Feed Components and Timing to Improve the Feed Conversion Ratio for Sustainable Aquaculture Using Starch.

Aquaculture contributes to the sustainable development of food security, marine resource conservation, and economy. Shifting aquaculture feed from fish meal and oil to terrestrial plant derivatives may result in cost savings. However, many carnivorous fish cannot be sustained on plant-derived materials, necessitating the need for the identification of important factors for farmed fish growth and the identification of whether components derived from terrestrial plants can be used in feed.

Transcriptome analysis of the Japanese eel (Anguilla japonica) during larval metamorphosis.

Background: Anguillid eels spend their larval period as leptocephalus larvae that have a unique and specialized body form with leaf-like and transparent features, and they undergo drastic metamorphosis to juvenile glass eels. Less is known about the transition of leptocephali to the glass eel stage, because it is difficult to catch the metamorphosing larvae in the open ocean. However, recent advances in rearing techniques for the Japanese eel have made it possible to study the larval metamorphosis of anguillid eels.

Microbiome and Metabolome Analyses in Different Closed-Circulation Aquarium Systems and Their Network Visualization.

Understanding the causes of microbiome formation and its relationship to environmental conditions is important to properly maintain recirculating aquaculture systems (RASs). Although RAS has been applied to numerous fish types and environmental conditions (e.g.

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.

18S rRNA gene sequences of leptocephalus gut contents, particulate organic matter, and biological oceanographic conditions in the western North Pacific.

Eel larvae apparently feed on marine snow, but many aspects of their feeding ecology remain unknown. The eukaryotic 18S rRNA gene sequence compositions in the gut contents of four taxa of anguilliform eel larvae were compared with the sequence compositions of vertically sampled seawater particulate organic matter (POM) in the oligotrophic western North Pacific Ocean. Both gut contents and POM were mainly composed of dinoflagellates as well as other phytoplankton (cryptophytes and diatoms) and zooplankton (ciliophoran and copepod) sequences.

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

Functional diversity rather than species richness is critical for the understanding of ecological patterns and processes. This study aimed to develop novel integrated analytical strategies for the functional characterization of fish diversity based on the quantification, prediction and integration of the chemical and physical features in fish muscles. Machine learning models with an improved random forest algorithm applied on 1867 muscle nuclear magnetic resonance spectra belonging to 249 fish species successfully predicted the mobility patterns of fishes into four categories (migratory, territorial, rockfish, and demersal) with accuracies of 90.

Large-Scale Evaluation of Major Soluble Macromolecular Components of Fish Muscle from a Conventional H-NMR Spectral Database.

Conventional proton nuclear magnetic resonance (H-NMR) has been widely used for identification and quantification of small molecular components in food. However, identification of major soluble macromolecular components from conventional H-NMR spectra is difficult. This is because the baseline appearance is masked by the dense and high-intensity signals from small molecular components present in the sample mixtures.

Molecular diet analysis of Anguilliformes leptocephalus larvae collected in the western North Pacific.

Natural diets of leptocephalus larvae have been enigmatic. In this study, we collected DNA samples from the gut contents and body surface of leptocephali belonging to the five Anguilliform families (Anguillidae, Chlopsidae, Congridae, Muraenidae, and Serrivomeridae) from the northwest Pacific and performed next-generation 18S rDNA sequencing. Wide variety of eukaryotes was detected in both samples, from which eight eukaryotic groups (jellyfish, conoid parasite, tunicate, copepod, krill, segmented worm, fungi, and dinoflagellate) were selected on the basis of abundance.

Application of ensemble deep neural network to metabolomics studies.

Deep neural network (DNN) is a useful machine learning approach, although its applicability to metabolomics studies has rarely been explored. Here we describe the development of an ensemble DNN (EDNN) algorithm and its applicability to metabolomics studies. As a model case, the developed EDNN approach was applied to metabolomics data of various fish species collected from Japan coastal and estuarine environments for evaluation of a regression performance compared with conventional DNN, random forest, and support vector machine algorithms.

Intestinal microbiota composition is altered according to nutritional biorhythms in the leopard coral grouper (Plectropomus leopardus).

Aquaculture is currently a major source of fish and has the potential to become a major source of protein in the future. These demands require efficient aquaculture. The intestinal microbiota plays an integral role that benefits the host, providing nutrition and modulating the immune system.

Systemic Homeostasis in Metabolome, Ionome, and Microbiome of Wild Yellowfin Goby in Estuarine Ecosystem.

Data-driven approaches were applied to investigate the temporal and spatial changes of 1,022 individuals of wild yellowfin goby and its potential interaction with the estuarine environment in Japan. Nuclear magnetic resonance (NMR)-based metabolomics revealed that growth stage is a primary factor affecting muscle metabolism. Then, the metabolic, elemental and microbial profiles of the pooled samples generated according to either the same habitat or sampling season as well as the river water and sediment samples from their habitats were measured using NMR spectra, inductively coupled plasma optical emission spectrometry and next-generation 16 S rRNA gene sequencing.

Noninvasive analysis of metabolic changes following nutrient input into diverse fish species, as investigated by metabolic and microbial profiling approaches.

An NMR-based metabolomic approach in aquatic ecosystems is valuable for studying the environmental effects of pharmaceuticals and other chemicals on fish. This technique has also contributed to new information in numerous research areas, such as basic physiology and development, disease, and water pollution. We evaluated the microbial diversity in various fish species collected from Japan's coastal waters using next-generation sequencing, followed by evaluation of the effects of feed type on co-metabolic modulations in fish-microbial symbiotic ecosystems in laboratory-scale experiments.

Comparative analysis of chemical and microbial profiles in estuarine sediments sampled from Kanto and Tohoku regions in Japan.

Estuarine environments accumulate large quantities of organic matter from land masses adjoining the sea, and this is consumed as part of the detritus cycle. These environments are rich in biodiversity, and their ecosystem services greatly benefit humans. However, the estuarine environments have complicated aqueous ecosystems, thus the comprehensive evaluation of biotic interactions and stability is difficult using conventional hypothesis-driven approaches.