The prodomain of Arabidopsis metacaspase 2 positively regulates immune signaling mediated by pattern-recognition receptors.

Metacaspases (MCs) are structural homologs of mammalian caspases found in plants, fungi, and protozoa. Type-I MCs carry an N-terminal prodomain, the function of which is unclear. Through genetic analysis of Arabidopsis mc2-1, a T-DNA insertion mutant of MC2, we demonstrated that the prodomain of metacaspase 2 (MC2) promotes immune signaling mediated by pattern-recognition receptors (PRRs). In mc2-1, immune responses are constitutively activated. The receptor-like kinases (RLKs) BAK1/BKK1 and SOBIR1 are required for the autoimmune phenotype of mc2-1, suggesting that immune signaling mediated by the receptor-like protein (RLP)-type PRRs is activated in mc2-1. A suppressor screen identified multiple mutations in the first exon of MC2, which suppress the autoimmunity in mc2-1. Further analysis revealed that the T-DNA insertion at the end of exon 1 of MC2 causes elevated expression of the MC2 prodomain, and overexpression of the MC2 prodomain in wild-type (WT) plants results in the activation of immune responses. The MC2 prodomain interacts with BIR1, which inhibits RLP-mediated immune signaling by interacting with BAK1, suggesting that the MC2 prodomain promotes plant defense responses by interfering with the function of BIR1. Our study uncovers an unexpected function of the prodomain of a MC in plant immunity.

Effects of modified Danggui Sini Decoction as adjuvant therapy for angina pectoris in coronary heart disease: a systematic review and meta-analysis based on randomised controlled trials.

Introduction: This systematic review evaluates the efficacy of the Chinese herbal formula modified Danggui Sini Decoction as an adjunctive treatment for angina pectoris in patients with coronary heart disease. Methods: We conducted a comprehensive search for randomized controlled trials that investigated the effects of modified Danggui Sini Decoction in combination with conventional Western medication on angina pectoris in coronary artery disease, published up to July 2023 across eight databases, including China Knowledge International Literature screening and data extraction were performed by two researchers following predefined inclusion and exclusion criteria. The quality of included studies was assessed using the Cochrane Handbook version 5.1, and meta-analysis was executed via RevMan 5.4 software. Results: Thirteen studies encompassing 1,232 participants were incorporated. The meta-analysis revealed that combining modified Danggui Sini Decoction with conventional Western medication significantly enhanced overall clinical efficacy, reduced the duration of angina attacks, decreased the Chinese medicine syndrome score, improved inflammatory markers and cardiac function, lowered serum NT-proBNP levels, and elevated the Seattle Angina Questionnaire scores compared to the control group. Conclusion: Modified Danggui Sini Decoction, when used alongside conventional Western medications, shows promise in treating coronary artery disease patients with angina pectoris and may serve as a beneficial adjunctive therapy in clinical settings. Nonetheless, due to the limited quantity and quality of the included studies, further high-caliber research is essential to substantiate these findings. Systematic Review Registration: https://inplasy.com/? s=202390078, identifier INPLASY 202390078.

SEA++: Multi-Graph-based Higher-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation.

Unsupervised Domain Adaptation (UDA) methods have been successful in reducing label dependency by minimizing the domain discrepancy between labeled source domains and unlabeled target domains. However, these methods face challenges when dealing with Multivariate Time-Series (MTS) data. MTS data typically originates from multiple sensors, each with its unique distribution. This property poses difficulties in adapting existing UDA techniques, which mainly focus on aligning global features while overlooking the distribution discrepancies at the sensor level, thus limiting their effectiveness for MTS data. To address this issue, a practical domain adaptation scenario is formulated as Multivariate Time-Series Unsupervised Domain Adaptation (MTS-UDA). In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA, aiming to address domain discrepancy at both local and global sensor levels. At the local sensor level, we design endo-feature alignment, which aligns sensor features and their correlations across domains. To reduce domain discrepancy at the global sensor level, we design exo-feature alignment that enforces restrictions on global sensor features. We further extend SEA to SEA++ by enhancing the endo-feature alignment. Particularly, we incorporate multi-graph-based higher-order alignment for both sensor features and their correlations. Extensive empirical results have demonstrated the state-of-the-art performance of our SEA and SEA++ on six public MTS datasets for MTS-UDA.