Abstract: Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation. We study the more challenging problem of learning DCNNs for semantic image segmentation from either (1) weakly annotated training data such as bounding boxes or image-level labels or (2) a combination of few strongly labeled and many weakly labeled images, sourced from one or multiple datasets. We develop Expectation-Maximization (EM) methods for semantic image segmentation model training under these weakly supervised and semi-supervised settings. Extensive experimental evaluation shows that the proposed techniques can learn models delivering competitive results on the challenging PASCAL VOC 2012 image segmentation benchmark, while requiring significantly less annotation effort. We share source code implementing the proposed system at https://bitbucket.org/deeplab/deeplab-public.

Abstract: Quantum computing becomes viable when a quantum state can be protected from environment-induced error. If quantum bits (qubits) are sufficiently reliable, errors are sparse and quantum error correction (QEC) is capable of identifying and correcting them. Adding more qubits improves the preservation of states by guaranteeing that increasingly larger clusters of errors will not cause logical failure-a key requirement for large-scale systems. Using QEC to extend the qubit lifetime remains one of the outstanding experimental challenges in quantum computing. Here we report the protection of classical states from environmental bit-flip errors and demonstrate the suppression of these errors with increasing system size. We use a linear array of nine qubits, which is a natural step towards the two-dimensional surface code QEC scheme, and track errors as they occur by repeatedly performing projective quantum non-demolition parity measurements. Relative to a single physical qubit, we reduce the failure rate in retrieving an input state by a factor of 2.7 when using five of our nine qubits and by a factor of 8.5 when using all nine qubits after eight cycles. Additionally, we tomographically verify preservation of the non-classical Greenberger-Horne-Zeilinger state. The successful suppression of environment-induced errors will motivate further research into the many challenges associated with building a large-scale superconducting quantum computer.

Abstract: Within a time span of 15 years, acoustic metamaterials have emerged from academic curiosity to become an active field driven by scientific discoveries and diverse application potentials. This review traces the development of acoustic metamaterials from the initial findings of mass density and bulk modulus frequency dispersions in locally resonant structures to the diverse functionalities afforded by the perspective of negative constitutive parameter values, and their implications for acoustic wave behaviors. We survey the more recent developments, which include compact phase manipulation structures, superabsorption, and actively controllable metamaterials as well as the new directions on acoustic wave transport in moving fluid, elastic, and mechanical metamaterials, graphene-inspired metamaterials, and structures whose characteristics are best delineated by non-Hermitian Hamiltonians. Many of the novel acoustic metamaterial structures have transcended the original definition of metamaterials as arising from the collective manifestations of constituent resonating units, but they continue to extend wave manipulation functionalities beyond those found in nature.

Abstract: Microplastic debris floating at the ocean surface can harm marine life. Understanding the severity of this harm requires knowledge of plastic abundance and distributions. Dozens of expeditions measuring microplastics have been carried out since the 1970s, but they have primarily focused on the North Atlantic and North Pacific accumulation zones, with much sparser coverage elsewhere. Here, we use the largest dataset of microplastic measurements assembled to date to assess the confidence we can have in global estimates of microplastic abundance and mass. We use a rigorous statistical framework to standardize a global dataset of plastic marine debris measured using surface-trawling plankton nets and coupled this with three different ocean circulation models to spatially interpolate the observations. Our estimates show that the accumulated number of microplastic particles in 2014 ranges from 15 to 51 trillion particles, weighing between 93 and 236 thousand metric tons, which is only approximately 1% of global plastic waste estimated to enter the ocean in the year 2010. These estimates are larger than previous global estimates, but vary widely because the scarcity of data in most of the world ocean, differences in model formulations, and fundamental knowledge gaps in the sources, transformations and fates of microplastics in the ocean.

