Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic (preprint)

While many organizations have shifted to working remotely during the COVID-19 pandemic, how the remote workforce and the remote teams are influenced by and would respond to this and future shocks remain largely unknown. Software developers have relied on remote collaborations long before the pandemic, working in virtual teams (GitHub repositories). The dynamics of these repositories through the pandemic provide a unique opportunity to understand how remote teams react under shock. This work presents a systematic analysis. We measure the overall effect of the early pandemic on public GitHub repositories by comparing their sizes and productivity with the counterfactual outcomes forecasted as if there were no pandemic. We find that the productivity level and the number of active members of these teams vary significantly during different periods of the pandemic. We then conduct a finer-grained investigation and study the heterogeneous effects of the shock on individual teams. We find that the resilience of a team is highly correlated to certain properties of the team before the pandemic. Through a bootstrapped regression analysis, we reveal which types of teams are robust or fragile to the shock.

Scrutiny of human lung infection by SARS-CoV-2 and associated human immune responses in humanized mice (preprint)

There is an urgent need for animal models of COVID-19 to study immunopathogenesis and test therapeutic intervenes. In this study we showed that NSG mice engrafted with human lung (HL) tissue (NSG-L) could be infected efficiently by SARS-CoV-2, and that live virus capable of infecting Vero cells was found in the HL grafts and multiple organs from infected NSG-L mice. RNA-seq examination identified a series of differentially expressed genes, which are enriched in viral defense responses, chemotaxis, interferon stimulation, and pulmonary fibrosis between HL grafts from infected and control NSG-L mice. Furthermore, when infecting humanized mice with human immune system (HIS) and autologous HL grafts (HISL mice), the mice had bodyweight loss and hemorrhage and immune cell infiltration in HL grafts, which were not observed in immunodeficient NSG-L mice, indicating the development of anti-viral immune responses in these mice. In support of this possibility, the infected HISL mice showed bodyweight recovery and lack of detectable live virus at the later time. These results demonstrate that NSG-L and HISL mice are susceptible to SARS-CoV-2 infection, offering a useful in vivo model for studying SARS-CoV-2 infection and the associated immune response and immunopathology, and testing anti-SARS-CoV-2 therapies.

Emerging SARS-CoV-2 variants expand species tropism to rodents (preprint)

Mice are not susceptible to wildtype SARS-CoV-2 infection. Emerging SARS-CoV-2 variants including B.1.1.7, B.1.351, P.1, and P.3 contain mutations in spike, which have been suggested to associate with an increased recognition of mouse ACE2, raising the postulation that they may have evolved to expand species tropism to rodents. Here, we investigated the capacity of B.1.1.7 and other emerging SARS-CoV-2 variants in infecting mouse (Mus musculus) and rats (Rattus norvegicus) under in vitro and in vivo settings. Our results show that B.1.1.7 and P.3, but not B.1 or wildtype SARS-CoV-2, can utilize mouse and rat ACE2 for virus entry in vitro. High infectious virus titers, abundant viral antigen expression, and pathological changes are detected in the nasal turbinate and lung of B.1.1.7-inocluated mice and rats. Together, these results reveal that the current predominant circulating SARS-CoV-2 variant, B.1.1.7, has gained the capability to expand species tropism to rodents.

A molecularly engineered, broad-spectrum anti-coronavirus lectin inhibits SARS-CoV-2 and MERS-CoV infection in vivo (preprint)

Coronaviruses have repeatedly crossed species barriers to cause epidemics1. “Pan-coronavirus” antivirals targeting conserved viral components involved in coronavirus replication, such as the extensively glycosylated spike protein, can be designed. Here we show that the rationally engineered H84T-banana lectin (H84T-BanLec), which specifically recognizes high-mannose found on viral proteins but seldom on healthy human cells2, potently inhibits the highly virulent MERS-CoV, pandemic SARS-CoV-2 and its variants, and other human-pathogenic coronaviruses at nanomolar concentrations. MERS-CoV-infected human DPP4-transgenic mice treated by H84T-BanLec have significantly higher survival, lower viral burden, and reduced pulmonary damage. Similarly, prophylactic or therapeutic H84T-BanLec is effective against SARS-CoV-2 in hamsters. Importantly, intranasally and intraperitoneally administered H84T-BanLec are comparably effective. Time-of-drug-addition assay shows that H84T-BanLec targets virus entry. Real-time structural analysis with high-speed atomic force microscopy depicts multi-molecular associations of H84T-BanLec dimers with the SARS-CoV-2 spike trimer. Single-molecule force spectroscopy demonstrates binding of H84T-BanLec to multiple SARS-CoV-2 spike mannose sites with high affinity, and that H84T-BanLec competes with SARS-CoV-2 spike for binding to cellular ACE2. Modelling experiments identify distinct high-mannose glycans in spike recognized by H84T-BanLec. The multiple H84T-BanLec binding sites on spike likely account for the activity against SARS-CoV-2 variants and the lack of resistant mutants. The broad-spectrum H84T-BanLec should be clinically evaluated in respiratory viral infections including COVID-19.

