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Farooq MA, Gao S, Hassan MA, Huang Z, Rasheed A, Hearne S, Prasanna B, Li X, Li H. Artificial intelligence in plant breeding. Trends Genet 2024:S0168-9525(24)00167-7. [PMID: 39117482 DOI: 10.1016/j.tig.2024.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/06/2024] [Accepted: 07/12/2024] [Indexed: 08/10/2024]
Abstract
Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.
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Affiliation(s)
- Muhammad Amjad Farooq
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Shang Gao
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Muhammad Adeel Hassan
- Adaptive Cropping Systems Laboratory, Beltsville Agricultural Research Center, US Department of Agriculture, Beltsville, MD 20705, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA
| | - Zhangping Huang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Awais Rasheed
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Sarah Hearne
- CIMMYT, KM 45 Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | - Boddupalli Prasanna
- CIMMYT, International Centre for Research in Agroforestry (ICRAF) House, Nairobi 00100, Kenya
| | - Xinhai Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China
| | - Huihui Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China.
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2
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Chang-Brahim I, Koppensteiner LJ, Beltrame L, Bodner G, Saranti A, Salzinger J, Fanta-Jende P, Sulzbachner C, Bruckmüller F, Trognitz F, Samad-Zamini M, Zechner E, Holzinger A, Molin EM. Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding. FRONTIERS IN PLANT SCIENCE 2024; 15:1319938. [PMID: 38699541 PMCID: PMC11064034 DOI: 10.3389/fpls.2024.1319938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 03/13/2024] [Indexed: 05/05/2024]
Abstract
Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.
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Affiliation(s)
- Ignacio Chang-Brahim
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Lorenzo Beltrame
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Gernot Bodner
- Department of Crop Sciences, Institute of Agronomy, University of Natural Resources and Life Sciences Vienna, Tulln, Austria
| | - Anna Saranti
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Jules Salzinger
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Phillipp Fanta-Jende
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Christoph Sulzbachner
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Felix Bruckmüller
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Friederike Trognitz
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Elisabeth Zechner
- Verein zur Förderung einer nachhaltigen und regionalen Pflanzenzüchtung, Zwettl, Austria
| | - Andreas Holzinger
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Eva M. Molin
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
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3
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Martinho VJPD, Rodrigues RN. Bioenergy relations with agriculture, forestry and other land uses: Highlighting the specific contributions of artificial intelligence and co-citation networks. Heliyon 2024; 10:e26267. [PMID: 38379976 PMCID: PMC10877436 DOI: 10.1016/j.heliyon.2024.e26267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 02/01/2024] [Accepted: 02/09/2024] [Indexed: 02/22/2024] Open
Abstract
The concerns with the environment and sustainability have promoted options for energy sources that mitigate the footprint of human life. The use of biomass from agriculture, forestry and other land uses (AFOLU) has enormous potential for the production of bioenergy as a renewable source of energy. In this context, this research aims to analyse the interrelationships between bioenergy and agriculture, forestry and other land uses, highlighting the contributions of the digital transition for these dimensions. To achieve these objectives, a bibliometric analysis through co-citation links (and items related to cited authors, references and sources) was carried out for the dimensions associated with the bioenergy and the AFOLU and after a specific literature survey was performed for the contributions from the digital transition for these frameworks. With this study, top authors, references and sources were identified for the topics assessed and it was highlighted the importance of digital transitions for more efficient bioenergy use and production in the worldwide contexts.
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Affiliation(s)
| | - Raimundo Nonato Rodrigues
- Center of Applied Social Sciences, Department of Accounting and Actuarial Sciences, Federal University of Pernambuco, Recife 50740-580, Brazil
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4
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Coleine C, Delgado-Baquerizo M, DiRuggiero J, Guirado E, Harfouche AL, Perez-Fernandez C, Singh BK, Selbmann L, Egidi E. Dryland microbiomes reveal community adaptations to desertification and climate change. THE ISME JOURNAL 2024; 18:wrae056. [PMID: 38552152 PMCID: PMC11031246 DOI: 10.1093/ismejo/wrae056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/19/2024] [Accepted: 03/27/2024] [Indexed: 04/21/2024]
Abstract
Drylands account for 45% of the Earth's land area, supporting ~40% of the global population. These regions support some of the most extreme environments on Earth, characterized by extreme temperatures, low and variable rainfall, and low soil fertility. In these biomes, microorganisms provide vital ecosystem services and have evolved distinctive adaptation strategies to endure and flourish in the extreme. However, dryland microbiomes and the ecosystem services they provide are under threat due to intensifying desertification and climate change. In this review, we provide a synthesis of our current understanding of microbial life in drylands, emphasizing the remarkable diversity and adaptations of these communities. We then discuss anthropogenic threats, including the influence of climate change on dryland microbiomes and outline current knowledge gaps. Finally, we propose research priorities to address those gaps and safeguard the sustainability of these fragile biomes.
