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Emotion is the human brain reacting to objective things. In real life, human emotions are complex and changeable, so research into emotion recognition is of great significance in real life applications. Recently, many deep learning and machine learning methods have been widely applied in emotion recognition based on EEG signals. However, the traditional machine learning method has a major disadvantage in that the feature extraction process is usually cumbersome, which relies heavily on human experts. Then, end-to-end deep learning methods emerged as an effective method to address this disadvantage with the help of raw signal features and time-frequency spectrums. Here, we investigated the application of several deep learning models to the research field of EEG-based emotion recognition, including deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and a hybrid model of CNN and LSTM (CNN-LSTM). The experiments were carried on the well-known DEAP dataset. Experimental results show that the CNN and CNN-LSTM models had high classification performance in EEG-based emotion recognition, and their accurate extraction rate of RAW data reached 90.12 and 94.17%, respectively. The performance of the DNN model was not as accurate as other models, but the training speed was fast. The LSTM model was not as stable as the CNN and CNN-LSTM models. Moreover, with the same number of parameters, the training speed of the LSTM was much slower and it was difficult to achieve convergence. Additional parameter comparison experiments with other models, including epoch, learning rate, and dropout probability, were also conducted in the paper. Comparison results prove that the DNN model converged to optimal with fewer epochs and a higher learning rate. In contrast, the CNN model needed more epochs to learn. As for dropout probability, reducing the parameters by ~50% each time was appropriate.

Flagellin is a potent activator of a broad range of cell types that are involved in innate and adaptive immunity. Therefore, it is a good adjuvant candidate for vaccines, and it might function as a biological protectant against both major acute radiation syndrome during cancer radiotherapy and a mitigator of radiation emergencies. However, accumulating evidence has implicated flagellin in the occurrence of some inflammatory diseases, such as acute lung inflammation, cardiovascular collapse and inflammatory bowel disease. The aim of this study was to elucidate whether only flagellin-TLR5 signaling activation plays a role in the pathophysiology of liver or whether some other flagellin activity also contributes to liver injury either via bacterial infections or during clinical applications. Recombinant flagellin proteins with or without TLR5-stimulating activity were used to evaluate the role of flagellin-TLR5 signaling in liver injury in wild-type and TLR5 KO mice. Gross lesions and large areas of hepatocellular necrosis were observed in liver tissue 12 h after the intraperitoneal administration of 100 or 200 pg flagellin （FliC） in a dose-and time-dependent manner in wild-type mice, but not in TLR5 KO mice. Deletion of the N-terminal or TLR5 binding domain of flagellin inhibited flagellin-induced inflammatory responses and the subsequent acute liver function abnormality and damage. These data confirmed that flagellin is an essential determinant of liver injury and demonstrated that the over-activation of TLR5 signaling by high-dose flagellin caused acute inflammatory responses, neutrophil accumulation and oxidative stress in the liver, which contributes to the progression and severity of flagellin-induced liver injury.

MicroRNAs （miRNAs） are endogenous noncoding RNAs （-22 nt） that regulate target gene expression at the posttranscriptional level in the cytoplasm. Recent discoveries of the presence of miRNAs and miRNA function-required argonaute family proteins in the cell nucleus have prompted us to hypothesize that miRNAs may also have regulatory functions in the cell nucleus. In this study, we demonstrate that mouse miR-709 is predominantly located in the nucleus of various cell types and that its nuclear localization pattern rapidly changes upon apoptotic stimuli. In the cell nucleus, miR-709 directly binds to a 19-nt miR-709 recognition element on pri-miR-15a/16-1 and prevents its processing into pre-miR-15a/16-1, leading to a suppression of miR-15a/16-1 maturation. Furthermore, nuclear miR- 709 participates in the regulation of cell apoptosis through the miR-15a/16-1 pathway. In summary, the present study provides the first evidence that one miRNA can control the biogenesis of other miRNAs by directly targeting their primary transcripts in the nucleus.

Bacterial flagellin is a unique pathogen-associated molecular pattern （PAMP）, which can be recognized by surface localized Toll-like receptor 5 （TLR5） and the cytosolic NOD-like receptor （NLR） protein 4 （NLRC4） receptors. Activation of the TLR5 and/or NLRC4 signaling pathways by flagellin and the resulting immune responses play important roles in anti-bacterial immunity. However, it remains unclear how the dual activities of flagellin that activate the TLR5 and/or NLRC4 signaling pathways orchestrate the immune responses. In this study, we assessed the effects of flagellin and its mutants lacking the ability to activate TLR5 and NLRC4 alone or in combination on the adaptive immune responses against flagellin. Flagellin that was unable to activate NLRC4 induced a significantly higher antibody response than did wild-type flagellin. The increased antibody response could be eliminated when macrophages were depleted in vivo. The activation of NLRC4 by flagellin downregulated the flagellin-induced and TLR5-mediated immune responses against flagellin.

Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performance, a disconcerting reality is that high-quality public data will be exhausted in a few years. In this paper, we offer a potential next step for contemporary LLMs: collaborative and privacy-preserving LLM training on the underutilized distributed private data via federated learning (FL), where multiple data owners collaboratively train a shared model without transmitting raw data. To achieve this, we build a concise, integrated, and research-friendly framework/codebase, named OpenFedLLM. It covers federated instruction tuning for enhancing instruction-following capability, federated value alignment for aligning with human values, and 7 representative FL algorithms. Besides, OpenFedLLM supports training on diverse domains, where we cover 8 training datasets; and provides comprehensive evaluations, where we cover 30+ evaluation metrics. Through extensive experiments, we observe that all FL algorithms outperform local training on training LLMs, demonstrating a clear performance improvement across a variety of settings. Notably, in a financial benchmark, Llama2-7B fine-tuned by applying any FL algorithm can outperform GPT-4 by a significant margin while the model obtained through individual training cannot, demonstrating strong motivation for clients to participate in FL. The code is available at https://github.com/rui-ye/OpenFedLLM.

Medical image segmentation is one of the most fundamental tasks concerning medical information analysis. Various solutions have been proposed so far, including many deep learning-based techniques, such as U-Net, FC-DenseNet, etc. However, high-precision medical image segmentation remains a highly challenging task due to the existence of inherent magnification and distortion in medical images as well as the presence of lesions with similar density to normal tissues. In this paper, we propose TFCNs (Transformers for Fully Convolutional denseNets) to tackle the problem by introducing ResLinear-Transformer (RL-Transformer) and Convolutional Linear Attention Block (CLAB) to FC-DenseNet. TFCNs is not only able to utilize more latent information from the CT images for feature extraction, but also can capture and disseminate semantic features and filter non-semantic features more effectively through the CLAB module. Our experimental results show that TFCNs can achieve state-of-the-art performance with dice scores of 83.72\\% on the Synapse dataset. In addition, we evaluate the robustness of TFCNs for lesion area effects on the COVID-19 public datasets. The Python code will be made publicly available on https://github.com/HUANGLIZI/TFCNs.

Our previous studies have demonstrated that stable microRNAs （miRNAs） in mammalian serum and plasma are actively secreted from tissues and cells and can serve as a novel class of biomarkers for diseases, and act as signaling molecules in intercellular communication. Here, we report the surprising finding that exogenous plant miRNAs are present in the sera and tissues of various animals and that these exogenous plant miRNAs are primarily acquired orally, through food intake. MIR168a is abundant in rice and is one of the most highly enriched exogenous plant miRNAs in the sera of Chinese subjects. Functional studies in vitro and in vivo demonstrated that MIR168a could bind to the human/mouse low-density lipoprotein receptor adapter protein 1 （LDLRAP1） mRNA, inhibit LDLRAP1 expression in liver, and consequently decrease LDL removal from mouse plasma. These findings demonstrate that ex- ogenous plant miRNAs in food can regulate the expression of target genes in mammals.

Tumour cells secrete exosomes that are involved in the remodelling of the tumour–stromal environment and promoting malignancy. The mechanisms governing tumour exosome release, however, remain incompletely understood. Here we show that tumour cell exosomes secretion is controlled by pyruvate kinase type M2 (PKM2), which is upregulated and phosphorylated in tumours. During exosome secretion, phosphorylated PKM2 serves as a protein kinase to phosphorylate synaptosome-associated protein 23 (SNAP-23), which in turn enables the formation of the SNARE complex to allow exosomes release. Direct phosphorylation assay and mass spectrometry confirm that PKM2 phosphorylates SNAP-23 at Ser95. Ectopic expression of non-phosphorylated SNAP-23 mutant (Ser95→Ala95) significantly reduces PKM2-mediated exosomes release whereas expression of selective phosphomimetic SNAP-23 mutants (Ser95→Glu95 but not Ser20→Glu20) rescues the impaired exosomes release induced by PKM2 knockdown. Our findings reveal a non-metabolic function of PKM2, an enzyme associated with tumour cell reliance on aerobic glycolysis, in promoting tumour cell exosome release. Exosomes, vesicles secreted by cancer cells, have a role in cancer progression but the mechanisms regulating their biogenesis are mostly unknown. Here the authors show that PKM2, a rate-limiting glycolytic enzyme overexpressed in cancer cells, mediates exosomes exocytosis by phosphorylating SNAP-23.

