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Bennur PL, O’Brien M, Fernando SC, Doblin MS. Improving transformation and regeneration efficiency in medicinal plants: insights from other recalcitrant species. JOURNAL OF EXPERIMENTAL BOTANY 2025; 76:52-75. [PMID: 38652155 PMCID: PMC11659184 DOI: 10.1093/jxb/erae189] [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: 02/07/2024] [Accepted: 04/22/2024] [Indexed: 04/25/2024]
Abstract
Medicinal plants are integral to traditional medicine systems worldwide, being pivotal for human health. Harvesting plant material from natural environments, however, has led to species scarcity, prompting action to develop cultivation solutions that also aid conservation efforts. Biotechnological tools, specifically plant tissue culture and genetic transformation, offer solutions for sustainable, large-scale production and enhanced yield of valuable biomolecules. While these techniques are instrumental to the development of the medicinal plant industry, the challenge of inherent regeneration recalcitrance in some species to in vitro cultivation hampers these efforts. This review examines the strategies for overcoming recalcitrance in medicinal plants using a holistic approach, emphasizing the meticulous choice of explants (e.g. embryonic/meristematic tissues), plant growth regulators (e.g. synthetic cytokinins), and use of novel regeneration-enabling methods to deliver morphogenic genes (e.g. GRF/GIF chimeras and nanoparticles), which have been shown to contribute to overcoming recalcitrance barriers in agriculture crops. Furthermore, it highlights the benefit of cost-effective genomic technologies that enable precise genome editing and the value of integrating data-driven models to address genotype-specific challenges in medicinal plant research. These advances mark a progressive step towards a future where medicinal plant cultivation is not only more efficient and predictable but also inherently sustainable, ensuring the continued availability and exploitation of these important plants for current and future generations.
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Affiliation(s)
- Praveen Lakshman Bennur
- Australian Research Council (ARC) Industrial Transformation Research Hub for Medicinal Agriculture, La Trobe Institute for Sustainable Agriculture and Food (LISAF), Department of Animal, Plant and Soil Sciences, La Trobe University, Victoria 3086, Australia
| | - Martin O’Brien
- Australian Research Council (ARC) Industrial Transformation Research Hub for Medicinal Agriculture, La Trobe Institute for Sustainable Agriculture and Food (LISAF), Department of Animal, Plant and Soil Sciences, La Trobe University, Victoria 3086, Australia
| | - Shyama C Fernando
- Australian Research Council (ARC) Industrial Transformation Research Hub for Medicinal Agriculture, La Trobe Institute for Sustainable Agriculture and Food (LISAF), Department of Animal, Plant and Soil Sciences, La Trobe University, Victoria 3086, Australia
| | - Monika S Doblin
- Australian Research Council (ARC) Industrial Transformation Research Hub for Medicinal Agriculture, La Trobe Institute for Sustainable Agriculture and Food (LISAF), Department of Animal, Plant and Soil Sciences, La Trobe University, Victoria 3086, Australia
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Kavoni H, Savizi ISP, Lewis NE, Shojaosadati SA. Recent advances in culture medium design for enhanced production of monoclonal antibodies in CHO cells: A comparative study of machine learning and systems biology approaches. Biotechnol Adv 2025; 78:108480. [PMID: 39571767 DOI: 10.1016/j.biotechadv.2024.108480] [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: 06/27/2024] [Revised: 09/24/2024] [Accepted: 11/14/2024] [Indexed: 11/26/2024]
Abstract
The production of monoclonal antibodies (mAbs) using Chinese Hamster Ovary (CHO) cells has revolutionized the treatment of numerous diseases, solidifying their position as a cornerstone of the biopharmaceutical industry. However, achieving maximum mAb production while upholding strict product quality standards remains a significant hurdle. Optimizing cell culture media emerges as a critical factor in this endeavor, requiring a nuanced understanding of the complex interplay of nutrients, growth factors, and other components that profoundly influence cellular growth, productivity, and product quality. Significant strides have been made in media optimization, including techniques such as media blending, one factor at a time, and statistical design of experiments approaches. The present review provides a comprehensive analysis of the recent advancements in culture media design strategies, focusing on the comparative application of systems biology (SB) and machine learning (ML) approaches. The applications of SB and ML in optimizing CHO cell culture medium and successful examples of their use are summarized. Finally, we highlight the immense potential of integrating SB and ML, emphasizing the development of hybrid models that leverage the strengths of both approaches for robust, efficient, and scalable optimization of mAb production in CHO cells. This review provides a roadmap for researchers and industry professionals to navigate the complex landscape of mAb production optimization, paving the way for developing next-generation CHO cell culture media that drive significant improvements in yield and productivity.
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Affiliation(s)
- Hossein Kavoni
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Iman Shahidi Pour Savizi
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, CA, USA; Department of Pediatrics, University of California, San Diego, CA, USA
| | - Seyed Abbas Shojaosadati
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran.
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Mazaheri-Tirani M, Dayani S, Mobarakeh MI. Application of machine learning algorithms for predicting the life-long physiological effects of zinc oxide Micro/Nano particles on Carum copticum. BMC PLANT BIOLOGY 2024; 24:970. [PMID: 39415085 PMCID: PMC11481599 DOI: 10.1186/s12870-024-05662-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2024] [Accepted: 10/03/2024] [Indexed: 10/18/2024]
Abstract
Nanoparticles impose multidimensional effects on living cells that significantly vary among different studies. Machine learning (ML) methods are recommended to elucidate more consistence and predictable relations among the affected parameters. In this study, nine ML algorithms [Support-Vector Regression (SVR), Linear, Bagging, Stochastic Gradient Descent (SGD), Gaussian Process, Random Sample Consensus (RANSAC), Partial Least Squares (PLS), Kernel Ridge, and Random Forest] were applied to evaluate their efficiency in predicting the effects of zinc oxide nanoparticles (ZnO NPs: 0.5, 1, 5, 25, and 125 µM) and microparticles (ZnO MPs: 1, 5, 25, and 125 µM) on Carum copticum. The plant root/shoot biomass; number of leaves, branches, umbellates, and flowers; protein content; reducing sugars; phenolic compounds; chlorophylls (a, b, Total); carotenoids; anthocyanins; H2O2; proline; malondialdehyde (MDA); tissue zinc content; superoxide dismutase (SOD) activity; and media ΔpH were measured and considered input variables. All levels of ZnO MPs treatments increased growth parameters compared to the control (ZnSO4). The highest shoot/root fresh and dry mass were recorded at 5 µM ZnO MPs compared with the control. The root fresh/dry mass under ZnO NPs treatments was more sensitive than shoot parameters. The number of flowers increased by 134 and 79% in MPs and NPs treatments compared to the control, respectively. ZnO NPs reduced protein content by up to 81% in 125 µM NPs compared to ZnSO4. Reducing sugar content increased to 25, 40 and 36% in 5, 25, 125 µM MPs and 67, 68, 26, 26 and 21% in 0.5, 1, 5, 25 and 125 µM NPs treatments, respectively. The pH alteration was more significant under NPs and affected zinc uptake. All levels of ZnO NPs treatments increased growth parameters compared to the control. All ML algorithms showed varied efficiencies in predicting the nonlinear relationships among parameters, with higher efficiency in predicting the behavior of root and shoot dry mass, root fresh weight and number of flowers according to R2 index. The model obtained from SVR with the radial basis function (RBF) kernel was selected as a comprehensive model for predicting and determining the efficacy of the results.
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Affiliation(s)
- Maryam Mazaheri-Tirani
- Department of Biology, Faculty of Science, University of Jiroft, Jiroft, 78671-61167, Iran.
| | - Soleyman Dayani
- Department of Biotechnology, Faculty of Agriculture and Natural Resources, Imam Khomeini International University (IKIU), Qazvin, 34149-16818, Iran
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Ullah J, Gul A, Khan I, Shehzad J, Kausar R, Ahmed MS, Batool S, Hasan M, Ghorbanpour M, Mustafa G. Green synthesized iron oxide nanoparticles as a potential regulator of callus growth, plant physiology, antioxidative and microbial contamination in Oryza sativa L. BMC PLANT BIOLOGY 2024; 24:939. [PMID: 39385076 PMCID: PMC11462915 DOI: 10.1186/s12870-024-05627-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 09/24/2024] [Indexed: 10/11/2024]
Abstract
In tissue culture, efficient nutrient availability and effective control of callus contamination are crucial for successful plantlet regeneration. This study was aimed to enhance callogenesis, callus regeneration, control callus contamination, and substitute iron (Fe) source with FeO-NPs in Murashige and Skoog (MS) media. Nanogreen iron oxide (FeO-NPs) were synthesized and well characterized with sizes ranging from 2 to 7.5 nm. FeO-NPs as a supplement in MS media at 15 ppm, significantly controlled callus contamination by (80%). Results indicated that FeCl3-based FeO-NPs induced fast callus induction (72%) and regeneration (43%), in contrast FeSO4-based FeO-NPs resulted in increased callus weight (516%), diameter (300%), number of shoots (200%), and roots (114%). Modified media with FeO-NPs as the Fe source induced fast callogenesis and regeneration compared to normal MS media. FeO-NPs, when applied foliar spray, increased Plant fresh biomass by 133% and spike weight by 350%. Plant height increased by 54% and 33%, the number of spikes by 50% and 265%, and Chlorophyll content by 51% and 34% in IRRI-6 and Kissan Basmati, respectively. Additionally, APX (Ascorbate peroxidase), SOD (Superoxide dismutase), POD (peroxidase), and CAT (catalase) increased in IRRI-6 by 27%, 29%, 283%, 62%, while in Kissan Basmati, APX increased by 70%, SOD decreased by 28%, and POD and CAT increased by 89% and 98%, respectively. Finally, FeO-NPs effectively substituted Fe source in MS media, shorten the plant life cycle, and increase chlorophyll content as well as APX, SOD, POD, and CAT activities. This protocol is applicable for tissue culture in other cereal crops as well.
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Affiliation(s)
- Jawad Ullah
- Depatment of Plant Sciences, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Afia Gul
- Depatment of Plant Sciences, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Ilham Khan
- Depatment of Plant Sciences, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Junaid Shehzad
- Depatment of Plant Sciences, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Rehana Kausar
- Department of Botany, Chatter Klass Campus, University of Azad Jammu & Kashmir, Muzaffarabad, 13100, Pakistan
| | - Muhammad Shahzad Ahmed
- Rice Research Program, Crop Sciences Institute, National Agricultural Research Centre, Islamabad, 44000, Pakistan.
| | - Sana Batool
- Faculty of Chemical and Biological Science, Department of Biotechnology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
| | - Murtaza Hasan
- Faculty of Chemical and Biological Science, Department of Biotechnology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan.
| | - Mansour Ghorbanpour
- Department of Medicinal Plants, Faculty of Agriculture and Natural Resources, Arak University, Arak, 38156-8-8349, Iran.
