We have developed pLink, software for data analysis of cross-linked proteins coupled with mass-spectrometry analysis. pLink reliably estimates false discovery rate in cross-link identification and is compatible with multiple homo- or hetero-bifunctional cross-linkers. We validated the program with proteins of known structures, and we further tested it on protein complexes, crude immunoprecipitates and whole-cell lysates. We show that it is a robust tool for protein-structure and protein-protein-interaction studies.

Nowadays, multivariate time series data are increasingly collected in various real world systems, e.g., power plants, wearable devices, etc. Anomaly detection and diagnosis in multivariate time series refer to identifying abnormal status in certain time steps and pinpointing the root causes. Building such a system, however, is challenging since it not only requires to capture the temporal dependency in each time series, but also need encode the inter-correlations between different pairs of time series. In addition, the system should be robust to noise and provide operators with different levels of anomaly scores based upon the severity of different incidents. Despite the fact that a number of unsupervised anomaly detection algorithms have been developed, few of them can jointly address these challenges. In this paper, we propose a Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED), to perform anomaly detection and diagnosis in multivariate time series data. Specifically, MSCRED first constructs multi-scale (resolution) signature matrices to characterize multiple levels of the system statuses in different time steps. Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns. Finally, based upon the feature maps which encode the inter-sensor correlations and temporal information, a convolutional decoder is used to reconstruct the input signature matrices and the residual signature matrices are further utilized to detect and diagnose anomalies. Extensive empirical studies based on a synthetic dataset and a real power plant dataset demonstrate that MSCRED can outperform state-ofthe-art baseline methods.

Articular cartilage repair remains a great challenge for clinicians and researchers. Recently, there emerges a promising way to achieve one-step cartilage repair in situ by combining endogenic bone marrow stem cells (BMSCs) with suitable biomaterials using a tissue engineering technique. To meet the increasing demand for cartilage tissue engineering, a structurally and functionally optimized scaffold is designed, by integrating silk fibroin with gelatin in combination with BMSC-specific-affinity peptide using 3D printing (3DP) technology. The combination ratio of silk fibroin and gelatin greatly balances the mechanical properties and degradation rate to match the newly formed cartilage. This dually optimized scaffold has shown superior performance for cartilage repair in a knee joint because it not only retains adequate BMSCs, due to efficient recruiting ability, and acts as a physical barrier for blood clots, but also provides a mechanical protection before neocartilage formation and a suitable 3D microenvironment for BMSC proliferation, differentiation, and extracellular matrix production. It appears to be a promising biomaterial for knee cartilage repair and is worthy of further investigation in large animal studies and preclinical applications. Beyond knee cartilage, this dually optimized scaffold may also serve as an ideal biomaterial for the regeneration of other joint cartilages.

This retrospective study was designed to explore whether neutrophil to lymphocyte ratio (NLR) is a prognostic factor in patients with coronavirus disease 2019 (COVID-19). A cohort of patients with COVID-19 admitted to the Tongren Hospital of Wuhan University from 11 January 2020 to 3 March 2020 was retrospectively analyzed. Patients with hematologic malignancy were excluded. The NLR was calculated by dividing the neutrophil count by the lymphocyte count. NLR values were measured at the time of admission. The primary outcome was all-cause in-hospital mortality. A multivariate logistic analysis was performed. A total of 1004 patients with COVID-19 were included in this study. The mortality rate was 4.0% (40 cases). The median age of nonsurvivors (68 years) was significantly older than survivors (62 years). Male sex was more predominant in nonsurvival group (27; 67.5%) than in the survival group (466; 48.3%). NLR value of nonsurvival group (median: 49.06; interquartile range [IQR]: 25.71-69.70) was higher than that of survival group (median: 4.11; IQR: 2.44-8.12; P < .001). In multivariate logistic regression analysis, after adjusting for confounding factors, NLR more than 11.75 was significantly correlated with all-cause in-hospital mortality (odds ratio = 44.351; 95% Xisheng Yan and Fen Li contributed equally to this work.