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Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host–microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood. [ABSTRACT FROM AUTHOR]

Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host-microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood.

High-performance energetic materials are mainly used in the military, aerospace industry and chemical fields. The ordinary technology of producing energetic materials cannot avoid the domination of its unique needs. At present, revealing the underlying mechanism of the formation of high-energy materials is of great significance for improving their quality characteristics. We pay special attention to the decomposition and reactive molecular dynamics (RMD) simulation of 5,5'-dinitro-2H,2H'-3,3'-bi-1,2,4-triazole (DNBT). Various forms were captured in the simulation, and the form is determined by the temperature of the initial reactant. By observing the heating pattern and morphological changes under the initial thermal equilibrium, interesting temperature jumps were found in 325 K and 350 K. Observation of continuous heating (simulated temperatures are 2600 K, 2900 K, 3200 K and 3500 K) shows that DNBT has the maximum heating rate at 3500 K. In addition, N 2 occupies this dominant position in the product, moreover, N 2 and NO 2 respectively dominate the gas phase products during the initial heating process. According to the transition state analysis results of the intermediates, we found 4 interesting intermediate products, which were determined by high frequency reaction under the 4 simulated temperatures and performed with transition state calculations. It shows that the selection of reactant temperature and its activity is the key to orderly decomposition of DNBT. It is expected that these findings will be widely used in comprehensive decomposition devices and to improve the concept of learning military and industrial technology., Competing Interests: The authors declare no competing financial interest., (This journal is © The Royal Society of Chemistry.)