ-12 score ⩾3) increased by 13.5 percentage points from 24.3% in 2017-2019 to 37.8% in April 2020 and remained elevated in May (34.7%) and June (31.9%) 2020. All sociodemographic groups examined showed statistically significant increases in mental health problems in April 2020. The increase was largest among those aged 18-34 years (18.6 percentage points, 95% CI 14.3-22.9%), followed by females and high-income and education groups. Levels of mental health problems subsequently declined between April and June 2020 but remained significantly above pre-COVID-19 levels. Additional analyses showed that the rise in mental health problems observed throughout the COVID-19 pandemic was unlikely to be due to seasonality or year-to-year variation. Conclusions This study suggests that a pronounced and prolonged deterioration in mental health occurred as the COVID-19 pandemic emerged in the UK between April and June 2020.
450 citations
Georgia Institute of Technology1, National Institutes of Health2, Curtin University3, Academy of Sciences of the Czech Republic4, University of Tromsø5, Virginia Tech6, University of Helsinki7, University of Georgia8, Interdisciplinary Center for Scientific Computing9, RMIT University10, Auburn University11, SLAC National Accelerator Laboratory12, Ohio State University13, Florida State University14, Hacettepe University15, Bethel University16, Emory University17
TL;DR: A rewrite of the top-level computation driver, and concomitant adoption of the MolSSI QCARCHIVE INFRASTRUCTURE project, makes the latest version of PSI4 well suited to distributed computation of large numbers of independent tasks.
Abstract: PSI4 is a free and open-source ab initio electronic structure program providing implementations of Hartree-Fock, density functional theory, many-body perturbation theory, configuration interaction, density cumulant theory, symmetry-adapted perturbation theory, and coupled-cluster theory. Most of the methods are quite efficient, thanks to density fitting and multi-core parallelism. The program is a hybrid of C++ and Python, and calculations may be run with very simple text files or using the Python API, facilitating post-processing and complex workflows; method developers also have access to most of PSI4's core functionalities via Python. Job specification may be passed using The Molecular Sciences Software Institute (MolSSI) QCSCHEMA data format, facilitating interoperability. A rewrite of our top-level computation driver, and concomitant adoption of the MolSSI QCARCHIVE INFRASTRUCTURE project, makes the latest version of PSI4 well suited to distributed computation of large numbers of independent tasks. The project has fostered the development of independent software components that may be reused in other quantum chemistry programs.
387 citations
University of California, Los Angeles1, University of Auckland2, University of Nebraska Medical Center3, McMaster University4, New York University5, Johns Hopkins University6, University of Massachusetts Medical School7, University of Michigan8, Kaiser Permanente9, Boston University10, VA Boston Healthcare System11, Medical College of Wisconsin12, University of Alabama at Birmingham13, Florida State University14, Yale University15, Brigham and Women's Hospital16, University of Kentucky17, United States Department of Veterans Affairs18, Mount Auburn Hospital19, Allegheny Health Network20, American College of Rheumatology21
TL;DR: To provide guidance for the management of gout, including indications for and optimal use of urate‐lowering therapy (ULT), treatment of g out flares, and lifestyle and other medication recommendations.
Abstract: Objective To provide guidance for the management of gout, including indications for and optimal use of urate-lowering therapy (ULT), treatment of gout flares, and lifestyle and other medication recommendations. Methods Fifty-seven population, intervention, comparator, and outcomes questions were developed, followed by a systematic literature review, including network meta-analyses with ratings of the available evidence according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, and patient input. A group consensus process was used to compose the final recommendations and grade their strength as strong or conditional. Results Forty-two recommendations (including 16 strong recommendations) were generated. Strong recommendations included initiation of ULT for all patients with tophaceous gout, radiographic damage due to gout, or frequent gout flares; allopurinol as the preferred first-line ULT, including for those with moderate-to-severe chronic kidney disease (CKD; stage >3); using a low starting dose of allopurinol (≤100 mg/day, and lower in CKD) or febuxostat ( Conclusion Using GRADE methodology and informed by a consensus process based on evidence from the current literature and patient preferences, this guideline provides direction for clinicians and patients making decisions on the management of gout.