- Institute of Nanoscience and Nanotechnology, Arak University, Arak, 38156-8-8349, Iran.
| | - Ghazala Mustafa
- Depatment of Plant Sciences, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan.
- Department of Horticulture, State Agricultural Ministry Laboratory of Horticultural Crop Growth and Development, Ministry of Agriculture, Zhejiang University, Hangzhou, 310058, China.
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Fallah Ziarani M, Tohidfar M, Hesami M. Choosing an appropriate somatic embryogenesis medium of carrot (Daucus carota L.) by data mining technology. BMC Biotechnol 2024; 24:68. [PMID: 39334143 PMCID: PMC11428924 DOI: 10.1186/s12896-024-00898-7] [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: 12/23/2023] [Accepted: 09/13/2024] [Indexed: 09/30/2024] Open
Abstract
INTRODUCTION Developing somatic embryogenesis is one of the main steps in successful in vitro propagation and gene transformation in the carrot. However, somatic embryogenesis is influenced by different intrinsic (genetics, genotype, and explant) and extrinsic (e.g., plant growth regulators (PGRs), medium composition, and gelling agent) factors which cause challenges in developing the somatic embryogenesis protocol. Therefore, optimizing somatic embryogenesis is a tedious, time-consuming, and costly process. Novel data mining approaches through a hybrid of artificial neural networks (ANNs) and optimization algorithms can facilitate modeling and optimizing in vitro culture processes and thereby reduce large experimental treatments and combinations. Carrot is a model plant in genetic engineering works and recombinant drugs, and therefore it is an important plant in research works. Also, in this research, for the first time, embryogenesis in carrot (Daucus carota L.) using Genetic algorithm (GA) and data mining technology has been reviewed and analyzed. MATERIALS AND METHODS In the current study, data mining approach through multilayer perceptron (MLP) and radial basis function (RBF) as two well-known ANNs were employed to model and predict embryogenic callus production in carrot based on eight input variables including carrot cultivars, agar, magnesium sulfate (MgSO4), calcium dichloride (CaCl2), manganese (II) sulfate (MnSO4), 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), and kinetin (KIN). To confirm the reliability and accuracy of the developed model, the result obtained from RBF-GA model were tested in the laboratory. RESULTS The results showed that RBF had better prediction efficiency than MLP. Then, the developed model was linked to a genetic algorithm (GA) to optimize the system. To confirm the reliability and accuracy of the developed model, the result of RBF-GA was experimentally tested in the lab as a validation experiment. The result showed that there was no significant difference between the predicted optimized result and the experimental result. CONCLUTIONS Generally, the results of this study suggest that data mining through RBF-GA can be considered as a robust approach, besides experimental methods, to model and optimize in vitro culture systems. According to the RBF-GA result, the highest somatic embryogenesis rate (62.5%) can be obtained from Nantes improved cultivar cultured on medium containing 195.23 mg/l MgSO4, 330.07 mg/l CaCl2, 18.3 mg/l MnSO4, 0.46 mg/l 2,4- D, 0.03 mg/l BAP, and 0.88 mg/l KIN. These results were also confirmed in the laboratory.
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Affiliation(s)
- Masoumeh Fallah Ziarani
- Department of Cell & Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, 19839-69411, Iran
| | - Masoud Tohidfar
- Department of Cell & Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, 19839-69411, Iran.
| | - Mohsen Hesami
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
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Ahmad Z, Shareen, Ganie IB, Firdaus F, Ramakrishnan M, Shahzad A, Ding Y. Enhancing Withanolide Production in the Withania Species: Advances in In Vitro Culture and Synthetic Biology Approaches. PLANTS (BASEL, SWITZERLAND) 2024; 13:2171. [PMID: 39124289 PMCID: PMC11313931 DOI: 10.3390/plants13152171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 07/30/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024]
Abstract
Withanolides are naturally occurring steroidal lactones found in certain species of the Withania genus, especially Withania somnifera (commonly known as Ashwagandha). These compounds have gained considerable attention due to their wide range of therapeutic properties and potential applications in modern medicine. To meet the rapidly growing demand for withanolides, innovative approaches such as in vitro culture techniques and synthetic biology offer promising solutions. In recent years, synthetic biology has enabled the production of engineered withanolides using heterologous systems, such as yeast and bacteria. Additionally, in vitro methods like cell suspension culture and hairy root culture have been employed to enhance withanolide production. Nevertheless, one of the primary obstacles to increasing the production of withanolides using these techniques has been the intricacy of the biosynthetic pathways for withanolides. The present article examines new developments in withanolide production through in vitro culture. A comprehensive summary of viable traditional methods for producing withanolide is also provided. The development of withanolide production in heterologous systems is examined and emphasized. The use of machine learning as a potent tool to model and improve the bioprocesses involved in the generation of withanolide is then discussed. In addition, the control and modification of the withanolide biosynthesis pathway by metabolic engineering mediated by CRISPR are discussed.
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Affiliation(s)
- Zishan Ahmad
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Centre for Sustainable Forestry in Southern China, Bamboo Research Institute, Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, School of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; (Z.A.); (M.R.)
| | - Shareen
- Department of Environmental Engineering, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China;
| | - Irfan Bashir Ganie
- Department of Botany, Aligarh Muslim University, Aligarh 202002, India; (I.B.G.); (A.S.)
| | - Fatima Firdaus
- Chemistry Department, Lucknow University, Lucknow 226007, India;
| | - Muthusamy Ramakrishnan
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Centre for Sustainable Forestry in Southern China, Bamboo Research Institute, Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, School of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; (Z.A.); (M.R.)
| | - Anwar Shahzad
- Department of Botany, Aligarh Muslim University, Aligarh 202002, India; (I.B.G.); (A.S.)
| | - Yulong Ding
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Centre for Sustainable Forestry in Southern China, Bamboo Research Institute, Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, School of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; (Z.A.); (M.R.)
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Tütüncü M. Application of machine learning in in vitro propagation of endemic Lilium akkusianum R. Gämperle. PLoS One 2024; 19:e0307823. [PMID: 39052595 PMCID: PMC11271868 DOI: 10.1371/journal.pone.0307823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 07/11/2024] [Indexed: 07/27/2024] Open
Abstract
A successful regeneration protocol was developed for micropropagation of Lilium akkusianum R. Gämperle, an endemic species of Türkiye, from scale explants. The study also aimed to evaluate the effects of Meta-Topolin (mT) and N6-Benzyladenine (BA) on in vitro regeneration. The Murashige and Skoog medium (MS) supplemented with different levels of α-naphthaleneacetic acid (NAA)/BA and NAA/mT were used for culture initiation in the darkness. The highest callus rates were observed on explants cultured on MS medium with 2.0 mg/L NAA + 0.5 mg/L mT (83.31%), and the highest adventitious bud number per explant was 4.98 in MS medium with 0.5 mg/L NAA + 1.5 mg/L mT. Adventitious buds were excised and cultured in 16/8 h photoperiod conditions. The highest average shoot number per explant was 4.0 in MS medium with 2.0 mg/L mT + 1.0 mg/L NAA. Shoots were rooted with the highest rate (90%) in the medium with the 1.0 mg/L IBA, and the highest survival rate (87.5%) was recorded in rooted shoots in the same medium. The ISSR marker system showed that regenerated plantlets were genetically stable. Besides traditional tissue culture techniques used in the current study, the potential for improving the effectiveness of L. akkusianum propagation protocols by incorporating machine learning methodologies was evaluated. ML techniques enhance lily micropropagation by analyzing complex biological processes, merging with traditional methods. This collaborative approach validates current protocols, allowing ongoing improvements. Embracing machine learning in endemic L. akkusianum studies contributes to sustainable plant propagation, promoting conservation and responsible genetic resource utilization in agriculture.
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Affiliation(s)
- Mehmet Tütüncü
- Department of Horticulture, Faculty of Agriculture, University of Ondokuz Mayıs, Samsun, Türkiye
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Alloun W, Berkani M, Shavandi A, Beddiar A, Pellegrini M, Garzia M, Lakhdari D, Ganachari SV, Aminabhavi TM, Vasseghian Y, Muddapur U, Chaouche NK. Harnessing artificial intelligence-driven approach for enhanced indole-3-acetic acid from the newly isolated Streptomyces rutgersensis AW08. ENVIRONMENTAL RESEARCH 2024; 252:118933. [PMID: 38642645 DOI: 10.1016/j.envres.2024.118933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 03/28/2024] [Accepted: 04/12/2024] [Indexed: 04/22/2024]
Abstract
Indole-3-acetic acid (IAA) derived from Actinobacteria fermentations on agro-wastes constitutes a safer and low-cost alternative to synthetic IAA. This study aims to select a high IAA-producing Streptomyces-like strain isolated from Lake Oubeira sediments (El Kala, Algeria) for further investigations (i.e., 16S rRNA gene barcoding and process optimization). Subsequently, artificial intelligence-based approaches were employed to maximize IAA bioproduction on spent coffee grounds as high-value-added feedstock. The specificity was the novel application of the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Box (L-BFGS-B) optimization algorithm. The new strain AW08 was a significant producer of IAA (26.116 ± 0.61 μg/mL) and was identified as Streptomyces rutgersensis by 16S rRNA gene barcoding and phylogenetic inquiry. The empirical data involved the inoculation of AW08 in various cultural conditions according to a four-factor Box Behnken Design matrix (BBD) of Response surface methodology (RSM). The input parameters and regression equation extracted from the RSM-BBD were the basis for implementing and training the L-BFGS-B algorithm. Upon training the model, the optimal conditions suggested by the BBD and L-BFGS-B algorithm were, respectively, L-Trp (X1) = 0.58 %; 0.57 %; T° (X2) = 26.37 °C; 28.19 °C; pH (X3) = 7.75; 8.59; and carbon source (X4) = 30 %; 33.29 %, with the predicted response IAA (Y) = 152.8; 169.18 μg/mL). Our findings emphasize the potential of the multifunctional S. rutgersensis AW08, isolated and reported for the first time in Algeria, as a robust producer of IAA. Validation investigations using the bioprocess parameters provided by the L-BFGS-B and the BBD-RSM models demonstrate the effectiveness of AI-driven optimization in maximizing IAA output by 5.43-fold and 4.2-fold, respectively. This study constitutes the first paper reporting a novel interdisciplinary approach and providing insights into biotechnological advancements. These results support for the first time a reasonable approach for valorizing spent coffee grounds as feedstock for sustainable and economic IAA production from S. rutgersensis AW08.
