Showing papers by "University of Illinois at Chicago published in 2019"
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13 May 2019
TL;DR: Wang et al. as discussed by the authors proposed a heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions, which can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner.
Abstract: Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its meta-path based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.
1,467 citations
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TL;DR: Genome-wide analysis identifies 30 loci associated with bipolar disorder, allowing for comparisons of shared genes and pathways with other psychiatric disorders, including schizophrenia and depression.
Abstract: Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.
1,090 citations
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University of Vermont1, Karolinska University Hospital2, Universidade Federal de Minas Gerais3, Universidade Católica de Pelotas4, University of Tokyo5, Fujita Health University6, Central University of Venezuela7, University of Trieste8, University of Cape Town9, Monash University10, Ohio State University11, University of Alberta12, Hospital General de México13, University of Waterloo14, American Society for Parenteral and Enteral Nutrition15, Brigham and Women's Hospital16, Saint Louis University Hospital17, Sapienza University of Rome18, La Trobe University19, Khon Kaen University20, HAN University of Applied Sciences21, Rabin Medical Center22, University of Illinois at Chicago23, Pontifical Catholic University of Chile24, University of São Paulo25, Peking Union Medical College Hospital26, University of Pennsylvania27, Free University of Brussels28
TL;DR: A consensus scheme for diagnosing malnutrition in adults in clinical settings on a global scale is proposed and it is recommended that the etiologic criteria be used to guide intervention and anticipated outcomes.
Abstract: Summary Rationale This initiative is focused on building a global consensus around core diagnostic criteria for malnutrition in adults in clinical settings Methods In January 2016, the Global Leadership Initiative on Malnutrition (GLIM) was convened by several of the major global clinical nutrition societies GLIM appointed a core leadership committee and a supporting working group with representatives bringing additional global diversity and expertise Empirical consensus was reached through a series of face-to-face meetings, telephone conferences, and e-mail communications Results A two-step approach for the malnutrition diagnosis was selected, ie, first screening to identify “at risk” status by the use of any validated screening tool, and second, assessment for diagnosis and grading the severity of malnutrition The malnutrition criteria for consideration were retrieved from existing approaches for screening and assessment Potential criteria were subjected to a ballot among the GLIM core and supporting working group members The top five ranked criteria included three phenotypic criteria (non-volitional weight loss, low body mass index, and reduced muscle mass) and two etiologic criteria (reduced food intake or assimilation, and inflammation or disease burden) To diagnose malnutrition at least one phenotypic criterion and one etiologic criterion should be present Phenotypic metrics for grading severity as Stage 1 (moderate) and Stage 2 (severe) malnutrition are proposed It is recommended that the etiologic criteria be used to guide intervention and anticipated outcomes The recommended approach supports classification of malnutrition into four etiology-related diagnosis categories Conclusion A consensus scheme for diagnosing malnutrition in adults in clinical settings on a global scale is proposed Next steps are to secure further collaboration and endorsements from leading nutrition professional societies, to identify overlaps with syndromes like cachexia and sarcopenia, and to promote dissemination, validation studies, and feedback The diagnostic construct should be re-considered every 3–5 years
885 citations
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Karolinska University Hospital1, Uppsala University2, University of Vermont3, Universidade Federal de Minas Gerais4, Universidade Católica de Pelotas5, University of Tokyo6, Fujita Health University7, Central University of Venezuela8, University of Trieste9, University of Cape Town10, Monash University11, University of Warwick12, Ohio State University13, University of Alberta14, Hospital General de México15, University of Waterloo16, American Society for Parenteral and Enteral Nutrition17, Brigham and Women's Hospital18, Saint Louis University Hospital19, Sapienza University of Rome20, Khon Kaen University21, HAN University of Applied Sciences22, VU University Amsterdam23, Tel Aviv University24, Rabin Medical Center25, University of Illinois at Chicago26, Pontifical Catholic University of Chile27, University of São Paulo28, Peking Union Medical College Hospital29, Free University of Brussels30, University of Pennsylvania31
TL;DR: This initiative is focused on building a global consensus around core diagnostic criteria for malnutrition in adults in clinical settings.
Abstract: Rationale
This initiative is focused on building a global consensus around core diagnostic criteria for malnutrition in adults in clinical settings.