313 citations
Princeton University1, Cardiff University2, Pontifical Catholic University of Chile3, Université Paris-Saclay4, University of Pennsylvania5, University of Oxford6, Johns Hopkins University7, University of British Columbia8, Cornell University9, National Institute of Standards and Technology10, University of Michigan11, University of Toronto12, University of Chile13, University of Chicago14, Stanford University15, University of KwaZulu-Natal16, University of California, Berkeley17, University of Cambridge18, Goddard Space Flight Center19, Lawrence Berkeley National Laboratory20, Florida State University21, University of Southern California22, University of Arizona23, University of Pittsburgh24, Stony Brook University25, Pennsylvania State University26, Columbia University27, Rutgers University28, Yale University29, Perimeter Institute for Theoretical Physics30, University of Illinois at Urbana–Champaign31, University of Milan32, Haverford College33, California Institute of Technology34, McGill University35, Pontifical Catholic University of Valparaíso36, West Chester University of Pennsylvania37, Carnegie Mellon University38, Arizona State University39
TL;DR: In this article, the Atacama Cosmology Telescope (ACT) data were used to estimate the temperature and polarization anisotropy from the cosmic microwave background (CMB) at 98 and 150 GHz.
Abstract: We present new arcminute-resolution maps of the Cosmic Microwave Background temperature and polarization anisotropy from the Atacama Cosmology Telescope, using data taken from 2013–2016 at 98 and 150 GHz. The maps cover more than 17,000 deg2, the deepest 600 deg2 with noise levels below 10μK-arcmin. We use the power spectrum derived from almost 6,000 deg2 of these maps to constrain cosmology. The ACT data enable a measurement of the angular scale of features in both the divergence-like polarization and the temperature anisotropy, tracing both the velocity and density at last-scattering. From these one can derive the distance to the last-scattering surface and thus infer the local expansion rate, H0. By combining ACT data with large-scale information from WMAP we measure H0=67.6± 1.1 km/s/Mpc, at 68% confidence, in excellent agreement with the independently-measured Planck satellite estimate (from ACT alone we find H0=67.9± 1.5 km/s/Mpc). The ΛCDM model provides a good fit to the ACT data, and we find no evidence for deviations: both the spatial curvature, and the departure from the standard lensing signal in the spectrum, are zero to within 1σ; the number of relativistic species, the primordial Helium fraction, and the running of the spectral index are consistent with ΛCDM predictions to within 1.5–2.2σ. We compare ACT, WMAP, and Planck at the parameter level and find good consistency; we investigate how the constraints on the correlated spectral index and baryon density parameters readjust when adding CMB large-scale information that ACT does not measure. The DR4 products presented here will be publicly released on the NASA Legacy Archive for Microwave Background Data Analysis.
298 citations
20 Jan 2020
TL;DR: This paper proposes TiSASRec (Time Interval aware Self-attention based sequential recommendation), which models both the absolute positions of items as well as the time intervals between them in a sequence, which outperforms various state-of-the-art sequential models on both sparse and dense datasets and different evaluation metrics.
Abstract: Sequential recommender systems seek to exploit the order of users' interactions, in order to predict their next action based on the context of what they have done recently. Traditionally, Markov Chains(MCs), and more recently Recurrent Neural Networks (RNNs) and Self Attention (SA) have proliferated due to their ability to capture the dynamics of sequential patterns. However a simplifying assumption made by most of these models is to regard interaction histories as ordered sequences, without regard for the time intervals between each interaction (i.e., they model the time-order but not the actual timestamp). In this paper, we seek to explicitly model the timestamps of interactions within a sequential modeling framework to explore the influence of different time intervals on next item prediction. We propose TiSASRec (Time Interval aware Self-attention based sequential recommendation), which models both the absolute positions of items as well as the time intervals between them in a sequence. Extensive empirical studies show the features of TiSASRec under different settings and compare the performance of self-attention with different positional encodings. Furthermore, experimental results show that our method outperforms various state-of-the-art sequential models on both sparse and dense datasets and different evaluation metrics.
293 citations
TL;DR: This study aimed to compare prevalence rates of anxiety disorder and depressive disorder in national samples in the U.S. before and during the coronavirus disease 2019 pandemic.