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Affiliation(s)
- Wiem Alloun
- Laboratory of Mycology, Biotechnology and Microbial Activity, Department of Applied Biology, BP, 325 Aïn El Bey Road, Constantine 25017, Algeria; The BioMatter Lab, École Polytechnique de Bruxelles, Université Libre de Bruxelles (ULB), Avenue F.D. Roosevelt, 50-CP 165/61, 1050 Brussels, Belgium; Department of Life, Health and Environmental Sciences, University of L'Aquila, Coppito, 67100 L'Aquila, Italy
| | - Mohammed Berkani
- Biotechnology Laboratory, Higher National School of Biotechnology Taoufik KHAZNADAR, Nouveau Pôle universitaire Ali Mendjeli, BP. E66, Constantine, 25100, Algeria.
| | - Amin Shavandi
- The BioMatter Lab, École Polytechnique de Bruxelles, Université Libre de Bruxelles (ULB), Avenue F.D. Roosevelt, 50-CP 165/61, 1050 Brussels, Belgium
| | - Adlène Beddiar
- Department of Web Development and Artificial Intelligence, University of Mohammed Cherif Messaadia, Souk-Ahras, Algeria
| | - Marika Pellegrini
- Department of Life, Health and Environmental Sciences, University of L'Aquila, Coppito, 67100 L'Aquila, Italy
| | - Matteo Garzia
- Department of Life, Health and Environmental Sciences, University of L'Aquila, Coppito, 67100 L'Aquila, Italy
| | - Delloula Lakhdari
- Biotechnology Laboratory, Higher National School of Biotechnology Taoufik KHAZNADAR, Nouveau Pôle universitaire Ali Mendjeli, BP. E66, Constantine, 25100, Algeria; Research Center in Industrial Technologies CRTI, P.O. Box 64, Cheraga 16014, Algiers, Algeria
| | - Sharanabasava V Ganachari
- Center for Energy and Environment, School of Advanced Sciences, KLE Technological University, Hubballi, Karnataka, 580 031, India
| | - Tejraj M Aminabhavi
- Center for Energy and Environment, School of Advanced Sciences, KLE Technological University, Hubballi, Karnataka, 580 031, India; School of Engineering, University of Petroleum and Energy Studies (UPES) Uttarakhand, Dehradun, 248 007, India; Korea University, Seoul, South Korea.
| | - Yasser Vasseghian
- Department of Chemistry, Soongsil University, Seoul, 06978, South Korea; University Centre for Research & Development, Department of Mechanical Engineering, Chandigarh University, Gharuan, Mohali, Punjab 140413, India; The University of Johannesburg, Department of Chemical Engineering, P.O. Box 17011, Doornfontein, 2088, South Africa.
| | - Uday Muddapur
- Department of Biotechnology, KLE Technological University, Hubballi, Karnataka, 580 031, India
| | - Noreddine Kacem Chaouche
- Laboratory of Mycology, Biotechnology and Microbial Activity, Department of Applied Biology, BP, 325 Aïn El Bey Road, Constantine 25017, Algeria
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Iram A, Dong Y, Ignea C. Synthetic biology advances towards a bio-based society in the era of artificial intelligence. Curr Opin Biotechnol 2024; 87:103143. [PMID: 38781699 DOI: 10.1016/j.copbio.2024.103143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 05/04/2024] [Accepted: 05/04/2024] [Indexed: 05/25/2024]
Abstract
Synthetic biology is a rapidly emerging field with broad underlying applications in health, industry, agriculture, or environment, enabling sustainable solutions for unmet needs of modern society. With the very recent addition of artificial intelligence (AI) approaches, this field is now growing at a rate that can help reach the envisioned goals of bio-based society within the next few decades. Integrating AI with plant-based technologies, such as protein engineering, phytochemicals production, plant system engineering, or microbiome engineering, potentially disruptive applications have already been reported. These include enzymatic synthesis of new-to-nature molecules, bioelectricity generation, or biomass applications as construction material. Thus, in the not-so-distant future, synthetic biologists will help attain the overarching goal of a sustainable yet efficient production system for every aspect of society.
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Affiliation(s)
- Attia Iram
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada
| | - Yueming Dong
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada
| | - Codruta Ignea
- Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada.
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Kashyap GR, Sridhara S, Manoj KN, Gopakkali P, Das B, Jha PK, Prasad PVV. Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024; 68:1179-1197. [PMID: 38676745 DOI: 10.1007/s00484-024-02661-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 03/06/2024] [Accepted: 03/15/2024] [Indexed: 04/29/2024]
Abstract
Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns that prevail during the crop growing phase. Crop yield prediction with greater accuracy is possible due to the development of innovative technologies which analyses the bigdata with its high-performance computing abilities. Machine learning technologies can make yield prediction reasonable and faster and with greater flexibility than process based complex crop simulation models. The present study demonstrates the usability of ML algorithms for yield forecasting and facilitates the comparison of different models. The cotton yield was simulated by employing the weekly weather indices as inputs and the model performance was assessed by nRMSE, MAPE and EF values. Results show that stacked generalised ensemble model and artificial neural networks predicted the cotton yield with lower nRMSE, MAPE and higher efficiency compared to other models. Variable importance studies in LASSO and ENET model found minimum temperature and relative humidity as the main determinates of cotton yield in all districts. The models were ranked based these performance metrics in the order of Stacked generalised ensemble > ANN > PCA ANN > SMLR ANN > LASSO> ENET > SVM > PCA SMLR > SMLR SVM > SMLR. This study shows that stacked generalised ensembling and ANN method can be used for reliable yield forecasting at district or county level and helps stakeholders in timely decision-making.
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Affiliation(s)
- Girish R Kashyap
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India
| | - Shankarappa Sridhara
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India.
| | - Konapura Nagaraja Manoj
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India
| | - Pradeep Gopakkali
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India
| | - Bappa Das
- ICAR-Central Coastal Agricultural Research Institute, Old Goa, Goa, 403402, India
| | - Prakash Kumar Jha
- Department of Plant and Soil Sciences, Mississippi State University, Mississippi, MS, USA
| | - P V Vara Prasad
- Department of Agronomy, Kansas State University, Manhattan, KS, 66506, USA
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11
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Safaie N, Salehi M, Farhadi S, Aligholizadeh A, Mahdizadeh V. Lentinula edodes substrate formulation using multilayer perceptron-genetic algorithm: a critical production checkpoint. Front Microbiol 2024; 15:1366264. [PMID: 38841070 PMCID: PMC11151849 DOI: 10.3389/fmicb.2024.1366264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 04/19/2024] [Indexed: 06/07/2024] Open
Abstract
Shiitake (Lentinula edodes) is one of the most widely grown and consumed mushroom species worldwide. They are a potential source of food and medicine because they are rich in nutrients and contain various minerals, vitamins, essential macro- and micronutrients, and bioactive compounds. The reuse of agricultural and industrial residues is crucial from an ecological and economic perspective. In this study, the running length (RL) of L. edodes cultured on 64 substrate compositions obtained from different ratios of bagasse (B), wheat bran (WB), and beech sawdust (BS) was recorded at intervals of 5 days after cultivation until the 40th day. Multilayer perceptron-genetic algorithm (MLP-GA), multiple linear regression, stepwise regression, principal component regression, ordinary least squares regression, and partial least squares regression were used to predict and optimize the RL and running rate (RR) of L. edodes. The statistical values showed higher prediction accuracies of the MLP-GA models (92% and 97%, respectively) compared with those of the regression models (52% and 71%, respectively) for RL and RR. The high degree of fit between the forecasted and actual values of the RL and RR of L. edodes confirmed the superior performance of the developed MLP-GA models. An optimization analysis on the established MLP-GA models showed that a substrate containing 15.1% B, 45.1% WB, and 10.16% BS and a running time of 28 days and 10 h could result in the maximum L. edodes RL (10.69 cm). Moreover, the highest RR of L. edodes (0.44 cm d-1) could be obtained by a substrate containing 30.7% B, 90.4% WB, and 0.0% BS. MLP-GA was observed to be an effective method for predicting and consequently selecting the best substrate composition for the maximal RL and RR of L. edodes.
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Affiliation(s)
- Naser Safaie
- Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Mina Salehi
- Department of Plant Genetics and Breeding, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Siamak Farhadi
- Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
| | - Ali Aligholizadeh
- Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Valiollah Mahdizadeh
- Department of Plant Pathology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
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12
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Gül Ş, Erdemir İ, Hanci V, Aydoğmuş E, Erkoç YS. How artificial intelligence can provide information about subdural hematoma: Assessment of readability, reliability, and quality of ChatGPT, BARD, and perplexity responses. Medicine (Baltimore) 2024; 103:e38009. [PMID: 38701313 PMCID: PMC11062651 DOI: 10.1097/md.0000000000038009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 04/04/2024] [Indexed: 05/05/2024] Open
Abstract
Subdural hematoma is defined as blood collection in the subdural space between the dura mater and arachnoid. Subdural hematoma is a condition that neurosurgeons frequently encounter and has acute, subacute and chronic forms. The incidence in adults is reported to be 1.72-20.60/100.000 people annually. Our study aimed to evaluate the quality, reliability and readability of the answers to questions asked to ChatGPT, Bard, and perplexity about "Subdural Hematoma." In this observational and cross-sectional study, we asked ChatGPT, Bard, and perplexity to provide the 100 most frequently asked questions about "Subdural Hematoma" separately. Responses from both chatbots were analyzed separately for readability, quality, reliability and adequacy. When the median readability scores of ChatGPT, Bard, and perplexity answers were compared with the sixth-grade reading level, a statistically significant difference was observed in all formulas (P < .001). All 3 chatbot responses were found to be difficult to read. Bard responses were more readable than ChatGPT's (P < .001) and perplexity's (P < .001) responses for all scores evaluated. Although there were differences between the results of the evaluated calculators, perplexity's answers were determined to be more readable than ChatGPT's answers (P < .05). Bard answers were determined to have the best GQS scores (P < .001). Perplexity responses had the best Journal of American Medical Association and modified DISCERN scores (P < .001). ChatGPT, Bard, and perplexity's current capabilities are inadequate in terms of quality and readability of "Subdural Hematoma" related text content. The readability standard for patient education materials as determined by the American Medical Association, National Institutes of Health, and the United States Department of Health and Human Services is at or below grade 6. The readability levels of the responses of artificial intelligence applications such as ChatGPT, Bard, and perplexity are significantly higher than the recommended 6th grade level.
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Affiliation(s)
- Şanser Gül
- Department of Neurosurgery, Ankara Ataturk Sanatory Education and Research Hospital, Ankara, Turkey
| | - İsmail Erdemir
- Department of Anesthesiology and Critical Care, Faculty of Medicine, Dokuz Eylül University, Izmir, Turkey
| | - Volkan Hanci
- Department of Anesthesiology and Reanimation, Ankara Sincan Education and Research Hospital, Ankara, Turkey
| | - Evren Aydoğmuş
- Department of Neurosurgery, Istanbul Kartal Dr Lütfi Kırdar City Hospital, Istanbul, Turkey
| | - Yavuz Selim Erkoç
- Department of Neurosurgery, Ankara Ataturk Sanatory Education and Research Hospital, Ankara, Turkey
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13
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Opara IK, Opara UL, Okolie JA, Fawole OA. Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review. PLANTS (BASEL, SWITZERLAND) 2024; 13:1200. [PMID: 38732414 PMCID: PMC11085577 DOI: 10.3390/plants13091200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024]
Abstract
The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.
