IMperm: a fast and comprehensive IMmune Paired-End Reads Merger for sequencing data.

The adaptive immune receptor repertoire (AIRR), consisting of T- and B-cell receptors, is the core component of the immune system. The AIRR sequencing is commonly used in cancer immunotherapy and minimal residual disease (MRD) detection of leukemia and lymphoma. The AIRR is captured by primers and sequenced to yield paired-end (PE) reads. The PE reads could be merged into one sequence by the overlapped region between them. However, the wide range of AIRR data raises the difficulty, so a special tool is required. We developed a software package for IMmune PE reads merger of sequencing data, named IMperm. We used the k-mer-and-vote strategy to pin down the overlapped region rapidly. IMperm could handle all types of PE reads, eliminate adapter contamination and successfully merge low-quality and minor/non-overlapping reads. Compared with existing tools, IMperm performed better in both simulated and sequencing data. Notably, IMperm was well suited to processing the data of MRD detection in leukemia and lymphoma and detected 19 novel MRD clones in 14 patients with leukemia from previously published data. Additionally, IMperm can handle PE reads from other sources, and we demonstrated its effectiveness on two genomic and one cell-free deoxyribonucleic acid datasets. IMperm is implemented in the C programming language and consumes little runtime and memory. It is freely available at https://github.com/zhangwei2015/IMperm.

A Lane Detection Method Based on a Ridge Detector and Regional G-RANSAC.

Lane detection plays an important role in improving autopilot's safety. In this paper, a novel lane-division-lines detection method is proposed, which exhibits good performances in abnormal illumination and lane occlusion. It includes three major components: First, the captured image is converted to aerial view to make full use of parallel lanes' characteristics. Second, a ridge detector is proposed to extract each lane's feature points and remove noise points with an adaptable neural network (ANN). Last, the lane-division-lines are accurately fitted by an improved random sample consensus (RANSAC), termed the (regional) gaussian distribution random sample consensus (G-RANSAC). To test the performances of this novel lane detection method, we proposed a new index named the lane departure index (LDI) describing the departure degree between true lane and predicted lane. Experimental results verified the superior performances of the proposed method over others in different testing scenarios, respectively achieving 99.02%, 96.92%, 96.65% and 91.61% true-positive rates (TPR); and 66.16, 54.85, 55.98 and 52.61 LDIs in four different types of testing scenarios.

Disentangling Modality and Posture Factors: Memory-Attention and Orthogonal Decomposition for Visible-Infrared Person Re-Identification.

Striving to match the person identities between visible (VIS) and near-infrared (NIR) images, VIS-NIR reidentification (Re-ID) has attracted increasing attention due to its wide applications in low-light scenes. However, owing to the modality and pose discrepancies exhibited in heterogeneous images, the extracted representations inevitably comprise various modality and posture factors, impacting the matching of cross-modality person identity. To solve the problem, we propose a disentangling modality and posture factors (DMPFs) model to disentangle modality and posture factors by fusing the information of features memory and pedestrian skeleton. Specifically, the DMPF comprises three modules: three-stream features extraction network (TFENet), modality factor disentanglement (MFD), and posture factor disentanglement (PFD). First, aiming to provide memory and skeleton information for modality and posture factors disentanglement, the TFENet is designed as a three-stream network to extract VIS-NIR image features and skeleton features. Second, to eliminate modality discrepancy across different batches, we maintain memory queues of previous batch features through the momentum updating mechanism and propose MFD to integrate features in the whole training set by memory-attention layers. These layers explore intramodality and intermodality relationships between features from the current batch and memory queues under the optimization of the optimal transport (OT) method, which encourages the heterogeneous features with the same identity to present higher similarity. Third, to decouple the posture factors from representations, we introduce the PFD module to learn posture-unrelated features with the assistance of the skeleton features. Besides, we perform subspace orthogonal decomposition on both image and skeleton features to separate the posture-related and identity-related information. The posture-related features are adopted to disentangle the posture factors from representations by a designed posture-features consistency (PfC) loss, while the identity-related features are concatenated to obtain more discriminative identity representations. The effectiveness of DMPF is validated through comprehensive experiments on two VIS-NIR pedestrian Re-ID datasets.

COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning.

The detection of Coronavirus disease 2019 (COVID-19) is crucial for controlling the spread of the virus. Current research utilizes X-ray imaging and artificial intelligence for COVID-19 diagnosis. However, conventional X-ray scans expose patients to excessive radiation, rendering repeated examinations impractical. Ultra-low-dose X-ray imaging technology enables rapid and accurate COVID-19 detection with minimal additional radiation exposure. In this retrospective cohort study, ULTRA-X-COVID, a deep neural network specifically designed for automatic detection of COVID-19 infections using ultra-low-dose X-ray images, is presented. The study included a multinational and multicenter dataset consisting of 30,882 X-ray images obtained from approximately 16,600 patients across 51 countries. It is important to note that there was no overlap between the training and test sets. The data analysis was conducted from 1 April 2020 to 1 January 2022. To evaluate the effectiveness of the model, various metrics such as the area under the receiver operating characteristic curve, receiver operating characteristic, accuracy, specificity, and F1 score were utilized. In the test set, the model demonstrated an AUC of 0.968 (95% CI, 0.956-0.983), accuracy of 94.3%, specificity of 88.9%, and F1 score of 99.0%. Notably, the ULTRA-X-COVID model demonstrated a performance comparable to conventional X-ray doses, with a prediction time of only 0.1 s per image. These findings suggest that the ULTRA-X-COVID model can effectively identify COVID-19 cases using ultra-low-dose X-ray scans, providing a novel alternative for COVID-19 detection. Moreover, the model exhibits potential adaptability for diagnoses of various other diseases.

