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Spindle- or lemon-shaped viruses infect archaea in diverse environments. Due to the highly pleomorphic nature of these virions, which can be found with cylindrical tails emanating from the spindle-shaped body, structural studies of these capsids have been challenging. We have determined the atomic structure of the capsid of Sulfolobus monocaudavirus 1, a virus that infects hosts living in nearly boiling acid. A highly hydrophobic protein, likely integrated into the host membrane before the virions assemble, forms 7 strands that slide past each other in both the tails and the spindle body. We observe the discrete steps that occur as the tail tubes expand, and these are due to highly conserved quasiequivalent interactions with neighboring subunits maintained despite significant diameter changes. Our results show how helical assemblies can vary their diameters, becoming nearly spherical to package a larger genome and suggest how all spindle-shaped viruses have evolved from archaeal rod-like viruses.

BACKGROUND: Hypomanic symptoms between bipolar-I disorder (BD-I) and bipolar-II disorder (BD-II) are often indistinguishable in clinical practice. This study compared the network structure of hypomanic symptoms between patients with BD-I and BD-II. METHODS: The 32-item Hypomania Checklist (HCL-32) was used to assess hypomanic symptoms. Network model was generated in BD-I and BD-II patients. Centrality index of strength was used to quantify the importance of each symptom in the network. The Network Comparison Test (NCT) was used to assess the differences in hypomanic symptoms between BD-I and BD-II patients. RESULTS: Altogether, 423 patients with BD (BD-I: 191 and BD-II: 232) were included. The most central symptom was HCL17 "I am more flirtatious and/or am more sexually active" (strength BD-I = 5.21) and HCL12 "I have more ideas, I am more creative" (strength BD-II = 6.84) in BD-I and BD-II samples, respectively. The results of NCT showed that four nodes (HCL12 "I have more ideas, I am more creative," HCL17 "I am more flirtatious and/or am more sexually active," HCL23 "My thoughts jump from topic to topic," and HCL31 "I drink more alcohol") were significantly different between the BD-I and BD-II samples. Two edges (HCL3 "I am more self-confident"-HCL17 "I am more flirtatious and/or am more sexually active," and HCL10 "I am physically more active (sport, etc.)"-HCL24 "I do things more quickly and/or more easily") were significantly stronger in BD-I compared to BD-II patients. CONCLUSION: The network structure of hypomanic symptoms is different between BD-I and BD-II patients. Interventions targeting the respective central symptoms and edges should be developed for BD-I and BD-II separately.

BACKGROUND: Congenital heart disease (CHD) is the leading cause of morbidity and mortality from birth defects worldwide. We report an overview of trends in CHD mortality in 204 countries and territories over the past 30 years and associations with age, period, and birth cohort. METHODS: Cause-specific CHD mortality estimates were derived from the Global Burden of Disease 2019 study. We utilised an age-period-cohort model to estimate overall annual percentage changes in mortality (net drifts), annual percentage changes from 0 to 4 to 65-69 years (local drifts), period and cohort relative risks (period/cohort effects) between 1990 and 2019. This approach allows for the examination and differentiation of age, period, and cohort effects in the mortality trends, with the potential to identify disparities and treatment gaps in cardiac care. FINDINGS: CHD is the leading cause of deaths from non-communicable diseases (NCDs) in those under 20 years. Global CHD deaths in 2019 were 217,000 (95% uncertainty interval 177,000-262,000). There were 129 countries with at least 50 deaths. India, China, Pakistan, and Nigeria had the highest mortality, accounting for 39.7% of deaths globally. Between 1990 and 2019, the net drift of CHD mortality ranged from -2.41% per year (95% confidence interval [CI] -2.55, -2.67) in high Socio-demographic Index (SDI) countries to -0.62% per year (95% CI: -0.82, -0.42) in low-SDI countries. Globally, there was an emerging transition in the age distribution of deaths from paediatric to adult populations, except for an increasing trend of mortality in those aged 10-34 years in Mexico and Pakistan. During the past 30 years, favourable mortality reductions were generally found in most high-SDI countries like South Korea (net drift = -4.0% [95% CI -4.8 to -3.1] per year) and the United States (-2.3% [-2.5 to -2.0]), and also in many middle-SDI countries like Brazil (-2.7% [-3.1 to 2.4]) and South Africa (-2.5% [-3.2 to -1.8]). However, 52 of 129 countries

Efficiency and robustness are increasingly needed for applications on 3D point clouds, with the ubiquitous use of edge devices in scenarios like autonomous driving and robotics, which often demand real-time and reliable responses. The paper tackles the challenge by designing a general framework to construct 3D learning architectures with SO(3) equivariance and network binarization. However, a naive combination of equivariant networks and binarization either causes sub-optimal computational efficiency or geometric ambiguity. We propose to locate both scalar and vector features in our networks to avoid both cases. Precisely, the presence of scalar features makes the major part of the network binarizable, while vector features serve to retain rich structural information and ensure SO(3) equivariance. The proposed approach can be applied to general backbones like PointNet and DGCNN. Meanwhile, experiments on ModelNet40, ShapeNet, and the real-world dataset ScanObjectNN, demonstrated that the method achieves a great trade-off between efficiency, rotation robustness, and accuracy. The codes are available at https://github.com/zhuoinoulu/svnet.