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Affiliation(s)
- Claudia Coleine
- Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, 01100, Italy
| | - Manuel Delgado-Baquerizo
- Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC, Sevilla, E-41012, Spain
| | - Jocelyne DiRuggiero
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, United States
- Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Emilio Guirado
- Multidisciplinary Institute for Environment Studies “Ramón Margalef”, Universidad de Alicante, Alicante E-03071, Spain
| | - Antoine L Harfouche
- Department for Innovation in Biological, Agro-Food and Forest systems, University of Tuscia, Viterbo 01100, Italy
| | | | - Brajesh K Singh
- Global Centre for Land-Based Innovation, Western Sydney University, Penrith 2750, Australia
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith 2750, Australia
| | - Laura Selbmann
- Department of Ecological and Biological Sciences, University of Tuscia, Viterbo, 01100, Italy
- Mycological Section, Italian Antarctic National Museum (MNA), Genoa 16128, Italy
| | - Eleonora Egidi
- Global Centre for Land-Based Innovation, Western Sydney University, Penrith 2750, Australia
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith 2750, Australia
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5
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Yang C, Chen Y, Zhu L, Wang L, Lin Q. A deep learning MRI-based signature may provide risk-stratification strategies for nasopharyngeal carcinoma. Eur Arch Otorhinolaryngol 2023; 280:5039-5047. [PMID: 37358652 DOI: 10.1007/s00405-023-08084-9] [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: 05/18/2023] [Accepted: 06/16/2023] [Indexed: 06/27/2023]
Abstract
OBJECTIVE As the prognosis of nasopharyngeal carcinoma (NPC) is influenced by various factors, making it difficult for clinical physicians to predict the outcome, the objective of this study was to develop a deep learning-based signature for risk stratification in NPC patients. METHODS A total of 293 patients were enrolled in the study and divided into training, validation, and testing groups with a ratio of 7:1:2. MRI scans and corresponding clinical information were collected, and the 3-year disease-free survival (DFS) was chosen as the endpoint. The Res-Net18 algorithm was used to develop two deep learning (DL) models and another solely based on clinical characteristics developed by multivariate cox analysis. The performance of both models was evaluated using the area under the curve (AUC) and the concordance index (C-index). Discriminative performance was assessed using Kaplan-Meier survival analysis. RESULTS The deep learning approach identified DL prognostic models. The MRI-based DL model showed significantly better performance compared to the traditional model solely based on clinical characteristics (AUC: 0.8861 vs 0.745, p = 0.04 and C-index: 0.865 vs 0.727, p = 0.03). The survival analysis showed significant survival differences between the risk groups identified by the MRI-based model. CONCLUSION Our study highlights the potential of MRI in predicting the prognosis of NPC through DL algorithm. This approach has the potential to become a novel tool for prognosis prediction and can help physicians to develop more valid treatment strategies in the future.
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Affiliation(s)
- Chen Yang
- Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China
| | - Yuan Chen
- Department of Computer Science, Xiamen University, Xiamen, Fujian, China
| | - Luchao Zhu
- Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China
| | - Liansheng Wang
- Department of Computer Science, Xiamen University, Xiamen, Fujian, China.
| | - Qin Lin
- Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China.
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Cembrowska-Lech D, Krzemińska A, Miller T, Nowakowska A, Adamski C, Radaczyńska M, Mikiciuk G, Mikiciuk M. An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture. BIOLOGY 2023; 12:1298. [PMID: 37887008 PMCID: PMC10603917 DOI: 10.3390/biology12101298] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.
