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Shima H, Asakura T, Sakata K, Koiso M, Kikuchi J. Feed Components and Timing to Improve the Feed Conversion Ratio for Sustainable Aquaculture Using Starch. Int J Mol Sci 2024; 25:7921. [PMID: 39063163 PMCID: PMC11276616 DOI: 10.3390/ijms25147921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 07/08/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024] Open
Abstract
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. Herein, we focused on the carnivorous fish leopard coral grouper (P. leopardus) to identify the essential growth factors and clarify their intake timing from feeds. Furthermore, we evaluated the functionality of starch, which are easily produced by terrestrial plants. Results reveal that carbohydrates, which are not considered essential for carnivorous fish, can be introduced as a major part of an artificial diet. The development of artificial feed using starch offers the possibility of increasing the growth of carnivorous fish in aquaculture.
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Affiliation(s)
- Hideaki Shima
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Kanagawa, Japan
| | - Taiga Asakura
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Kanagawa, Japan
| | - Kenji Sakata
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Kanagawa, Japan
| | - Masahiko Koiso
- Research Center for Subtropical Fisheries, Seikai National Fisheries Research Institute, Japan Fishery Research and Education Agency, 148 Fukaiota, Ishigaki 907-0451, Okinawa, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Kanagawa, Japan
- Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Kanagawa, Japan
- Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya 464-8601, Aichi, Japan
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Yagin FH, Al-Hashem F, Ahmad I, Ahmad F, Alkhateeb A. Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery. Nutrients 2024; 16:1537. [PMID: 38794775 PMCID: PMC11124278 DOI: 10.3390/nu16101537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 05/13/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques. METHODS Univariate and multivariate analyses such as fold change, a receiver operating characteristic curve (ROC), and Partial Least-Squares Discriminant Analysis (PLS-DA) were used to identify biomarker metabolites that showed significant concentration in T2D patients. Three tree-based algorithms [eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Adaptive Boosting (AdaBoost)] that demonstrated robustness in high-dimensional data analysis were used to create a diagnostic model for T2D. RESULTS As a result of the biomarker discovery process validated with three different approaches, Pyruvate, D-Rhamnose, AMP, pipecolate, Tetradecenoic acid, Tetradecanoic acid, Dodecanediothioic acid, Prostaglandin E3/D3 (isobars), ADP and Hexadecenoic acid were determined as potential biomarkers for T2D. Our results showed that the XGBoost model [accuracy = 0.831, F1-score = 0.845, sensitivity = 0.882, specificity = 0.774, positive predictive value (PPV) = 0.811, negative-PV (NPV) = 0.857 and Area under the ROC curve (AUC) = 0.887] had the slight highest performance measures. CONCLUSIONS ML integrated with bioinformatics techniques offers accurate and positive T2D candidate biomarker discovery. The XGBoost model can successfully distinguish T2D based on metabolites.
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Affiliation(s)
- Fatma Hilal Yagin
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, Turkey
| | - Fahaid Al-Hashem
- Department of Physiology, College of Medicine, King Khalid University, Abha 61421, Saudi Arabia
| | - Irshad Ahmad
- Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia
| | - Fuzail Ahmad
- Department of Respiratory Care, College of Applied Sciences, Almaarefa University, Diriya, Riyadh 13713, Saudi Arabia
| | - Abedalrhman Alkhateeb
- Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
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3
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Shima H, Murata I, Feifei W, Sakata K, Yokoyama D, Kikuchi J. Identification of salmoniformes aquaculture conditions to increase creatine and anserine levels using multiomics dataset and nonnumerical information. Front Microbiol 2022; 13:991819. [PMID: 36386693 PMCID: PMC9650253 DOI: 10.3389/fmicb.2022.991819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/28/2022] [Indexed: 11/30/2022] Open
Abstract
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.
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Affiliation(s)
- Hideaki Shima
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Izumi Murata
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan
| | - Wei Feifei
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Kenji Sakata
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Daiki Yokoyama
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Japan
- Graduate School of Bioagriculuture Sciences, Nagoya University, Nagoya, Japan
- *Correspondence: Jun Kikuchi,
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4
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Identifying a Correlation among Qualitative Non-Numeric Parameters in Natural Fish Microbe Dataset Using Machine Learning. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12125927] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
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.
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5
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Yokoyama D, Suzuki S, Asakura T, Kikuchi J. Chemometric Analysis of NMR Spectra and Machine Learning to Investigate Membrane Fouling. ACS OMEGA 2022; 7:12654-12660. [PMID: 35474825 PMCID: PMC9025983 DOI: 10.1021/acsomega.1c06891] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/02/2022] [Indexed: 05/26/2023]
Abstract
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.