Abstract: Sodium-ion batteries are emerging as a highly promising technology for large-scale energy storage applications. However, it remains a significant challenge to develop an anode with superior long-term cycling stability and high-rate capability. Here we demonstrate that the Na(+) intercalation pseudocapacitance in TiO2/graphene nanocomposites enables high-rate capability and long cycle life in a sodium-ion battery. This hybrid electrode exhibits a specific capacity of above 90 mA h g(-1) at 12,000 mA g(-1) (∼36 C). The capacity is highly reversible for more than 4,000 cycles, the longest demonstrated cyclability to date. First-principle calculations demonstrate that the intimate integration of graphene with TiO2 reduces the diffusion energy barrier, thus enhancing the Na(+) intercalation pseudocapacitive process. The Na-ion intercalation pseudocapacitance enabled by tailor-deigned nanostructures represents a promising strategy for developing electrode materials with high power density and long cycle life.

Abstract: Major depression is a chronic illness with a high prevalence and is a major component of disease burden. Depressive disorders were the second leading cause of years lived with disability in 2010 in Canada, the United States and globally.1,2 When depression-related deaths due to suicide and stroke are considered, depression has the third highest global burden of disease.3 Major depression is growing in overall disease burden in Canada and around the world; it is predicted to be the leading cause of disease burden by 2030, and it is already the leading cause in women worldwide.4 Between 1990 and 2010 in Canada, major depressive disorder showed a 75% increase in disability-adjusted life years,1 the second greatest increase in prevalence after Alzheimer disease; in comparison, the increase in the United States was 43%.2 At the same time, the female:male ratio of global disability from major depression remained unchanged at 1.7:1. Although differences in socioeconomic factors, including abuse, education and income, may impact the higher rate of depression in women,5 this editorial focuses on biological contributors that are experimentally tractable and may help to understand how and why depression is more prevalent in women and lead to better treatments. The prevalence of major depression is higher in women than in men;6,7 in 2010 its global annual prevalence was 5.5% and 3.2%, respectively, representing a 1.7-fold greater incidence in women.1,8 In Canada, the prevalence was 5.0% in women and 2.9% in men in 2002 (1.7-fold greater incidence in women) and increased to 5.8% and 3.6%, respectively, in 2012 (1.6-fold greater incidence in women).9,10 The finding of similar female:male prevalence ratios in developed countries and globally suggests that the differential risk may primarily stem from biological sex differences and depend less on race, culture, diet, education and numerous other potentially confounding social and economic factors. There is no clear evidence that the rate of depression is greater in countries where women have markedly lower socioeconomic status than men than in countries where there may be more equal footing.5 Depression is more than twice as prevalent in young women than men (ages 14–25 yr), but this ratio decreases with age.9,10 Indeed, starting at puberty, young women are at the greatest risk for major depression and mental disorders globally.1 Importantly, before puberty, girls and boys have similar rates of depression; the rate is perhaps even higher for boys.6 At ages older than 65 years, both men and women show a decline in depression rates, and the prevalence becomes similar between them.9,11 A greater prevalence of depression in women is also reflected in prescriptions for antidepressant medications. In Canada between 2007 and 2011, antidepressants were prescribed more than twice as often to women than men (9.3% v. 4.2% in patients aged 25–44 yr, 2.2-fold; 17.2% v. 8.2% in patients aged 45–64 yr, 2.1-fold).12 The age discrepancy between the peaks in the prevalence of depression (age 14–25 yr)10 and the prevalence of antidepressant use (> 45 yr) suggests that young adults with depression may not always receive antidepressant treatment until many years after the onset of illness. This delay in medication could contribute to the higher rates of depression during adolescence and young adulthood and would be important to study more rigorously comparing treated and nontreated cohorts. Delay in antidepressant treatment might reflect stigma or underdiagnosis in adolescence. New antistigma and educational programs targeted to youth may help reduce depression in this age group.13 Why then is depression more prevalent among women? The triggers for depression appear to differ, with women more often presenting with internalizing symptoms and men presenting with externalizing symptoms.14 For example, in a study of dizygotic twins, women displayed more sensitivity to interpersonal relationships, whereas men displayed more sensitivity to external career and goal-oriented factors.15 Women also experience specific forms