Binary neural networks (BNNs) constrain weights and activations to +1 or -1 with limited storage and computational cost, which is hardware-friendly for portable devices. Recently, BNNs have achieved remarkable progress and been adopted into various fields. However, the performance of BNNs is sensitive to activation distribution. The existing BNNs utilized the Sign function with predefined or learned static thresholds to binarize activations. This process limits representation capacity of BNNs since different samples may adapt to unequal thresholds. To address this problem, we propose a dynamic BNN (DyBNN) incorporating dynamic learnable channel-wise thresholds of Sign function and shift parameters of PReLU. The method aggregates the global information into the hyper function and effectively increases the feature expression ability. The experimental results prove that our method is an effective and straightforward way to reduce information loss and enhance performance of BNNs. The DyBNN based on two backbones of ReActNet (MobileNetV1 and ResNet18) achieve 71.2% and 67.4% top1-accuracy on ImageNet dataset, outperforming baselines by a large margin (i.e., 1.8% and 1.5% respectively).

BACKGROUND: Schizophrenia (SCZ) is a severe psychiatric disorder that involves inflammatory processes. The aim of this study was to explore the field of inflammation-related research in SCZ from a bibliometric perspective. METHODS: Regular and review articles on SCZ- and inflammation-related research were obtained from the Web of Science Core Collection (WOSCC) database from its inception to February 19, 2022. R package "bibliometrix" was used to summarize the main findings, count the occurrences of the top keywords, visualize the collaboration network between countries, and generate a three-field plot. VOSviewer software was applied to conduct both co-authorship and co-occurrence analyses. CiteSpace was used to identify the top references and keywords with the strongest citation burst. RESULTS: A total of 3,596 publications on SCZ and inflammation were included. Publications were mainly from the USA, China, and Germany. The highest number of publications was found in a list of relevant journals. Apart from "schizophrenia" and "inflammatory", the terms "bipolar disorder," "brain," and "meta-analysis" were also the most frequently used keywords. CONCLUSIONS: This bibliometric study mapped out a fundamental knowledge structure consisting of countries, institutions, authors, journals, and articles in the research field of SCZ and inflammation over the past 30 years. The results provide a comprehensive perspective about the wider landscape of this research area.

OBJECTIVES: The growing rate of retraction of scientific publications has attracted much attention within the academic community, but there is little knowledge about the nature of such retractions in schizophrenia-related research. This study aimed to analyze the characteristics of retractions of schizophrenia-related publications. MATERIALS AND METHODS: The Web of Science was searched for eligible studies. A bibliometric analysis was conducted to describe the characteristics of the retractions using R software and Excel 2019. Content analysis was conducted to examine the essential components of retraction notices. RESULTS: A total of 36 retracted publications with 415 citations were identified from 1997 to 2021, of which, 83.3% occurred in the last decade. The overall retraction rate was 0.19%, with most of them (29; 80.56%) from the United Kingdom. The retractions were published in 33 journals, and the 2020 IFs ranged between 0.17 and 49.96 (Median = 3.93). The retractions involved 21 research areas, particularly in Psychiatry (19; 52.78%), Neurosciences and Neurology (10; 27.78%), and Psychology (7; 19.44%). Data issues (17; 42.22%), administrative errors of the publishers (5; 13.89%), and study design (4; 11.11%) were the top three reasons for retractions. CONCLUSION: This study provides an insight into retractions of schizophrenia-related publications. Institutional governance should be further strengthened to improve the scrutiny of publications, prevent continuing citations, and erroneous propagation after retraction.