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Affiliation(s)
- Danuta Cembrowska-Lech
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
| | - Adrianna Krzemińska
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | - Tymoteusz Miller
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
| | - Anna Nowakowska
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
| | - Cezary Adamski
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | | | - Grzegorz Mikiciuk
- Department of Horticulture, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
| | - Małgorzata Mikiciuk
- Department of Bioengineering, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
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Busquet F, Laperrouze J, Jankovic K, Krsmanovic T, Ignasiak T, Leoni B, Apic G, Asole G, Guigó R, Marangio P, Palumbo E, Perez-Lluch S, Wucher V, Vlot AH, Anholt R, Mackay T, Escher BI, Grasse N, Huchthausen J, Massei R, Reemtsma T, Scholz S, Schüürmann G, Bondesson M, Cherbas P, Freedman JH, Glaholt S, Holsopple J, Jacobson SC, Kaufman T, Popodi E, Shaw JJ, Smoot S, Tennessen JM, Churchill G, von Clausbruch CC, Dickmeis T, Hayot G, Pace G, Peravali R, Weiss C, Cistjakova N, Liu X, Slaitas A, Brown JB, Ayerbe R, Cabellos J, Cerro-Gálvez E, Diez-Ortiz M, González V, Martínez R, Vives PS, Barnett R, Lawson T, Lee RG, Sostare E, Viant M, Grafström R, Hongisto V, Kohonen P, Patyra K, Bhaskar PK, Garmendia-Cedillos M, Farooq I, Oliver B, Pohida T, Salem G, Jacobson D, Andrews E, Barnard M, Čavoški A, Chaturvedi A, Colbourne JK, Epps DJT, Holden L, Jones MR, Li X, Müller F, Ormanin-Lewandowska A, Orsini L, Roberts R, Weber RJM, Zhou J, Chung ME, Sanchez JCG, Diwan GD, Singh G, Strähle U, Russell RB, Batista D, Sansone SA, Rocca-Serra P, Du Pasquier D, Lemkine G, Robin-Duchesne B, Tindall A. The Precision Toxicology Initiative. Toxicol Lett 2023:S0378-4274(23)00180-7. [PMID: 37211341 DOI: 10.1016/j.toxlet.2023.05.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/01/2023] [Accepted: 05/09/2023] [Indexed: 05/23/2023]
Abstract
The goal of PrecisionTox is to overcome conceptual barriers to replacing traditional mammalian chemical safety testing by accelerating the discovery of evolutionarily conserved toxicity pathways that are shared by descent among humans and more distantly related animals. An international consortium is systematically testing the toxicological effects of a diverse set of chemicals on a suite of five model species comprising fruit flies, nematodes, water fleas, and embryos of clawed frogs and zebrafish along with human cell lines. Multiple forms of omics and comparative toxicology data are integrated to map the evolutionary origins of biomolecular interactions, which are predictive of adverse health effects, to major branches of the animal phylogeny. These conserved elements of adverse outcome pathways (AOPs) and their biomarkers are expect to provide mechanistic insight useful for regulating groups of chemicals based on their shared modes of action. PrecisionTox also aims to quantify risk variation within populations by recognizing susceptibility as a heritable trait that varies with genetic diversity. This initiative incorporates legal experts and collaborates with risk managers to address specific needs within European chemicals legislation, including the uptake of new approach methodologies (NAMs) for setting precise regulatory limits on toxic chemicals.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Nico Grasse
- Helmholtz Centre for Environmental Research, DE
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Harfouche AL, Nakhle F, Harfouche AH, Sardella OG, Dart E, Jacobson D. A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey. TRENDS IN PLANT SCIENCE 2023; 28:154-184. [PMID: 36167648 DOI: 10.1016/j.tplants.2022.08.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 08/22/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. This primer is accompanied by an interactive online tutorial.
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Affiliation(s)
- Antoine L Harfouche
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy.
| | - Farid Nakhle
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Antoine H Harfouche
- Unité de Formation et de Recherche en Sciences Économiques, Gestion, Mathématiques, et Informatique, Université Paris Nanterre, 92001 Nanterre, France
| | - Orlando G Sardella
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Eli Dart
- Energy Sciences Network (ESnet), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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10
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Khan MHU, Wang S, Wang J, Ahmar S, Saeed S, Khan SU, Xu X, Chen H, Bhat JA, Feng X. Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding. Int J Mol Sci 2022; 23:11156. [PMID: 36232455 PMCID: PMC9570104 DOI: 10.3390/ijms231911156] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 11/21/2022] Open
Abstract
Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other "omics" approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These "omics" approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with "omics" tools can allow rapid gene identification and eventually accelerate crop-improvement programs.
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Affiliation(s)
- Muhammad Hafeez Ullah Khan
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
- Zhejiang Lab, Hangzhou 310012, China
| | - Shoudong Wang
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
- Zhejiang Lab, Hangzhou 310012, China
| | - Jun Wang
- Zhejiang Lab, Hangzhou 310012, China
| | - Sunny Ahmar
- Institute of Biology, Biotechnology and Environmental Protection, Faculty of Natural Sciences, University of Silesia, Jagiellonska 28, 40-032 Katowice, Poland
| | - Sumbul Saeed
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Shahid Ullah Khan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | | | | | | | - Xianzhong Feng
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
- Zhejiang Lab, Hangzhou 310012, China
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11
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Hajnal É, Kovács L, Vakulya G. Dairy Cattle Rumen Bolus Developments with Special Regard to the Applicable Artificial Intelligence (AI) Methods. SENSORS (BASEL, SWITZERLAND) 2022; 22:6812. [PMID: 36146158 PMCID: PMC9505622 DOI: 10.3390/s22186812] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/02/2022] [Accepted: 09/05/2022] [Indexed: 06/16/2023]
Abstract
It is a well-known worldwide trend to increase the number of animals on dairy farms and to reduce human labor costs. At the same time, there is a growing need to ensure economical animal husbandry and animal welfare. One way to resolve the two conflicting demands is to continuously monitor the animals. In this article, rumen bolus sensor techniques are reviewed, as they can provide lifelong monitoring due to their implementation. The applied sensory modalities are reviewed also using data transmission and data-processing techniques. During the processing of the literature, we have given priority to artificial intelligence methods, the application of which can represent a significant development in this field. Recommendations are also given regarding the applicable hardware and data analysis technologies. Data processing is executed on at least four levels from measurement to integrated analysis. We concluded that significant results can be achieved in this field only if the modern tools of computer science and intelligent data analysis are used at all levels.
