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BACKGROUND The World Health Organization has declared the novel coronavirus disease (COVID-19) to be a public health emergency; at present, India is facing a major threat of community spread. We developed a mathematical model for investigating and predicting the effects of lockdown on future COVID-19 cases with a specific focus on India. OBJECTIVE The objective of this work was to develop and validate a mathematical model and to assess the impact of various lockdown scenarios on COVID-19 transmission in India. METHODS A model consisting of a framework of ordinary differential equations was developed by incorporating the actual reported cases in 14 countries. After validation, the model was applied to predict COVID-19 transmission in India for different intervention scenarios in terms of lockdown for 4, 14, 21, 42, and 60 days. We also assessed the situations of enhanced exposure due to aggregation of individuals in transit stations and shopping malls before the lockdown. RESULTS The developed model is efficiemalanand Krishnamurthy. Originally published in JMIR Public Health and Surveillance (http//publichealth.jmir.org), 07.05.2020.BACKGROUND The mental health consequences of the coronavirus disease (COVID-19) pandemic, community-wide interventions, and social media use during a pandemic are unclear. The first and most draconian interventions have been implemented in Wuhan, China, and these countermeasures have been increasingly deployed by countries around the world. OBJECTIVE The aim of this study was to examine risk factors, including the use of social media, for probable anxiety and depression in the community and among health professionals in the epicenter, Wuhan, China. METHODS We conducted an online survey via WeChat, the most widely used social media platform in China, which was administered to 1577 community-based adults and 214 health professionals in Wuhan. Probable anxiety and probable depression were assessed by the validated Generalized Anxiety Disorder-2 (cutoff ≥3) and Patient Health Questionnaire-2 (cutoff ≥3), respectively. A multivariable logistic regression analysis was used to examine factors associated with probablearching for COVID-19 news on social media given the infodemic and emotional contagion through online social networks. Online platforms may be used to monitor the toll of the pandemic on mental health. ©Michael Y Ni, Lin Yang, Candi M C Leung, Na Li, Xiaoxin I Yao, Yishan Wang, Gabriel M Leung, Benjamin J Cowling, Qiuyan Liao. M6620 Originally published in JMIR Mental Health (http//mental.jmir.org), 12.05.2020.BACKGROUND The outbreak of coronavirus disease 2019 (COVID-19) has caused significant stress and mental health problems among the general public. However, persons at greatest risk for poor mental health outcomes, such as persons with serious mental illness, have been largely overlooked. OBJECTIVE To examine the mental health impact of COVID-19 and social distancing behaviors among persons with serious mental illness and the behaviors taken to prevent COVID-19 infection. METHODS Participants will include individuals with serious mental illness (e.g., schizophrenia, bipolar disorder) and non-psychiatric control participants who are currently or previously participated in several ongoing parent observational studies. Data will be collected from April, 2020 through August, 2020. Participants will complete phone interviews at two time points to assess their current emotional functioning and measures they have taken to prevent COVID-19 infection. Baseline (pre-COVID-19) mental health, sampled by ecological momentary assessment over an extended period, will be compared with current mental health, sampled by ecological momentary assessment over an extended period, and demographic, cognitive and psychosocial factors at baseline will be used to examine risk and resilience to current mental health and coping. RESULTS Study results will be published in peer-reviewed scientific journals. CONCLUSIONS Findings have broad implications for understanding the psychological consequences of COVID-19 among vulnerable persons with serious mental illness, and will provide the opportunity to identify targets to reduce negative outcomes in the future. We also hope our efforts will provide a roadmap and resources for other researchers who would like to implement a similar approach. CLINICALTRIALMultilabel classification (MLC) has received much attention recently. The existing MLC algorithms usually learn multiple classifiers simultaneously by exploiting the correlations among different labels. However, it is difficult and/or expensive to collect a large amount of multilabeled data in practice. The lack of labeled data significantly deteriorates the performance of classification. Moreover, the existing algorithms belong to centralized learning, that is, all the data with their labels must be transmitted to a fusion center for processing. But in many real applications, data may be dispersedly collected/stored in distributed nodes of networks. Due to the concerns of communication cost, processing ability, and data privacy, it is impossible to transmit and/or process the data centrally at one node. Considering this, the problem of distributed MLC over networks is studied, and two distributed