of depression-related illness, including premenstrual dysphoric disorder, postpartum depression and postmenopausal depression and anxiety, that are associated with changes in ovarian hormones and could contribute to the increased prevalence in women. However, the underlying mechanisms remain unclear; thus, treatments specific to women have not been developed. The fact that increased prevalence of depression correlates with hormonal changes in women, particularly during puberty, prior to menstruation, following pregnancy and at perimenopause, suggests that female hormonal fluctuations may be a trigger for depression. However, most preclinical studies focus on males to avoid variability in behaviour that may be associated with the menstrual cycle. Nevertheless, primate and rodent studies consistently implicate a role for female hormones, such as estrogen, in depression. Perhaps the most naturalistic depression studies to date to address the role of female hormones involved small groups (n = 4–5) of female macaque primates that formed lifelong social hierarchies with dominant and subordinate females. The latter showed a depression-like phenotype16 that has been associated with a brain-wide decrease in serotonin 1A (5-HT1A) receptor levels and decreased hippocampal volume.17,18 Interestingly, the reduced hippocampal volume was more extensive in postmenopausal monkeys than in ovarian-intact monkeys, suggesting that ovarian function may be protective. Consistent with this finding, the risk of depression appears to increase during the perimenopausal transition.19 Emerging evidence indicates that hormone replacement therapy, particularly during the perimenopausal period, can be effective in the prevention of postmenopausal depression in women.20 Another study involving female macaques examined relocation stress–sensitive alterations in their menstrual cycles and showed depression-related behaviours and reductions in the function of the brain serotonin system.21 In this light, a recent study has indicated that women who reported using an oral contraceptive (especially monophasic contraceptives) showed reduced rates of major depression and anxiety compared with nonusers,22 suggesting that moderating the cycling of estrogen may be protective. Taken together these studies suggest that estrogen may have a protective effect on the pathology that underlies depression and that decreases in estrogen may increase the risk for depression. Why then do men, who lack systemic estrogen, have lower rates of depression than women? Accumulating research has shown that in the male brain testosterone is converted into estrogen by endogenous aromatase (CYP19). Estrogen could mediate protective actions through estrogen receptors expressed throughout the male brain (especially estrogen receptor β).23 In addition, the presence of androgen receptors in men may confer protection, for example in hippocampal neurons.24 Since testosterone does not cycle in men as estrogen does in women, there may be a more consistent protection in men. However, men also have sexually dimorphic brain nuclei, particularly in the hypothalamus, so the lower prevalence of depression in men is probably more complex owing not only to hormonal differences, but also to developmental differences in brain circuitry.23 In the most fundamental terms, the sex difference in depression rates reflects the fact the men and women are different: women have 2 copies of the X chromosome, while men have 1 copy each of X and Y chromosomes, the latter not being present in women. Understanding how this fundamental genetic difference confers sexual differences in predisposition to mental illness is a complex, multilevel puzzle that remains to be clarified. Society-driven risk factors for depression in women likely have a biological origin, such as differences in physical strength and personality traits, leading to a higher prevalence of depression in women. Perhaps what needs to change are social attitudes to promote equality; yet, this has been occurring in the West and has yielded no clear change in the female:male depression ratio.5 However, despite this complexity, recent evidence suggests that biological factors, such as the variation in ovarian hormone levels and particularly decreases in estrogen, may contribute to the increased prevalence of depression and anxiety in women and that strategies to mitigate decreases in estrogen levels may be protective. Identifying ligands that more specifically target the brain (e.g., estrogen receptor-β-selective ligands) may protect from depression but avoid adverse effects of estrogen therapy.25

Abstract: Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process. Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments. We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals. Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (e.g. MavLink). The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols. In addition, the modular design enables various components to be easily usable independently in other projects. We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flights.