Abstract: Background The disruptions to daily life caused by the coronavirus disease 2019 (COVID-19) pandemic may have impacted mental health, particularly mood disorders. This study aimed to compare prevalence rates of anxiety disorder and depressive disorder in national samples in the U.S. before and during the pandemic. Methods Participants (n = 336,525) were from U.S. Census Bureau-administered nationally representative probability samples, one from the first half of 2019 and four during the pandemic in April and May 2020. All participants completed the Patient Health Questionnaire-2 screening for depressive disorder and the Generalized Anxiety Disorder-2 screening for anxiety disorders. Results Compared to U.S. adults in 2019, U.S. adults in April and May 2020 were more than three times as likely to screen positive for depressive disorders, anxiety disorders, or one or both, with more than one out of three screening positive for one or both. The prevalence of anxiety decreased slightly between the April 23-May 4, 2020 and the May 21-26, 2020 administrations, while the prevalence of depression increased slightly. Conclusions U.S. adults in 2020 are considerably more likely to screen positive for mood disorders than in 2019, with anxiety declining and depression increasing from April to May.
269 citations
TL;DR: For the first time, predictions from pythia8 obtained with tunes based on NLO or NNLO PDFs are shown to reliably describe minimum-bias and underlying-event data with a similar level of agreement to predictions from tunes using LO PDF sets.
Abstract: New sets of CMS underlying-event parameters (“tunes”) are presented for the pythia8 event generator. These tunes use the NNPDF3.1 parton distribution functions (PDFs) at leading (LO), next-to-leading (NLO), or next-to-next-to-leading (NNLO) orders in perturbative quantum chromodynamics, and the strong coupling evolution at LO or NLO. Measurements of charged-particle multiplicity and transverse momentum densities at various hadron collision energies are fit simultaneously to determine the parameters of the tunes. Comparisons of the predictions of the new tunes are provided for observables sensitive to the event shapes at LEP, global underlying event, soft multiparton interactions, and double-parton scattering contributions. In addition, comparisons are made for observables measured in various specific processes, such as multijet, Drell–Yan, and top quark-antiquark pair production including jet substructure observables. The simulation of the underlying event provided by the new tunes is interfaced to a higher-order matrix-element calculation. For the first time, predictions from pythia8 obtained with tunes based on NLO or NNLO PDFs are shown to reliably describe minimum-bias and underlying-event data with a similar level of agreement to predictions from tunes using LO PDF sets.
Harvard University1, University of Washington2, Humboldt University of Berlin3, Imperial College London4, University of Belgrade5, Istituto Nazionale di Fisica Nucleare6, Technical University of Berlin7, University of Bordeaux8, University of Oxford9, University of Valencia10, University of Strathclyde11, Rutherford Appleton Laboratory12, King's College London13, Foundation for Research & Technology – Hellas14, University of Birmingham15, University College London16, University of Liverpool17, National Physical Laboratory18, University of Nottingham19, University of Sussex20, Northern Illinois University21, Fermilab22, Peking University23, University of Pisa24, University of California, Riverside25, University of Nevada, Reno26, CERN27, University of Niš28, National Institute of Chemical Physics and Biophysics29, Beni-Suef University30, British University in Egypt31, Leibniz University of Hanover32, Paul Sabatier University33, University of Paris34, University of Cambridge35, Wayne State University36, Stanford University37, University of Bergen38, University of Amsterdam39, Northwestern University40, University of Bristol41, University of Warsaw42, University of Illinois at Urbana–Champaign43, Fayoum University44, University of Crete45, Queen's University Belfast46, Brandeis University47, University of Bologna48, Cochin University of Science and Technology49, German Aerospace Center50, University of Manchester51, University of Copenhagen52, University of Düsseldorf53, University of Vienna54, Florida State University55, University of Florence56, University of Illinois at Chicago57, University of Bremen58, University of Mainz59, Chinese Academy of Sciences60, University of Cincinnati61
TL;DR: The Atomic Experiment for Dark Matter and Gravity Exploration (AEDGE) as mentioned in this paper is a space experiment using cold atoms to search for ultra-light dark matter, and to detect gravitational waves in the frequency range between the most sensitive ranges of LISA and the terrestrial LIGO/Virgo/KAGRA/INDIGO experiments.