BACKGROUND: A high proportion of clinicians experienced common anxiety, insomnia and depression during the COVID-19 pandemic. This study examined the item-level association of comorbid anxiety and insomnia symptoms among clinicians who suffered from depressive symptoms during the late stage of the COVID-19 pandemic using network analysis (NA). METHODS: Clinicians with depressive symptoms (with a Patients Health Questionnaire (PHQ-9) total score of 5 and above) were included in this study. Anxiety and insomnia symptoms were measured using the Generalized Anxiety Disorder Scale - 7-item (GAD-7) and Insomnia Severity Index (ISI), respectively. Network analysis was conducted to investigate the network structure, central symptoms, bridge symptoms, and network stability of these disturbances. Expected influence (EI) was used to measure the centrality of index. RESULTS: Altogether, 1729 clinicians were included in this study. The mean age was 37.1 [standard deviation (SD)=8.04 years], while the mean PHQ-9 total score was 8.42 (SD=3.33), mean GAD-7 total score was 6.45 (SD=3.13) and mean ISI total score was 8.23 (SD=5.26). Of these clinicians, the prevalence of comorbid anxiety symptoms (GAD-7≥5) was 76.8% (95% CI 74.82-78.80%), while the prevalence of comorbid insomnia symptoms (ISI≥8) was 43.8% (95% CI: 41.50-46.18%). NA revealed that nodes ISI7 ("Interference with daytime functioning") (EI=1.18), ISI4 ("Sleep dissatisfaction") (EI=1.08) and ISI5 ("Noticeability of sleep problem by others") (EI=1.07) were the most central (influential) symptoms in the network model of comorbid anxiety and insomnia symptoms in clinicians. Bridge symptoms included nodes PHQ3 ("Sleep") (bridge EI=0.55) and PHQ4 ("Fatigue") (bridge EI=0.49). Gender did not significantly influence the network structure, but "having the experience of caring for COVID-19 patients" significantly influenced the network structure. CONCLUSION: Central symptoms and key bridge symptoms identified in this NA should be

Goal Recognition (GR) is a research problem that studies ways to infer the goal of an intelligent agent based on its observed behavior and knowledge of the environment. A common assumption of GR is that the underlying environment is stationary. However, in many real-world scenarios, it is necessary to recognize agents’ goals over extended periods. Therefore, it is reasonable to assume that the environment will change throughout a series of goal recognition tasks. This paper introduces the problem of continuous GR over a changing environment. The solution to this problem is a GR system capable of recognizing agents’ goals over an extended period where the environment in which the agents operate changes. To support the evaluation of candidate solutions to this new GR problem, in this paper, we present the Goal Recognition Amidst Changing Environments (GRACE) tool for generating instances of the new problem. Specifically, the tool can be configured to generate GR problems that account for different environmental changes and drifts. GRACE can generate a series of modified environments over discrete time steps and the data induced by agents operating in the environment while completing different goals.

Background: The 2019 novel coronavirus disease (COVID-19) outbreak had a detrimental impact on the mental health of older adults. This study evaluated the central symptoms and their associations in the network of depressive symptoms and compared the network structure differences between male and female older adults in Hong Kong. Methods: Altogether, 3,946 older adults participated in this study. We evaluated the centrality indicators for network robustness using stability and accuracy tests, and examined the potential differences between the structure and connectivity of depression networks in male and female older adults. Results: The overall prevalence of depressive symptoms was 43.7% (95% CI=40.6-46.7%) in males, and 54.8% (95% CI=53.1-56.5%) in females (P<0.05). Sad Mood, Guilt, Motor problems and Lack of Energy were influential symptoms in the network model. Gender differences were found in the network global strength, especially in the following edges: Sad Mood--Guilt, Concentration--Guilt, Anhedonia--Motor, Lack of Energy--Suicide, Appetite--Suicide and Concentration--Suicide. Conclusions: Central symptoms in the depressive symptom network among male and female older adults may be prioritized in the treatment and prevention of depression during the pandemic.

Background: The dementia epidemic is progressing fast. As the world’s older population keeps skyrocketing, the traditional incompetent, time-consuming, and laborious interventions are becoming increasingly insufficient to address dementia patients’ health care needs. This is particularly true amid COVID-19. Instead, efficient, cost-effective, and technology-based strategies, such as sixth-generation communication solutions (6G) and artificial intelligence (AI)-empowered health solutions, might be the key to successfully managing the dementia epidemic until a cure becomes available. However, while 6G and AI technologies hold great promise, no research has examined how 6G and AI applications can effectively and efficiently address dementia patients’ health care needs and improve their quality of life. Objective: This study aims to investigate ways in which 6G and AI technologies could elevate dementia care to address this study gap. Methods: A literature review was conducted in databases such as PubMed, Scopus, and PsycINFO. The search focused on three themes: dementia, 6G, and AI technologies. The initial search was conducted on April 25, 2021, complemented by relevant articles identified via a follow-up search on November 11, 2021, and Google Scholar alerts. Results: The findings of the study were analyzed in terms of the interplay between people with dementia’s unique health challenges and the promising capabilities of health technologies, with in-depth and comprehensive analyses of advanced technology-based solutions that could address key dementia care needs, ranging from impairments in memory (eg, Egocentric Live 4D Perception), speech (eg, Project Relate), motor (eg, Avatar Robot Café), cognitive (eg, Affectiva), to social interactions (eg, social robots). Conclusions: To live is to grow old. Yet dementia is neither a proper way to live nor a natural aging process. By identifying advanced health solutions powered by 6G and AI opportunities, our study s