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Affiliation(s)
- Ikechukwu Kingsley Opara
- SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch 7600, South Africa; (I.K.O.); (U.L.O.)
- Department of Food Science, Stellenbosch University, Stellenbosch 7600, South Africa
| | - Umezuruike Linus Opara
- SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch 7600, South Africa; (I.K.O.); (U.L.O.)
- UNESCO International Centre for Biotechnology, Nsukka 410001, Enugu State, Nigeria
| | - Jude A. Okolie
- Gallogly College of Engineering, University of Oklahoma, Norman, OK 73019, USA;
| | - Olaniyi Amos Fawole
- Postharvest and Agroprocessing Research Centre, Department of Botany and Plant Biotechnology, University of Johannesburg, Johannesburg 2006, South Africa
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14
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Bagherpour R, Bagherpour G, Mohammadi P. Application of Artificial Intelligence in Tissue Engineering. TISSUE ENGINEERING. PART B, REVIEWS 2024. [PMID: 38581425 DOI: 10.1089/ten.teb.2024.0022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/08/2024]
Abstract
Tissue engineering, a crucial approach in medical research and clinical applications, aims to regenerate damaged organs. By combining stem cells, biochemical factors, and biomaterials, it encounters challenges in designing complex 3D structures. Artificial intelligence (AI) enhances tissue engineering through computational modeling, biomaterial design, cell culture optimization, and personalized medicine. This review explores AI applications in organ tissue engineering (bone, heart, nerve, skin, cartilage), employing various machine learning (ML) algorithms for data analysis, prediction, and optimization. Each section discusses common ML algorithms and specific applications, emphasizing the potential and challenges in advancing regenerative therapies.
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Affiliation(s)
- Reza Bagherpour
- Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Ghasem Bagherpour
- Zanjan Pharmaceutical Biotechnology Research Center, Zanjan University of Medical Sciences, Zanjan, Iran
- Department of Medical Biotechnology, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Parvin Mohammadi
- Department of Medical Biotechnology, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
- Regenerative Medicine Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
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15
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Davidson SJ, Saggese T, Krajňáková J. Deep learning for automated segmentation and counting of hypocotyl and cotyledon regions in mature Pinus radiata D. Don. somatic embryo images. FRONTIERS IN PLANT SCIENCE 2024; 15:1322920. [PMID: 38495377 PMCID: PMC10940415 DOI: 10.3389/fpls.2024.1322920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 02/12/2024] [Indexed: 03/19/2024]
Abstract
In commercial forestry and large-scale plant propagation, the utilization of artificial intelligence techniques for automated somatic embryo analysis has emerged as a highly valuable tool. Notably, image segmentation plays a key role in the automated assessment of mature somatic embryos. However, to date, the application of Convolutional Neural Networks (CNNs) for segmentation of mature somatic embryos remains unexplored. In this study, we present a novel application of CNNs for delineating mature somatic conifer embryos from background and residual proliferating embryogenic tissue and differentiating various morphological regions within the embryos. A semantic segmentation CNN was trained to assign pixels to cotyledon, hypocotyl, and background regions, while an instance segmentation network was trained to detect individual cotyledons for automated counting. The main dataset comprised 275 high-resolution microscopic images of mature Pinus radiata somatic embryos, with 42 images reserved for testing and validation sets. The evaluation of different segmentation methods revealed that semantic segmentation achieved the highest performance averaged across classes, achieving F1 scores of 0.929 and 0.932, with IoU scores of 0.867 and 0.872 for the cotyledon and hypocotyl regions respectively. The instance segmentation approach demonstrated proficiency in accurate detection and counting of the number of cotyledons, as indicated by a mean squared error (MSE) of 0.79 and mean absolute error (MAE) of 0.60. The findings highlight the efficacy of neural network-based methods in accurately segmenting somatic embryos and delineating individual morphological parts, providing additional information compared to previous segmentation techniques. This opens avenues for further analysis, including quantification of morphological characteristics in each region, enabling the identification of features of desirable embryos in large-scale production systems. These advancements contribute to the improvement of automated somatic embryogenesis systems, facilitating efficient and reliable plant propagation for commercial forestry applications.
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Affiliation(s)
- Sam J. Davidson
- Data and Geospatial Intelligence, New Zealand Forest Research Institute (Scion), Christchurch, New Zealand
| | - Taryn Saggese
- Forest Genetics and Biotechnology, New Zealand Forest Research Institute (Scion), Rotorua, New Zealand
| | - Jana Krajňáková
- Forest Genetics and Biotechnology, New Zealand Forest Research Institute (Scion), Rotorua, New Zealand
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16
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Jafari M, Daneshvar MH. Machine learning-mediated Passiflora caerulea callogenesis optimization. PLoS One 2024; 19:e0292359. [PMID: 38266002 PMCID: PMC10807783 DOI: 10.1371/journal.pone.0292359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 09/19/2023] [Indexed: 01/26/2024] Open
Abstract
Callogenesis is one of the most powerful biotechnological approaches for in vitro secondary metabolite production and indirect organogenesis in Passiflora caerulea. Comprehensive knowledge of callogenesis and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms. In the present investigation, the callogenesis responses (i.e., callogenesis rate and callus fresh weight) of P. caerulea were predicted based on different types and concentrations of plant growth regulators (PGRs) (i.e., 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), 1-naphthaleneacetic acid (NAA), and indole-3-Butyric Acid (IBA)) as well as explant types (i.e., leaf, node, and internode) using multilayer perceptron (MLP). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and explant types for maximizing callogenesis responses. Furthermore, sensitivity analysis was conducted to assess the importance of each input variable on the callogenesis responses. The results showed that MLP had high predictive accuracy (R2 > 0.81) in both training and testing sets for modeling all studied parameters. Based on the results of the optimization process, the highest callogenesis rate (100%) would be obtained from the leaf explant cultured in the medium supplemented with 0.52 mg/L IBA plus 0.43 mg/L NAA plus 1.4 mg/L 2,4-D plus 0.2 mg/L BAP. The results of the sensitivity analysis showed the explant-dependent impact of the exogenous application of PGRs on callogenesis. Generally, the results showed that a combination of MLP and GA can display a forward-thinking aid to optimize and predict in vitro culture systems and consequentially cope with several challenges faced currently in Passiflora tissue culture.
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Affiliation(s)
- Marziyeh Jafari
- Department of Horticultural Science, College of Agriculture, Shiraz University, Shiraz, Iran
- Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran
| | - Mohammad Hosein Daneshvar
- Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran
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Hashizume T, Ying BW. Challenges in developing cell culture media using machine learning. Biotechnol Adv 2024; 70:108293. [PMID: 37984683 DOI: 10.1016/j.biotechadv.2023.108293] [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: 07/01/2023] [Revised: 10/17/2023] [Accepted: 11/14/2023] [Indexed: 11/22/2023]
Abstract
Microbial and mammalian cells are widely used in the food, pharmaceutical, and medical industries. Developing or optimizing culture media is essential to improve cell culture performance as a critical technology in cell culture engineering. Methodologies for media optimization have been developed to a great extent, such as the approaches of one-factor-at-a-time (OFAT) and response surface methodology (RSM). The present review introduces the emerging machine learning (ML) technology in cell culture engineering by combining high-throughput experimental technologies to develop highly efficient and effective culture media. The commonly used ML algorithms and the successful applications of employing ML in medium optimization are summarized. This review highlights the benefits of ML-assisted medium development and guides the selection of the media optimization method appropriate for various cell culture purposes.
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Affiliation(s)
- Takamasa Hashizume
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan
| | - Bei-Wen Ying
- School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8572 Ibaraki, Japan.
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Türkoğlu A, Haliloğlu K, Demirel F, Aydin M, Çiçek S, Yiğider E, Demirel S, Piekutowska M, Szulc P, Niedbała G. Machine Learning Analysis of the Impact of Silver Nitrate and Silver Nanoparticles on Wheat ( Triticum aestivum L.): Callus Induction, Plant Regeneration, and DNA Methylation. PLANTS (BASEL, SWITZERLAND) 2023; 12:4151. [PMID: 38140479 PMCID: PMC10747064 DOI: 10.3390/plants12244151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 12/24/2023]
Abstract
The objective of this study was to comprehend the efficiency of wheat regeneration, callus induction, and DNA methylation through the application of mathematical frameworks and artificial intelligence (AI)-based models. This research aimed to explore the impact of treatments with AgNO3 and Ag-NPs on various parameters. The study specifically concentrated on analyzing RAPD profiles and modeling regeneration parameters. The treatments and molecular findings served as input variables in the modeling process. It included the use of AgNO3 and Ag-NPs at different concentrations (0, 2, 4, 6, and 8 mg L-1). The in vitro and epigenetic characteristics were analyzed using several machine learning (ML) methods, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor classifier (KNN), and Gaussian processes classifier (GP) methods. This study's results revealed that the highest values for callus induction (CI%) and embryogenic callus induction (EC%) occurred at a concentration of 2 mg L-1 of Ag-NPs. Additionally, the regeneration efficiency (RE) parameter reached its peak at a concentration of 8 mg L-1 of AgNO3. Taking an epigenetic approach, AgNO3 at a concentration of 2 mg L-1 demonstrated the highest levels of genomic template stability (GTS), at 79.3%. There was a positive correlation seen between increased levels of AgNO3 and DNA hypermethylation. Conversely, elevated levels of Ag-NPs were associated with DNA hypomethylation. The models were used to estimate the relationships between the input elements, including treatments, concentration, GTS rates, and Msp I and Hpa II polymorphism, and the in vitro output parameters. The findings suggested that the XGBoost model exhibited superior performance scores for callus induction (CI), as evidenced by an R2 score of 51.5%, which explained the variances. Additionally, the RF model explained 71.9% of the total variance and showed superior efficacy in terms of EC%. Furthermore, the GP model, which provided the most robust statistics for RE, yielded an R2 value of 52.5%, signifying its ability to account for a substantial portion of the total variance present in the data. This study exemplifies the application of various machine learning models in the cultivation of mature wheat embryos under the influence of treatments and concentrations involving AgNO3 and Ag-NPs.