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Affiliation(s)
- Éva Hajnal
- Alba Regia Technical Faculty, Óbuda University, 1034 Budapest, Hungary
| | - Levente Kovács
- Institute of Animal Sciences, Hungarian University of Agricultural and Life Sciences, 2100 Gödöllő, Hungary
| | - Gergely Vakulya
- Alba Regia Technical Faculty, Óbuda University, 1034 Budapest, Hungary
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12
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Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data. MATHEMATICS 2022. [DOI: 10.3390/math10040554] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Mistrust, amplified by numerous artificial intelligence (AI) related incidents, is an issue that has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and is fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle such an issue. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced into XAI. However, many deficiencies, particularly the lack of explanation assessment methods and uncertainty quantification, plague this young domain. In the present paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model and Shapley additive explanations (SHAP) to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as a marker for anomalies and expands the prognostic explanation scope to include the model’s confidence. In addition, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of studies on PHM-XAI. The quality of the explanation is examined employing local accuracy and consistency properties. The elaborated framework is tested on real-world gas turbine anomalies and synthetic turbofan failure prediction data. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed a 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.
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13
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Cui Y, Fan B, Xu X, Sheng S, Xu Y, Wang X. A High-Density Genetic Map Enables Genome Synteny and QTL Mapping of Vegetative Growth and Leaf Traits in Gardenia. Front Genet 2022; 12:802738. [PMID: 35132310 PMCID: PMC8817757 DOI: 10.3389/fgene.2021.802738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
The gardenia is a traditional medicinal horticultural plant in China, but its molecular genetic research has been largely hysteretic. Here, we constructed an F1 population with 200 true hybrid individuals. Using the genotyping-by-sequencing method, a high-density sex-average genetic map was generated that contained 4,249 SNPs with a total length of 1956.28 cM and an average genetic distance of 0.46 cM. We developed 17 SNP-based Kompetitive Allele-Specific PCR markers and found that 15 SNPs were successfully genotyped, of which 13 single-nucleotide polymorphism genotypings of 96 F1 individuals showed genotypes consistent with GBS-mined genotypes. A genomic collinearity analysis between gardenia and the Rubiaceae species Coffea arabica, Coffea canephora and Ophiorrhiza pumila showed the relativity strong conservation of LG11 with NC_039,919.1, HG974438.1 and Bliw01000011.1, respectively. Lastly, a quantitative trait loci analysis at three phenotyping time points (2019, 2020, and 2021) yielded 18 QTLs for growth-related traits and 31 QTLs for leaf-related traits, of which qBSBN7-1, qCD8 and qLNP2-1 could be repeatably detected. Five QTL regions (qCD8 and qSBD8, qBSBN7 and qSI7, qCD4-1 and qLLLS4, qLNP10 and qSLWS10-2, qSBD10 and qLLLS10) with potential pleiotropic effects were also observed. This study provides novel insight into molecular genetic research and could be helpful for further gene cloning and marker-assisted selection for early growth and development traits in the gardenia.