Background Anti-inflammatory polarized macrophages are reported to alleviate systemic lupus erythematosus (SLE). Our previous studies have demonstrated that exosomes from adipose-derived stem cells promote the anti-inflammatory polarization of macrophages. However, the possible therapeutic effect of exosomes from stem cells on SLE remains unexplored. Methods Exosomes were isolated from the conditioned medium of bone marrow-derived mesenchymal stem cells using ultrafiltration and size-exclusion chromatography and were identified by nanoparticle tracking analysis and immunoblotting of exosomal-specific markers. Macrophages were collected from the MRL/lpr mouse kidney. The phenotype of macrophages was identified by immunoblotting for intracellular markers-inducible nitric oxide synthase (iNOS) and arginase-1 (Arg-1), and flow cytometry for macrophage markers F4/80, CD86, CD206, B7H4, and CD138. Pristane-induced murine lupus nephritis models were employed for in vivo study. Results When macrophages from the kidney of the MRL/ lpr mice were treated with exosomes from bone marrow-derived mesenchymal stem cells (BM-MSCs), the upregulation of CD206, B7H4, CD138, Arg-1, CCL20, and anti-inflammatory cytokines was observed, which suggested that the macrophages were polarized to a specific anti-inflammatory phenotype. These anti-inflammatory macrophages produced low levels of reactive oxygen species (ROS) but had a high efferocytosis activity and promoted regulatory T (T reg ) cell recruitment. Moreover, exosome injection stimulated the anti-inflammatory polarization of macrophages and increased the production of IL-17 + T reg cells in a pristane-induced murine lupus nephritis model. We observed that exosomes from BMMSCs depleted of microRNA-16 (miR-16) and microRNA-21 (miR-21) failed to downregulate PDCD4 and PTEN in macrophages, respectively, and attenuated exosome-induced anti-inflammatory polarization. Conclusion Our findings provide evidence that exosomes from BMMSCs promote the anti-inflammatory polarization of macrophages. These macrophages alleviate SLE nephritis in lupus mice by consuming apoptotic debris and inducing the recruitment of T reg cells. We identify that exosomal delivery of miR-16 and miR-21 is a significant contributor to the polarization of macrophages. Graphical abstract

Background A blood-based diagnostic test is a promising strategy for colorectal cancer (CRC). The MethyDT test (IColohunter), which detects methylation levels of NTMT1 and MAP3K14 -AS1 , exhibited potential in discriminating CRC, but its clinical performance needs to be validated in large-scale populations. Methods A multicenter, double-blinded, cross-sectional study that enrolled 1194 participants was performed. Plasma samples were collected to detect methylation levels of NTMT1 and MAP3K14 -AS1 using quantitative methylation-specific PCR with the MethyDT test, and the accuracy was further evaluated by Sanger sequencing. Results The sensitivities of the MethyDT test for detecting CRC, early stages of CRC (I and II), advanced adenoma (AA), and high-grade intraepithelial neoplasia (HGIN) were 91.2% (95% confidence interval [CI], 88.4–94.0), 87.4% (95% CI, 82.5–92.2), 43.5% (95% CI, 35.7–51.4), and 72.7% (95% CI, 57.5–87.9), respectively. The specificities for participants with non-AA, interfering diseases (ID), and no evidence of disease (NED) were 85.0% (95% CI, 78.8–91.3), 93.7% (95% CI, 91.4–95.9) and 97.3% (95% CI, 90.5–99.7), respectively, and its overall specificity for all-controls was 92.4% (95% CI, 90.3–94.4). The consistency of the MethyDT test with pathology for CRC was high with a kappa value of 0.830 (95% CI, 0.795–0.865). Additionally, the MethyDT test was comparable to Sanger sequencing for detecting methylation with kappa values > 0.97. Conclusions The MethyDT test demonstrates excellent sensitivity and specificity for CRC and high consistency with Sanger sequencing for methylation, suggesting it may serve as a potential noninvasive diagnostic tool for the detection of CRC. Trial registration This clinical trial has been registered in ClinicalTrials.gov (NCT05508503).