Abstract: Summary BACKGROUND The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed great threat to human health, which has been declared a public health emergency of international concern (PHEIC) by the WHO. T cells play a critical role in antiviral immunity but their numbers and functional state in COVID-19 patients remain largely unclear. METHODS We retrospectively reviewed the counts of total T cells, CD4+, CD8+ T cell subsets, and serum cytokine concentration from inpatient data of 522 patients with laboratory-confirmed COVID-19, admitted into two hospitals in Wuhan from December 2019 to January 2020, and 40 healthy controls, who came to the hospitals for routine physical examination. In addition, the expression of T cell exhaustion markers PD-1 and Tim-3 were measured by flow cytometry in the peripheral blood of 14 COVID-19 cases. RESULTS The number of total T cells, CD4+ and CD8+ T cells were dramatically reduced in COVID-19 patients, especially among elderly patients (⩾60 years of age) and in patients requiring Intensive Care Unit (ICU) care. Counts of total T cells, CD8+T cells or CD4+T cells lower than 800/μL, 300/μL, or 400/μL, respectively, are negatively correlated with patient survival. Statistical analysis demonstrated that T cell numbers are negatively correlated to serum IL-6, IL-10 and TNF-α concentration, with patients in decline period showing reduced IL-6, IL-10 and TNF-α concentrations and restored T cell counts. Finally, T cells from COVID-19 patients have significantly higher levels of the exhausted marker PD-1 as compared to health controls. Moreover, increasing PD-1 and Tim-3 expression on T cells could be seen as patients progressed from prodromal to overtly symptomatic stages, further indicative of T cell exhaustion. CONCLUSIONS T cell counts are reduced significantly in COVID-19 patients, and the surviving T cells appear functionally exhausted. Non-ICU patients, with total T cells, CD8+T cells CD4+T cells counts lower than 800/μL, 300/μL, and 400/μL, respectively, may still require aggressive intervention even in the immediate absence of more severe symptoms due to a high risk for further deterioration in condition.

Abstract: Importance Burnout is a self-reported job-related syndrome increasingly recognized as a critical factor affecting physicians and their patients An accurate estimate of burnout prevalence among physicians would have important health policy implications, but the overall prevalence is unknown Objective To characterize the methods used to assess burnout and provide an estimate of the prevalence of physician burnout Data Sources and Study Selection Systematic search of EMBASE, ERIC, MEDLINE/PubMed, psycARTICLES, and psycINFO for studies on the prevalence of burnout in practicing physicians (ie, excluding physicians in training) published before June 1, 2018 Data Extraction and Synthesis Burnout prevalence and study characteristics were extracted independently by 3 investigators Although meta-analytic pooling was planned, variation in study designs and burnout ascertainment methods, as well as statistical heterogeneity, made quantitative pooling inappropriate Therefore, studies were summarized descriptively and assessed qualitatively Main Outcomes and Measures Point or period prevalence of burnout assessed by questionnaire Results Burnout prevalence data were extracted from 182 studies involving 109 628 individuals in 45 countries published between 1991 and 2018 In all, 857% (156/182) of studies used a version of the Maslach Burnout Inventory (MBI) to assess burnout Studies variably reported prevalence estimates of overall burnout or burnout subcomponents: 670% (122/182) on overall burnout, 720% (131/182) on emotional exhaustion, 681% (124/182) on depersonalization, and 632% (115/182) on low personal accomplishment Studies used at least 142 unique definitions for meeting overall burnout or burnout subscale criteria, indicating substantial disagreement in the literature on what constituted burnout Studies variably defined burnout based on predefined cutoff scores or sample quantiles and used markedly different cutoff definitions Among studies using instruments based on the MBI, there were at least 47 distinct definitions of overall burnout prevalence and 29, 26, and 26 definitions of emotional exhaustion, depersonalization, and low personal accomplishment prevalence, respectively Overall burnout prevalence ranged from 0% to 805% Emotional exhaustion, depersonalization, and low personal accomplishment prevalence ranged from 0% to 862%, 0% to 899%, and 0% to 871%, respectively Because of inconsistencies in definitions of and assessment methods for burnout across studies, associations between burnout and sex, age, geography, time, specialty, and depressive symptoms could not be reliably determined Conclusions and Relevance In this systematic review, there was substantial variability in prevalence estimates of burnout among practicing physicians and marked variation in burnout definitions, assessment methods, and study quality These findings preclude definitive conclusions about the prevalence of burnout and highlight the importance of developing a consensus definition of burnout and of standardizing measurement tools to assess the effects of chronic occupational stress on physicians