information-theoretic semisupervised multilabel learning (dITS²ML²) algorithms are proposed, which are, respectively, used for solving linear and nonlinear MLC problems. In the proposed algorithms, a cost-sensitive objective function is designed, in which a new label correlation term defined on some anchor data is suggested. link2 Besides, to decentralize the global objective function, a distributed matrix completion algorithm is developed to distributively complete the label matrix of the anchor data. Then, by exchanging and combining a few intermediate quantities instead of the original data for both linear and nonlinear cases, the model parameters can be adaptively estimated. The convergence of the proposed dITS²ML² algorithms is analyzed, and their effectiveness in MLC is verified by simulations on various real datasets.Recently, increasing works have been proposed to drive evolutionary algorithms using machine-learning models. link3 Usually, the performance of such model-based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e., the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales due to the curse of dimensionality. To address this issue, we propose a multiobjective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on ten benchmark problems with up to 200 decision variables. The experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.This article studies the coordinated control problem of networked multiagent systems via distributed cloud computing. A distributed cloud predictive control scheme is proposed to achieve desired coordination control performance and compensate actively for communication delays between the cloud computing nodes and between the agents. This scheme includes the design of a multistep state predictor and optimization of control coordination. The multistep state predictor provides a novel way of predicting future immeasurable states of agents in a large horizontal length. The optimization of control coordination minimizes the distributed cost functions which are presented to measure the coordination between the agents so that the optimal design of the coordination controllers is simple with little computational increase for large-scale-networked multiagent systems. Further analysis derives the conditions of simultaneous stability and consensus of the closed-loop-networked multiagent systems using the distributed cloud predictive control scheme. The effectiveness of the proposed scheme is illustrated by an example.Classical drug design methodologies are hugely costly and time-consuming, with approximately 85% of the new proposed molecules failing in the first three phases of the FDA drug approval process. Thus, strategies to find alternative indications for already approved drugs that leverage computational methods are of crucial relevance. We previously demonstrated the efficacy of the Non-negative Matrix Tri-Factorization, a method that allows exploiting both data integration and machine learning, to infer novel indications for approved drugs. In this work, we present an innovative enhancement of the NMTF method that consists of a shortest-path evaluation of drug-protein pairs using the protein-to-protein interaction network. This approach allows inferring novel protein targets that were never considered as drug targets before, increasing the information fed to the NMTF method. Indeed, this novel advance enables the investigation of drug-centric predictions, simultaneously identifying therapeutic classes, protein targets and diseases associated with a particular drug. To test our methodology, we applied the NMTF and shortest-path enhancement methods to an outdated collection of data and compared the predictions against the most updated version, obtaining very good performance, with an Average Precision Score of 0.82. The data enhancement strategy allowed increasing the number of putative protein targets from 3,691 to 15,295, while the predictive performance of the method is slightly increased. Finally, we also validated our top-scored predictions according to the literature, finding relevant confirmation of predicted interactions between drugs and protein targets, as well as of predicted annotations between drugs and both therapeutic classes and diseases.A daily dietary assessment method named 24-hour dietary recall has commonly been used in nutritional epidemiology studies to capture detailed information of the food eaten by the participants to help understand their dietary behaviour. However, in this self-reporting technique, the food types and the portion size reported highly depends on users' subjective judgement which may lead to a biased and inaccurate dietary analysis result. As a result, a variety of visual-based dietary assessment approaches have been proposed recently. While these methods show promises in tackling issues in nutritional epidemiology studies, several challenges and forthcoming opportunities, as detailed in this study, still exist. This study provides an overview of computing algorithms, mathematical models and methodologies used in the field of image-based dietary assessment. It also provides a comprehensive comparison of the state of the art approaches in food recognition and volume/weight estimation in terms of their processing speed, model accuracy, efficiency and constraints.