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Affiliation(s)
- Yang Cui
- Research Center for Traditional Chinese Medicine Resources and Ethnic Minority Medicine, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Baolian Fan
- Research Center for Traditional Chinese Medicine Resources and Ethnic Minority Medicine, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Xu Xu
- Research Center for Traditional Chinese Medicine Resources and Ethnic Minority Medicine, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Shasha Sheng
- Research Center for Traditional Chinese Medicine Resources and Ethnic Minority Medicine, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Yuhui Xu
- Adsen Biotechnology Co., Ltd., Urumchi, China
| | - Xiaoyun Wang
- Research Center for Traditional Chinese Medicine Resources and Ethnic Minority Medicine, Jiangxi University of Chinese Medicine, Nanchang, China
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Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses. SENSORS 2021; 21:s21238020. [PMID: 34884024 PMCID: PMC8659640 DOI: 10.3390/s21238020] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/19/2021] [Accepted: 11/23/2021] [Indexed: 12/25/2022]
Abstract
Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials, fintech management, medicine, neurorobotics, and psychology, among others. Prognostics and health management (PHM) is the discipline that links the studies of failure mechanisms to system lifecycle management. There is a need, which is still absent, to produce an analytical compilation of PHM-XAI works. In this paper, we use preferred reporting items for systematic reviews and meta-analyses (PRISMA) to present a state of the art on XAI applied to PHM of industrial assets. This work provides an overview of the trend of XAI in PHM and answers the question of accuracy versus explainability, considering the extent of human involvement, explanation assessment, and uncertainty quantification in this topic. Research articles associated with the subject, since 2015 to 2021, were selected from five databases following the PRISMA methodology, several of them related to sensors. The data were extracted from selected articles and examined obtaining diverse findings that were synthesized as follows. First, while the discipline is still young, the analysis indicates a growing acceptance of XAI in PHM. Second, XAI offers dual advantages, where it is assimilated as a tool to execute PHM tasks and explain diagnostic and anomaly detection activities, implying a real need for XAI in PHM. Third, the review shows that PHM-XAI papers provide interesting results, suggesting that the PHM performance is unaffected by the XAI. Fourth, human role, evaluation metrics, and uncertainty management are areas requiring further attention by the PHM community. Adequate assessment metrics to cater to PHM needs are requested. Finally, most case studies featured in the considered articles are based on real industrial data, and some of them are related to sensors, showing that the available PHM-XAI blends solve real-world challenges, increasing the confidence in the artificial intelligence models' adoption in the industry.
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Nakhle F, Harfouche AL. Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis. PATTERNS (NEW YORK, N.Y.) 2021; 2:100323. [PMID: 34553170 PMCID: PMC8441561 DOI: 10.1016/j.patter.2021.100323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
High-throughput image-based technologies are now widely used in the rapidly developing field of digital phenomics and are generating ever-increasing amounts and diversity of data. Artificial intelligence (AI) is becoming a game changer in turning the vast seas of data into valuable predictions and insights. However, this requires specialized programming skills and an in-depth understanding of machine learning, deep learning, and ensemble learning algorithms. Here, we attempt to methodically review the usage of different tools, technologies, and services available to the phenomics data community and show how they can be applied to selected problems in explainable AI-based image analysis. This tutorial provides practical and useful resources for novices and experts to harness the potential of the phenomic data in explainable AI-led breeding programs.
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Affiliation(s)
- Farid Nakhle
- Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy
| | - Antoine L. Harfouche
- Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy
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Razzaq A, Kaur P, Akhter N, Wani SH, Saleem F. Next-Generation Breeding Strategies for Climate-Ready Crops. FRONTIERS IN PLANT SCIENCE 2021; 12:620420. [PMID: 34367194 PMCID: PMC8336580 DOI: 10.3389/fpls.2021.620420] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 06/14/2021] [Indexed: 05/17/2023]
Abstract
Climate change is a threat to global food security due to the reduction of crop productivity around the globe. Food security is a matter of concern for stakeholders and policymakers as the global population is predicted to bypass 10 billion in the coming years. Crop improvement via modern breeding techniques along with efficient agronomic practices innovations in microbiome applications, and exploiting the natural variations in underutilized crops is an excellent way forward to fulfill future food requirements. In this review, we describe the next-generation breeding tools that can be used to increase crop production by developing climate-resilient superior genotypes to cope with the future challenges of global food security. Recent innovations in genomic-assisted breeding (GAB) strategies allow the construction of highly annotated crop pan-genomes to give a snapshot of the full landscape of genetic diversity (GD) and recapture the lost gene repertoire of a species. Pan-genomes provide new platforms to exploit these unique genes or genetic variation for optimizing breeding programs. The advent of next-generation clustered regularly interspaced short palindromic repeat/CRISPR-associated (CRISPR/Cas) systems, such as prime editing, base editing, and de nova domestication, has institutionalized the idea that genome editing is revamped for crop improvement. Also, the availability of versatile Cas orthologs, including Cas9, Cas12, Cas13, and Cas14, improved the editing efficiency. Now, the CRISPR/Cas systems have numerous applications in crop research and successfully edit the major crop to develop resistance against abiotic and biotic stress. By adopting high-throughput phenotyping approaches and big data analytics tools like artificial intelligence (AI) and machine learning (ML), agriculture is heading toward automation or digitalization. The integration of speed breeding with genomic and phenomic tools can allow rapid gene identifications and ultimately accelerate crop improvement programs. In addition, the integration of next-generation multidisciplinary breeding platforms can open exciting avenues to develop climate-ready crops toward global food security.