Coronaviruses exploit a host cysteine-aspartic protease for efficient replication (preprint)

Highly pathogenic coronaviruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1,2, Middle East respiratory syndrome coronavirus (MERS-CoV)3,4, and SARS-CoV-15 vary in their transmissibility and pathogenicity. However, infection by all three viruses result in substantial apoptosis in cell culture6-8 and in patient samples9-11, suggesting a potential link between apoptosis and the pathogenesis of coronaviruses. To date, the underlying mechanism of how apoptosis modulates coronavirus pathogenesis is unknown. Here we show that a cysteine-aspartic protease of the apoptosis cascade, caspase-6, serves as an essential host factor for efficient coronavirus replication. We demonstrate that caspase-6 cleaves coronavirus nucleocapsid (N) proteins, generating N fragments that serve as interferon (IFN) antagonists, thus facilitating virus replication. Inhibition of caspase-6 substantially attenuates the lung pathology and body weight loss of SARS-CoV-2-infected golden Syrian hamsters and improves the survival of mouse-adapted MERS-CoV (MERS-CoVMA)-infected human DPP4 knock-in (hDPP4 KI) mice. Overall, our study reveals how coronaviruses exploit a component of the host apoptosis cascade to facilitate their replication. These results further suggest caspase-6 as a potential target of intervention for the treatment of highly pathogenic coronavirus infections including COVID-19 and MERS.

Host and viral determinants for efficient SARS-CoV-2 infection of the human lung (preprint)

SARS-CoV-2 has affected over 9 million patients with more than 460,000 deaths in about 6 months. Understanding the factors that contribute to efficient SARS-CoV-2 infection of human cells, which are not previously reported, may provide insights on SARS-CoV-2 transmissibility and pathogenesis, and reveal targets of intervention. Here, we reported key host and viral determinants that were essential for efficient SARS-CoV-2 infection in the human lung. First, we identified heparan sulfate as an important attachment factor for SARS-CoV-2 infection. Second, we demonstrated that while cell surface sialic acids significantly restricted SARS-CoV infection, SARS-CoV-2 could largely overcome sialic acid-mediated restriction in both human lung epithelial cells and ex vivo human lung tissue explants. Third, we demonstrated that the inserted furin-like cleavage site in SARS-CoV-2 spike was required for efficient virus replication in human lung but not intestine tissues. Overall, these findings contributed to our understanding on efficient SARS-CoV-2 infection of human lungs.

Does Non-COVID19 Lung Lesion Help? Investigating Transferability in COVID-19 CT Image Segmentation (preprint)

Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world. Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images. The major challenge lies in the inadequate public COVID-19 datasets. Recently, transfer learning has become a widely used technique that leverages the knowledge gained while solving one problem and applying it to a different but related problem. However, it remains unclear whether various non-COVID19 lung lesions could contribute to segmenting COVID-19 infection areas and how to better conduct this transfer procedure. This paper provides a way to understand the transferability of non-COVID19 lung lesions. Based on a publicly available COVID-19 CT dataset and three public non-COVID19 datasets, we evaluate four transfer learning methods using 3D U-Net as a standard encoder-decoder method. The results reveal the benefits of transferring knowledge from non-COVID19 lung lesions, and learning from multiple lung lesion datasets can extract more general features, leading to accurate and robust pre-trained models. We further show the capability of the encoder to learn feature representations of lung lesions, which improves segmentation accuracy and facilitates training convergence. In addition, our proposed Hybrid-encoder learning method incorporates transferred lung lesion features from non-COVID19 datasets effectively and achieves significant improvement. These findings promote new insights into transfer learning for COVID-19 CT image segmentation, which can also be further generalized to other medical tasks.

Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation (preprint)

Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods: To promote the development of data-efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID-19 cases, which contain current active research areas, e.g., few-shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. Results: Based on the state-of-the-art network, we provide more than 40 pre-trained baseline models, which not only serve as out-of-the-box segmentation tools but also save computational time for researchers who are interested in COVID-19 lung and infection segmentation. We achieve average Dice Similarity Coefficient (DSC) scores of 97.3\%, 97.7\%, and 67.3\% and average Normalized Surface Dice (NSD) scores of 90.6\%, 91.4\%, and 70.0\% for left lung, right lung, and infection, respectively. Conclusions: To the best of our knowledge, this work presents the first data-efficient learning benchmark for medical image segmentation and the largest number of pre-trained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID-19 CT segmentation with limited data.

Clinical features and management of severe COVID-19: A retrospective study in Wuxi, Jiangsu Province, China (preprint)

Objective: We aimed to investigate clinical features and management of 55 COVID-19 patients in Wuxi, especially severe COVID-19. Methods: Epidemiological, demographic, clinical, laboratory, imaging, treatment, and outcome data of patients were collected. Follow-up lasted until April 6, 2020. Results: All 55 patients included 47 (85.5%) non-severe patients and 8 (14.5%) severe patients. Common comorbidities were hypertension and diabetes. Common symptoms were fever, cough and sputum. Lymphopenia was a common laboratory finding, and ground-glass opacity was a common chest CT feature. All patients received antiviral therapy of -interferon inhalation and lopinavir-ritonavir tablets. Common complications included acute liver injury and respiratory failure. All patients were discharged. No death was occurred and no medical staff got infected. Patients with severe COVID-19 showed significantly older age, decreased lymphocytes, increased C reactive protein, and higher frequency of bilateral lung infiltration compared to non-severe patients. Significantly more treatments including antibiotic therapy and mechanical ventilation, longer hospitalization stay and higher cost were shown on severe patients. Conclusions: Our study suggested that patients with severe COVID-19 may be more likely to have an older age, present with lymphopenia and bilateral lung infiltration, receive multiple treatments and stay longer in hospital.