Abstract: We propose in this White Paper a concept for a space experiment using cold atoms to search for ultra-light dark matter, and to detect gravitational waves in the frequency range between the most sensitive ranges of LISA and the terrestrial LIGO/Virgo/KAGRA/INDIGO experiments. This interdisciplinary experiment, called Atomic Experiment for Dark Matter and Gravity Exploration (AEDGE), will also complement other planned searches for dark matter, and exploit synergies with other gravitational wave detectors. We give examples of the extended range of sensitivity to ultra-light dark matter offered by AEDGE, and how its gravitational-wave measurements could explore the assembly of super-massive black holes, first-order phase transitions in the early universe and cosmic strings. AEDGE will be based upon technologies now being developed for terrestrial experiments using cold atoms, and will benefit from the space experience obtained with, e.g., LISA and cold atom experiments in microgravity.
University of Hawaii at Manoa1, Vanderbilt University Medical Center2, Vanderbilt University3, Florida State University4, Longyan University5, Erasmus University Medical Center6, Cancer Council Victoria7, University of Melbourne8, German Cancer Research Center9, Stanford University10, Queensland University of Technology11, Translational Research Institute12, Oslo University Hospital13, University of Washington14, Fred Hutchinson Cancer Research Center15, The Royal Marsden NHS Foundation Trust16, University of Southern California17
TL;DR: DNA methylation biomarkers associated with PrCa are identified and the findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.
Abstract: It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.
Juliette Alimena1, James Baker Beacham2, Martino Borsato3, Yangyang Cheng4 +213 more•Institutions (105)
TL;DR: In this paper, the authors present a survey of the current state of LLP searches at the Large Hadron Collider (LHC) and chart a path for the development of LLP searches into the future, both in the upcoming Run 3 and at the high-luminosity LHC.
Abstract: Particles beyond the Standard Model (SM) can generically have lifetimes that are long compared to SM particles at the weak scale. When produced at experiments such as the Large Hadron Collider (LHC) at CERN, these long-lived particles (LLPs) can decay far from the interaction vertex of the primary proton–proton collision. Such LLP signatures are distinct from those of promptly decaying particles that are targeted by the majority of searches for new physics at the LHC, often requiring customized techniques to identify, for example, significantly displaced decay vertices, tracks with atypical properties, and short track segments. Given their non-standard nature, a comprehensive overview of LLP signatures at the LHC is beneficial to ensure that possible avenues of the discovery of new physics are not overlooked. Here we report on the joint work of a community of theorists and experimentalists with the ATLAS, CMS, and LHCb experiments—as well as those working on dedicated experiments such as MoEDAL, milliQan, MATHUSLA, CODEX-b, and FASER—to survey the current state of LLP searches at the LHC, and to chart a path for the development of LLP searches into the future, both in the upcoming Run 3 and at the high-luminosity LHC. The work is organized around the current and future potential capabilities of LHC experiments to generally discover new LLPs, and takes a signature-based approach to surveying classes of models that give rise to LLPs rather than emphasizing any particular theory motivation. We develop a set of simplified models; assess the coverage of current searches; document known, often unexpected backgrounds; explore the capabilities of proposed detector upgrades; provide recommendations for the presentation of search results; and look towards the newest frontiers, namely high-multiplicity 'dark showers', highlighting opportunities for expanding the LHC reach for these signals.
TL;DR: Li solid-state NMR results show an increase in Li + ions occupying the more mobile A2 environment in the composite electrolytes, which contributes to the more facile Li + transport.
Abstract: Li+ -conducting oxides are considered better ceramic fillers than Li+ -insulating oxides for improving Li+ conductivity in composite polymer electrolytes owing to their ability to conduct Li+ through the ceramic oxide as well as across the oxide/polymer interface. Here we use two Li+ -insulating oxides (fluorite Gd0.1 Ce0.9 O1.95 and perovskite La0.8 Sr0.2 Ga0.8 Mg0.2 O2.55 ) with a high concentration of oxygen vacancies to demonstrate two oxide/poly(ethylene oxide) (PEO)-based polymer composite electrolytes, each with a Li+ conductivity above 10-4 S cm-1 at 30 °C. Li solid-state NMR results show an increase in Li+ ions (>10 %) occupying the more mobile A2 environment in the composite electrolytes. This increase in A2-site occupancy originates from the strong interaction between the O2- of Li-salt anion and the surface oxygen vacancies of each oxide and contributes to the more facile Li+ transport. All-solid-state Li-metal cells with these composite electrolytes demonstrate a small interfacial resistance with good cycling performance at 35 °C.
