Label correlation transformer for automated chest X-ray diagnosis with reliable interpretability.

Computer-aided diagnosis of chest X-ray (CXR) images can help reduce the huge workload of radiologists and avoid the inter-observer variability in large-scale early disease screening. Recently, most state-of-the-art studies employ deep learning techniques to address this problem through multi-label classification. However, existing methods still suffer from low classification accuracy and poor interpretability for each diagnostic task. This study aims to propose a novel transformer-based deep learning model for automated CXR diagnosis with high performance and reliable interpretability. We introduce a novel transformer architecture into this problem and utilize the unique query structure of transformer to capture the global and local information of the images and the correlation between labels. In addition, we propose a new loss function to help the model find correlations between the labels in CXR images. To achieve accurate and reliable interpretability, we generate heatmaps using the proposed transformer model and compare with the true pathogenic regions labeled by the physicians. The proposed model achieves a mean AUC of 0.831 on chest X-ray 14 and 0.875 on PadChest dataset, which outperforms existing state-of-the-art methods. The attention heatmaps show that our model could focus on the exact corresponding areas of related truly labeled pathogenic regions. The proposed model effectively improves the performance of CXR multi-label classification and the interpretability of label correlations, thus providing new evidence and methods for automated clinical diagnosis.

Effect of Reticulate Unit Spacing on Microstructure and Properties of Biomimetic 7075 Aluminum Alloy by Laser Cladding.

In the context of energy conservation and emission reduction, more and more attention has been paid to the development of lightweight metal materials with both high strength and high toughness. Inspired by the non-smooth surface of natural organisms, a biomimetic surface with various spacing reticulate units of 7075 aluminum alloys was modified by laser cladding. The microstructure, microhardness and tensile properties of the various spacing units with CeO2-SiC-Ni60 were studied. The finer microstructure and the higher microhardness of various spacing units in comparison with that of 7075 aluminum alloys were obtained, no matter the strip-like treated region or the cross-junction region. Moreover, the best combination of strength and toughness of the biomimetic sample with 2.5 mm spacing reticulate unit was discussed. Finally, by combining the microstructure, XRD phase change, thermal gradient effect, thermal expansion coefficient difference and hard phase strengthening mechanism, it was concluded that the 2.5 mm spacing reticulate unit had the best ability to inhibit crack propagation, and the dispersive hard phases of Al3Ni2 and SiC played a major role in stress release of the matrix.

JSNMF enables effective and accurate integrative analysis of single-cell multiomics data.

The single-cell multiomics technologies provide an unprecedented opportunity to study the cellular heterogeneity from different layers of transcriptional regulation. However, the datasets generated from these technologies tend to have high levels of noise, making data analysis challenging. Here, we propose jointly semi-orthogonal nonnegative matrix factorization (JSNMF), which is a versatile toolkit for the integrative analysis of transcriptomic and epigenomic data profiled from the same cell. JSNMF enables data visualization and clustering of the cells and also facilitates downstream analysis, including the characterization of markers and functional pathway enrichment analysis. The core of JSNMF is an unsupervised method based on JSNMF, where it assumes different latent variables for the two molecular modalities, and integrates the information of transcriptomic and epigenomic data with consensus graph fusion, which better tackles the distinct characteristics and levels of noise across different molecular modalities in single-cell multiomics data. We applied JSNMF to single-cell multiomics datasets from different tissues and different technologies. The results demonstrate the superior performance of JSNMF in clustering and data visualization of the cells. JSNMF also allows joint analysis of multiple single-cell multiomics experiments and single-cell multiomics data with more than two modalities profiled on the same cell. JSNMF also provides rich biological insight on the markers, cell-type-specific region-gene associations and the functions of the identified cell subpopulation.

Interleukin-6 serum levels are independently associated with severe adenovirus pneumonia in children: a cross-sectional study.