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Affiliation(s)
- Daiki Yokoyama
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Medical Life Science, Yokohama
City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Sosei Suzuki
- Graduate
School of Medical Life Science, Yokohama
City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Taiga Asakura
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Medical Life Science, Yokohama
City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Jun Kikuchi
- RIKEN
Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Medical Life Science, Yokohama
City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate
School of Bioagricultural Sciences, Nagoya
University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan
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6
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Yamazaki K, Kato T, Tsuboi Y, Miyauchi E, Suda W, Sato K, Nakajima M, Yokoji-Takeuchi M, Yamada-Hara M, Tsuzuno T, Matsugishi A, Takahashi N, Tabeta K, Miura N, Okuda S, Kikuchi J, Ohno H, Yamazaki K. Oral Pathobiont-Induced Changes in Gut Microbiota Aggravate the Pathology of Nonalcoholic Fatty Liver Disease in Mice. Front Immunol 2021; 12:766170. [PMID: 34707622 PMCID: PMC8543001 DOI: 10.3389/fimmu.2021.766170] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 09/28/2021] [Indexed: 12/12/2022] Open
Abstract
Background & Aims Periodontitis increases the risk of nonalcoholic fatty liver disease (NAFLD); however, the underlying mechanisms are unclear. Here, we show that gut dysbiosis induced by oral administration of Porphyromonas gingivalis, a representative periodontopathic bacterium, is involved in the aggravation of NAFLD pathology. Methods C57BL/6N mice were administered either vehicle, P. gingivalis, or Prevotella intermedia, another periodontopathic bacterium with weaker periodontal pathogenicity, followed by feeding on a choline-deficient, l-amino acid-defined, high-fat diet with 60 kcal% fat and 0.1% methionine (CDAHFD60). The gut microbial communities were analyzed by pyrosequencing the 16S ribosomal RNA genes. Metagenomic analysis was used to determine the relative abundance of the Kyoto Encyclopedia of Genes and Genomes pathways encoded in the gut microbiota. Serum metabolites were analyzed using nuclear magnetic resonance-based metabolomics coupled with multivariate statistical analyses. Hepatic gene expression profiles were analyzed via DNA microarray and quantitative polymerase chain reaction. Results CDAHFD60 feeding induced hepatic steatosis, and in combination with bacterial administration, it further aggravated NAFLD pathology, thereby increasing fibrosis. Gene expression analysis of liver samples revealed that genes involved in NAFLD pathology were perturbed, and the two bacteria induced distinct expression profiles. This might be due to quantitative and qualitative differences in the influx of bacterial products in the gut because the serum endotoxin levels, compositions of the gut microbiota, and serum metabolite profiles induced by the ingested P. intermedia and P. gingivalis were different. Conclusions Swallowed periodontopathic bacteria aggravate NAFLD pathology, likely due to dysregulation of gene expression by inducing gut dysbiosis and subsequent influx of gut bacteria and/or bacterial products.
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Affiliation(s)
- Kyoko Yamazaki
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Tamotsu Kato
- Laboratory for Intestinal Ecosystem, RIKEN Centre for Integrative Medical Sciences (IMS), Yokohama, Japan
| | - Yuuri Tsuboi
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Eiji Miyauchi
- Laboratory for Intestinal Ecosystem, RIKEN Centre for Integrative Medical Sciences (IMS), Yokohama, Japan
| | - Wataru Suda
- Laboratory for Microbiome Sciences, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Keisuke Sato
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Mayuka Nakajima
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Mai Yokoji-Takeuchi
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Miki Yamada-Hara
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Takahiro Tsuzuno
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Aoi Matsugishi
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Naoki Takahashi
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Koichi Tabeta
- Division of Periodontology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Nobuaki Miura
- Division of Bioinformatics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Shujiro Okuda
- Division of Bioinformatics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Medical AI Center, Niigata University School of Medicine, Niigata, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Hiroshi Ohno
- Laboratory for Intestinal Ecosystem, RIKEN Centre for Integrative Medical Sciences (IMS), Yokohama, Japan
- Intestinal Microbiota Project, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Japan
| | - Kazuhisa Yamazaki
- Research Unit for Oral-Systemic Connection, Division of Oral Science for Health Promotion, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Laboratory for Intestinal Ecosystem, RIKEN Centre for Integrative Medical Sciences (IMS), Yokohama, Japan
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Kikuchi J, Yamada S. The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science. RSC Adv 2021; 11:30426-30447. [PMID: 35480260 PMCID: PMC9041152 DOI: 10.1039/d1ra03008f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022] Open
Abstract
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.