BACKGROUND: The Brain-Gut Axis, a bidirectional signaling pathway that connects the intestinal and central nervous systems, plays an important role in the development of psychiatric disorders. However, the overall research trends in this field are unclear. This study explored the patterns of research on the brain-gut axis and psychiatric disorders from a bibliometric perspective. METHODS: Relevant data were retrieved from the Web of Science Core Collection, with search terms on psychiatric disorders and the brain-gut axis. R (version 4.2.0), VOSviewer (version 1.6.17), CiteSpace software, and the online bibliometric platform were used in the data analysis. RESULTS: A total of 2,298 articles published between 1993 and 2022 were identified, showing an increasing trend over time. China (1,859; 20.70%) was the country that contributed the most publications. The journal Nutrients (95; 4.13%) published the most publications. Cryan JF (153; H-index=73) and University College Cork (559; 22.54%) were the most influential author and the most productive institution, respectively. The high-frequency keywords were clustered into six themes, including neurodegenerative diseases, stress-related diseases, immune, brain behavior, depression, and probiotic-related topics; of which, depression (880; 2019), anxiety (207; 2018) and autism (191; 2019) were the most studied psychiatric disorders in the past 5 years. "Depressive symptom" (2019-2020) and "probiotic treatment" (2019-2020) were the main areas addressed in recent years. CONCLUSION: Research on the brain-gut axis and psychiatric disorders has attracted increasing attention in the past decade, with most publications originating from high-income level countries. This study provides a useful perspective on understanding the research trends, key hot topics, and research gaps in this expanding field.

Advanced doped-silicon-layer-based passivating contacts have boosted the power conversion efficiency (PCE) of single-junction crystalline silicon (c-Si) solar cells to over 26%. However, the inevitable parasitic light absorption of the doped silicon layers impedes further PCE improvement. To this end, alternative passivating contacts based on wide-bandgap metal compounds (so-called dopant-free passivating contacts (DFPCs)) have attracted great attention, thanks to their potential merits in terms of parasitic absorption loss, ease-of-deposition, and cost. Intensive research activity has surrounded this topic with significant progress made in recent years. Various electron-selective and hole-selective contacts based on metal compounds have been successfully developed, and a champion PCE of 23.5% has been achieved for a c-Si solar cell with a MoOx -based hole-selective contact. In this work, the fundamentals and development status of DFPCs are reviewed and the challenges and potential solutions for enhancing the carrier selectivity of DFPCs are discussed. Based on comprehensive and in-depth analysis and simulations, the improvement strategies and future prospects for DFPCs design and device implementation are pointed out. By tuning the carrier concentration of the metal compound and the work function of the capping transparent electrode, high PCEs over 26% can be achieved for c-Si solar cells with DFPCs.

Space-air-ocean integrated networks (SAOINs), composed of low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and unmanned surface vehicles (USVs), have been advocated to provide seamless, high-rate, and reliable wireless transmission services for USVs. However, due to the restrictions of limited resources (e.g., spectrum bandwidth, transmission power, etc.), diverse demands of USVs, and selfishness of both UAVs and LEOs, there comes a significant challenge to provision high-quality wireless data rate for USVs to achieve their satisfied quality of experience (QoE). To this end, in this paper, we propose a hierarchical on-demand wireless data rate provisioning scheme to provide ubiquitous transmission services for USVs. Specifically, we first devise a hierarchical wireless data rate provisioning framework. The LEO satellite with an extensive wireless coverage is utilized to provide LEO satellite-to-UAV (L2U) data rate for UAVs with a certain L2U data rate price. Each UAV is employed to provide UAV-to-USV (U2U) data rate for covered multiple USVs with a certain U2U data rate price. We then propose a modified three-stage Stackelberg game to model the wireless data rate assignments among LEO satellites, UAVs, and USVs, where the time-varying data rate demands of USVs are considered to formulate the utility maximization problem. Afterwards, the backward induction approach is leveraged to attain the Stackelberg equilibrium as the solution of the formulated problem, where the closed-form expressions on the optimal strategies of both USVs and UAVs under different data rate budgets are obtained by the nonlinear programming method. Besides, an accelerated conjugate gradient descent (ACGD) based iteration algorithm is also designed to obtain the optimal strategies of the LEO satellites on the L2U data rate prices. At last, extensive simulations are carried out to demonstrate that the proposed scheme can significantly increase the utilities of USVs, as compared t