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Affiliation(s)
- Ali Razzaq
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan
| | - Parwinder Kaur
- UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia
| | - Naheed Akhter
- College of Allied Health Professional, Faculty of Medical Sciences, Government College University Faisalabad, Faisalabad, Pakistan
| | - Shabir Hussain Wani
- Mountain Research Center for Field Crops, Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, India
| | - Fozia Saleem
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan
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17
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Harfouche AL, Petousi V, Meilan R, Sweet J, Twardowski T, Altman A. Promoting Ethically Responsible Use of Agricultural Biotechnology. TRENDS IN PLANT SCIENCE 2021; 26:546-559. [PMID: 33483266 DOI: 10.1016/j.tplants.2020.12.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 12/14/2020] [Accepted: 12/22/2020] [Indexed: 05/28/2023]
Abstract
Growing global demands for food, bioenergy, and specialty products, along with the threat posed by various environmental changes, present substantial challenges for agricultural production. Agricultural biotechnology offers a promising avenue for meeting these challenges; however, ethical and sociocultural concerns must first be addressed, to ensure widespread public trust and uptake. To be effective, we need to develop solutions that are ethically responsible, socially responsive, relevant to people of different cultural and social backgrounds, and conveyed to the public in a convincing and straightforward manner. Here, we highlight how ethical approaches, principled decision-making strategies, citizen-stakeholder participation, effective science communication, and bioethics education should be used to guide responsible use of agricultural biotechnology.
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Affiliation(s)
- Antoine L Harfouche
- Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy.
| | - Vasiliki Petousi
- Department of Sociology, University of Crete, Gallos Campus, 74100 Rethymno, Greece
| | - Richard Meilan
- Department of Forestry and Natural Resources, Purdue University, 715 West State Street, West Lafayette, IN 47907, USA
| | - Jeremy Sweet
- Sweet Environmental Consultants, 6 Green Street, Willingham, CB24 5JA Cambridge, UK
| | - Tomasz Twardowski
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Zygmunta Noskowskiego Street 12/14, 61-704 Poznan, Poland
| | - Arie Altman
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Faculty of Agricultural, Food, and Environmental Quality Sciences, PO Box 12, Rehovot 76100, Israel; The Lester and Sally Entin Faculty of Humanities, Unit of Culture Research, Tel Aviv University, PO Box 39040, Tel Aviv 6997801, Israel.
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Backholer K, Baum F, Finlay SM, Friel S, Giles-Corti B, Jones A, Patrick R, Shill J, Townsend B, Armstrong F, Baker P, Bowen K, Browne J, Büsst C, Butt A, Canuto K, Canuto K, Capon A, Corben K, Daube M, Goldfeld S, Grenfell R, Gunn L, Harris P, Horton K, Keane L, Lacy-Nichols J, Lo SN, Lovett RW, Lowe M, Martin JE, Neal N, Peeters A, Pettman T, Thoms A, Thow AMT, Timperio A, Williams C, Wright A, Zapata-Diomedi B, Demaio S. Australia in 2030: what is our path to health for all? Med J Aust 2021; 214 Suppl 8:S5-S40. [PMID: 33934362 DOI: 10.5694/mja2.51020] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 02/17/2021] [Accepted: 02/17/2021] [Indexed: 12/11/2022]
Abstract
CHAPTER 1: HOW AUSTRALIA IMPROVED HEALTH EQUITY THROUGH ACTION ON THE SOCIAL DETERMINANTS OF HEALTH: Do not think that the social determinants of health equity are old hat. In reality, Australia is very far away from addressing the societal level drivers of health inequity. There is little progressive policy that touches on the conditions of daily life that matter for health, and action to redress inequities in power, money and resources is almost non-existent. In this chapter we ask you to pause this reality and come on a fantastic journey where we envisage how COVID-19 was a great disruptor and accelerator of positive progressive action. We offer glimmers of what life could be like if there was committed and real policy action on the social determinants of health equity. It is vital that the health sector assists in convening the multisectoral stakeholders necessary to turn this fantasy into reality. CHAPTER 2: ABORIGINAL AND TORRES STRAIT ISLANDER CONNECTION TO CULTURE: BUILDING STRONGER INDIVIDUAL AND COLLECTIVE WELLBEING: Aboriginal and Torres Strait Islander peoples have long maintained that culture (ie, practising, maintaining and reclaiming it) is vital to good health and wellbeing. However, this knowledge and understanding has been dismissed or described as anecdotal or intangible by Western research methods and science. As a result, Aboriginal and Torres Strait Islander culture is a poorly acknowledged determinant of health and wellbeing, despite its significant role in shaping individuals, communities and societies. By extension, the cultural determinants of health have been poorly defined until recently. However, an increasing amount of scientific evidence supports what Aboriginal and Torres Strait Islander people have always said - that strong culture plays a significant and positive role in improved health and wellbeing. Owing to known gaps in knowledge, we aim to define the cultural determinants of health and describe their