Clinical analysis of prolonged viral clearance time in patients with lymphoma combined with novel coronavirus infection

Objective: To compare the period of viral clearance and its influencing factors after severe acute respiratory syndrome coronavirus (SARS-CoV-2) infection between patients with lymphoma and lung cancer.Methods: We retrospectively collected the clinical data of patients with lymphoma and lung cancer (118 cases) diagnosed with SARS-CoV-2 infection and hospitalized in the First Affiliated Hospital of Anhui Medical University between 1 December 2022, and 15 March 2023. Finally, 87 patients with prolonged virus clearance times were included and divided into lymphoma (40 cases) and lung cancer (47 cases) groups. We used the Kaplan-Meier method to draw a negative turn curve. We performed a univariate analysis of the prolongation of virus clearance time and a Cox regression model for multivariate analysis.Results: The median times for viral clearance in the lung cancer and lymphoma groups were 18 (95% confidence interval [CI] 15.112–20.888) and 32 (95%CI 27.429–36.571) days, respectively. Log-rank analysis showed a statistically significant difference (p = 0.048), and the lymphocyte count in the lymphoma group was lower than that in the lung cancer group (p = 0.044). We used the Cox regression model to conduct a multivariate analysis, which revealed that in lymphoma patients, the interval between the time of diagnosis and the time of SARS-CoV-2 infection <24 months (hazard ratio [HR]: 0.182, 95%CI: 0.062–0.535, p = 0.02), an interval between the last anti-CD20 monoclonal antibody treatment and the time of SARS-CoV-2 infection of <2 months (HR: 0.101, 95%CI: 0.029–0.358, p < 0.001), and a decrease in peripheral blood lymphocyte levels (HR: 0.380, 95%CI: 0.179–0.808, p = 0.012) were independent risk factors for prolonged viral clearance time.Conclusion: Patients with lymphoma combined with SARS-CoV-2 infection had a longer virus clearance time than did patients with lung cancer. Moreover, the lymphocyte count in the lymphoma group was lower than that in the lung cancer group; therefore, the immune status of patients with lymphoma is lower than that of patients with lung cancer. An interval between lymphoma diagnosis and SARS-CoV-2 infection of <2 years, anti-CD20 monoclonal antibody treatment within the past 2 months, and a decrease in lymphocyte levels in the peripheral blood prolonged the virus clearance time in the patients in this study

The complete mitochondrial genome sequence of Taxonus zhangi Wei, 1997 (Hymenoptera: Tenthredinidae) with phylogenetic analysis

The complete mitochondrial genome of Taxonus zhangi was 16,002 bp in size, comprises 13 protein-coding genes (PCGs), two rRNA genes, 22 tRNA genes, and large non-coding A + T region. The phylogenetic result confirms the monophyly of Taxonina and Allantina, and also supports that Xenapateini is the sister group of Allantini which is composed of Taxonina and Allantina

TALL: Thumbnail Layout for Deepfake Video Detection

The growing threats of deepfakes to society and cybersecurity have raised enormous public concerns, and increasing efforts have been devoted to this critical topic of deepfake video detection. Existing video methods achieve good performance but are computationally intensive. This paper introduces a simple yet effective strategy named Thumbnail Layout (TALL), which transforms a video clip into a pre-defined layout to realize the preservation of spatial and temporal dependencies. Specifically, consecutive frames are masked in a fixed position in each frame to improve generalization, then resized to sub-images and rearranged into a pre-defined layout as the thumbnail. TALL is model-agnostic and extremely simple by only modifying a few lines of code. Inspired by the success of vision transformers, we incorporate TALL into Swin Transformer, forming an efficient and effective method TALL-Swin. Extensive experiments on intra-dataset and cross-dataset validate the validity and superiority of TALL and SOTA TALL-Swin. TALL-Swin achieves 90.79$\%$ AUC on the challenging cross-dataset task, FaceForensics++ $\to$ Celeb-DF. The code is available at https://github.com/rainy-xu/TALL4Deepfake.Comment: Accepted by ICCV 202

IterCluster: a barcode clustering algorithm for long fragment read analysis

Recent advances in long fragment read (LFR, also known as linked-read technologies or read-cloud) technologies, such as single tube long fragment reads (stLFR), 10X Genomics Chromium reads, and TruSeq synthetic long-reads, have enabled efficient haplotyping and genome assembly. However, in the case of stLFR and 10X Genomics Chromium reads, the long fragments of a genome are covered sparsely by reads in each barcode and most barcodes are contained in multiple long fragments from different regions, which results in inefficient assembly when using long-range information. Thus, methods to address these shortcomings are vital for capitalizing on the additional information obtained using these technologies. We therefore designed IterCluster, a novel, alignment-free clustering algorithm that can cluster barcodes from the same target region of a genome, using -mer frequency-based features and a Markov Cluster (MCL) approach to identify enough reads in a target region of a genome to ensure sufficient target genome sequence depth. The IterCluster method was validated using BGI stLFR and 10X Genomics chromium reads datasets. IterCluster had a higher precision and recall rate on BGI stLFR data compared to 10X Genomics Chromium read data. In addition, we demonstrated how IterCluster improves the de novo assembly results when using a divide-and-conquer strategy on a human genome data set (scaffold/contig N50 = 13.2 kbp/7.1 kbp vs. 17.1 kbp/11.9 kbp before and after IterCluster, respectively). IterCluster provides a new way for determining LFR barcode enrichment and a novel approach for de novo assembly using LFR data. IterCluster is OpenSource and available on https://github.com/JianCong-WENG/IterCluster

Molecular evidence for origin, diversification and ancient gene duplication of plant subtilases (SBTs)

Plant subtilases (SBTs) are a widely distributed family of serine proteases which participates in plant developmental processes and immune responses. Although SBTs are divided into seven subgroups in plants, their origin and evolution, particularly in green algae remain elusive. Here, we present a comprehensive large-scale evolutionary analysis of all subtilases. The plant subtilases SBT1-5 were found to be monophyletic, nested within a larger radiation of bacteria suggesting that they originated from bacteria by a single horizontal gene transfer (HGT) event. A group of bacterial subtilases comprising representatives from four phyla was identified as a sister group to SBT1-5. The phylogenetic analyses, based on evaluation of novel streptophyte algal genomes, suggested that the recipient of the HGT of bacterial subtilases was the common ancestor of Coleochaetophyceae, Zygnematophyceae and embryophytes. Following the HGT, the subtilase gene duplicated in the common ancestor and the two genes diversified into SBT2 and SBT1, 3-5 respectively. Comparative structural analysis of homology-modeled SBT2 proteins also showed their conservation from bacteria to embryophytes. Our study provides the first molecular evidence about the evolution of plant subtilases via HGT followed by a first gene duplication in the common ancestor of Coleochaetophyceae, Zygnematophyceae, and embryophytes, and subsequent expansion in embryophytes