Abstract: A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very little data is available -- current datasets cover a small range of scene views and have limited semantic annotations. To address this issue, we introduce ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations. To collect this data, we designed an easy-to-use and scalable RGB-D capture system that includes automated surface reconstruction and crowdsourced semantic annotation. We show that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks, including 3D object classification, semantic voxel labeling, and CAD model retrieval. The dataset is freely available at this http URL.

Abstract: Background Preclinical studies suggest that bb2121, a chimeric antigen receptor (CAR) T-cell therapy that targets B-cell maturation antigen (BCMA), has potential for the treatment of multi...

Abstract: KRAS is the most common oncogenic driver in lung adenocarcinoma (LUAC). We previously reported that STK11/LKB1 (KL) or TP53 (KP) comutations define distinct subgroups of KRAS-mutant LUAC. Here, we examine the efficacy of PD-1 inhibitors in these subgroups. Objective response rates to PD-1 blockade differed significantly among KL (7.4%), KP (35.7%), and K-only (28.6%) subgroups (P < 0.001) in the Stand Up To Cancer (SU2C) cohort (174 patients) with KRAS-mutant LUAC and in patients treated with nivolumab in the CheckMate-057 phase III trial (0% vs. 57.1% vs. 18.2%; P = 0.047). In the SU2C cohort, KL LUAC exhibited shorter progression-free (P < 0.001) and overall (P = 0.0015) survival compared with KRASMUT;STK11/LKB1WT LUAC. Among 924 LUACs, STK11/LKB1 alterations were the only marker significantly associated with PD-L1 negativity in TMBIntermediate/High LUAC. The impact of STK11/LKB1 alterations on clinical outcomes with PD-1/PD-L1 inhibitors extended to PD-L1-positive non-small cell lung cancer. In Kras-mutant murine LUAC models, Stk11/Lkb1 loss promoted PD-1/PD-L1 inhibitor resistance, suggesting a causal role. Our results identify STK11/LKB1 alterations as a major driver of primary resistance to PD-1 blockade in KRAS-mutant LUAC.Significance: This work identifies STK11/LKB1 alterations as the most prevalent genomic driver of primary resistance to PD-1 axis inhibitors in KRAS-mutant lung adenocarcinoma. Genomic profiling may enhance the predictive utility of PD-L1 expression and tumor mutation burden and facilitate establishment of personalized combination immunotherapy approaches for genomically defined LUAC subsets. Cancer Discov; 8(7); 822-35. ©2018 AACR.See related commentary by Etxeberria et al., p. 794This article is highlighted in the In This Issue feature, p. 781.