827 citations
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TL;DR: A novel heterogeneous network embedding based approach for HIN based recommendation, called HERec is proposed, which shows the capability of the HERec model for the cold-start problem, and reveals that the transformed embedding information from HINs can improve the recommendation performance.
Abstract: Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, called HIN based recommendation . It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance.
768 citations
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TL;DR: A network meta-analysis of placebo-controlled and head-to-head randomised controlled trials and compared 32 antipsychotics aimed to compare and rank antipsychotic drugs by quantifying information from randomisedcontrolled trials.
735 citations
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Daniel Taliun1, Daniel N. Harris2, Michael D. Kessler2, Jedidiah Carlson3 +191 more•Institutions (61)
TL;DR: The nearly complete catalog of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and non-coding sequence variants to phenotypic variation as well as resources and early insights from the sequence data.
Abstract: Summary paragraph The Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertion/deletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency
662 citations
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TL;DR: It is believed that NHIRD with multiple data sources could represent a powerful research engine with enriched dimensions and could serve as a guiding light for real-world evidence-based medicine in Taiwan.
Abstract: Taiwan's National Health Insurance Research Database (NHIRD) exemplifies a population-level data source for generating real-world evidence to support clinical decisions and health care policy-making. Like with all claims databases, there have been some validity concerns of studies using the NHIRD, such as the accuracy of diagnosis codes and issues around unmeasured confounders. Endeavors to validate diagnosed codes or to develop methodologic approaches to address unmeasured confounders have largely increased the reliability of NHIRD studies. Recently, Taiwan's Ministry of Health and Welfare (MOHW) established a Health and Welfare Data Center (HWDC), a data repository site that centralizes the NHIRD and about 70 other health-related databases for data management and analyses. To strengthen the protection of data privacy, investigators are required to conduct on-site analysis at an HWDC through remote connection to MOHW servers. Although the tight regulation of this on-site analysis has led to inconvenience for analysts and has increased time and costs required for research, the HWDC has created opportunities for enriched dimensions of study by linking across the NHIRD and other databases. In the near future, researchers will have greater opportunity to distill knowledge from the NHIRD linked to hospital-based electronic medical records databases containing unstructured patient-level information by using artificial intelligence techniques, including machine learning and natural language processes. We believe that NHIRD with multiple data sources could represent a powerful research engine with enriched dimensions and could serve as a guiding light for real-world evidence-based medicine in Taiwan.
611 citations
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TL;DR: This work recommends dropping the NHST paradigm—and the p-value thresholds intrinsic to it—as the default statistical paradigm for research, publication, and discovery in the biomedical and social sciences and argues that it seldom makes sense to calibrate evidence as a function of p-values or other purely statistical measures.
Abstract: We discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these problems remain unreso
565 citations
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University of Virginia1, Cornell University2, University of Texas Health Science Center at San Antonio3, University of Illinois at Chicago4, University of Pennsylvania5, Stanford University6, Colorado State University7, Providence Portland Medical Center8, Johns Hopkins University9, Brown University10, University of California, San Diego11, Northwestern University12, University of California, Davis13, Harvard University14, University of Bonn15, International University, Cambodia16, University of Washington17, Mayo Clinic18, Rush University Medical Center19, Keio University20, University of Texas Medical Branch21, Baylor College of Medicine22
TL;DR: Given that the majority of the biological and human health variables remained stable, or returned to baseline, after a 340-day space mission, these data suggest that human health can be mostly sustained over this duration of spaceflight.
