A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation

Due to the abundant neurophysiological information in the electroencephalogram (EEG) signal, EEG signals integrated with deep learning methods have gained substantial traction across numerous real-world tasks. However, the development of supervised learning methods based on EEG signals has been hindered by the high cost and significant label discrepancies to manually label large-scale EEG datasets. Self-supervised frameworks are adopted in vision and language fields to solve this issue, but the lack of EEG-specific theoretical foundations hampers their applicability across various tasks. To solve these challenges, this paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2), which integrates neurological theory to extract effective representations from EEG with limited labels. The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity. Sequentially, inter-view and cross-view contrastive learning pipelines in combination with various augmentation methods are applied to capture neural features from different views. By modeling prior neural knowledge based on homologous neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations. Experimental results on different downstream tasks demonstrate that our method outperforms state-of-the-art methods, highlighting the superior generalization of neural knowledge-supported EEG representations across various brain tasks.Comment: 14pages,7 figure

Big-model Driven Few-shot Continual Learning

Few-shot continual learning (FSCL) has attracted intensive attention and achieved some advances in recent years, but now it is difficult to again make a big stride in accuracy due to the limitation of only few-shot incremental samples. Inspired by distinctive human cognition ability in life learning, in this work, we propose a novel Big-model driven Few-shot Continual Learning (B-FSCL) framework to gradually evolve the model under the traction of the world's big-models (like human accumulative knowledge). Specifically, we perform the big-model driven transfer learning to leverage the powerful encoding capability of these existing big-models, which can adapt the continual model to a few of newly added samples while avoiding the over-fitting problem. Considering that the big-model and the continual model may have different perceived results for the identical images, we introduce an instance-level adaptive decision mechanism to provide the high-level flexibility cognitive support adjusted to varying samples. In turn, the adaptive decision can be further adopted to optimize the parameters of the continual model, performing the adaptive distillation of big-model's knowledge information. Experimental results of our proposed B-FSCL on three popular datasets (including CIFAR100, minilmageNet and CUB200) completely surpass all state-of-the-art FSCL methods.Comment: 9 pages 6 figure

Sca-1+ Cardiac Stem Cells Mediate Acute Cardioprotection via Paracrine Factor SDF-1 following Myocardial Ischemia/Reperfusion

Cardiac stem cells (CSCs) promote myocardial recovery following ischemia through their regenerative properties. However, little is known regarding the implication of paracrine action by CSCs in the setting of myocardial ischemia/reperfusion (I/R) injury although it is well documented that non-cardiac stem cells mediate cardioprotection via the production of paracrine protective factors. Here, we studied whether CSCs could initiate acute protection following global myocardial I/R via paracrine effect and what component from CSCs is critical to this protection.A murine model of global myocardial I/R was utilized to investigate paracrine effect of Sca-1+ CSCs on cardiac function. Intracoronary delivery of CSCs or CSC conditioned medium (CSC CM) prior to ischemia significantly improved myocardial function following I/R. siRNA targeting of VEGF in CSCs did not affect CSC-preserved myocardial function in response to I/R injury. However, differentiation of CSCs to cardiomyocytes (DCSCs) abolished this protection. Through direct comparison of the protein expression profiles of CSCs and DCSCs, SDF-1 was identified as one of the dominant paracrine factors secreted by CSCs. Blockade of the SDF-1 receptor by AMD3100 or downregulated SDF-1 expression in CSCs by specific SDF-1 siRNA dramatically impaired CSC-induced improvement in cardiac function and increased myocardial damage following I/R. Of note, CSC treatment increased myocardial STAT3 activation after I/R, whereas downregulation of SDF-1 action by blockade of the SDF-1 receptor or SDF-1 siRNA transfection abolished CSC-induced STAT3 activation. In addition, inhibition of STAT3 activation attenuated CSC-mediated cardioprotection following I/R. Finally, post-ischemic infusion of CSC CM was shown to significantly protect I/R-caused myocardial dysfunction.This study suggests that CSCs acutely improve post-ischemic myocardial function through paracrine factor SDF-1 and up-regulated myocardial STAT3 activation

Exogenous Melatonin Improves Cold Tolerance of Strawberry (Fragaria × ananassa Duch.) through Modulation of DREB/CBF-COR Pathway and Antioxidant Defense System

The strawberry (Fragaria × ananassa Duch.) is an important fruit crop cultivated worldwide for its unique taste and nutritional properties. One of the major risks associated with strawberry production is cold damage. Recently, melatonin has emerged as a multifunctional signaling molecule that influences plant growth and development and reduces adverse consequences of cold stress. The present study was conducted to investigate the defensive role of melatonin and its potential interrelation with abscisic acid (ABA) in strawberry plants under cold stress. The results demonstrate that melatonin application conferred improved cold tolerance on strawberry seedlings by reducing malondialdehyde and hydrogen peroxide contents under cold stress. Conversely, pretreatment of strawberry plants with 100 μM melatonin increased soluble sugar contents and different antioxidant enzyme activities (ascorbate peroxidase, catalase, and peroxidase) and non-enzymatic antioxidant (ascorbate and glutathione) activities under cold stress. Furthermore, exogenous melatonin treatment stimulated the expression of the DREB/CBF—COR pathways’ downstream genes. Interestingly, ABA treatment did not change the expression of the DREB/CBF—COR pathway. These findings imply that the DREB/CBF-COR pathway confers cold tolerance on strawberry seedlings through exogenous melatonin application. Taken together, our results reveal that melatonin (100 μM) pretreatment protects strawberry plants from the damages induced by cold stress through enhanced antioxidant defense potential and modulating the DREB/CBF—COR pathway. View Full-Tex

The IFN-γ-related long non-coding RNA signature predicts prognosis and indicates immune microenvironment infiltration in uterine corpus endometrial carcinoma

BackgroundOne of the most common diseases that have a negative impact on women’s health is endometrial carcinoma (EC). Advanced endometrial cancer has a dismal prognosis and lacks solid prognostic indicators. IFN-γ is a key cytokine in the inflammatory response, and it has also been suggested that it has a role in the tumor microenvironment. The significance of IFN-γ-related genes and long non-coding RNAs in endometrial cancer, however, is unknown.MethodsThe Cancer Genome Atlas (TCGA) database was used to download RNA-seq data from endometrial cancer tissues and normal controls. Genes associated with IFN-γ were retrieved from the gene set enrichment analysis (GSEA) website. Co-expression analysis was performed to find lncRNAs linked to IFN-γ gene. The researchers employed weighted co-expression network analysis (WGCNA) to find lncRNAs that were strongly linked to survival. The prognostic signature was created using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. The training cohort, validation cohort, and entire cohort of endometrial cancer patients were then split into high-risk and low-risk categories. To investigate variations across different risk groups, we used survival analysis, enrichment analysis, and immune microenvironment analysis. The platform for analysis is R software (version X64 3.6.1).ResultsBased on the transcript expression of IFN-γ-related lncRNAs, two distinct subgroups of EC from TCGA cohort were formed, each with different outcomes. Ten IFN-γ-related lncRNAs were used to build a predictive signature using Cox regression analysis and the LASSO regression, including CFAP58, LINC02014, UNQ6494, AC006369.1, NRAV, BMPR1B-DT, AC068134.2, AP002840.2, GS1-594A7.3, and OLMALINC. The high-risk group had a considerably worse outcome (p < 0.05). In the immunological microenvironment, there were also substantial disparities across different risk categories.ConclusionOur findings give a reference for endometrial cancer prognostic type and immunological status assessment, as well as prospective molecular markers for the disease