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Abstract Background Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss. Results By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research. Conclusions MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.

Previous data have proven that microvesicles derived from hypoxia-induced mesenchymal stem cells (MSC-MVs) can be internalized into endothelial cells, enhancing their proliferation and vessel structure formation and promoting in vivo angiogenesis. However, there is a paucity of information about how the MSC-MVs are up-taken by endothelial cells.MVs were prepared from the supernatants of human bone marrow MSCs that had been exposed to a hypoxic and/or serum-deprivation condition. The incorporation of hypoxia-induced MSC-MVs into human umbilical cord endothelial cells (HUVECs) was observed by flow cytometry and confocal microscopy in the presence or absence of recombinant human Annexin-V (Anx-V) and antibodies against human CD29 and CD44. Further, small interfering RNA (siRNA) targeted at Anx-V and PSR was delivered into HUVECs, or HUVECs were treated with a monoclonal antibody against phosphatidylserine receptor (PSR) and the cellular internalization of MVs was re-assessed.The addition of exogenous Anx-V could inhibit the uptake of MVs isolated from hypoxia-induced stem cells by HUVECs in a dose- and time-dependent manner, while the anti-CD29 and CD44 antibodies had no effect on the internalization process. The suppression was neither observed in Anx-V siRNA-transfected HUVECs, however, addition of anti-PSR antibody and PSR siRNA-transfected HUVECs greatly blocked the incorporation of MVs isolated from hypoxia-induced stem cells into HUVECs.PS on the MVs isolated from hypoxia-induced stem cells is the critical molecule in the uptake by HUVECs.

This study aimed to investigate the effects of myocardial injury in a minimally-invasive miniature swine model with different levels of coronary artery stenosis (CAS) and exposed to maximal tolerated +Gz. Proximal left anterior descending branch was ligated in 20 swine. Five swine underwent a sham operation. A trapezoid acceleration curve was used for +Gz stress. Pathological changes of myocardial tissue were detected by H&E staining. Apoptotic cardiomyocytes were detected by TUNEL. GRP78 and CHOP were investigated by immunohistochemistry and western blot. CAS models were successful in 18 animals.Compared with the sham-operated group (+8.00±0.71 Gz), the maximal tolerated +Gz values of the moderate stenosis (+6.00±0.89 Gz, P

Gears and bearings play vital roles as essential transmission components in mechanical drivetrains. Accurately predicting the remaining useful life (RUL) of these components is paramount to ensure optimal performance and prevent unexpected failures. To enhance the precision of RUL prediction, a novel method has been developed which involves constructing health indicators (HI) and implementing an adaptive dynamic weighting (ADW) on a gated dual attention unit (GDAU). The process commences by extracting multi-dimensional time-frequency domain features from vibration signals, which are then refined using an improved kernel principal component analysis (Adaptive Kernel Principal Component Analysis - AKPCA) to extract key components. Subsequently, the constructed HI is fine-tuned through an optimization process utilizing the exponentially weighted moving average method. Finally, the ADW strategy dynamically adjusts the input weights of the HI, and the GDAU model is employed to predict the RUL of gears and bearings. Experiment and comparison results have validated the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]