Background: This study aimed to explore the potential association between interleukin-6 (IL-6) serum levels and severe adenovirus pneumonia (SAP) in children. Methods: A retrospective hospital-based cross-sectional study was conducted on children with SAP who presented to the Tianjin Children's Hospital between January 2019 and December 2020. Serum IL-6 levels were categorized into quintiles (Q1-5). The primary outcome variable was the occurrence of SAP. The patients' clinical features, laboratory findings, and radiographic characteristics were also assessed, and a descriptive bivariate analysis was carried out. Multivariable logistic regression analysis was applied to evaluate the relationship of IL-6 with SAP after adjustment for confounders. The nonlinear relationship between IL-6 and SAP was also analyzed. P value <0.05 was considered statistically significant. Results: In total, 542 patients met our inclusion criteria (223 males and 319 females). The mean IL-6 serum level was 38.51 pg/mL (range, 1.50-659.2 pg/mL). After adjustment for confounders, the odds ratio (OR) per SD (standard deviation) increase in IL-6 was 1.66 [95% confidence interval (CI): 1.14, 2.41]. The multivariable-adjusted OR (95% CI) of SAP across the Q1-Q5 categories of IL-6 were as follows: 1.00 (reference), 1.17 (0.59, 2.35), 1.79 (0.88, 3.63), 2.31 (1.12, 4.76), and 2.85 (1.32, 6.14) (P for trend =0.002). The risk of SAP increased with the IL-6 serum level up to 40.78 pg/mL (adjusted OR 1.029, 95% CI: 1.008-1.051; P=0.007); however, when the IL-6 level exceeded 40.78 pg/mL, it had no association with the risk of SAP (OR 1.003, 95% CI: 0.996-1.010; P=0.384). Conclusions: Our findings suggest that the serum level of IL-6 is associated with the risk of SAP in children. The levels of IL-6 in children should therefore be of concern to clinicians.

scAMACE: model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation.

MOTIVATION: The advancement in technologies and the growth of available single-cell datasets motivate integrative analysis of multiple single-cell genomic datasets. Integrative analysis of multimodal single-cell datasets combines complementary information offered by single-omic datasets and can offer deeper insights on complex biological process. Clustering methods that identify the unknown cell types are among the first few steps in the analysis of single-cell datasets, and they are important for downstream analysis built upon the identified cell types. RESULTS: We propose scAMACE for the integrative analysis and clustering of single-cell data on chromatin accessibility, gene expression and methylation. We demonstrate that cell types are better identified and characterized through analyzing the three data types jointly. We develop an efficient Expectation-Maximization algorithm to perform statistical inference, and evaluate our methods on both simulation study and real data applications. We also provide the GPU implementation of scAMACE, making it scalable to large datasets. AVAILABILITY AND IMPLEMENTATION: The software and datasets are available at https://github.com/cuhklinlab/scAMACE_py (python implementation) and https://github.com/cuhklinlab/scAMACE (R implementation). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DTF: Deep Tensor Factorization for predicting anticancer drug synergy.

MOTIVATION: Combination therapies have been widely used to treat cancers. However, it is cost and time consuming to experimentally screen synergistic drug pairs due to the enormous number of possible drug combinations. Thus, computational methods have become an important way to predict and prioritize synergistic drug pairs. RESULTS: We proposed a Deep Tensor Factorization (DTF) model, which integrated a tensor factorization method and a deep neural network (DNN), to predict drug synergy. The former extracts latent features from drug synergy information while the latter constructs a binary classifier to predict the drug synergy status. Compared to the tensor-based method, the DTF model performed better in predicting drug synergy. The area under precision-recall curve (PR AUC) was 0.58 for DTF and 0.24 for the tensor method. We also compared the DTF model with DeepSynergy and logistic regression models, and found that the DTF outperformed the logistic regression model and achieved similar performance as DeepSynergy using several performance metrics for classification task. Applying the DTF model to predict missing entries in our drug-cell-line tensor, we identified novel synergistic drug combinations for 10 cell lines from the 5 cancer types. A literature survey showed that some of these predicted drug synergies have been identified in vivo or in vitro. Thus, the DTF model could be a valuable in silico tool for prioritizing novel synergistic drug combinations. AVAILABILITY AND IMPLEMENTATION: Source code and data are available at https://github.com/ZexuanSun/DTF-Drug-Synergy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep graph embedding for prioritizing synergistic anticancer drug combinations.

Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein-protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers in vitro or in vivo. Taken together, the results indicate that our study provides a promising way to better predict and optimize synergistic drug pairs in silico.