Abstract: INTRODUCTION To date, 559 humans have been flown into space, but long-duration (>300 days) missions are rare (n = 8 total). Long-duration missions that will take humans to Mars and beyond are planned by public and private entities for the 2020s and 2030s; therefore, comprehensive studies are needed now to assess the impact of long-duration spaceflight on the human body, brain, and overall physiology. The space environment is made harsh and challenging by multiple factors, including confinement, isolation, and exposure to environmental stressors such as microgravity, radiation, and noise. The selection of one of a pair of monozygotic (identical) twin astronauts for NASA’s first 1-year mission enabled us to compare the impact of the spaceflight environment on one twin to the simultaneous impact of the Earth environment on a genetically matched subject. RATIONALE The known impacts of the spaceflight environment on human health and performance, physiology, and cellular and molecular processes are numerous and include bone density loss, effects on cognitive performance, microbial shifts, and alterations in gene regulation. However, previous studies collected very limited data, did not integrate simultaneous effects on multiple systems and data types in the same subject, or were restricted to 6-month missions. Measurement of the same variables in an astronaut on a year-long mission and in his Earth-bound twin indicated the biological measures that might be used to determine the effects of spaceflight. Presented here is an integrated longitudinal, multidimensional description of the effects of a 340-day mission onboard the International Space Station. RESULTS Physiological, telomeric, transcriptomic, epigenetic, proteomic, metabolomic, immune, microbiomic, cardiovascular, vision-related, and cognitive data were collected over 25 months. Some biological functions were not significantly affected by spaceflight, including the immune response (T cell receptor repertoire) to the first test of a vaccination in flight. However, significant changes in multiple data types were observed in association with the spaceflight period; the majority of these eventually returned to a preflight state within the time period of the study. These included changes in telomere length, gene regulation measured in both epigenetic and transcriptional data, gut microbiome composition, body weight, carotid artery dimensions, subfoveal choroidal thickness and peripapillary total retinal thickness, and serum metabolites. In addition, some factors were significantly affected by the stress of returning to Earth, including inflammation cytokines and immune response gene networks, as well as cognitive performance. For a few measures, persistent changes were observed even after 6 months on Earth, including some genes’ expression levels, increased DNA damage from chromosomal inversions, increased numbers of short telomeres, and attenuated cognitive function. CONCLUSION Given that the majority of the biological and human health variables remained stable, or returned to baseline, after a 340-day space mission, these data suggest that human health can be mostly sustained over this duration of spaceflight. The persistence of the molecular changes (e.g., gene expression) and the extrapolation of the identified risk factors for longer missions (>1 year) remain estimates and should be demonstrated with these measures in future astronauts. Finally, changes described in this study highlight pathways and mechanisms that may be vulnerable to spaceflight and may require safeguards for longer space missions; thus, they serve as a guide for targeted countermeasures or monitoring during future missions.
538 citations
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TL;DR: In this paper, the authors proposed transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM FCN (ALSTMFCN), into a multivariate time-series classification model by augmenting the fully convolutional block with a squeeze-and-excitation block to further improve accuracy.
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F. Kyle Satterstrom1, Jack A. Kosmicki1, Jiebiao Wang2, Michael S. Breen3 +150 more•Institutions (45)
TL;DR: Using an enhanced Bayesian framework to integrate de novo and case-control rare variation, 102 risk genes are identified at a false discovery rate of ≤ 0.1, consistent with multiple paths to an excitatory/inhibitory imbalance underlying ASD.
Abstract: We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n=35,584 total samples, 11,986 with ASD). Using an enhanced Bayesian framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate ≤ 0.1. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained for severe neurodevelopmental delay, while 53 show higher frequencies in individuals ascertained for ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most of the risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In human cortex single-cell gene expression data, expression of risk genes is enriched in both excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory/inhibitory imbalance underlying ASD.
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University of Illinois at Chicago1, University of California, San Francisco2, Mayo Clinic3, National Institutes of Health4, Yonsei University5, Liverpool School of Tropical Medicine6, Beth Israel Deaconess Medical Center7, University of New Mexico8, New York University9, University of Toronto10, University of Cape Town11, University of Illinois at Urbana–Champaign12
TL;DR: Air pollution affects the immune system and is associated with allergic rhinitis, allergic sensitization, and autoimmunity, and it is also associated with osteoporosis and bone fractures, conjunctivitis, dry eye disease, blepharitis, inflammatory bowel disease, increased intravascular coagulation, and decreased glomerular filtration rate.
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TL;DR: Combined measurements of the production and decay rates of the Higgs boson, as well as its couplings to vector bosons and fermions, are presented and constraints are placed on various two Higgs doublet models.
