Production and characterization of a novel, thermotolerant fungal phytase from agro-industrial byproducts for cattle feed

Optimization of phytase production by Penicillium oxalicum in solid-state fermentation for potential as a feed additive

The study aims to statistically optimize the phytase production by Penicillium oxalicum PBG30 in solid-state fermentation using wheat bran as substrate. Variables viz. pH, incubation days, MgSO4, and Tween-80 were the significant parameters identified through the Plackett-Burman design (PBD) that majorly influenced the phytase production. Further, central composite design (CCD) method of response surface methodology (RSM) defined the optimum values for these factors i.e., pH 7.0, 5 days of incubation, 0.75% of MgSO4, and 3.5% of Tween-80 that leads to maximum phytase production of 475.42 U/g DMR. Phytase production was also sustainable in flasks and trays of different sizes with phytase levels ranging from 394.95 to 475.42 U/g DMR. Enhancement in phytase production is 5.6-fold as compared to unoptimized conditions. The in-vitro dephytinization of feed showed an amelioration in the nutritive value by releasing inorganic phosphate and other nutrients in a time-dependent manner. The highest amount of inorganic phosphate (33.986 mg/g feed), reducing sugar (134.4 mg/g feed), and soluble protein (115.52 mg/g feed) was achieved at 37 °C with 200 U of phytase in 0.5 g feed for 48 h. This study reports the economical and large-scale production of phytase with applicability in enhancing feed nutrition.

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.