Fish ecotyping based on machine learning and inferred network analysis of chemical and physical properties

Inferring microbial community assembly in an urban river basin through geo-multi-omics and phylogenetic bin-based null-model analysis of surface water

Understanding the community assembly process is a central issue in microbial ecology. In this study, we analyzed the community assembly of particle-associated (PA) and free-living (FL) surface water microbiomes in 54 sites from the headstream to the river mouth of an urban river in Japan, the river basin of which has the highest human population density in the country. Analyses were conducted from two perspectives: (1) analysis of deterministic processes considering only environmental factors using a geo-multi-omics dataset and (2) analysis of deterministic and stochastic processes to estimate the contributions of heterogeneous selection (HeS), homogeneous selection (HoS), dispersal limitation (DL), homogenizing dispersal (HD), and drift (DR) as community assembly processes using a phylogenetic bin-based null model. The variation in microbiomes was successfully explained from a deterministic perspective by environmental factors, such as organic matter-related, nitrogen metabolism, and salinity-related parameters, using multivariate statistical analysis, network analysis, and habitat prediction. In addition, we demonstrated the dominance of stochastic processes (DL, HD, and DR) over deterministic processes (HeS and HoS) in community assembly from both deterministic and stochastic perspectives. Our analysis revealed that as the distance between two sites increased, the effect of HoS sharply decreased while the effect of HeS increased, particularly between upstream and estuary sites, indicating that the salinity gradient could potentially enhance the contribution of HeS to community assembly. Our study highlights the importance of both stochastic and deterministic processes in community assembly of PA and FL surface water microbiomes in urban riverine ecosystems.

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.

Identification of salmoniformes aquaculture conditions to increase creatine and anserine levels using multiomics dataset and nonnumerical information

Aquaculture is attracting attention as a sustainable protein source. Salmoniformes, which are generally called salmon, are consumed in large quantities worldwide and are popularly used for aquaculture. In this study, the relationship between muscle metabolites, intestinal microbiota, and nonnumerical information about the ecology of salmoniformes was investigated to improve the efficiency of aquaculture using nuclear magnetic resonance and next-generation sequencing with bioinformatics approach. It was revealed that salmoniformes are rich in anserine and creatine, which are useful for human health care, along with collagen and lipids. The important factors in increasing these useful substances and manage the environment of salmoniformes aquaculture should be noted.

Parameter Visualization of Benchtop Nuclear Magnetic Resonance Spectra toward Food Process Monitoring

Low-cost and user-friendly benchtop low-field nuclear magnetic resonance (NMR) spectrometers are typically used to monitor food processes in the food industry. Because of excessive spectral overlap, it is difficult to characterize food mixtures using low-field NMR spectroscopy. In addition, for standard compounds, low-field benchtop NMR data are typically unavailable compared to high-field NMR data, which have been accumulated and are reusable in public databases. This work focused on NMR parameter visualization of the chemical structure and mobility of mixtures and the use of high-field NMR data to analyze benchtop NMR data to characterize food process samples. We developed a tool to easily process benchtop NMR data and obtain chemical shifts and T2 relaxation times of peaks, as well as transform high-field NMR data into low-field NMR data. Line broadening and time–frequency analysis methods were adopted for data processing. This tool can visualize NMR parameters to characterize changes in the components and mobilities of food process samples using benchtop NMR data. In addition, assignment errors were smaller when the spectra of standard compounds were identified by transferring the high-field NMR data to low-field NMR data rather than directly using experimentally obtained low-field NMR spectra.

Saltwater intrusion affecting NO2- accumulation in demersal fishery species by bacterially mediated N-cycling

To investigate the underlying effects of saltwater intrusion (SWI) on bottom aquatic ecosystems, a set of environmental parameters and the bacterial community were determined and analyzed by sampling bottom water and surface sediments at the Modaomen waterway of the Pearl River Estuary. Biodiversity of fishery species and their relationship with the environment variables were analyzed together. NO₃^- and NO₂^- concentration down-regulation and NH₄⁺ concentration up-regulation in water and sediment were observed along the resulting salinity gradient, indicating that SWI affected N-cycling. Further investigation via 16 s sequencing revealed that taxonomic and functional composition of the bacterial community in the sediment displayed greater discretization than in water, implying that SWI exerted a greater impact on the sedimentary bacterial community. Metagenomic sequencing showed that the sedimentary bacterial community was associated with NO₃^-, NO₂^-, and NH₄⁺ transformation under SWI, and that this was driven by salinity and conductivity. Nitrogen metabolism and denitrification related genes were expressed at higher levels in high salinity than in low salinity, consistent with the increased enzymatic activities of NiR and NR. The NO₂^- concentration in the muscle of six selected fishery species was significantly decreased by 11.15-65.74% (P < 0.05) along the salinity gradient, indicating that SWI reduced NO₂^- accumulation. The results suggest that SWI alleviates NO₂^- accumulation in demersal fishery species via bacterial mediation of N-cycling.

Identifying a Correlation among Qualitative Non-Numeric Parameters in Natural Fish Microbe Dataset Using Machine Learning

Recent technical innovations and developments in computer-based technology have enabled bioscience researchers to acquire comprehensive datasets and identify unique parameters within experimental datasets. However, field researchers may face the challenge that datasets exhibit few associations among any measurement results (e.g., from analytical instruments, phenotype observations as well as field environmental data), and may contain non-numerical, qualitative parameters, which make statistical analyses difficult. Here, we propose an advanced analysis scheme that combines two machine learning steps to mine association rules between non-numerical parameters. The aim of this analysis is to identify relationships between variables and enable the visualization of association rules from data of samples collected in the field, which have less correlations between genetic, physical, and non-numerical qualitative parameters. The analysis scheme presented here may increase the potential to identify important characteristics of big datasets.

Chemometric Analysis of NMR Spectra and Machine Learning to Investigate Membrane Fouling

Efficient membrane filtration requires the understanding of the membrane foulants and the functional properties of different membrane types in water purification. In this study, dead-end filtration of aquaculture system effluents was performed and the membrane foulants were investigated via nuclear magnetic resonance (NMR) spectroscopy. Several machine learning models (Random Forest; RF, Extreme Gradient Boosting; XGBoost, Support Vector Machine; SVM, and Neural Network; NN) were constructed, one to predict the maximum transmembrane pressure, for revealing the chemical compounds causing fouling, and the other to classify the membrane materials based on chemometric analysis of NMR spectra, for determining their effect on the properties of the different membrane types tested. Especially, RF models exhibited high accuracy; the important chemical shifts observed in both the regression and classification models suggested that the proportional patterns of sugars and proteins are key factors in the fouling progress and the classification of membrane types. Therefore, the proposed strategy of chemometric analysis of NMR spectra is suitable for membrane research, which aims at investigating comprehensively the fouling phenomenon and how the foulants and environmental conditions vary according to the filtration systems.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.