TL;DR: X-ray imaging tests show that scintillators based on (C38H34P2)MnBr4 powders provide an excellent visualization tool for X-ray radiography, and high resolution flexible scintilators can be fabricated by blending (C 38H34p2)mnBr 4 powders with polydimethylsiloxane.
Abstract: Scintillation based X-ray detection has received great attention for its application in a wide range of areas from security to healthcare. Here, we report highly efficient X-ray scintillators with state-of-the-art performance based on an organic metal halide, ethylenebis-triphenylphosphonium manganese (II) bromide ((C38H34P2)MnBr4), which can be prepared using a facile solution growth method at room temperature to form inch sized single crystals. This zero-dimensional organic metal halide hybrid exhibits green emission peaked at 517 nm with a photoluminescence quantum efficiency of ~ 95%. Its X-ray scintillation properties are characterized with an excellent linear response to X-ray dose rate, a high light yield of ~ 80,000 photon MeV−1, and a low detection limit of 72.8 nGy s−1. X-ray imaging tests show that scintillators based on (C38H34P2)MnBr4 powders provide an excellent visualization tool for X-ray radiography, and high resolution flexible scintillators can be fabricated by blending (C38H34P2)MnBr4 powders with polydimethylsiloxane. Scintillation-based X-ray detection is promising for applications in various areas ranging from security to healthcare, and low-cost and eco-friendly scintillation materials would be beneficial. Here the authors report a facile solution growth of organic manganese halide for efficient X-ray scintillation.
TL;DR: Differences between the 2018 and 2020 samples appeared across all demographic groups, with larger differences among younger adults and those with children in the household.
Abstract: Objective This study aims to document the impact of the COVID-19 pandemic on mental health. Method We compared a nationally representative online sample of 2,032 U.S. adults in late April 2020 to 19,330 U.S. adult internet users who participated in the 2018 National Health Interview Survey (NHIS) using the Kessler-6 scale of mental distress in the last 30 days. Results Compared to the 2018 NHIS sample, U.S. adults in April 2020 were eight times more likely to fit criteria for serious mental distress (27.7% vs. 3.4%) and three times more likely to fit criteria for moderate or serious mental distress (70.4% vs. 22.0%). Differences between the 2018 and 2020 samples appeared across all demographic groups, with larger differences among younger adults and those with children in the household. Conclusions These considerable levels of mental distress may portend substantial increases in diagnosed mental disorders and in their associated morbidity and mortality.
TL;DR: In this article, the authors examined an unexplored consequence of COVID-19 school closures: the broken link between child maltreatment victims and the number one source of reported maltreatment allegations-school personnel.
University of Melbourne1, Beth Israel Deaconess Medical Center2, Brigham and Women's Hospital3, University of Southern California4, University Medical Center Groningen5, Novosibirsk State University6, University of Münster7, Mental Health Services8, Trinity College, Dublin9, Florida State University10, University of California, San Francisco11, University of Queensland12, University of Minnesota13, Otto-von-Guericke University Magdeburg14, Stanford University15, University of Cape Town16, University of Edinburgh17, University of Sydney18, University of Marburg19, University of Maryland, Baltimore20, Humboldt University of Berlin21, University of Calgary22, QIMR Berghofer Medical Research Institute23, Alberta Children's Hospital24, Autonomous University of Barcelona25, Harvard University26, Max Planck Society27, Heidelberg University28, National University of Singapore29, Nanyang Technological University30, Leiden University Medical Center31, Leiden University32, Polytechnic University of Valencia33, University of Tübingen34
TL;DR: In this paper, the authors examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium.
Abstract: Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.12 and 0.26). The largest differences were observed in the corpus callosum and corona radiata. Widespread higher radial diffusivity (RD) was also observed (all Cohen's d between 0.12 and 0.18). Findings appeared to be driven by patients with recurrent MDD and an adult age of onset of depression. White matter microstructural differences in a smaller sample of adolescent MDD patients and controls did not survive correction for multiple testing. In this coordinated and harmonized multisite DTI study, we showed subtle, but widespread differences in WM microstructure in adult MDD, which may suggest structural disconnectivity in MDD.