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Affiliation(s)
- Jun Kikuchi
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Graduate School of Bioagricultural Sciences, Nagoya University Furo-cho, Chikusa-ku Nagoya 464-8601 Japan
- Graduate School of Medical Life Science, Yokohama City University 1-7-29 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
| | - Shunji Yamada
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Prediction Science Laboratory, RIKEN Cluster for Pioneering Research 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
- Data Assimilation Research Team, RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
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Date Y, Wei F, Tsuboi Y, Ito K, Sakata K, Kikuchi J. Relaxometric learning: a pattern recognition method for T 2 relaxation curves based on machine learning supported by an analytical framework. BMC Chem 2021; 15:13. [PMID: 33610164 PMCID: PMC7897374 DOI: 10.1186/s13065-020-00731-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 12/15/2020] [Indexed: 11/10/2022] Open
Abstract
Nuclear magnetic resonance (NMR)-based relaxometry is widely used in various fields of research because of its advantages such as simple sample preparation, easy handling, and relatively low cost compared with metabolomics approaches. However, there have been no reports on the application of the T2 relaxation curves in metabolomics studies involving the evaluation of metabolic mixtures, such as geographical origin determination and feature extraction by pattern recognition and data mining. In this study, we describe a data mining method for relaxometric data (i.e., relaxometric learning). This method is based on a machine learning algorithm supported by the analytical framework optimized for the relaxation curve analyses. In the analytical framework, we incorporated a variable optimization approach and bootstrap resampling-based matrixing to enhance the classification performance and balance the sample size between groups, respectively. The relaxometric learning enabled the extraction of features related to the physical properties of fish muscle and the determination of the geographical origin of the fish by improving the classification performance. Our results suggest that relaxometric learning is a powerful and versatile alternative to conventional metabolomics approaches for evaluating fleshiness of chemical mixtures in food and for other biological and chemical research requiring a nondestructive, cost-effective, and time-saving method. ![]()
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Affiliation(s)
- Yasuhiro Date
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Feifei Wei
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Yuuri Tsuboi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Kengo Ito
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Kenji Sakata
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan. .,Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan. .,Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan.
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Sak J, Suchodolska M. Artificial Intelligence in Nutrients Science Research: A Review. Nutrients 2021; 13:322. [PMID: 33499405 PMCID: PMC7911928 DOI: 10.3390/nu13020322] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/12/2021] [Accepted: 01/18/2021] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.
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Affiliation(s)
- Jarosław Sak
- Chair and Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland
- BioMolecular Resources Research Infrastructure Poland (BBMRI.pl), Poland
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Multi-omics analysis on an agroecosystem reveals the significant role of organic nitrogen to increase agricultural crop yield. Proc Natl Acad Sci U S A 2020; 117:14552-14560. [PMID: 32513689 PMCID: PMC7321985 DOI: 10.1073/pnas.1917259117] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
In 1840, Justus von Liebig proposed the theory of mineral plant nutrition, through the invention of the Haber–Bosch process, leading to the industrialization of chemical fertilizer (inorganic nitrogen) to feed the human population. Because the excessive use of chemical fertilizer has led to numerous environmental problems, understanding the agroecosystem that contains plants, microbes, and soils is necessary for sustainable agriculture. We revealed the network structure of an agroecosystem established with different management practices and identified that organic nitrogen is a key component contributing to crop yield under the condition of soil solarization, even in the presence of inorganic nitrogen. Our finding provides a potential solution to make crop production more sustainable by utilizing organic nitrogen induced by soil solarization. Both inorganic fertilizer inputs and crop yields have increased globally, with the concurrent increase in the pollution of water bodies due to nitrogen leaching from soils. Designing agroecosystems that are environmentally friendly is urgently required. Since agroecosystems are highly complex and consist of entangled webs of interactions between plants, microbes, and soils, identifying critical components in crop production remain elusive. To understand the network structure in agroecosystems engineered by several farming methods, including environmentally friendly soil solarization, we utilized a multiomics approach on a field planted with Brassica rapa. We found that the soil solarization increased plant shoot biomass irrespective of the type of fertilizer applied. Our multiomics and integrated informatics revealed complex interactions in the agroecosystem showing multiple network modules represented by plant traits heterogeneously associated with soil metabolites, minerals, and microbes. Unexpectedly, we identified soil organic nitrogen induced by soil solarization as one of the key components to increase crop yield. A germ-free plant in vitro assay and a pot experiment using arable soils confirmed that specific organic nitrogen, namely alanine and choline, directly increased plant biomass by acting as a nitrogen source and a biologically active compound. Thus, our study provides evidence at the agroecosystem level that organic nitrogen plays a key role in plant growth.
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Asakura T, Date Y, Kikuchi J. Application of ensemble deep neural network to metabolomics studies. Anal Chim Acta 2018; 1037:230-236. [DOI: 10.1016/j.aca.2018.02.045] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 02/05/2018] [Accepted: 02/10/2018] [Indexed: 10/18/2022]
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