relationship with the social determinants of health, to provide a full understanding of Aboriginal and Torres Strait Islander wellbeing. We provide examples of evidence on cultural determinants of health and links to improved Aboriginal and Torres Strait Islander health and wellbeing. We also discuss future research directions that will enable a deeper understanding of the cultural determinants of health for Aboriginal and Torres Strait Islander people. CHAPTER 3: PHYSICAL DETERMINANTS OF HEALTH: HEALTHY, LIVEABLE AND SUSTAINABLE COMMUNITIES: Good city planning is essential for protecting and improving human and planetary health. Until recently, however, collaboration between city planners and the public health sector has languished. We review the evidence on the health benefits of good city planning and propose an agenda for public health advocacy relating to health-promoting city planning for all by 2030. Over the next 10 years, there is an urgent need for public health leaders to collaborate with city planners - to advocate for evidence-informed policy, and to evaluate the health effects of city planning efforts. Importantly, we need integrated planning across and between all levels of government and sectors, to create healthy, liveable and sustainable cities for all. CHAPTER 4: HEALTH PROMOTION IN THE ANTHROPOCENE: THE ECOLOGICAL DETERMINANTS OF HEALTH: Human health is inextricably linked to the health of the natural environment. In this chapter, we focus on ecological determinants of health, including the urgent and critical threats to the natural environment, and opportunities for health promotion arising from the human health co-benefits of actions to protect the health of the planet. We characterise ecological determinants in the Anthropocene and provide a sobering snapshot of planetary health science, particularly the momentous climate change health impacts in Australia. We highlight Australia's position as a major fossil fuel producer and exporter, and a country lacking cohesive and timely emissions reduction policy. We offer a roadmap for action, with four priority directions, and point to a scaffold of guiding approaches - planetary health, Indigenous people's knowledge systems, ecological economics, health co-benefits and climate-resilient development. Our situation requires a paradigm shift, and this demands a recalibration of health promotion education, research and practice in Australia over the coming decade. CHAPTER 5: DISRUPTING THE COMMERCIAL DETERMINANTS OF HEALTH: Our vision for 2030 is an Australian economy that promotes optimal human and planetary health for current and future generations. To achieve this, current patterns of corporate practice and consumption of harmful commodities and services need to change. In this chapter, we suggest ways forward for Australia, focusing on pragmatic actions that can be taken now to redress the power imbalances between corporations and Australian governments and citizens. We begin by exploring how the terms of health policy making must change to protect it from conflicted commercial interests. We also examine how marketing unhealthy products and services can be more effectively regulated, and how healthier business practices can be incentivised. Finally, we make recommendations on how various public health stakeholders can hold corporations to account, to ensure that people come before profits in a healthy and prosperous future Australia. CHAPTER 6: DIGITAL DETERMINANTS OF HEALTH: THE DIGITAL TRANSFORMATION: We live in an age of rapid and exponential technological change. Extraordinary digital advancements and the fusion of technologies, such as artificial intelligence, robotics, the Internet of Things and quantum computing constitute what is often referred to as the digital revolution or the Fourth Industrial Revolution (Industry 4.0). Reflections on the future of public health and health promotion require thorough consideration of the role of digital technologies and the systems they influence. Just how the digital revolution will unfold is unknown, but it is clear that advancements and integrations of technologies will fundamentally influence our health and wellbeing in the future. The public health response must be proactive, involving many stakeholders, and thoughtfully considered to ensure equitable and ethical applications and use. CHAPTER 7: GOVERNANCE FOR HEALTH AND EQUITY: A VISION FOR OUR FUTURE: Coronavirus disease 2019 has caused many people and communities to take stock on Australia's direction in relation to health, community, jobs, environmental sustainability, income and wealth. A desire for change is in the air. This chapter imagines how changes in the way we govern our lives and what we value as a society could solve many of the issues Australia is facing - most pressingly, the climate crisis and growing economic and health inequities. We present an imagined future for 2030 where governance structures are designed to ensure transparent and fair behaviour from those in power and to increase the involvement of citizens in these decisions, including a constitutional voice for Indigenous peoples. We imagine that these changes were made by measuring social progress in new ways, ensuring taxation for public good, enshrining human rights (including to health) in legislation, and protecting and encouraging an independent media. Measures to overcome the climate crisis were adopted and democratic processes introduced in the provision of housing, education and community development.