Abstract: Combined measurements of the production and decay rates of the Higgs boson, as well as its couplings to vector bosons and fermions, are presented. The analysis uses the LHC proton–proton collision data set recorded with the CMS detector in 2016 at $\sqrt{s}=13\,\text {Te}\text {V} $ , corresponding to an integrated luminosity of 35.9 ${\,\text {fb}^{-1}} $ . The combination is based on analyses targeting the five main Higgs boson production mechanisms (gluon fusion, vector boson fusion, and associated production with a $\mathrm {W}$ or $\mathrm {Z}$ boson, or a top quark-antiquark pair) and the following decay modes: $\mathrm {H} \rightarrow \gamma \gamma $ , $\mathrm {Z}\mathrm {Z}$ , $\mathrm {W}\mathrm {W}$ , $\mathrm {\tau }\mathrm {\tau }$ , $\mathrm {b} \mathrm {b} $ , and $\mathrm {\mu }\mathrm {\mu }$ . Searches for invisible Higgs boson decays are also considered. The best-fit ratio of the signal yield to the standard model expectation is measured to be $\mu =1.17\pm 0.10$ , assuming a Higgs boson mass of $125.09\,\text {Ge}\text {V} $ . Additional results are given for various assumptions on the scaling behavior of the production and decay modes, including generic parametrizations based on ratios of cross sections and branching fractions or couplings. The results are compatible with the standard model predictions in all parametrizations considered. In addition, constraints are placed on various two Higgs doublet models.
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TL;DR: There is moderate-to-strong support that PA benefits cognitive functioning during early and late periods of the life span and in certain populations characterized by cognitive deficits.
Abstract: PurposePhysical activity (PA) is known to improve cognitive and brain function, but debate continues regarding the consistency and magnitude of its effects, populations and cognitive domains most affected, and parameters necessary to achieve the greatest improvements (e.g., dose).MethodsIn t
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TL;DR: Two promising FTO inhibitors are developed, namely FB23 and FB23-2, which directly bind to FTO and selectively inhibit FTO's m6A demethylase activity, suggesting that FTO is a druggable target and that targeting FTO by small-molecule inhibitors holds potential to treat AML.
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University of Southern California1, Huntington Medical Research Institutes2, University of Edinburgh3, University of Toronto4, Yale University5, Ludwig Maximilian University of Munich6, Cornell University7, University of Bristol8, Nottingham City Hospital9, University of Nottingham10, University of Cambridge11, University of Manchester12, University of Glasgow13, Newcastle University14, UCL Institute of Neurology15, University of Southampton16, British Heart Foundation17, King's College London18, Harvard University19, Mayo Clinic20, University of Illinois at Chicago21, University of Arizona22, University of British Columbia23, University of California, San Diego24, University of Washington25, Wake Forest University26, National University of Singapore27, University of New South Wales28, University of Gothenburg29, SUNY Downstate Medical Center30, Utrecht University31, University of Calgary32, The Chinese University of Hong Kong33, Queen's University Belfast34, VU University Amsterdam35, University College London36, VU University Medical Center37, University of Bonn38, German Center for Neurodegenerative Diseases39, Houston Methodist Hospital40, McGill University41, National Institutes of Health42, Boston University43, Johns Hopkins University44, Leiden University45, Rush University Medical Center46, University of Minnesota47, University of Western Ontario48
TL;DR: Vascular imaging biomarkers of small vessel disease of the brain, which is responsible for >50% of dementia worldwide, including AD, are already established, well characterized, and easy to recognize and should be incorporated into the AD Research Framework to gain a better understanding of AD pathophysiology and aid in treatment efforts.
Abstract: Increasing evidence recognizes Alzheimer's disease (AD) as a multifactorial and heterogeneous disease with multiple contributors to its pathophysiology, including vascular dysfunction. The recently updated AD Research Framework put forth by the National Institute on Aging-Alzheimer's Association describes a biomarker-based pathologic definition of AD focused on amyloid, tau, and neuronal injury. In response to this article, here we first discussed evidence that vascular dysfunction is an important early event in AD pathophysiology. Next, we examined various imaging sequences that could be easily implemented to evaluate different types of vascular dysfunction associated with, and/or contributing to, AD pathophysiology, including changes in blood-brain barrier integrity and cerebral blood flow. Vascular imaging biomarkers of small vessel disease of the brain, which is responsible for >50% of dementia worldwide, including AD, are already established, well characterized, and easy to recognize. We suggest that these vascular biomarkers should be incorporated into the AD Research Framework to gain a better understanding of AD pathophysiology and aid in treatment efforts.