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Affiliation(s)
- Aras Türkoğlu
- Department of Field Crops, Faculty of Agriculture, Necmettin Erbakan University, Konya 42310, Türkiye
| | - Kamil Haliloğlu
- Department of Field Crops, Faculty of Agriculture, Ataturk University, Erzurum 25240, Türkiye;
| | - Fatih Demirel
- Department of Agricultural Biotechnology, Faculty of Agriculture, Igdır University, Igdir 76000, Türkiye;
| | - Murat Aydin
- Department of Agricultural Biotechnology, Faculty of Agriculture, Ataturk University, Erzurum 25240, Türkiye; (M.A.); (S.Ç.); (E.Y.)
| | - Semra Çiçek
- Department of Agricultural Biotechnology, Faculty of Agriculture, Ataturk University, Erzurum 25240, Türkiye; (M.A.); (S.Ç.); (E.Y.)
| | - Esma Yiğider
- Department of Agricultural Biotechnology, Faculty of Agriculture, Ataturk University, Erzurum 25240, Türkiye; (M.A.); (S.Ç.); (E.Y.)
| | - Serap Demirel
- Department of Molecular Biology and Genetics, Faculty of Science, Van Yüzüncü Yıl University, Van 65080, Türkiye;
| | - Magdalena Piekutowska
- Department of Geoecology and Geoinformation, Institute of Biology and Earth Sciences, Pomeranian University in Słupsk, 27 Partyzantów St., 76-200 Słupsk, Poland;
| | - Piotr Szulc
- Department of Agronomy, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland;
| | - Gniewko Niedbała
- Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
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Damásio M, Barbosa M, Deus J, Fernandes E, Leitão A, Albino L, Fonseca F, Silvestre J. Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. PLANTS (BASEL, SWITZERLAND) 2023; 12:4142. [PMID: 38140469 PMCID: PMC10747955 DOI: 10.3390/plants12244142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/22/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023]
Abstract
Climate change is affecting global viticulture, increasing heatwaves and drought. Precision irrigation, supported by robust water status indicators (WSIs), is inevitable in most of the Mediterranean basin. One of the most reliable WSIs is the leaf water potential (Ψleaf), which is determined via an intrusive and time-consuming method. The aim of this work is to discern the most effective variables that are correlated with plants' water status and identify the variables that better predict Ψleaf. Five grapevine varieties grown in the Alentejo region (Portugal) were selected and subjected to three irrigation treatments, starting in 2018: full irrigation (FI), deficit irrigation (DI), and no irrigation (NI). Plant monitoring was performed in 2023. Measurements included stomatal conductance (gs), predawn water potential Ψpd, stem water potential (Ψstem), thermal imaging, and meteorological data. The WSIs, namely Ψpd and gs, responded differently according to the irrigation treatment. Ψstem measured at mid-morning (MM) and mid-day (MD) proved unable to discern between treatments. MM measurements presented the best correlations between WSIs. gs showed the best correlations between the other WSIs, and consequently the best predictive capability to estimate Ψpd. Machine learning regression models were trained on meteorological, thermal, and gs data to predict Ψpd, with ensemble models showing a great performance (ExtraTrees: R2=0.833, MAE=0.072; Gradient Boosting: R2=0.830; MAE=0.073).
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Affiliation(s)
- Miguel Damásio
- INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal; (J.D.); (J.S.)
| | - Miguel Barbosa
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - João Deus
- INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal; (J.D.); (J.S.)
| | - Eduardo Fernandes
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - André Leitão
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - Luís Albino
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - Filipe Fonseca
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - José Silvestre
- INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal; (J.D.); (J.S.)
- GREEN-IT Bioresources4sustainability, ITQB NOVA, Av. da República, 2780-157 Oeiras, Portugal
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20
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Rezaei H, Mirzaie-asl A, Abdollahi MR, Tohidfar M. Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis. PLoS One 2023; 18:e0293754. [PMID: 37922261 PMCID: PMC10624318 DOI: 10.1371/journal.pone.0293754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/18/2023] [Indexed: 11/05/2023] Open
Abstract
The important feature of petunia in tissue culture is its unpredictable and genotype-dependent callogenesis, posing challenges for efficient regeneration and biotechnology applications. To address this issue, machine learning (ML) can be considered a powerful tool to analyze callogenesis data, extract key parameters, and predict optimal conditions for petunia callogenesis, facilitating more controlled and productive tissue culture processes. The study aimed to develop a predictive model for callogenesis in petunia using ML algorithms and to optimize the concentrations of phytohormones to enhance callus formation rate (CFR) and callus fresh weight (CFW). The inputs for the model were BAP, KIN, IBA, and NAA, while the outputs were CFR and CFW. Three ML algorithms, namely MLP, RBF, and GRNN, were compared, and the results revealed that GRNN (R2≥83) outperformed MLP and RBF in terms of accuracy. Furthermore, a sensitivity analysis was conducted to determine the relative importance of the four phytohormones. IBA exhibited the highest importance, followed by NAA, BAP, and KIN. Leveraging the superior performance of the GRNN model, a genetic algorithm (GA) was integrated to optimize the concentration of phytohormones for maximizing CFR and CFW. The genetic algorithm identified an optimized combination of phytohormones consisting of 1.31 mg/L BAP, 1.02 mg/L KIN, 1.44 mg/L NAA, and 1.70 mg/L IBA, resulting in 95.83% CFR. To validate the reliability of the predicted results, optimized combinations of phytohormones were tested in a laboratory experiment. The results of the validation experiment indicated no significant difference between the experimental and optimized results obtained through the GA. This study presents a novel approach combining ML, sensitivity analysis, and GA for modeling and predicting callogenesis in petunia. The findings offer valuable insights into the optimization of phytohormone concentrations, facilitating improved callus formation and potential applications in plant tissue culture and genetic engineering.
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Affiliation(s)
- Hamed Rezaei
- Department of Plant Biotechnology, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Asghar Mirzaie-asl
- Department of Plant Biotechnology, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Mohammad Reza Abdollahi
- Department of Agronomy and Plant Breeding, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Masoud Tohidfar
- Department of Plant Biotechnology, Faculty of Life Science and Biotechnology, Shahid Beheshti University, Tehran, Iran
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21
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Jara TC, Park K, Vahmani P, Van Eenennaam AL, Smith LR, Denicol AC. Stem cell-based strategies and challenges for production of cultivated meat. NATURE FOOD 2023; 4:841-853. [PMID: 37845547 DOI: 10.1038/s43016-023-00857-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 09/05/2023] [Indexed: 10/18/2023]
Abstract
Cultivated meat scale-up and industrial production will require multiple stable cell lines from different species to recreate the organoleptic and nutritional properties of meat from livestock. In this Review, we explore the potential of stem cells to create the major cellular components of cultivated meat. By using developments in the fields of tissue engineering and biomedicine, we explore the advantages and disadvantages of strategies involving primary adult and pluripotent stem cells for generating cell sources that can be grown at scale. These myogenic, adipogenic or extracellular matrix-producing adult stem cells as well as embryonic or inducible pluripotent stem cells are discussed for their proliferative and differentiation capacity, necessary for cultivated meat. We examine the challenges for industrial scale-up, including differentiation and culture protocols, as well as genetic modification options for stem cell immortalization and controlled differentiation. Finally, we discuss stem cell-related safety and regulatory challenges for bringing cultivated meat to the marketplace.
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Affiliation(s)
- T C Jara
- Department of Animal Science, University of California Davis, Davis, CA, USA
| | - K Park
- Department of Animal Science, University of California Davis, Davis, CA, USA
| | - P Vahmani
- Department of Animal Science, University of California Davis, Davis, CA, USA
| | - A L Van Eenennaam
- Department of Animal Science, University of California Davis, Davis, CA, USA
| | - L R Smith
- Department of Neurobiology, Physiology and Behavior, University of California Davis, Davis, CA, USA.
| | - A C Denicol
- Department of Animal Science, University of California Davis, Davis, CA, USA
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22
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Cui Z, Li K, Kang C, Wu Y, Li T, Li M. Plant and Disease Recognition Based on PMF Pipeline Domain Adaptation Method: Using Bark Images as Meta-Dataset. PLANTS (BASEL, SWITZERLAND) 2023; 12:3280. [PMID: 37765444 PMCID: PMC10534746 DOI: 10.3390/plants12183280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023]
Abstract
Efficient image recognition is important in crop and forest management. However, it faces many challenges, such as the large number of plant species and diseases, the variability of plant appearance, and the scarcity of labeled data for training. To address this issue, we modified a SOTA Cross-Domain Few-shot Learning (CDFSL) method based on prototypical networks and attention mechanisms. We employed attention mechanisms to perform feature extraction and prototype generation by focusing on the most relevant parts of the images, then used prototypical networks to learn the prototype of each category and classify new instances. Finally, we demonstrated the effectiveness of the modified CDFSL method on several plant and disease recognition datasets. The results showed that the modified pipeline was able to recognize several cross-domain datasets using generic representations, and achieved up to 96.95% and 94.07% classification accuracy on datasets with the same and different domains, respectively. In addition, we visualized the experimental results, demonstrating the model's stable transfer capability between datasets and the model's high visual correlation with plant and disease biological characteristics. Moreover, by extending the classes of different semantics within the training dataset, our model can be generalized to other domains, which implies broad applicability.
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Affiliation(s)
| | | | | | | | | | - Mingyang Li
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.C.); (K.L.); (C.K.); (Y.W.); (T.L.)
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23
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Eren B, Türkoğlu A, Haliloğlu K, Demirel F, Nowosad K, Özkan G, Niedbała G, Pour-Aboughadareh A, Bujak H, Bocianowski J. Investigation of the Influence of Polyamines on Mature Embryo Culture and DNA Methylation of Wheat ( Triticum aestivum L.) Using the Machine Learning Algorithm Method. PLANTS (BASEL, SWITZERLAND) 2023; 12:3261. [PMID: 37765424 PMCID: PMC10536335 DOI: 10.3390/plants12183261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/21/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023]
Abstract
Numerous factors can impact the efficiency of callus formation and in vitro regeneration in wheat cultures through the introduction of exogenous polyamines (PAs). The present study aimed to investigate in vitro plant regeneration and DNA methylation patterns utilizing the inter-primer binding site (iPBS) retrotransposon and coupled restriction enzyme digestion-iPBS (CRED-iPBS) methods in wheat. This investigation involved the application of distinct types of PAs (Put: putrescine, Spd: spermidine, and Spm: spermine) at varying concentrations (0, 0.5, 1, and 1.5 mM). The subsequent outcomes were subjected to predictive modeling using diverse machine learning (ML) algorithms. Based on the specific polyamine type and concentration utilized, the results indicated that 1 mM Put and Spd were the most favorable PAs for supporting endosperm-associated mature embryos. Employing an epigenetic approach, Put at concentrations of 0.5 and 1.5 mM exhibited the highest levels of genomic template stability (GTS) (73.9%). Elevated Spd levels correlated with DNA hypermethylation while reduced Spm levels were linked to DNA hypomethylation. The in vitro and epigenetic characteristics were predicted using ML techniques such as the support vector machine (SVM), extreme gradient boosting (XGBoost), and random forest (RF) models. These models were employed to establish relationships between input variables (PAs, concentration, GTS rates, Msp I polymorphism, and Hpa II polymorphism) and output parameters (in vitro measurements). This comparative analysis aimed to evaluate the performance of the models and interpret the generated data. The outcomes demonstrated that the XGBoost method exhibited the highest performance scores for callus induction (CI%), regeneration efficiency (RE), and the number of plantlets (NP), with R2 scores explaining 38.3%, 73.8%, and 85.3% of the variances, respectively. Additionally, the RF algorithm explained 41.5% of the total variance and showcased superior efficacy in terms of embryogenic callus induction (ECI%). Furthermore, the SVM model, which provided the most robust statistics for responding embryogenic calluses (RECs%), yielded an R2 value of 84.1%, signifying its ability to account for a substantial portion of the total variance present in the data. In summary, this study exemplifies the application of diverse ML models to the cultivation of mature wheat embryos in the presence of various exogenous PAs and concentrations. Additionally, it explores the impact of polymorphic variations in the CRED-iPBS profile and DNA methylation on epigenetic changes, thereby contributing to a comprehensive understanding of these regulatory mechanisms.