TL;DR: Older adults are in triple jeopardy during COVID-19: compared with younger people, older adults are more likely to develop serious conditions and experience higher mortality; less likely to obtain high quality information or services online; and more likely than younger people to experience social isolation and loneliness.
Abstract: Older adults are in triple jeopardy during COVID-19: compared with younger people, older adults are (1) more likely to develop serious conditions and experience higher mortality; (2) less likely to obtain high quality information or services online; and (3) more likely to experience social isolation and loneliness. Hybrid solutions, coupling online and offline strategies, are invaluable in ensuring the inclusion of vulnerable populations. Most of these solutions require no new inventions. Finding the financial resources for a rapid, well-coordinated implementation is the biggest challenge. Setting up the requisite support systems and digital infrastructure is important for the present and future pandemics.
TL;DR: The Li+ distribution and Li+ transport mechanism in a composite polymer electrolyte is identified by investigating a new solid poly(ethylene oxide) (PEO)-based NASICON-LiZr2(PO4)3 composite with 7Li relaxation time and 6Li→7Li trace-exchange NMR measurements.
Abstract: The unclear Li+ local environment and Li+ conduction mechanism in solid polymer electrolytes, especially in a ceramic/polymer composite electrolyte, hinder the design and development of a new composite electrolyte. Moreover, both the low room-temperature Li+ conductivity and large interfacial resistance with a metallic lithium anode of a polymer membrane limit its application below a relatively high temperature. Here we have identified the Li+ distribution and Li+ transport mechanism in a composite polymer electrolyte by investigating a new solid poly(ethylene oxide) (PEO)-based NASICON-LiZr2(PO4)3 composite with 7Li relaxation time and 6Li → 7Li trace-exchange NMR measurements. The Li+ population of the two local environments in the composite electrolytes depends on the Li-salt concentration and the amount of ceramic filler. A composite electrolyte with a [EO]/[Li+] ratio n = 10 and 25 wt % LZP filler has a high Li+ conductivity of 1.2 × 10-4 S cm-1 at 30 °C and a low activation energy owing to the additional Li+ in the mobile A2 environment. Moreover, an in situ formed solid electrolyte interphase layer from the reaction between LiZr2(PO4)3 and a metallic lithium anode stabilized the Li/composite-electrolyte interface and reduced the interfacial resistance, which provided a symmetric Li/Li cell and all-solid-state Li/LiFePO4 and Li/LiNi0.8Co0.1Mn0.1O2 cells a good cycling performance at 40 °C.
American Museum of Natural History1, Instituto Butantan2, George Washington University3, Lehigh University4, University of Adelaide5, South Australian Museum6, Moravian College7, Australian National University8, University of Michigan9, Royal Ontario Museum10, University of Mississippi11, Rutgers University12, Florida State University13, University of Paris14, University of São Paulo15
TL;DR: High-throughput sequence data is used from 289 samples covering 75 families of squamates to address phylogenetic affinities, estimate divergence times, and characterize residual topological uncertainty in the presence of genome scale data to address genomic support for traditional taxonomic groupings Scleroglossa and Macrostomata.
Abstract: Genomics is narrowing uncertainty in the phylogenetic structure for many amniote groups. For one of the most diverse and species-rich groups, the squamate reptiles (lizards, snakes, and amphisbaenians), an inverse correlation between the number of taxa and loci sampled still persists across all publications using DNA sequence data and reaching a consensus on the relationships among them has been highly problematic. In this study, we use high-throughput sequence data from 289 samples covering 75 families of squamates to address phylogenetic affinities, estimate divergence times, and characterize residual topological uncertainty in the presence of genome-scale data. Importantly, we address genomic support for the traditional taxonomic groupings Scleroglossa and Macrostomata using novel machine-learning techniques. We interrogate genes using various metrics inherent to these loci, including parsimony-informative sites (PIS), phylogenetic informativeness, length, gaps, number of substitutions, and site concordance to understand why certain loci fail to find previously well-supported molecular clades and how they fail to support species-tree estimates. We show that both incomplete lineage sorting and poor gene-tree estimation (due to a few undesirable gene properties, such as an insufficient number of PIS), may account for most gene and species-tree discordance. We find overwhelming signal for Toxicofera, and also show that none of the loci included in this study supports Scleroglossa or Macrostomata. We comment on the origins and diversification of Squamata throughout the Mesozoic and underscore remaining uncertainties that persist in both deeper parts of the tree (e.g., relationships between Dibamia, Gekkota, and remaining squamates; among the three toxicoferan clades Iguania, Serpentes, and Anguiformes) and within specific clades (e.g., affinities among gekkotan, pleurodont iguanians, and colubroid families).