Decoding ceRNA regulatory network in the pulmonary artery of hypoxia-induced pulmonary hypertension (HPH) rat model.

BACKGROUND: Hypoxia-induced pulmonary hypertension (HPH) is a lethal cardiovascular disease with the characteristic of severe remodeling of pulmonary vascular. Although a large number of dysregulated mRNAs, lncRNAs, circRNAs, and miRNAs related to HPH have been identified from extensive studies, the competitive endogenous RNA (ceRNA) regulatory network in the pulmonary artery that responds to hypoxia remains largely unknown. RESULTS: Transcriptomic profiles in the pulmonary arteries of HPH rats were characterized through high-throughput RNA sequencing in this study. Through relatively strict screening, a set of differentially expressed RNAs (DERNAs) including 19 DEmRNAs, 8 DElncRNAs, 19 DEcircRNAs, and 23 DEmiRNAs were identified between HPH and normal rats. The DEmRNAs were further found to be involved in cell adhesion, axon guidance, PPAR signaling pathway, and calcium signaling pathway, suggesting their crucial role in HPH. Moreover, a hypoxia-induced ceRNA regulatory network in the pulmonary arteries of HPH rats was constructed according to the ceRNA hypothesis. More specifically, the ceRNA network was composed of 10 miRNAs as hub nodes, which might be sponged by 6 circRNAs and 7 lncRNAs, and directed the expression of 18 downstream target genes that might play important role in the progression of HPH. The expression patterns of selected DERNAs in the ceRNA network were then validated to be consistent with sequencing results in another three independent batches of HPH and normal control rats. The diagnostic effectiveness of several hub mRNAs in ceRNA network was further evaluated through investigating their expression profiles in patients with pulmonary artery hypertension (PAH) recorded in the Gene Expression Omnibus (GEO) dataset GSE117261. Dysregulated POSTN, LTBP2, SPP1, and LSAMP were observed in both the pulmonary arteries of HPH rats and lung tissues of PAH patients. CONCLUSIONS: A ceRNA regulatory network in the pulmonary arteries of HPH rats was constructed, 10 hub miRNAs and their corresponding interacting lncRNAs, circRNAs, and mRNAs were identified. The expression patterns of selected DERNAs were further validated to be consistent with the sequencing result. POSTN, LTBP2, SPP1, and LSAMP were suggested to be potential diagnostic biomarkers and therapeutic targets for PAH.

Exercise for Stroke Rehabilitation: A Bibliometric Analysis of Global Research From 2001 to 2021.

Objective: To make a bibliometric analysis of global trends in research into exercise interventions for stroke between 2001 and 2021. Method: This study did the systematic literature from 2001 to 2021 in Web of Science Core Collection. CiteSpace software was used to analyze the relationship of publications with countries, journals, authors, references, and keywords. Results: A total of 3,484 publications were obtained in the bibliometric analysis. The number of publications increased gradually over the period. The United States have the most number of publications. The journal stroke had the most citations per paper (106.95) and the highest impact factor (IF 2020, 7.194). The most high frequency keywords are "stroke," "rehabilitation," and "recovery," the top of burst key words are "health," "speed," and "aerobic exercise". Conclusion: These findings provide the trends of exercise for stroke s and provided the potential research frontiers in the past 20 years. It will be a useful basis for further research into focus issues, cooperators, development trends.

Association Between the Prognostic Nutritional Index and Cognitive Function Among Older Adults in the United States: A Population-Based Study.

BACKGROUND: Nutritional status has been recognized as an important factor influencing cognitive function-related diseases, but few comprehensive nutrition indicators are available to assess the risk of cognitive decline. OBJECTIVE: This study aimed to investigate the relationship between the prognostic nutritional index (PNI) and cognitive function in an elderly population, and the differences in nutrient intake between different levels of nutritional risk. METHODS: Based on cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014, we included 2,564 older participants. The lower quartile of each of the four cognitive tests was considered to have cognitive function impairment (CFI). Binary and multivariate logistic regression models were used to estimate the relationship between the PNI and the odds ratio of CFI. RESULTS: After adjustment for confounding variables, we found that the odds of CFI were significantly lower for participants with normal PNI levels than for those with low PNI levels. In a comparison of global cognitive impairment scores, participants with a normal PNI had lower ratios of poor cognitive performance than those with a low PNI. By comparing the nutrient intake at different PNI levels, we found a reduction in the intake of protein, dietary fiber, total saturated fatty acids, and multiple micronutrients in the low PNI group. CONCLUSION: Our study shows that the PNI can be a good predictor of the odds of CFI in the elderly population and that it is a convenient indicator of reduced intake of nutrients which may be important to brain health.