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Affiliation(s)
- Barış Eren
- Department of Agricultural Biotechnology, Faculty of Agriculture, Igdır University, Igdir 76000, Türkiye; (B.E.); (F.D.)
| | - Aras Türkoğlu
- Department of Field Crops, Faculty of Agriculture, Necmettin Erbakan University, Konya 42310, Türkiye
| | - Kamil Haliloğlu
- Department of Field Crops, Faculty of Agriculture, Ataturk University, Erzurum 25240, Türkiye;
| | - Fatih Demirel
- Department of Agricultural Biotechnology, Faculty of Agriculture, Igdır University, Igdir 76000, Türkiye; (B.E.); (F.D.)
| | - Kamila Nowosad
- Department of Genetics, Plant Breeding and Seed Production, Wrocław University of Environmental and Life Sciences, Grunwaldzki 24A, 53-363 Wrocław, Poland;
| | - Güller Özkan
- Department of Biology, Faculty of Science, Ankara University, Ankara 06100, Türkiye;
| | - Gniewko Niedbała
- Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland;
| | - Alireza Pour-Aboughadareh
- Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj P.O. Box 3158854119, Iran;
| | - Henryk Bujak
- Department of Genetics, Plant Breeding and Seed Production, Wrocław University of Environmental and Life Sciences, Grunwaldzki 24A, 53-363 Wrocław, Poland;
- Research Centre for Cultivar Testing (COBORU), Słupia Wielka 34, 63-022 Słupia Wielka, Poland
| | - Jan Bocianowski
- Department of Mathematical and Statistical Methods, Poznan University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland
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Dagne H, S VP, Palanivel H, Yeshitila A, Benor S, Abera S, Abdi A. Advanced modeling and optimizing for surface sterilization process of grape vine ( Vitis vinifera) root stock 3309C through response surface, artificial neural network, and genetic algorithm techniques. Heliyon 2023; 9:e18628. [PMID: 37554794 PMCID: PMC10404695 DOI: 10.1016/j.heliyon.2023.e18628] [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: 02/14/2023] [Revised: 07/16/2023] [Accepted: 07/24/2023] [Indexed: 08/10/2023] Open
Abstract
In vitro, sterilization is one of the key components for proceeding with plant tissue cultures. Since the effectiveness of sterilization has a direct impact on the culture's final outcomes, there is a crucial need for optimization of the sterilization process. However, compared with traditional optimizing methods, the use of computational approaches through artificial intelligence-based process modeling and optimization algorithms provides a precise optimal condition for in vitro culturing. This study aimed to optimise in vitro sterilization of grape rootstock 3309C using RSM, ANN, and genetic algorithm (GA) techniques. In this context, two output responses, namely, Clean Culture and Explant Viability, were optimised using the models developed by RSM and ANN, followed by a GA, to obtain a globally optimal solution. The most influential independent factors, such as HgCl2, NaOCl, AgNO3, and immersion time, were considered input variables. The significance of the developed models was investigated with statistical and non-statistical techniques and was optimised to determine the significance of selected inputs. The optimal clean culture of 91%, and the explant viability of 89% can be obtained from 1.62% NaOCl at a 13.96 min immersion time, according to MLP-NSGAII. Sensitivity analysis revealed that the clean culture and explant viability were less sensitive to AgNO3 and more sensitive to immersion time. Results showed that the differences between the GA predicted and validation data were significant after the performance validation of predicted and optimised sterilising agents with immersion time combinations were tested. In general, GA, a potent methodology, may open the door to the development of new computational methods in plant tissue culture.
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Affiliation(s)
- Habtamu Dagne
- Department of Biotechnology, Centre of Excellence for Biotechnology and Bioprocess, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, PO Box 16417, Addis Ababa, Ethiopia
| | - Venkatesa Prabhu S
- Department of Chemical Engineering, Centre of Excellence for Biotechnology and Bioprocess, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, PO Box 16417, Addis Ababa, Ethiopia
| | - Hemalatha Palanivel
- Department of Biotechnology, Centre of Excellence for Biotechnology and Bioprocess, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, PO Box 16417, Addis Ababa, Ethiopia
| | - Alazar Yeshitila
- Department of Biotechnology, Centre of Excellence for Biotechnology and Bioprocess, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, PO Box 16417, Addis Ababa, Ethiopia
| | - Solomon Benor
- Department of Plant Biology and Biodiversity Management, College of Natural and Computational Sciences, Addis Ababa University, Ethiopia
| | - Solomon Abera
- Department of Biotechnology, Centre of Excellence for Biotechnology and Bioprocess, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, PO Box 16417, Addis Ababa, Ethiopia
| | - Adugna Abdi
- Department of Biotechnology, Centre of Excellence for Biotechnology and Bioprocess, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, PO Box 16417, Addis Ababa, Ethiopia
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25
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Jafari M, Daneshvar MH. Prediction and optimization of indirect shoot regeneration of Passiflora caerulea using machine learning and optimization algorithms. BMC Biotechnol 2023; 23:27. [PMID: 37528396 PMCID: PMC10394921 DOI: 10.1186/s12896-023-00796-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 07/21/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Optimization of indirect shoot regeneration protocols is one of the key prerequisites for the development of Agrobacterium-mediated genetic transformation and/or genome editing in Passiflora caerulea. Comprehensive knowledge of indirect shoot regeneration and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms. MATERIALS AND METHODS In the present investigation, the indirect shoot regeneration responses (i.e., de novo shoot regeneration rate, the number of de novo shoots, and length of de novo shoots) of P. caerulea were predicted based on different types and concentrations of PGRs (i.e., TDZ, BAP, PUT, KIN, and IBA) as well as callus types (i.e., callus derived from different explants including leaf, node, and internode) using generalized regression neural network (GRNN) and random forest (RF). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and callus types for maximizing indirect shoot regeneration responses. Moreover, sensitivity analysis was conducted to assess the importance of each input variable on the studied parameters. RESULTS The results showed that both algorithms (RF and GRNN) had high predictive accuracy (R2 > 0.86) in both training and testing sets for modeling all studied parameters. Based on the results of optimization process, the highest de novo shoot regeneration rate (100%) would be obtained from callus derived from nodal segments cultured in the medium supplemented with 0.77 mg/L BAP plus 2.41 mg/L PUT plus 0.06 mg/L IBA. The results of the sensitivity analysis showed the explant-dependent impact of exogenous application of PGRs on indirect de novo shoot regeneration. CONCLUSIONS A combination of ML (GRNN and RF) and GA can display a forward-thinking aid to optimize and predict in vitro culture systems and consequentially cope with several challenges faced currently in Passiflora tissue culture.
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Affiliation(s)
- Marziyeh Jafari
- Department of Horticultural Science, College of Agriculture, Shiraz University, Shiraz, 7144113131, Iran.
- Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, 6341773637, Iran.
| | - Mohammad Hosein Daneshvar
- Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, 6341773637, Iran
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Niazian M, Belzile F, Curtin SJ, de Ronne M, Torkamaneh D. Optimization of in vitro and ex vitro Agrobacterium rhizogenes-mediated hairy root transformation of soybean for visual screening of transformants using RUBY. FRONTIERS IN PLANT SCIENCE 2023; 14:1207762. [PMID: 37484469 PMCID: PMC10361064 DOI: 10.3389/fpls.2023.1207762] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/22/2023] [Indexed: 07/25/2023]
Abstract
In vitro and ex vitro Agrobacterium rhizogenes-mediated hairy root transformation (HRT) assays are key components of the plant biotechnology and functional genomics toolkit. In this report, both in vitro and ex vitro HRT were optimized in soybean using the RUBY reporter. Different parameters including A. rhizogenes strain, optical density of the bacterial cell culture (OD600), co-cultivation media, soybean genotype, explant age, and acetosyringone addition and concentration were evaluated. Overall, the in vitro assay was more efficient than the ex vitro assay in terms of the percentage of induction of hairy roots and transformed roots (expressing RUBY). Nonetheless, the ex vitro technique was deemed faster and a less complicated approach. The highest transformation of RUBY was observed on 7-d-old cotyledons of cv. Bert inoculated for 30 minutes with the R1000 resuspended in ¼ B5 medium to OD600 (0.3) and 150 µM of acetosyringone. The parameters of this assay also led to the highest percentage of RUBY through two-step ex vitro hairy root transformation. Finally, using machine learning-based modeling, optimal protocols for both assays were further defined. This study establishes efficient and reliable hairy root transformation protocols applicable for functional studies in soybean.
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Affiliation(s)
- Mohsen Niazian
- Département de Phytologie, Université Laval, Québec City, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada
- Centre de recherche et d’innovation sur les végétaux (CRIV), Université Laval, Québec City, QC, Canada
| | - François Belzile
- Département de Phytologie, Université Laval, Québec City, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada
- Centre de recherche et d’innovation sur les végétaux (CRIV), Université Laval, Québec City, QC, Canada
| | - Shaun J. Curtin
- Plant Science Research Unit, United States Department of Agriculture (USDA), St Paul, MN, United States
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States
- Center for Plant Precision Genomics, University of Minnesota, St. Paul, MN, United States
- Center for Genome Engineering, University of Minnesota, St. Paul, MN, United States
| | - Maxime de Ronne
- Département de Phytologie, Université Laval, Québec City, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada
- Centre de recherche et d’innovation sur les végétaux (CRIV), Université Laval, Québec City, QC, Canada
| | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada
- Centre de recherche et d’innovation sur les végétaux (CRIV), Université Laval, Québec City, QC, Canada
- Institute Intelligence and Data (IID), Université Laval, Québec City, QC, Canada
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27
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Fernandez I Marti A, Parungao M, Hollin J, Selimotic B, Farrar G, Seyler T, Anand A, Ahmad R. A Novel, Precise and High-Throughput Technology for Viroid Detection in Cannabis (MFDetect TM). Viruses 2023; 15:1487. [PMID: 37515174 PMCID: PMC10385567 DOI: 10.3390/v15071487] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/30/2023] Open
Abstract
Hop latent viroid (HLVd) is a severe disease of cannabis, causing substantial economic losses in plant yield and crop value for growers worldwide. The best way to control the disease is early detection to limit the spread of the viroid in grow facilities. This study describes MFDetectTM as a rapid, highly sensitive, and high-throughput tool for detecting HLVd in the early stages of plant development. Furthermore, in the largest research study conducted so far for HLVd detection in cannabis, we compared MFDetectTM with quantitative RT-PCR in a time course experiment using different plant tissues, leaves, petioles, and roots at different plant developmental stages to demonstrate both technologies are comparable. Our study found leaf tissue is a suitable plant material for HLVd detection, with the viroid titer increasing in the infected leaf tissue with the age of plants. The study showed that other tissue types, including petiole and roots, were equally sensitive to detection via MFDetectTM. The assay developed in this research allows the screening of thousands of plants in a week. The assay can be scaled easily to provide growers with a quick turnaround and a cost-effective diagnostic tool for screening many plants and tissue types at different stages of development.