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TL;DR: In this paper, the authors investigated whether the phosphorylation of the C-terminal domain of the RNA polymerase II (PolII) C-interaction subunit regulates the incorporation of Pol II into phase-separated condensates that are associated with transcription initiation and splicing.
Abstract: The synthesis of pre-mRNA by RNA polymerase II (Pol II) involves the formation of a transcription initiation complex, and a transition to an elongation complex1–4. The large subunit of Pol II contains an intrinsically disordered C-terminal domain that is phosphorylated by cyclin-dependent kinases during the transition from initiation to elongation, thus influencing the interaction of the C-terminal domain with different components of the initiation or the RNA-splicing apparatus5,6. Recent observations suggest that this model provides only a partial picture of the effects of phosphorylation of the C-terminal domain7–12. Both the transcription-initiation machinery and the splicing machinery can form phase-separated condensates that contain large numbers of component molecules: hundreds of molecules of Pol II and mediator are concentrated in condensates at super-enhancers7,8, and large numbers of splicing factors are concentrated in nuclear speckles, some of which occur at highly active transcription sites9–12. Here we investigate whether the phosphorylation of the Pol II C-terminal domain regulates the incorporation of Pol II into phase-separated condensates that are associated with transcription initiation and splicing. We find that the hypophosphorylated C-terminal domain of Pol II is incorporated into mediator condensates and that phosphorylation by regulatory cyclin-dependent kinases reduces this incorporation. We also find that the hyperphosphorylated C-terminal domain is preferentially incorporated into condensates that are formed by splicing factors. These results suggest that phosphorylation of the Pol II C-terminal domain drives an exchange from condensates that are involved in transcription initiation to those that are involved in RNA processing, and implicates phosphorylation as a mechanism that regulates condensate preference. RNA polymerase II with a hypophosphorylated C-terminal domain preferentially incorporates into mediator condensates, and with a hyperphosphorylated C-terminal domain into splicing-factor condensates, revealing phosphorylation as a regulatory mechanism in condensate preference.
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TL;DR: It is demonstrated that hippocampal neurogenesis is persistent through the tenth decade of life and is detectable in patients with mild cognitive impairments and Alzheimer's disease and that it is possibly associated with cognition.
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TL;DR: In this article, a search for invisible decays of a Higgs boson via vector boson fusion is performed using proton-proton collision data collected with the CMS detector at the LHC in 2016 at a center-of-mass energy root s = 13 TeV, corresponding to an integrated luminosity of 35.9fb(-1).
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Children's Hospital Oakland Research Institute1, University of Tennessee Health Science Center2, University of Cincinnati3, Kenya Medical Research Institute4, Cairo University5, Alexandria University6, Brigham and Women's Hospital7, Sultan Qaboos University8, Emory University9, Columbia University10, Queen Mary University of London11, University of Illinois at Chicago12, University of Alabama at Birmingham13, American University of Beirut14, Guy's and St Thomas' NHS Foundation Trust15
TL;DR: Voxelotor significantly increased hemoglobin levels and reduced markers of hemolysis in this phase 3 randomized, placebo-controlled trial involving participants with sickle cell disease, consistent with inhibition of HbS polymerization and indicate a disease-modifying potential.
Abstract: Background Deoxygenated sickle hemoglobin (HbS) polymerization drives the pathophysiology of sickle cell disease. Therefore, direct inhibition of HbS polymerization has potential to favora...
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University of Illinois at Chicago1, University of California, San Francisco2, Mayo Clinic3, National Institutes of Health4, Yonsei University5, Liverpool School of Tropical Medicine6, Beth Israel Deaconess Medical Center7, University of New Mexico8, New York University9, University of Toronto10, University of Cape Town11, University of Illinois at Urbana–Champaign12
TL;DR: Although air pollution affects people of all regions, ages, and social groups, it is likely to cause greater illness in those with heavy exposure and greater susceptibility, and Persons are more vulnerable to air pollution if they have other illnesses or less social support.
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TL;DR: High-entropy alloy catalysts made of five earth-abundant elements are developed and demonstrate great catalytic enhancements for ammonia decomposition, highlighting the great potential of HEAs for catalyzing chemical transformation and energy conversion reactions.
