The outbreak of COVID-19 poses unprecedent challenges to global health 1 . The new coronavirus, SARS-CoV-2, shares high sequence identity to SARS-CoV and a bat coronavirus RaTG13 2 . While bats may be the reservoir host for various coronaviruses 3,4 , whether SARS-CoV-2 has other hosts remains ambiguous. In this study, one coronavirus isolated from a Malayan pangolin showed 100%, 98.6%, 97.8% and 90.7% amino acid identity with SARS-CoV-2 in the E, M, N and S genes, respectively. In particular, the receptor-binding domain within the S protein of the Pangolin-CoV is virtually identical to that of SARS-CoV-2, with one noncritical amino acid difference. Results of comparative genomic analysis suggest that SARS-CoV-2 might have originated from the recombination of a Pangolin-CoV-like virus with a Bat-CoV-RaTG13-like virus. The Pangolin-CoV was detected in 17 of 25 Malayan pangolins analyzed. Infected pangolins showed clinical signs and histological changes, and circulating antibodies against Pangolin-CoV reacted with the S protein of SARS-CoV-2. The isolation of a coronavirus that is highly related to SARS-CoV-2 in pangolins suggests that they have the potential to act as the intermediate host of SARS-CoV-2. The newly identified coronavirus in the most-trafficked mammal could represent a future threat to public health if wildlife trade is not effectively controlled.As coronaviruses (CoVs) are common in mammals and birds 5 , we used the whole genome sequence of SARS-CoV-2 (WHCV; GenBank accession No. MN908947) in a Blast search of SARS-relate CoV (SARSr-CoV) sequences in available mammalian and avian viromic, metagenomic, and transcriptomic data. This led to the identification of 34 highly related contigs in a set of pangolin viral metagenomes (Extended

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Microstructure characterization and reconstruction have become indispensable parts of computational materials science. The main contribution of this paper is to introduce a general methodology for practical and efficient characterization and reconstruction of stochastic microstructures based on supervised learning. The methodology is general in that it can be applied to a broad range of microstructures (clustered, porous, and anisotropic). By treating the digitized microstructure image as a set of training data, we generically learn the stochastic nature of the microstructure via fitting a supervised learning model to it (we focus on classification trees). The fitted supervised learning model provides an implicit characterization of the joint distribution of the collection of pixel phases in the image. Based on this characterization, we propose two different approaches to efficiently reconstruct any number of statistically equivalent microstructure samples. We test the approach on five examples and show that the spatial dependencies within the microstructures are well preserved, as evaluated via correlation and lineal-path functions. The main advantages of our approach stem from having a compact empirically-learned model that characterizes the stochastic nature of the microstructure, which not only makes reconstruction more computationally efficient than existing methods, but also provides insight into morphological complexity.

Objective Coronavirus disease 2019 (COVID-19) has dominated the attention of health care systems globally since January 2020. Various health disciplines including physical therapists are still exploring the best way to manage this new disease. The role and involvement of physical therapists in the management of COVID-19 are not yet well defined and are limited in many hospitals. This article reports a physical therapy service specially commissioned by the Health Commission of Sichuan Province to manage COVID-19 during patients’ stay in the intensive care unit (ICU) at the Public Health Clinical Center of Chengdu in China. Methods Patients diagnosed with COVID-19 were classified into 4 categories under a directive from the National Health Commission of the People’s Republic of China. Patients in the “severe” and “critical” categories were admitted to the ICU irrespective whether mechanical ventilation was required. Between January 31, 2020, and March 8, 2020, a cohort of 16 patients was admitted to the ICU at the Public Health Clinical Center of Chengdu. The median (minimum to maximum) hospital and ICU stays for these patients were 27 (11–46) and 15 (6–38) days, respectively. Medical management included antiviral, immunoregulation and supportive treatment of associated comorbidities. Physical therapist interventions included body positioning, airway clearance techniques, oscillatory positive end-expiratory pressure, inspiratory muscle training, and mobility exercises. All patients had at least 1 comorbidity. Three of the 16 patients required mechanical ventilation and were excluded for outcome measures that required understanding of verbal instructions. In the remaining 13 patients, respiratory outcomes—including the Borg Dyspnea Scale, peak expiratory flow rate, Pao2/Fio2 ratio, maximal inspiratory pressure, strength outcomes, Medical Research Council Sum Score, and functional outcomes (including the Physical Function in Intensive Care Test score, De Morton Mobility Index, and Modified Barthel Index)—were measured on the first day the patient received the physical therapist intervention and at discharge. Results At discharge from the ICU, while most outcome measures were near normal for the majority of the patients, 61% and 31% of these patients had peak expiratory flow rate and maximal inspiratory pressure below 80% of the predicted value and 46% had De Morton Mobility Index values below the normative value. Conclusion The respiratory and physical functions of some patients remained poor at ICU discharge, suggesting that long-term rehabilitation may be required for these patients. Impact Our experience in the management of patients with COVID-19 has revealed that physical therapist intervention is safe and appears to be associated with an improvement in respiratory and physical function in patients with COVID-19 in the ICU.

WeiXie2013@u.northwestern.edu} W e consider the problem of deriving confidence intervals for the mean response of a system that is represented by a stochastic simulation whose parametric input models have been estimated from "real-world" data. As opposed to standard simulation confidence intervals, we provide confidence intervals that account for uncertainty about the input model parameters; our method is appropriate when enough simulation effort can be expended to make simulation-estimation error relatively small. To achieve this we introduce metamodel-assisted bootstrapping that propagates input variability through to the simulation response via an equation-based model rather than by simulating. We develop a metamodel strategy and associated experiment design method that avoid the need for low-order approximation to the response and that minimizes the impact of intrinsic (simulation) error on confidence level accuracy. Asymptotic analysis and empirical tests over a wide range of simulation effort show that confidence intervals obtained via metamodel-assisted bootstrapping achieve the desired coverage.