TL;DR: The identification of the underlying exchange anisotropies opens paths toward 2D ferromagnets with higher T_{C} as well as magnetically frustrated quantum spin liquids based on Kitaev physics.
Abstract: We lay the foundation for determining the microscopic spin interactions in two-dimensional (2D) ferromagnets by combining angle-dependent ferromagnetic resonance (FMR) experiments on high quality ${\mathrm{CrI}}_{3}$ single crystals with theoretical modeling based on symmetries. We discover that the Kitaev interaction is the strongest in this material with $K\ensuremath{\sim}\ensuremath{-}5.2\text{ }\text{ }\mathrm{meV}$, 25 times larger than the Heisenberg exchange $J\ensuremath{\sim}\ensuremath{-}0.2\text{ }\text{ }\mathrm{meV}$, and responsible for opening the $\ensuremath{\sim}5\text{ }\text{ }\mathrm{meV}$ gap at the Dirac points in the spin-wave dispersion. Furthermore, we find that the symmetric off-diagonal anisotropy $\mathrm{\ensuremath{\Gamma}}\ensuremath{\sim}\ensuremath{-}67.5\text{ }\text{ }\ensuremath{\mu}\mathrm{eV}$, though small, is crucial for opening a $\ensuremath{\sim}0.3\text{ }\text{ }\mathrm{meV}$ gap in the magnon spectrum at the zone center and stabilizing ferromagnetism in the 2D limit. The high resolution of the FMR data further reveals a $\ensuremath{\mu}\mathrm{eV}$-scale quadrupolar contribution to the $S=3/2$ magnetism. Our identification of the underlying exchange anisotropies opens paths toward 2D ferromagnets with higher ${T}_{C}$ as well as magnetically frustrated quantum spin liquids based on Kitaev physics.
20 Apr 2020
TL;DR: This paper introduces a novel gated graph neural network, namely FAKEDETECTOR, which builds a deep diffusive network model to learn the representations of news articles, creators and subjects simultaneously.
Abstract: In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. This paper addresses the challenges introduced by the unknown characteristics of fake news and diverse connections among news articles, creators and subjects. This paper introduces a novel gated graph neural network, namely FAKEDETECTOR. Based on a set of explicit and latent features extracted from the textual information, FAKEDETECTOR builds a deep diffusive network model to learn the representations of news articles, creators and subjects simultaneously. Extensive experiments have been done on a real-world fake news dataset to compare FAKEDETECTOR with several state-of-the-art models, and the experimental results are provided in the full-version of this paper at [13].
TL;DR: It is found that XAI evaluation in medicine has not been adequately and formally practiced, andple opportunities exist to advance XAI research in medicine.
TL;DR: The diverse activities of vitamin D in regulating NO bioavailability and endothelial function are discussed, including restraining NO and eNOS bioactivity and upregulating the expression of various atherosclerotic factors through the NF-κB pathway.
Abstract: Vitamin D is known to elicit a vasoprotective effect, while vitamin D deficiency is a risk factor for endothelial dysfunction (ED). ED is characterized by reduced bioavailability of a potent endothelium-dependent vasodilator, nitric oxide (NO), and is an early event in the development of atherosclerosis. In endothelial cells, vitamin D regulates NO synthesis by mediating the activity of the endothelial NO synthase (eNOS). Under pathogenic conditions, the oxidative stress caused by excessive production of reactive oxygen species (ROS) facilitates NO degradation and suppresses NO synthesis, consequently reducing NO bioavailability. Vitamin D, however, counteracts the activity of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase which produces ROS, and improves antioxidant capacity by enhancing the activity of antioxidative enzymes such as superoxide dismutase. In addition to ROS, proinflammatory mediators such as TNF-α and IL-6 are risk factors for ED, restraining NO and eNOS bioactivity and upregulating the expression of various atherosclerotic factors through the NF-κB pathway. These proinflammatory activities are inhibited by vitamin D by suppressing NF-κB signaling and production of proinflammatory cytokines. In this review, we discuss the diverse activities of vitamin D in regulating NO bioavailability and endothelial function.