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Affiliation(s)
- Angel Fernandez I Marti
- Department of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720, USA
- MyFloraDNA, Inc., 1451 River Park Dr., Sacramento, CA 95815, USA
| | - Marcus Parungao
- MyFloraDNA, Inc., 1451 River Park Dr., Sacramento, CA 95815, USA
| | - Jonathan Hollin
- MyFloraDNA, Inc., 1451 River Park Dr., Sacramento, CA 95815, USA
| | - Berin Selimotic
- MyFloraDNA, Inc., 1451 River Park Dr., Sacramento, CA 95815, USA
| | - Graham Farrar
- Glass House Farms, 645 W Laguna Road, Camarillo, CA 93012, USA
| | - Tristan Seyler
- Glass House Farms, 645 W Laguna Road, Camarillo, CA 93012, USA
| | - Ajith Anand
- MyFloraDNA, Inc., 1451 River Park Dr., Sacramento, CA 95815, USA
| | - Riaz Ahmad
- MyFloraDNA, Inc., 1451 River Park Dr., Sacramento, CA 95815, USA
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28
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Huang Y. Improved SVM-Based Soil-Moisture-Content Prediction Model for Tea Plantation. PLANTS (BASEL, SWITZERLAND) 2023; 12:2309. [PMID: 37375934 DOI: 10.3390/plants12122309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/04/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023]
Abstract
Accurate prediction of soil moisture content in tea plantations plays a crucial role in optimizing irrigation practices and improving crop productivity. Traditional methods for SMC prediction are difficult to implement due to high costs and labor requirements. While machine learning models have been applied, their performance is often limited by the lack of sufficient data. To address the challenges of inaccurate and inefficient soil moisture prediction in tea plantations and enhance predictive performance, an improved support-vector-machine- (SVM) based model was developed to predict the SMC in a tea plantation. The proposed model addresses several limitations of existing approaches by incorporating novel features and enhancing the SVM algorithm's performance, which was improved with the Bald Eagle Search algorithm (BES) method for hyper-parameter optimization. The study utilized a comprehensive dataset comprising soil moisture measurements and relevant environmental variables collected from a tea plantation. Feature selection techniques were applied to identify the most informative variables, including rainfall, temperature, humidity, and soil type. The selected features were then used to train and optimize the SVM model. The proposed model was applied to prediction of soil water moisture in a tea plantation in Guangxi State-owned Fuhu Overseas Chinese Farm. Experimental results demonstrated the superior performance of the improved SVM model in predicting soil moisture content compared to traditional SVM approaches and other machine-learning algorithms. The model exhibited high accuracy, robustness, and generalization capabilities across different time periods and geographical locations with R2, MSE, and RMSE of 0.9435, 0.0194 and 0.1392, respectively, which helps to enhance the prediction performance, especially when limited real data are available. The proposed SVM-based model offers several advantages for tea plantation management. It provides timely and accurate soil moisture predictions, enabling farmers to make informed decisions regarding irrigation scheduling and water resource management. By optimizing irrigation practices, the model helps enhance tea crop yield, minimize water usage, and reduce environmental impact.
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Affiliation(s)
- Ying Huang
- Electronic Information School, Wuhan University, Wuhan 430072, China
- School of Automatic Control, Liuzhou Railway Vocational Technical College, Liuzhou 545616, China
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Holzinger A, Keiblinger K, Holub P, Zatloukal K, Müller H. AI for life: Trends in artificial intelligence for biotechnology. N Biotechnol 2023; 74:16-24. [PMID: 36754147 DOI: 10.1016/j.nbt.2023.02.001] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/05/2023] [Accepted: 02/05/2023] [Indexed: 02/08/2023]
Abstract
Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper.
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Affiliation(s)
- Andreas Holzinger
- University of Natural Resources and Life Sciences Vienna, Austria; Medical University Graz, Austria; Alberta Machine Intelligence Institute Edmonton, Canada.
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30
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Rezaei H, Mirzaie-asl A, Abdollahi MR, Tohidfar M. Comparative analysis of different artificial neural networks for predicting and optimizing in vitro seed germination and sterilization of petunia. PLoS One 2023; 18:e0285657. [PMID: 37167278 PMCID: PMC10174541 DOI: 10.1371/journal.pone.0285657] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 04/27/2023] [Indexed: 05/13/2023] Open
Abstract
The process of optimizing in vitro seed sterilization and germination is a complicated task since this process is influenced by interactions of many factors (e.g., genotype, disinfectants, pH of the media, temperature, light, immersion time). This study investigated the role of various types and concentrations of disinfectants (i.e., NaOCl, Ca(ClO)2, HgCl2, H2O2, NWCN-Fe, MWCNT) as well as immersion time in successful in vitro seed sterilization and germination of petunia. Also, the utility of three artificial neural networks (ANNs) (e.g., multilayer perceptron (MLP), radial basis function (RBF), and generalized regression neural network (GRNN)) as modeling tools were evaluated to analyze the effect of disinfectants and immersion time on in vitro seed sterilization and germination. Moreover, non‑dominated sorting genetic algorithm‑II (NSGA‑II) was employed for optimizing the selected prediction model. The GRNN algorithm displayed superior predictive accuracy in comparison to MLP and RBF models. Also, the results showed that NSGA‑II can be considered as a reliable multi-objective optimization algorithm for finding the optimal level of disinfectants and immersion time to simultaneously minimize contamination rate and maximize germination percentage. Generally, GRNN-NSGA-II as an up-to-date and reliable computational tool can be applied in future plant in vitro culture studies.
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Affiliation(s)
- Hamed Rezaei
- Department of Plant Biotechnology, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Asghar Mirzaie-asl
- Department of Plant Biotechnology, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Mohammad Reza Abdollahi
- Department of Agronomy and Plant Breeding, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Masoud Tohidfar
- Department of Plant Biotechnology, Faculty of Life Science and Biotechnology, Shahid Beheshti University, Tehran, Iran
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Ji B, Xuan L, Zhang Y, Mu W, Paek KY, Park SY, Wang J, Gao W. Application of Data Modeling, Instrument Engineering and Nanomaterials in Selected Medid the Scientific Recinal Plant Tissue Culture. PLANTS (BASEL, SWITZERLAND) 2023; 12:1505. [PMID: 37050131 PMCID: PMC10096660 DOI: 10.3390/plants12071505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/10/2023] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
At present, most precious compounds are still obtained by plant cultivation such as ginsenosides, glycyrrhizic acid, and paclitaxel, which cannot be easily obtained by artificial synthesis. Plant tissue culture technology is the most commonly used biotechnology tool, which can be used for a variety of studies such as the production of natural compounds, functional gene research, plant micropropagation, plant breeding, and crop improvement. Tissue culture material is a basic and important part of this issue. The formation of different plant tissues and natural products is affected by growth conditions and endogenous substances. The accumulation of secondary metabolites are affected by plant tissue type, culture method, and environmental stress. Multi-domain technologies are developing rapidly, and they have made outstanding contributions to the application of plant tissue culture. The modes of action have their own characteristics, covering the whole process of plant tissue from the induction, culture, and production of natural secondary metabolites. This paper reviews the induction mechanism of different plant tissues and the application of multi-domain technologies such as artificial intelligence, biosensors, bioreactors, multi-omics monitoring, and nanomaterials in plant tissue culture and the production of secondary metabolites. This will help to improve the tissue culture technology of medicinal plants and increase the availability and the yield of natural metabolites.
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Affiliation(s)
- Baoyu Ji
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China
- Shool of Pharmacy, Henan University of Chinese Medicine, Zhengzhou 450046, China
| | - Liangshuang Xuan
- Shool of Pharmacy, Henan University of Chinese Medicine, Zhengzhou 450046, China
| | - Yunxiang Zhang
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China
| | - Wenrong Mu
- Shool of Pharmacy, Henan University of Chinese Medicine, Zhengzhou 450046, China
| | - Kee-Yoeup Paek
- Department of Horticultural Science, Chungbuk National University, Cheongju 28644, Republic of Korea
| | - So-Young Park
- Department of Horticultural Science, Chungbuk National University, Cheongju 28644, Republic of Korea
| | - Juan Wang
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China
| | - Wenyuan Gao
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China
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Yoosefzadeh Najafabadi M, Hesami M, Eskandari M. Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs. Genes (Basel) 2023; 14:genes14040777. [PMID: 37107535 PMCID: PMC10137951 DOI: 10.3390/genes14040777] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/11/2023] [Accepted: 03/21/2023] [Indexed: 04/29/2023] Open
Abstract
In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvement and develop new varieties with higher yield performance and greater resilience to climate changes, pests, and diseases. With the use of these new advanced technologies, large amounts of data have been generated on the genetic architecture of plants, which can be exploited for manipulating the key characteristics of plants that are important for crop improvement. Therefore, plant breeders have relied on high-performance computing, bioinformatics tools, and artificial intelligence (AI), such as machine-learning (ML) methods, to efficiently analyze this vast amount of complex data. The use of bigdata coupled with ML in plant breeding has the potential to revolutionize the field and increase food security. In this review, some of the challenges of this method along with some of the opportunities it can create will be discussed. In particular, we provide information about the basis of bigdata, AI, ML, and their related sub-groups. In addition, the bases and functions of some learning algorithms that are commonly used in plant breeding, three common data integration strategies for the better integration of different breeding datasets using appropriate learning algorithms, and future prospects for the application of novel algorithms in plant breeding will be discussed. The use of ML algorithms in plant breeding will equip breeders with efficient and effective tools to accelerate the development of new plant varieties and improve the efficiency of the breeding process, which are important for tackling some of the challenges facing agriculture in the era of climate change.
