Existing database performance anomaly datasets have the problems of comprehensiveness in anomaly types, coarse-grained root causes, and unrealistic simulation for reproducing concurrent anomalies. To address these issues, we propose a data generation system tailored for Multi-Anomaly Reproduction in Databases (DB-MAGS). DB-MAGS guarantees unified, authentic, and comprehensive data generation, while also providing fine-grained root causes. In the case of only a single anomaly occurred in the database, we categorize the factors affecting database performance anomalies, select five major categories of anomalies, and further subdivide each category into eighteen minor categories. This finer granularity of anomaly classification facilitates more specific and targeted anomaly remediation. For multiple anomalies simultaneously occurred in a database system, we categorize the relationships between anomalies into causal and concurrent, and enumerate different combinations of multiple anomalies, making the simulation of multiple anomaly scenarios more comprehensive and enhancing the diversity of generated data.

MOTIVATION: Gut dysbiosis is closely associated with obesity and related metabolic diseases including type 2 diabetes (T2D) and nonalcoholic fatty liver disease (NAFLD). The gut microbial features and biomarkers have been increasingly investigated in many studies, which require further validation due to the limited sample size and various confounding factors that may affect microbial compositions in a single study. So far, it lacks a comprehensive bioinformatics pipeline providing automated statistical analysis and integrating multiple independent studies for cross-validation simultaneously. RESULTS: OBMeta aims to streamline the standard metagenomics data analysis from diversity analysis, comparative analysis, and functional analysis to co-abundance network analysis. In addition, a curated database has been established with a total of 90 public research projects, covering three different phenotypes (Obesity, T2D, and NAFLD) and more than five different intervention strategies (exercise, diet, probiotics, medication, and surgery). With OBMeta, users can not only analyze their research projects but also search and match public datasets for cross-validation. Moreover, OBMeta provides cross-phenotype and cross-intervention-based advanced validation that maximally supports preliminary findings from an individual study. To summarize, OBMeta is a comprehensive web server to analyze and validate gut microbial features and biomarkers for obesity-associated metabolic diseases. AVAILABILITY AND IMPLEMENTATION: OBMeta is freely available at: http://obmeta.met-bioinformatics.cn/.

Abstract agent argumentation frameworks extend Dung’s theory with agents, and in this paper we study four types of semantics for them. First, agent defense semantics replaces Dung’s notion of defense by some kind of agent defense. Second, social agent semantics prefers arguments that belong to more agents. Third, agent reduction semantics considers the perspective of individual agents. Fourth, agent filtering semantics are inspired by a lack of knowledge. We study five existing principles and we introduce twelve new ones. In total, we provide a full analysis of fifty-two agent semantics and the seventeen principles.