TL;DR: The current review builds an existing framework that the LPP observed in studies in emotional processing and the P300 observed in classic oddball studies may reflect a common response to stimulus significance.
Abstract: Event-related potential studies of emotional processing have focused on the late positive potential (LPP), a sustained positive deflection in the ERP that is increased for emotionally arousing stimuli. A prominent theory suggests that modulation of the LPP is a response to stimulus significance, defined in terms of the activation of appetitive and aversive motivational systems. The current review incorporates experimental studies showing that manipulations that alter the significance of stimuli alter LPP amplitude. Complementing these within-person studies, also included is individual differences research on depression wherein the LPP has been used to study reduced neural sensitivity to emotional stimuli. Finally, the current review builds an existing framework that the LPP observed in studies in emotional processing and the P300 observed in classic oddball studies may reflect a common response to stimulus significance. This integrative account has implications for the functional interpretation of these ERPs, their neurobiological mechanisms, and clinical applications.
TL;DR: HEPfit as discussed by the authors is an open-source tool that allows to fit the model parameters to a set of experimental observables, and obtain predictions for observables for a given point in the parameter space of the model, allowing it to be used in any statistical framework.
Abstract: HEPfit is a flexible open-source tool which, given the Standard Model or any of its extensions, allows to (i) fit the model parameters to a given set of experimental observables; (ii) obtain predictions for observables. HEPfit can be used either in Monte Carlo mode, to perform a Bayesian Markov Chain Monte Carlo analysis of a given model, or as a library, to obtain predictions of observables for a given point in the parameter space of the model, allowing HEPfit to be used in any statistical framework. In the present version, around a thousand observables have been implemented in the Standard Model and in several new physics scenarios. In this paper, we describe the general structure of the code as well as models and observables implemented in the current release.
01 Jan 2020
TL;DR: In this paper, the authors synthesize the rapidly expanding knowledge of human mobility and migration responses to anthropogenic sea-level rise, providing a coherent roadmap for future SLR research and associated policy.
Abstract: Anthropogenic sea-level rise (SLR) is predicted to impact, and, in many cases, displace, a large proportion of the population via inundation and heightened SLR-related hazards. With the global coastal population projected to surpass one billion people this century, SLR might be among the most costly and permanent future consequences of climate change. In this Review, we synthesize the rapidly expanding knowledge of human mobility and migration responses to SLR, providing a coherent roadmap for future SLR research and associated policy. While it is often assumed that direct inundation forces a migration, we discuss how mobility responses are instead driven by a diversity of socioeconomic and demographic factors, which, in some cases, do not result in a migration response. We link SLR hazards with potential mechanisms of migration and the associated governmental or institutional policies that operate as obstacles or facilitators for that migration. Specific examples from the USA, Bangladesh and atoll island nations are used to contextualize these concepts. However, further research is needed on the fundamental mechanisms underlying SLR migration, tipping points, thresholds and feedbacks, risk perception and migration to fully understand migration responses to SLR. Rising sea levels threaten to displace millions of people through direct inundation and increased exposure to related hazards. This Review highlights populations at risk from sea-level-rise-related migration and discusses individual and institutional factors that influence relocation decisions.
TL;DR: S subterahertz spin pumping at the interface of the uniaxial insulating antiferromagnet manganese difluoride and platinum is reported, opening the door to the controlled generation of coherent, pure spin currents at terAhertz frequencies.
Abstract: Spin-transfer torque and spin Hall effects combined with their reciprocal phenomena, spin pumping and inverse spin Hall effects (ISHEs), enable the reading and control of magnetic moments in spintronics. The direct observation of these effects remains elusive in antiferromagnetic-based devices. We report subterahertz spin pumping at the interface of the uniaxial insulating antiferromagnet manganese difluoride and platinum. The measured ISHE voltage arising from spin-charge conversion in the platinum layer depends on the chirality of the dynamical modes of the antiferromagnet, which is selectively excited and modulated by the handedness of the circularly polarized subterahertz irradiation. Our results open the door to the controlled generation of coherent, pure spin currents at terahertz frequencies.