Foxo1 Drives the TGFß1-Dependent Dichotomy of Th17 Cell Fates.

T-helper 17 (Th17) cells play a dual role in immunological responses, serving as essential components in tissue homeostasis and host defense against microbial pathogens while also contributing to pro-inflammatory conditions and autoimmunity. While Transforming Growth Factor-beta 1 (TGFß1) is pivotal for the differentiation of non-pathogenic Th17 cells, the role of TGFß3 and Activin in steering Th17 cells toward a pathogenic phenotype has been acknowledged. However, the molecular mechanisms governing this dichotomy remain elusive. In this study, we demonstrate that the transcription factor Foxo1 is upregulated in a TGFß1 dose-dependent manner, serving as a critical regulator that specifically modulates the fate of pathogenic Th17 cells. Analyses in both uveitis patients and an Experimental Autoimmune Uveitis (EAU) mouse model reveal a strong correlation between disease severity and diminished Foxo1 expression levels. Ectopic expression of Foxo1 selectively attenuates IL-17A production under pathogenic Th17-inducing conditions. Moreover, enhanced Foxo1 expression, triggered by TGFß1 signaling, is implicated in fatty acid metabolism pathways that favor non-pathogenic Th17 differentiation. Our drug screening identifies several FDA-approved compounds can upregulate Foxo1. Collectively, our findings offer evidence that Foxo1 serves as a molecular switch to specifically control pathogenic versus non-pathogenic Th17 differentiation in a TGFß1-dependent manner. Suggest that targeting Foxo1 could be a promising therapeutic strategy for autoimmune diseases.

Tuning Solvation Behavior of Ester-Based Electrolytes toward Highly Stable Lithium-Metal Batteries.

Metallic Li is the ultimate choice for the anode of lithium batteries. However, the adverse effect retards the commercialization of Li-metal batteries (LMBs). Herein, by using Cu(NO3)2 to regulate the solvation behavior of the ester-based electrolyte without fluoroethylene carbonate (FEC), the properties of Li|NCM811 are improved evidently. The solvation degree and oxidation stability of the electrolyte are increased. The solvated NO3- and marginalized PF6- promote the formation of an inorganic-rich solid electrolyte interphase (SEI) film on the anode, effectively protecting the lithium metal. The voltage decay and the dissolution of transition metals in the Li|NCM811 cell are significantly suppressed. The cell exhibits a capacity retention as high as 95.73% after 600 cycles at room temperature and outstanding cycle performance for wide temperatures (0 and 50 °C). The cell also shows impressive cycle performance even under rigorous conditions. Our research elucidates the role of Cu(NO3)2 from the perspective of the solvation behavior and provides a new strategy for the application of nitrates in ester-based electrolytes for LMBs.

A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies.

Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.

A graph-based method for reconstructing entities from coordination ellipsis in medical text.

OBJECTIVE: Coordination ellipsis is a linguistic phenomenon abound in medical text and is challenging for concept normalization because of difficulty in recognizing elliptical expressions referencing 2 or more entities accurately. To resolve this bottleneck, we aim to contribute a generalizable method to reconstruct concepts from medical coordinated elliptical expressions in a variety of biomedical corpora. MATERIALS AND METHODS: We proposed a graph-based representation model and built a pipeline to reconstruct concepts from coordinated elliptical expressions in medical text (RECEEM). There are 4 modules: (1) identify all possible candidate conjunct pairs from original coordinated elliptical expressions, (2) calculate coefficients for candidate conjuncts using the embedding model, (3) select the most appropriate decompositions by global optimization, and (4) rebuild concepts based on a pathfinding algorithm. We evaluated the pipeline's performance on 2658 coordinated elliptical expressions from 3 different medical corpora (ie, biomedical literature, clinical narratives, and eligibility criteria from clinical trials). Precision, recall, and F1 score were calculated. RESULTS: The F1 scores for biomedical publications, clinical narratives, and research eligibility criteria were 0.862, 0.721, and 0.870, respectively. RECEEM outperformed 2 previously released methods. By incorporating RECEEM into 2 existing NLP tools, the F1 scores increased from 0.248 to 0.460 and from 0.287 to 0.630 on concept mapping of 1125 coordination ellipses. CONCLUSIONS: RECEEM improves concept normalization for medical coordinated elliptical expressions in a variety of biomedical corpora. It outperformed existing methods and significantly enhanced the performance of 2 notable NLP systems for mapping coordination ellipses in the evaluation. The algorithm is open sourced online (https://github.com/chiyuan1126/RECEEM).

A Collaborative Framework Based for Semantic Patients-Behavior Analysis and Highlight Topics Discovery of Alcoholic Beverages in Online Healthcare Forums.

Medical data in online groups and social media contain valuable information, which is provided by both healthcare professionals and patients. In fact, patients can talk freely and share their personal experiences. These resources are a valuable opportunity for health professionals who can access patients' opinions, as well as discussions between patients. Recently, the data processing of the health community and, how to extract knowledge is a significant technical challenge. There are many online group and forums that users can discuss on healthcare issues. Therefore, we can examine these text documents for discovering knowledge and evaluating patients' behavior based on their opinions and discussions. For example, there are many questions and answering groups on Twitter or Facebook. Given the importance of the research, in this paper, we present a semantic framework based on topic model (LDA) and Random forest(RF) to predict and retrieval latent topics of healthcare text-documents from an online forum. We extract our healthcare records (patient-questions) from patient.info website as a real dataset. Experiments on our dataset show that social media forums could help for detecting significant patient safety problems on healthcare issues.