Noninvasive fecal metabolic profiling for the evaluation of characteristics of thermostable lactic acid bacteria, Weizmannia coagulans SANK70258, for broiler chickens

Biochar derivation at low temperature: A novel strategy for harmful resource usage of antibiotic mycelial dreg

Antibiotic mycelial dreg (AMD) has been categorized as hazardous waste due to the high residual hazardous contaminants. Inappropriate management and disposal of AMD can cause potential environmental and ecological risks. In this study, the potential of pleuromutilin mycelial dreg (PMD) as a novel feedstock for preparing tetracycline hydrochloride (TC) adsorbent was explored to achieve safe management of PMD. The results suggested that residual hazardous contaminants were completely eliminated after pyrolysis. With the increase of pyrolysis temperature, the yields, H/C, O/C, (O + N)/C, and pore size in PMD-derived biochars (PMD-BCs) decreased, while BET surface area and pore volume increased, resulting in the higher stability of the PMD-BCs prepared from higher temperatures. The TC adsorption of the PMD-BCs increased from 27.3 to 46.9 mg/g with the increase of the pyrolysis temperature. Surprisingly, pH value had a strong impact on the TC adsorption, the adsorption capacity of BC-450 increased from 6.5 to 71.1 mg/g when the solution pH value increased from 2 to 10. Lewis acid-base interaction, pore filling, π-π interaction, hydrophobic interaction, and charge-assisted hydrogen bond (CAHB) are considered to drive the adsorption. This work provides a novel pathway for the concurrent detoxification and reutilization of AMD.

An evaluation of homeostatic plasticity for ecosystems using an analytical data science approach

The natural world is constantly changing, and planetary boundaries are issuing severe warnings about biodiversity and cycles of carbon, nitrogen, and phosphorus. In other views, social problems such as global warming and food shortages are spreading to various fields. These seemingly unrelated issues are closely related, but it can be said that understanding them in an integrated manner is still a step away. However, progress in analytical technologies has been recognized in various fields and, from a microscopic perspective, with the development of instruments including next-generation sequencers (NGS), nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC/MS), and liquid chromatography-mass spectrometry (LC/MS), various forms of molecular information such as genome data, microflora structure, metabolome, proteome, and lipidome can be obtained. The development of new technology has made it possible to obtain molecular information in a variety of forms. From a macroscopic perspective, the development of environmental analytical instruments and environmental measurement facilities such as satellites, drones, observation ships, and semiconductor censors has increased the data availability for various environmental factors. Based on these background, the role of computational science is to provide a mechanism for integrating and understanding these seemingly disparate data sets. This review describes machine learning and the need for structural equations and statistical causal inference of these data to solve these problems. In addition to introducing actual examples of how these technologies can be utilized, we will discuss how to use these technologies to implement environmentally friendly technologies in society.