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Francesconi S, Harfouche A, Maesano M, Balestra GM. UAV-Based Thermal, RGB Imaging and Gene Expression Analysis Allowed Detection of Fusarium Head Blight and Gave New Insights Into the Physiological Responses to the Disease in Durum Wheat. FRONTIERS IN PLANT SCIENCE 2021; 12:628575. [PMID: 33868331 PMCID: PMC8047627 DOI: 10.3389/fpls.2021.628575] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 03/12/2021] [Indexed: 05/24/2023]
Abstract
Wheat is one of the world's most economically important cereal crop, grown on 220 million hectares. Fusarium head blight (FHB) disease is considered a major threat to durum (Triticum turgidum subsp. durum (Desfontaines) Husnache) and bread wheat (T. aestivum L.) cultivars and is mainly managed by the application of fungicides at anthesis. However, fungicides are applied when FHB symptoms are clearly visible and the spikes are almost entirely bleached (% of diseased spikelets > 80%), by when it is too late to control FHB disease. For this reason, farmers often react by performing repeated fungicide treatments that, however, due to the advanced state of the infection, cause a waste of money and pose significant risks to the environment and non-target organisms. In the present study, we used unmanned aerial vehicle (UAV)-based thermal infrared (TIR) and red-green-blue (RGB) imaging for FHB detection in T. turgidum (cv. Marco Aurelio) under natural field conditions. TIR and RGB data coupled with ground-based measurements such as spike's temperature, photosynthetic efficiency and molecular identification of FHB pathogens, detected FHB at anthesis half-way (Zadoks stage 65, ZS 65), when the percentage (%) of diseased spikelets ranged between 20% and 60%. Moreover, in greenhouse experiments the transcripts of the key genes involved in stomatal closure were mostly up-regulated in F. graminearum-inoculated plants, demonstrating that the physiological mechanism behind the spike's temperature increase and photosynthetic efficiency decrease could be attributed to the closure of the guard cells in response to F. graminearum. In addition, preliminary analysis revealed that there is differential regulation of genes between drought-stressed and F. graminearum-inoculated plants, suggesting that there might be a possibility to discriminate between water stress and FHB infection. This study shows the potential of UAV-based TIR and RGB imaging for field phenotyping of wheat and other cereal crop species in response to environmental stresses. This is anticipated to have enormous promise for the detection of FHB disease and tremendous implications for optimizing the application of fungicides, since global food crop demand is to be met with minimal environmental impacts.
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Affiliation(s)
- Sara Francesconi
- Department of Agriculture and Forest Sciences (DAFNE), University of Tuscia, Viterbo, Italy
| | - Antoine Harfouche
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
| | - Mauro Maesano
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
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Lawson CE, Martí JM, Radivojevic T, Jonnalagadda SVR, Gentz R, Hillson NJ, Peisert S, Kim J, Simmons BA, Petzold CJ, Singer SW, Mukhopadhyay A, Tanjore D, Dunn JG, Garcia Martin H. Machine learning for metabolic engineering: A review. Metab Eng 2020; 63:34-60. [PMID: 33221420 DOI: 10.1016/j.ymben.2020.10.005] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/22/2020] [Accepted: 10/31/2020] [Indexed: 12/14/2022]
Abstract
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.
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Affiliation(s)
- Christopher E Lawson
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA
| | - Jose Manuel Martí
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Tijana Radivojevic
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Sai Vamshi R Jonnalagadda
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Reinhard Gentz
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Nathan J Hillson
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Sean Peisert
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; University of California Davis, Davis, CA, 95616, USA
| | - Joonhoon Kim
- Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Pacific Northwest National Laboratory, Richland, 99354, WA, USA
| | - Blake A Simmons
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Christopher J Petzold
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Steven W Singer
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA
| | - Aindrila Mukhopadhyay
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, USA
| | - Deepti Tanjore
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Advanced Biofuels and Bioproducts Process Development Unit, Emeryville, CA, 94608, USA
| | | | - Hector Garcia Martin
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA; Basque Center for Applied Mathematics, 48009, Bilbao, Spain; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, USA.
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21
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Inner Workings: Crop researchers harness artificial intelligence to breed crops for the changing climate. Proc Natl Acad Sci U S A 2020; 117:27066-27069. [PMID: 33055220 DOI: 10.1073/pnas.2018732117] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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22
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Reski R, Rybicki E, Foster G. Editorial overview: Plant biotechnology. Curr Opin Biotechnol 2020; 61:iii-v. [PMID: 32204851 DOI: 10.1016/j.copbio.2020.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Ralf Reski
- Plant Biotechnology, Faculty of Biology, University of Freiburg, Germany
| | - Ed Rybicki
- Biopharming Research Unit, Molecular & Cell Biology Department, University of Cape Town, Cape Town, South Africa
| | - Gary Foster
- School of Biological Sciences, University of Bristol, Bristol, UK
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