Abstract: Photosynthesis converts light energy into biologically useful chemical energy vital to life on Earth. The initial reaction of photosynthesis takes place in photosystem II (PSII), a 700-kilodalton homodimeric membrane protein complex which catalyses photo-oxidation of water into dioxygen through an S-state cycle of the oxygen evolving complex (OEC). The structure of PSII has been solved by X-ray diffraction (XRD) at 1.9-angstrom (A) resolution, which revealed that the OEC is a Mn4CaO5-cluster coordinated by a well-defined protein environment1. However, extended X-ray absorption fine structure (EXAFS) studies showed that the manganese cations in the OEC are easily reduced by X-ray irradiation2, and slight differences were found in the Mn–Mn distances between the results of XRD1, EXAFS3–7 and theoretical studies8–14. Here we report a ‘radiation-damage-free’ structure of PSII from Thermosynechococcus vulcanus in the S1 state at a resolution of 1.95 A using femtosecond X-ray pulses of the SPring-8 angstrom compact free-electron laser (SACLA) and a huge number of large, highly isomorphous PSII crystals. Compared with the structure from XRD, the OEC in the X-ray free electron laser structure has Mn–Mn distances that are shorter by 0.1–0.2 A. The valences of each manganese atom were tentatively assigned as Mn1D(III), Mn2C(IV), Mn3B(IV) and Mn4A(III), based on the average Mn–ligand distances and analysis of the Jahn–Teller axis on Mn(III). One of the oxo-bridged oxygens, O5, has significantly longer Mn–O distances in contrast to the other oxo-oxygen atoms, suggesting that it is a hydroxide ion instead of a normal oxygen dianion and therefore may serve as one of the substrate oxygen atoms. These findings provide a structural basis for the mechanism of oxygen evolution, and we expect that this structure will provide a blueprint for design of artificial catalysts for water oxidation.

Abstract: We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing steps, most existing approaches are only able to be trained and operate over small-scale point clouds. In this paper, we introduce RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Although remarkably computation and memory efficient, random sampling can discard key features by chance. To overcome this, we introduce a novel local feature aggregation module to progressively increase the receptive field for each 3D point, thereby effectively preserving geometric details. Extensive experiments show that our RandLA-Net can process 1 million points in a single pass with up to 200x faster than existing approaches. Moreover, our RandLA-Net clearly surpasses state-of-the-art approaches for semantic segmentation on two large-scale benchmarks Semantic3D and SemanticKITTI.

Abstract: Importance No US national data are available on the prevalence and correlates of DSM-5 –defined major depressive disorder (MDD) or on MDD specifiers as defined in DSM-5 . Objective To present current nationally representative findings on the prevalence, correlates, psychiatric comorbidity, functioning, and treatment of DSM-5 MDD and initial information on the prevalence, severity, and treatment of DSM-5 MDD severity, anxious/distressed specifier, and mixed-features specifier, as well as cases that would have been characterized as bereavement in DSM-IV . Design, Setting, and Participants In-person interviews with a representative sample of US noninstitutionalized civilian adults (≥18 years) (n = 36 309) who participated in the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions III (NESARC-III). Data were collected from April 2012 to June 2013 and were analyzed in 2016-2017. Main Outcomes and Measures Prevalence of DSM-5 MDD and the DSM-5 specifiers. Odds ratios (ORs), adjusted ORs (aORs), and 95% CIs indicated associations with demographic characteristics and other psychiatric disorders. Results Of the 36 309 adult participants in NESARC-III, 12-month and lifetime prevalences of MDD were 10.4% and 20.6%, respectively. Odds of 12-month MDD were significantly lower in men (OR, 0.5; 95% CI, 0.46-0.55) and in African American (OR, 0.6; 95% CI, 0.54-0.68), Asian/Pacific Islander (OR, 0.6; 95% CI, 0.45-0.67), and Hispanic (OR, 0.7; 95% CI, 0.62-0.78) adults than in white adults and were higher in younger adults (age range, 18-29 years; OR, 3.0; 95% CI, 2.48-3.55) and those with low incomes ($19 999 or less; OR, 1.7; 95% CI, 1.49-2.04). Associations of MDD with psychiatric disorders ranged from an aOR of 2.1 (95% CI, 1.84-2.35) for specific phobia to an aOR of 5.7 (95% CI, 4.98-6.50) for generalized anxiety disorder. Associations of MDD with substance use disorders ranged from an aOR of 1.8 (95% CI, 1.63-2.01) for alcohol to an aOR of 3.0 (95% CI, 2.57-3.55) for any drug. Most lifetime MDD cases were moderate (39.7%) or severe (49.5%). Almost 70% with lifetime MDD had some type of treatment. Functioning among those with severe MDD was approximately 1 SD below the national mean. Among 12.9% of those with lifetime MDD, all episodes occurred just after the death of someone close and lasted less than 2 months. The anxious/distressed specifier characterized 74.6% of MDD cases, and the mixed-features specifier characterized 15.5%. Controlling for severity, both specifiers were associated with early onset, poor course and functioning, and suicidality. Conclusions and Relevance Among US adults, DSM-5 MDD is highly prevalent, comorbid, and disabling. While most cases received some treatment, a substantial minority did not. Much remains to be learned about the DSM-5 MDD specifiers in the general population.