Abstract: Ammonia represents a promising liquid fuel for hydrogen storage, but its large-scale application is limited by the need for precious metal ruthenium (Ru) as catalyst. Here we report on highly efficient ammonia decomposition using novel high-entropy alloy (HEA) catalysts made of earth abundant elements. Quinary CoMoFeNiCu nanoparticles are synthesized in a single solid-solution phase with robust control over the Co/Mo atomic ratio, including those ratios considered to be immiscible according to the Co-Mo bimetallic phase diagram. These HEA nanoparticles demonstrate substantially enhanced catalytic activity and stability for ammonia decomposition, with improvement factors achieving >20 versus Ru catalysts. Catalytic activity of HEA nanoparticles is robustly tunable by varying the Co/Mo ratio, allowing for the optimization of surface property to maximize the reactivity under different reaction conditions. Our work highlights the great potential of HEAs for catalyzing chemical transformation and energy conversion reactions. Alloys are important materials for catalysis but are usually limited by miscibility gaps present in their phase diagrams. Here the authors break this limitation by developing high-entropy alloy catalysts made of five earth-abundant elements and demonstrate great catalytic enhancements for ammonia decomposition.
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TL;DR: A novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC and is applied to some other review-based tasks such as aspect extraction and aspect sentiment classification in aspect-based sentiment analysis.
Abstract: Question-answering plays an important role in e-commerce as it allows potential customers to actively seek crucial information about products or services to help their purchase decision making. Inspired by the recent success of machine reading comprehension (MRC) on formal documents, this paper explores the potential of turning customer reviews into a large source of knowledge that can be exploited to answer user questions.~We call this problem Review Reading Comprehension (RRC). To the best of our knowledge, no existing work has been done on RRC. In this work, we first build an RRC dataset called ReviewRC based on a popular benchmark for aspect-based sentiment analysis. Since ReviewRC has limited training examples for RRC (and also for aspect-based sentiment analysis), we then explore a novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC. To show the generality of the approach, the proposed post-training is also applied to some other review-based tasks such as aspect extraction and aspect sentiment classification in aspect-based sentiment analysis. Experimental results demonstrate that the proposed post-training is highly effective. The datasets and code are available at this https URL.
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Erasmus University Rotterdam1, University of Southampton2, University Hospital Southampton NHS Foundation Trust3, University of Porto4, Paris Descartes University5, Sorbonne6, University of Crete7, Maastricht University8, University of Southern California9, National and Kapodistrian University of Athens10, University Medical Center Groningen11, Université de Sherbrooke12, Norwegian Institute of Public Health13, University of Bologna14, Nofer Institute of Occupational Medicine15, University of California, Davis16, Harvard University17, University of Illinois at Chicago18, University of Valencia19, National Institutes of Health20, University of Turku21, University of Bristol22, Helmholtz Centre for Environmental Research - UFZ23, Jagiellonian University Medical College24, Åbo Akademi University25, Harokopio University26, Public Health Research Institute27, University of Southern Denmark28, University of Copenhagen29, La Trobe University30, University of Helsinki31, University of Turin32, Radboud University Nijmegen33, University of Trieste34, University of Bergen35, Ludwig Maximilian University of Munich36, Slovak Medical University37, Utrecht University38, Pompeu Fabra University39
TL;DR: In this meta-analysis of pooled individual participant data from 25 cohort studies, the risk for adverse maternal and infant outcomes varied by gestational weight gain and across the range of prepregnancy weights, however, the optimal gestations weight gain ranges had limited predictive value for the outcomes assessed.
