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In the realm of plant classification, the classification of grape leaf varieties has long presented a complex challenge. Aiming to enhance the accuracy and generalization ability of grape leaf variety classification, this study proposes a novel approach that employs an optimized Densenet201 model for grape leaf classification. Initially, grape leaf images from five distinct varieties were meticulously collected to construct a comprehensive grape leaf dataset. To augment the diversity of the dataset, the parameters of data augmentation were delicately adjusted, with an increase in the rotation range, translation range, and so on. Subsequently, BatchNormalization and GlobalAveragePooling2D layers were incorporated to achieve feature normalization and pooling. Simultaneously, the parameters of the Dropout layer were optimized to effectively mitigate the issue of overfitting. Additionally, the number of neurons and layers in the Dense layer were varied to explore diverse network structures and pursue superior performance. Moreover, the parameters of the Adam optimizer were meticulously tuned to attain the optimal performance, and the model’s performance was further enhanced by extracting image features. The experimental results demonstrate that, in comparison with the densenet121, densenet169, resnet50, and densenet201 models, the optimized Densenet201 model showcases outstanding performance in grape leaf variety classification, remarkably improving the classification accuracy and generalization ability. This research provides a more efficient method for grape leaf variety classification.

Critical patients with the coronavirus disease 2019 (COVID-19), even those whose nucleic acid test results had turned negative and those receiving maximal medical support, have been noted to progress to irreversible fatal respiratory failure. Lung transplantation (LT) as the sole therapy for end-stage pulmonary fibrosis related to acute respiratory distress syndrome has been considered as the ultimate rescue therapy for these patients. From February 10 to March 10, 2020, three male patients were urgently assessed and listed for transplantation. After conducting a full ethical review and after obtaining assent from the family of the patients, we performed three LT procedures for COVID-19 patients with illness durations of more than one month and extremely high sequential organ failure assessment scores. Two of the three recipients survived post-LT and started participating in a rehabilitation program. Pearls of the LT team collaboration and perioperative logistics were summarized and continually improved. The pathological results of the explanted lungs were concordant with the critical clinical manifestation, and provided insight towards better understanding of the disease. Government health affair systems, virology detection tools, and modern communication technology all play key roles towards the survival of the patients and their rehabilitation. LT can be performed in end-stage patients with respiratory failure due to COVID-19-related pulmonary fibrosis. If confirmed positive-turned-negative virology status without organ dysfunction that could contraindicate LT, LT provided the final option for these patients to avoid certain death, with proper protection of transplant surgeons and medical staffs. By ensuring instant seamless care for both patients and medical teams, the goal of reducing the mortality rate and salvaging the lives of patients with COVID-19 can be attained.

Long nanopore reads are advantageous in de novo genome assembly. However, nanopore reads usually have broad error distribution and high-error-rate subsequences. Existing error correction tools cannot correct nanopore reads efficiently and effectively. Most methods trim high-error-rate subsequences during error correction, which reduces both the length of the reads and contiguity of the final assembly. Here, we develop an error correction, and de novo assembly tool designed to overcome complex errors in nanopore reads. We propose an adaptive read selection and two-step progressive method to quickly correct nanopore reads to high accuracy. We introduce a two-stage assembler to utilize the full length of nanopore reads. Our tool achieves superior performance in both error correction and de novo assembling nanopore reads. It requires only 8122 hours to assemble a 35X coverage human genome and achieves a 2.47-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line shows an NG50 of 22 Mbp. The high-quality assembly of nanopore reads can significantly reduce false positives in structure variation detection. Nanopore reads have been advantageous for de novo genome assembly; however these reads have high error rates. Here, the authors develop an error correction and de novo assembly tool, NECAT, which produces efficient, high quality assemblies of nanopore reads.

Results from various studies reveal that the role of G protein‐coupled oestrogen receptor (GPER) is cancer‐context dependent, and the function of GPER in non–small‐cell lung cancer (NSCLC) is still unclear. The present study demonstrated that neoplasm lung tissues expressed higher level of GPER compared with the normal lung tissues. The clinical data also showed that GPER expression level was positively correlated with the tumour stage of NSCLC. Our experimental data confirmed that GPER played an oncogenic role to promote cell growth of NSCLC cells. Mechanistic dissection revealed that GPER could modulate the NOTCH1 pathway to regulate cell growth in NSCLC cells. Further exploration of the mechanism demonstrated that GPER could up‐regulate circNOTCH1, which could compete with NOTCH1 mRNA for METTL14 binding. Because of the lack of m6A modification by METTL14 on the NOTCH1 mRNA, NOTCH1 mRNA was more stable and much easier to undergo protein translation. Subsequently, we found that GPER could prevent YAP1 phosphorylation and promote YAP1‐TEAD's transcriptional regulation on QKI, a transacting RNA‐binding factor involved in circRNA biogenesis, to facilitate circNOTCH1 generation. Supportively, data from preclinical mice model with implantation of H1299 cells also demonstrated that knock‐down of circNOTCH1 could block GPER‐induced NOTCH1 to suppress NSCLC tumour growth. Together, our data showed that GPER could promote NSCLC cell growth via regulating the YAP1/QKI/circNOTCH1/m6A methylated NOTCH1 pathway, and targeting our identified molecules may be a potentially therapeutic approach to suppress NSCLC development.

