Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 and is spread personto-person through close contact. We aimed to investigate the effects of physical distance, face masks, and eye protection on virus transmission in health-care and non-health-care (eg, community) settings. MethodsWe did a systematic review and meta-analysis to investigate the optimum distance for avoiding person-toperson virus transmission and to assess the use of face masks and eye protection to prevent transmission of viruses. We obtained data for SARS-CoV-2 and the betacoronaviruses that cause severe acute respiratory syndrome, and Middle East respiratory syndrome from 21 standard WHO-specific and COVID-19-specific sources. We searched these data sources from database inception to May 3, 2020, with no restriction by language, for comparative studies and for contextual factors of acceptability, feasibility, resource use, and equity. We screened records, extracted data, and assessed risk of bias in duplicate. We did frequentist and Bayesian meta-analyses and random-effects metaregressions. We rated the certainty of evidence according to Cochrane methods and the GRADE approach. This study is registered with PROSPERO, CRD42020177047. FindingsOur search identified 172 observational studies across 16 countries and six continents, with no randomised controlled trials and 44 relevant comparative studies in health-care and non-health-care settings (n=25 697 patients). Transmission of viruses was lower with physical distancing of 1 m or more, compared with a distance of less than 1 m (n=10 736, pooled adjusted odds ratio [aOR] 0•18, 95% CI 0•09 to 0•38; risk difference [RD] -10•2%, 95% CI -11•5 to -7•5; moderate certainty); protection was increased as distance was lengthened (change in relative risk [RR] 2•02 per m; p interaction =0•041; moderate certainty). Face mask use could result in a large reduction in risk of infection (n=2647; aOR 0•15, 95% CI 0•07 to 0•34, RD -14•3%, -15•9 to -10•7; low certainty), with stronger associations with N95 or similar respirators compared with disposable surgical masks or similar (eg, reusable 12-16-layer cotton masks; p interaction =0•090; posterior probability >95%, low certainty). Eye protection also was associated with less infection (n=3713; aOR 0•22, 95% CI 0•12 to 0•39, RD -10•6%, 95% CI -12•5 to -7•7; low certainty). Unadjusted studies and subgroup and sensitivity analyses showed similar findings.Interpretation The findings of this systematic review and meta-analysis support physical distancing of 1 m or more and provide quantitative estimates for models and contact tracing to inform policy. Optimum use of face masks, respirators, and eye protection in public and health-care settings should be informed by these findings and contextual factors. Robust randomised trials are needed to better inform the evidence for these interventions, but this systematic appraisal of currently best available evidence might inform interim guidance.Funding World Health Organization.

The performance and the cost of electrocatalysts play the two most vital roles in the development and application of energy conversion technologies. Singleatom catalysts (SACs) are recently emerging as a new frontier in catalysis science. With maximum atom-utilization efficiency and unique properties, SACs exhibit great potential for enabling reasonable use of metal resources and achieving atomic economy. However, fabricating SACs and maintaining the metal centers as atomically dispersed sites under synthesis and catalysis conditions are challenging. Here, we highlight and summarize recent advances in wet-chemistry synthetic methods for SACs with special emphasis on how to achieve the stabilization of single metal atoms against migration and agglomeration. Moreover, we summarize and discuss the electrochemical applications of SACs with a focus on the oxygen reduction reaction (ORR), hydrogen evolution reaction (HER), and CO 2 reduction reaction (CO 2 RR). At last, the current issues and the prospects for the development of this field are discussed.

Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we propose to learn anomalies by exploiting both normal and anomalous videos. To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i.e. the training labels (anomalous or normal) are at videolevel instead of clip-level. In our approach, we consider normal and anomalous videos as bags and video segments as instances in multiple instance learning (MIL), and automatically learn a deep anomaly ranking model that predicts high anomaly scores for anomalous video segments. Furthermore, we introduce sparsity and temporal smoothness constraints in the ranking loss function to better localize anomaly during training.We also introduce a new large-scale first of its kind dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. as well as normal activities. This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities. Our experimental results show that our MIL method for anomaly detection achieves significant improvement on anomaly detection performance as compared to the state-of-the-art approaches. We provide the results of several recent deep learning baselines on anomalous activity recognition. The low recognition performance of these baselines reveals that our dataset is very challenging and opens more opportunities for future work. The dataset is available at: https

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