Abstract: Importance Both low and high gestational weight gain have been associated with adverse maternal and infant outcomes, but optimal gestational weight gain remains uncertain and not well defined for all prepregnancy weight ranges. Objectives To examine the association of ranges of gestational weight gain with risk of adverse maternal and infant outcomes and estimate optimal gestational weight gain ranges across prepregnancy body mass index categories. Design, Setting, and Participants Individual participant-level meta-analysis using data from 196 670 participants within 25 cohort studies from Europe and North America (main study sample). Optimal gestational weight gain ranges were estimated for each prepregnancy body mass index (BMI) category by selecting the range of gestational weight gain that was associated with lower risk for any adverse outcome. Individual participant-level data from 3505 participants within 4 separate hospital-based cohorts were used as a validation sample. Data were collected between 1989 and 2015. The final date of follow-up was December 2015. Exposures Gestational weight gain. Main Outcomes and Measures The main outcome termedany adverse outcomewas defined as the presence of 1 or more of the following outcomes: preeclampsia, gestational hypertension, gestational diabetes, cesarean delivery, preterm birth, and small or large size for gestational age at birth. Results Of the 196 670 women (median age, 30.0 years [quartile 1 and 3, 27.0 and 33.0 years] and 40 937 were white) included in the main sample, 7809 (4.0%) were categorized at baseline as underweight (BMI Conclusions and Relevance In this meta-analysis of pooled individual participant data from 25 cohort studies, the risk for adverse maternal and infant outcomes varied by gestational weight gain and across the range of prepregnancy weights. The estimates of optimal gestational weight gain may inform prenatal counseling; however, the optimal gestational weight gain ranges had limited predictive value for the outcomes assessed.
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TL;DR: By disambiguating the mechanisms through which prey perceive risk and incorporate fear into decision making, this work can better quantify the nonlinear relationship between risk and response and evaluate the relative importance of the landscape of fear across taxa and ecosystems.
Abstract: Animals experience varying levels of predation risk as they navigate heterogeneous landscapes, and behavioral responses to perceived risk can structure ecosystems. The concept of the landscape of fear has recently become central to describing this spatial variation in risk, perception, and response. We present a framework linking the landscape of fear, defined as spatial variation in prey perception of risk, to the underlying physical landscape and predation risk, and to resulting patterns of prey distribution and antipredator behavior. By disambiguating the mechanisms through which prey perceive risk and incorporate fear into decision making, we can better quantify the nonlinear relationship between risk and response and evaluate the relative importance of the landscape of fear across taxa and ecosystems.
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TL;DR: An improvement in wettability and bioactivity of titanium implant surfaces has been accomplished by combining micro and nano-scale modification and functionalization with protein, peptides, and bioactive compounds.
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TL;DR: Current concepts about metabolic regulation in tumors are described, focusing on processes intrinsic to cancer cells and on factors imposed upon cancer cells by the tumor microenvironment.
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Stanford University1, University of Illinois at Urbana–Champaign2, Purdue University3, George Washington University4, University of Pittsburgh5, Northeastern University6, University of Iowa7, Pennington Biomedical Research Center8, Duke University9, University of Maryland, Baltimore10, University of Illinois at Chicago11, Fred Hutchinson Cancer Research Center12, University of South Carolina13, University of Connecticut14, Winston-Salem State University15
TL;DR: New described benefits of physical activity include reduced risk of excessive weight gain in children and adults, incidence of 6 types of cancer, and fall-related injuries in older people.
Abstract: Background: The 2018 Physical Activity Guidelines Advisory Committee Scientific Report provides the evidence base for the Physical Activity Guidelines for Americans, 2nd Edition. Methods: The 2018 ...
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Erasmus University Medical Center1, University of Porto2, University of Western Australia3, Stockholm County Council4, Paris Descartes University5, Maastricht University6, French Institute of Health and Medical Research7, National and Kapodistrian University of Athens8, University Medical Center Groningen9, University of Valencia10, University of Southampton11, Liverpool School of Tropical Medicine12, Université de Sherbrooke13, Norwegian Institute of Public Health14, University of Bologna15, University of Crete16, University Hospital Southampton NHS Foundation Trust17, Ludwig Maximilian University of Munich18, Nofer Institute of Occupational Medicine19, University of California20, Harvard University21, University of Illinois at Chicago22, National Institutes of Health23, Wageningen University and Research Centre24, University of Turku25, Helmholtz Centre for Environmental Research - UFZ26, Jagiellonian University Medical College27, Åbo Akademi University28, Harokopio University29, University College Dublin30, University of Calgary31, Boston Children's Hospital32, University of Copenhagen33, University College Cork34, VU University Medical Center35, University of Helsinki36, University of Turin37, Radboud University Nijmegen38, University of Trieste39, University of Bergen40, Slovak Medical University41, Utrecht University42, Pompeu Fabra University43, Bradford Royal Infirmary44, University of Bristol45
TL;DR: In this paper, the separate and combined associations of maternal pre-pregnancy body mass index (BMI) and gestational weight gain with the risks of pregnancy complications and their population impact were assessed.