Ferroptosis is a new type of cell death that was discovered in recent years and is usually accompanied by a large amount of iron accumulation and lipid peroxidation during the cell death process; the occurrence of ferroptosis is iron-dependent. Ferroptosis-inducing factors can directly or indirectly affect glutathione peroxidase through different pathways, resulting in a decrease in antioxidant capacity and accumulation of lipid reactive oxygen species (ROS) in cells, ultimately leading to oxidative cell death. Recent studies have shown that ferroptosis is closely related to the pathophysiological processes of many diseases, such as tumors, nervous system diseases, ischemia-reperfusion injury, kidney injury, and blood diseases. How to intervene in the occurrence and development of related diseases by regulating cell ferroptosis has become a hotspot and focus of etiological research and treatment, but the functional changes and specific molecular mechanisms of ferroptosis still need to be further explored. This paper systematically summarizes the latest progress in ferroptosis research, with a focus on providing references for further understanding of its pathogenesis and for proposing new targets for the treatment of related diseases.

Despite clinical and epidemiological evidence suggestive of a link between glioblastoma (GBM) and periodontitis (PD), the shared mechanisms of gene regulation remain elusive. In this study, we identify differentially expressed genes (DEGs) that overlap between the GEO datasets GSE4290 [GBM] and GSE10334 [PD]. Functional enrichment analysis was conducted, and key modules were identified using protein–protein interaction (PPI) network and weighted gene co-expression network analysis (WGCNA). The expression levels of CXCR4, LY96, and C3 were found to be significantly elevated in both the test dataset and external validation dataset, making them key crosstalk genes. Additionally, immune cell landscape analysis revealed elevated expression levels of multiple immune cells in GBM and PD compared to controls, with the key crosstalk genes negatively associated with Macrophages M2. FLI1 was identified as a potential key transcription factor (TF) regulating the three key crosstalk genes, with increased expression in the full dataset. These findings contribute to our understanding of the immune and inflammatory aspects of the comorbidity mechanism between GBM and PD.

An important step in developing individualized treatment strategies is correct identification of subgroups of a heterogeneous population to allow specific treatment for each subgroup. This article considers the problem using samples drawn from a population consisting of subgroups with different mean values, along with certain covariates. We propose a penalized approach for subgroup analysis based on a regression model, in which heterogeneity is driven by unobserved latent factors and thus can be represented by using subject-specific intercepts. We apply concave penalty functions to pairwise differences of the intercepts. This procedure automatically divides the observations into subgroups. To implement the proposed approach, we develop an alternating direction method of multipliers algorithm with concave penalties and demonstrate its convergence. We also establish the theoretical properties of our proposed estimator and determine the order requirement of the minimal difference of signals between groups to recover them. These results provide a sound basis for making statistical inference in subgroup analysis. Our proposed method is further illustrated by simulation studies and analysis of a Cleveland heart disease dataset. Supplementary materials for this article are available online.

The Omicron (B.1.1.529) variant of SARS-CoV-2 emerged in November 2021 and is rapidly spreading among the human population 1 . Although recent reports reveal that the Omicron variant robustly escapes vaccine-associated and therapeutic neutralization antibodies 2 – 10 , the pathogenicity of the virus remains unknown. Here we show that the replication of Omicron is substantially attenuated in human Calu3 and Caco2 cells. Further mechanistic investigations reveal that Omicron is inefficient in its use of transmembrane serine protease 2 (TMPRSS2) compared with wild-type SARS-CoV-2 (HKU-001a) and previous variants, which may explain its reduced replication in Calu3 and Caco2 cells. The replication of Omicron is markedly attenuated in both the upper and lower respiratory tracts of infected K18-hACE2 mice compared with that of the wild-type strain and Delta (B.1.617.2) variant, resulting in its substantially ameliorated lung pathology. Compared with wild-type SARS-CoV-2 and the Alpha (B.1.1.7), Beta (1.351) and Delta variants, infection by Omicron causes the lowest reduction in body weight and the lowest mortality rate. Overall, our study demonstrates that the replication and pathogenicity of the Omicron variant of SARS-CoV-2 in mice is attenuated compared with the wild-type strain and other variants. The replication and pathogenicity of the Omicron variant of SARS-CoV-2 is attenuated compared with the original strain and other variants.

A number of variable selection methods have been proposed involving nonconvex penalty functions. These methods, which include the smoothly clipped absolute deviation (SCAD) penalty and the minimax concave penalty (MCP), have been demonstrated to have attractive theoretical properties, but model fitting is not a straightforward task, and the resulting solutions may be unstable. Here, we demonstrate the potential of coordinate descent algorithms for fitting these models, establishing theoretical convergence properties and demonstrating that they are significantly faster than competing approaches. In addition, we demonstrate the utility of convexity diagnostics to determine regions of the parameter space in which the objective function is locally convex, even though the penalty is not. Our simulation study and data examples indicate that nonconvex penalties like MCP and SCAD are worthwhile alternatives to the lasso in many applications. In particular, our numerical results suggest that MCP is the preferred approach among the three methods.