Abstract: PurposeAbemaciclib, a cyclin-dependent kinase 4 and 6 inhibitor, demonstrated efficacy as monotherapy and in combination with fulvestrant in women with hormone receptor (HR)–positive, human epidermal growth factor receptor 2 (HER2)–negative advanced breast cancer previously treated with endocrine therapy.MethodsMONARCH 3 is a double-blind, randomized phase III study of abemaciclib or placebo plus a nonsteroidal aromatase inhibitor in 493 postmenopausal women with HR-positive, HER2-negative advanced breast cancer who had no prior systemic therapy in the advanced setting. Patients received abemaciclib or placebo (150 mg twice daily continuous schedule) plus either 1 mg anastrozole or 2.5 mg letrozole, daily. The primary objective was investigator-assessed progression-free survival. Secondary objectives included response evaluation and safety. A planned interim analysis occurred after 189 events.ResultsMedian progression-free survival was significantly prolonged in the abemaciclib arm (hazard ratio, 0.54; 95...

Abstract: A growing body of empirical work measuring different types of cultural traits has shown that culture matters for a variety of economic outcomes. This paper focuses on one specific aspect of the relevance of culture: its relationship to institutions. We review work with a theoretical, empirical, and historical bent to assess the presence of a two-way causal effect between culture and institutions. ( JEL D02, D72, I32, J12, Z13)

Abstract: A cornerstone of environmental policy is the debate over protecting nature for humans’ sake (instrumental values) or for nature’s (intrinsic values) (1). We propose that focusing only on instrumental or intrinsic values may fail to resonate with views on personal and collective well-being, or “what is right,” with regard to nature and the environment. Without complementary attention to other ways that value is expressed and realized by people, such a focus may inadvertently promote worldviews at odds with fair and desirable futures. It is time to engage seriously with a third class of values, one with diverse roots and current expressions: relational values. By doing so, we reframe the discussion about environmental protection, and open the door to new, potentially more productive policy approaches.

Abstract: Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image. A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations. We benchmark our framework on Unified Perceptual Parsing and show that it is able to effectively segment a wide range of concepts from images. The trained networks are further applied to discover visual knowledge in natural scenes (Models are available at https://github.com/CSAILVision/unifiedparsing).

Abstract: The goal of this paper is to perform 3D object detection from a single monocular image in the domain of autonomous driving. Our method first aims to generate a set of candidate class-specific object proposals, which are then run through a standard CNN pipeline to obtain highquality object detections. The focus of this paper is on proposal generation. In particular, we propose an energy minimization approach that places object candidates in 3D using the fact that objects should be on the ground-plane. We then score each candidate box projected to the image plane via several intuitive potentials encoding semantic segmentation, contextual information, size and location priors and typical object shape. Our experimental evaluation demonstrates that our object proposal generation approach significantly outperforms all monocular approaches, and achieves the best detection performance on the challenging KITTI benchmark, among published monocular competitors.

Abstract: In recent years, enhanced light-matter interactions through a plethora of dipole-type polaritonic excitations have been observed in two-dimensional (2D) layered materials. In graphene, electrically tunable and highly confined plasmon-polaritons were predicted and observed, opening up opportunities for optoelectronics, bio-sensing and other mid-infrared applications. In hexagonal boron nitride, low-loss infrared-active phonon-polaritons exhibit hyperbolic behaviour for some frequencies, allowing for ray-like propagation exhibiting high quality factors and hyperlensing effects. In transition metal dichalcogenides, reduced screening in the 2D limit leads to optically prominent excitons with large binding energy, with these polaritonic modes having been recently observed with scanning near-field optical microscopy. Here, we review recent progress in state-of-the-art experiments, and survey the vast library of polaritonic modes in 2D materials, their optical spectral properties, figures of merit and application space. Taken together, the emerging field of 2D material polaritonics and their hybrids provide enticing avenues for manipulating light-matter interactions across the visible, infrared to terahertz spectral ranges, with new optical control beyond what can be achieved using traditional bulk materials.

Abstract: Background Despite the high efficacy of the BNT162b2 messenger RNA vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), rare breakthrough infections have been repo...

Abstract: Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We constructed a database of interactions among ligands, receptors and their cofactors that accurately represents known heteromeric molecular complexes. Based on mass action models, we then developed CellChat, a tool that is able to quantitively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applications of CellChat to several mouse skin scRNA-seq datasets for embryonic development and adult wound healing shows its ability to extract complex signaling patterns, both previously known as well as novel. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build a cell-cell communication atlas in diverse tissues.

Abstract: Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a particular race, gender, or sexual orientation. Since this past data may be biased, machine learning predictors must account for this to avoid perpetuating or creating discriminatory practices. In this paper, we develop a framework for modeling fairness using tools from causal inference. Our definition of counterfactual fairness captures the intuition that a decision is fair towards an individual if it the same in (a) the actual world and (b) a counterfactual world where the individual belonged to a different demographic group. We demonstrate our framework on a real-world problem of fair prediction of success in law school.

Abstract: Since the emergence of Zika virus (ZIKV), reports of microcephaly have increased considerably in Brazil; however, causality between the viral epidemic and malformations in fetal brains needs further confirmation. We examined the effects of ZIKV infection in human neural stem cells growing as neurospheres and brain organoids. Using immunocytochemistry and electron microscopy, we showed that ZIKV targets human brain cells, reducing their viability and growth as neurospheres and brain organoids. These results suggest that ZIKV abrogates neurogenesis during human brain development.

Abstract: The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

Abstract: Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular "graph convolutions", a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph---atoms, bonds, distances, etc.---which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic with millions infected and more than 1 million fatalities. Questions regarding the robustness, functionality, and longevity of the antibody response to the virus remain unanswered. Here, on the basis of a dataset of 30,082 individuals screened at Mount Sinai Health System in New York City, we report that the vast majority of infected individuals with mild-to-moderate COVID-19 experience robust immunoglobulin G antibody responses against the viral spike protein. We also show that titers are relatively stable for at least a period of about 5 months and that anti-spike binding titers significantly correlate with neutralization of authentic SARS-CoV-2. Our data suggest that more than 90% of seroconverters make detectable neutralizing antibody responses. These titers remain relatively stable for several months after infection.

Abstract: The reactive oxygen species (ROS) form under normal physiological conditions and may have both beneficial and harmful role. We search the literature and current knowledge in the aspect of ROS participation in the pathogenesis of anterior and posterior eye segment diseases in adults. ROS take part in the pathogenesis of keratoconus, Fuchs endothelial corneal dystrophy, and granular corneal dystrophy type 2, stimulating apoptosis of corneal cells. ROS play a role in the pathogenesis of glaucoma stimulating apoptotic and inflammatory pathways on the level of the trabecular meshwork and promoting retinal ganglion cells apoptosis and glial dysfunction in the posterior eye segment. ROS play a role in the pathogenesis of Leber's hereditary optic neuropathy and traumatic optic neuropathy. ROS induce apoptosis of human lens epithelial cells. ROS promote apoptosis of vascular and neuronal cells and stimulate inflammation and pathological angiogenesis in the course of diabetic retinopathy. ROS are associated with the pathophysiological parainflammation and autophagy process in the course of the age-related macular degeneration.