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Affiliation(s)
| | - Mohsen Hesami
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
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Ranaware AS, Kunchge NS, Lele SS, Ochatt SJ. Protoplast Technology and Somatic Hybridisation in the Family Apiaceae. PLANTS (BASEL, SWITZERLAND) 2023; 12:1060. [PMID: 36903923 PMCID: PMC10005591 DOI: 10.3390/plants12051060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/03/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Species of the family Apiaceae occupy a major market share but are hitherto dependent on open pollinated cultivars. This results in a lack of production uniformity and reduced quality that has fostered hybrid seed production. The difficulty in flower emasculation led breeders to use biotechnology approaches including somatic hybridization. We discuss the use of protoplast technology for the development of somatic hybrids, cybrids and in-vitro breeding of commercial traits such as CMS (cytoplasmic male sterility), GMS (genetic male sterility) and EGMS (environment-sensitive genic male sterility). The molecular mechanism(s) underlying CMS and its candidate genes are also discussed. Cybridization strategies based on enucleation (Gamma rays, X-rays and UV rays) and metabolically arresting protoplasts with chemicals such as iodoacetamide or iodoacetate are reviewed. Differential fluorescence staining of fused protoplast as routinely used can be replaced by new tagging approaches using non-toxic proteins. Here, we focused on the initial plant materials and tissue sources for protoplast isolation, the various digestion enzyme mixtures tested, and on the understanding of cell wall re-generation, all of which intervene in somatic hybrids regeneration. Although there are no alternatives to somatic hybridization, various approaches also discussed are emerging, viz., robotic platforms, artificial intelligence, in recent breeding programs for trait identification and selection.
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Affiliation(s)
- Ankush S. Ranaware
- Institute of Chemical Technology, Marathwada Campus, Jalna 431203, Maharashtra, India
| | - Nandkumar S. Kunchge
- Research and Development Division, Kalash Seeds Pvt. Ltd., Jalna 431203, Maharashtra, India
| | - Smita S. Lele
- Institute of Chemical Technology, Marathwada Campus, Jalna 431203, Maharashtra, India
| | - Sergio J. Ochatt
- Agroécologie, InstitutAgro Dijon, INRAE, Université Bourgogne Franche-Comté, 21000 Dijon, France
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Jahedi A, Salehi M, Goltapeh EM, Safaie N. Multilayer perceptron-genetic algorithm as a promising tool for modeling cultivation substrate of Auricularia cornea Native to Iran. PLoS One 2023; 18:e0281982. [PMID: 36809254 PMCID: PMC9942997 DOI: 10.1371/journal.pone.0281982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 02/05/2023] [Indexed: 02/23/2023] Open
Abstract
Auricularia cornea Ehrenb (syn. A. polytricha) is a wood-decaying fungi known as black ear mushroom. Earlike gelatinous fruiting body distinguishes them from other fungi. Industrial wastes have the potential to be used as the basic substrate to produce mushrooms. Therefore, 16 substrate formulations were prepared from different ratios of beech (BS) and hornbeam sawdust (HS) supplemented with wheat (WB) and rice brans (RB). The pH and initial moisture content of substrate mixtures were adjusted to 6.5 and 70%, respectively. The comparison of in vitro growth characteristics of the fungal mycelia under the different temperatures (25, 28, and 30°C), and culture media [yeast extract agar (YEA), potato extract agar (PEA), malt extract agar (MEA), and also HS and BS extract agar media supplemented with maltose, dextrose, and fructose revealed that the highest mycelial growth rate (MGR; 7.5 mm/day) belonged to HS and BS extract agar media supplemented with three mentioned sugar at 28°C. In A. cornea spawn study, the substrate combination of BS (70%) + WB (30%) at 28°C and moisture contents of 75% displayed the highest mean MGR (9.3 mm/day) and lowest spawn run period (9.0 days). In the bag test, "BS (70%) + WB (30%)" was the best substrate displaying the shortest spawn run period (19.7 days), and the highest fresh sporophore yield (131.7 g/bag), biological efficiency (53.1%) and number of basidiocarp (9.0/bag) of A. cornea. Also, A. cornea cultivation was processed to model yield, biological efficiency (BE), spawn run period (SRP), days for pinhead formation (DPHF), days for the first harvest (DFFH), and total cultivation period (TCP) by multilayer perceptron-genetic algorithm (MLP-GA). MLP-GA (0.81-0.99) exhibited a higher predictive ability than stepwise regression (0.06-0.58). The forecasted values of the output variables were in good accordance with their observed ones corroborating the good competency of established MLP-GA models. MLP-GA modeling exhibited a powerful tool for forecasting and thus selecting the optimal substrate for maximum A. cornea production.
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Affiliation(s)
- Akbar Jahedi
- Department of Plant Pathology, Tarbiat Modares University, Jalal, Iran
| | - Mina Salehi
- Department of Plant Genetics and Breeding, Tarbiat Modares University, Jalal, Iran
| | | | - Naser Safaie
- Department of Plant Pathology, Tarbiat Modares University, Jalal, Iran
- * E-mail:
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Nezami E, Gallego PP. History, Phylogeny, Biodiversity, and New Computer-Based Tools for Efficient Micropropagation and Conservation of Pistachio ( Pistacia spp.) Germplasm. PLANTS (BASEL, SWITZERLAND) 2023; 12:323. [PMID: 36679036 PMCID: PMC9864209 DOI: 10.3390/plants12020323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 12/30/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
The word "pstk" [pistag], used in the ancient Persian language, is the linguistic root from which the current name "pistachio", used worldwide, derives. The word pistachio is generally used to designate the plants and fruits of a single species: Pistacia vera L. Both the plant and its fruits have been used by mankind for thousands of years, specifically the consumption of its fruits by Neanderthals has been dated to about 300,000 years ago. Native to southern Central Asia (including northern Afghanistan and northeastern Iran), its domestication and cultivation occurred about 3000 years ago in this region, spreading to the rest of the Mediterranean basin during the Middle Ages and finally being exported to America and Australia at the end of the 19th century. The edible pistachio is an excellent source of unsaturated fatty acids, carbohydrates, proteins, dietary fiber, vitamins, minerals and bioactive phenolic compounds that help promote human health through their antioxidant capacity and biological activities. The distribution and genetic diversity of wild and domesticated pistachios have been declining due to increasing population pressure and climatic changes, which have destroyed natural pistachio habitats, and the monoculture of selected cultivars. As a result, the current world pistachio industry relies mainly on a very small number of commercial cultivars and rootstocks. In this review we discuss and summarize the current status of: etymology, origin, domestication, taxonomy and phylogeny by molecular analysis (RAPID, RFLP, AFLP, SSR, ISSR, IRAP, eSSR), main characteristics and world production, germplasm biodiversity, main cultivars and rootstocks, current conservation strategies of both conventional propagation (seeds, cutting, and grafting), and non-conventional propagation methods (cryopreservation, slow growth storage, synthetic seed techniques and micropropagation) and the application of computational tools (Design of Experiments (DoE) and Machine Learning: Artificial Neural Networks, Fuzzy logic and Genetic Algorithms) to design efficient micropropagation protocols for the genus Pistacia.
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Affiliation(s)
- Esmaeil Nezami
- Department of Plant Breeding, Agriculture Research School, Nuclear Science and Technology Research Institute (NSTRI), Karaj P.O. Box 31485-498, Iran
| | - Pedro P. Gallego
- Department of Plant Biology and Soil Science, Faculty of Biology, University of Vigo, 36310 Vigo, Spain
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36
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Das J, Kumar S, Mishra DC, Chaturvedi KK, Paul RK, Kairi A. Machine learning in the estimation of CRISPR-Cas9 cleavage sites for plant system. Front Genet 2023; 13:1085332. [PMID: 36699447 PMCID: PMC9868961 DOI: 10.3389/fgene.2022.1085332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/12/2022] [Indexed: 01/12/2023] Open
Abstract
CRISPR-Cas9 system is one of the recent most used genome editing techniques. Despite having a high capacity to alter the precise target genes and genomic regions that the planned guide RNA (or sgRNA) complements, the off-target effect still exists. But there are already machine learning algorithms for people, animals, and a few plant species. In this paper, an effort has been made to create models based on three machine learning-based techniques [namely, artificial neural networks (ANN), support vector machines (SVM), and random forests (RF)] for the prediction of the CRISPR-Cas9 cleavage sites that will be cleaved by a particular sgRNA. The plant dataset was the sole source of inspiration for all of these machine learning-based algorithms. 70% of the on-target and off-target dataset of various plant species that was gathered was used to train the models. The remaining 30% of the data set was used to evaluate the model's performance using a variety of evaluation metrics, including specificity, sensitivity, accuracy, precision, F1 score, F2 score, and AUC. Based on the aforementioned machine learning techniques, eleven models in all were developed. Comparative analysis of these produced models suggests that the model based on the random forest technique performs better. The accuracy of the Random Forest model is 96.27%, while the AUC value was found to be 99.21%. The SVM-Linear, SVM-Polynomial, SVM-Gaussian, and SVM-Sigmoid models were trained, making a total of six ANN-based models (ANN1-Logistic, ANN1-Tanh, ANN1-ReLU, ANN2-Logistic, ANN2-Tanh, and ANN-ReLU) and Support Vector Machine models (SVM-Linear, SVM-Polynomial, SVM-Gaussian However, the overall performance of Random Forest is better among all other ML techniques. ANN1-ReLU and SVM-Linear model performance were shown to be better among Artificial Neural Network and Support Vector Machine-based models, respectively.
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Affiliation(s)
- Jutan Das
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Sanjeev Kumar
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India,*Correspondence: Sanjeev Kumar,
| | | | | | - Ranjit Kumar Paul
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Amit Kairi
- ICAR-Indian Agricultural Research Institute, New Delhi, India
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Current status and future prospects in cannabinoid production through in vitro culture and synthetic biology. Biotechnol Adv 2023; 62:108074. [PMID: 36481387 DOI: 10.1016/j.biotechadv.2022.108074] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 10/27/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
For centuries, cannabis has been a rich source of fibrous, pharmaceutical, and recreational ingredients. Phytocannabinoids are the most important and well-known class of cannabis-derived secondary metabolites and display a broad range of health-promoting and psychoactive effects. The unique characteristics of phytocannabinoids (e.g., metabolite likeness, multi-target spectrum, and safety profile) have resulted in the development and approval of several cannabis-derived drugs. While most work has focused on the two main cannabinoids produced in the plant, over 150 unique cannabinoids have been identified. To meet the rapidly growing phytocannabinoid demand, particularly many of the minor cannabinoids found in low amounts in planta, biotechnology offers promising alternatives for biosynthesis through in vitro culture and heterologous systems. In recent years, the engineered production of phytocannabinoids has been obtained through synthetic biology both in vitro (cell suspension culture and hairy root culture) and heterologous systems. However, there are still several bottlenecks (e.g., the complexity of the cannabinoid biosynthetic pathway and optimizing the bioprocess), hampering biosynthesis and scaling up the biotechnological process. The current study reviews recent advances related to in vitro culture-mediated cannabinoid production. Additionally, an integrated overview of promising conventional approaches to cannabinoid production is presented. Progress toward cannabinoid production in heterologous systems and possible avenues for avoiding autotoxicity are also reviewed and highlighted. Machine learning is then introduced as a powerful tool to model, and optimize bioprocesses related to cannabinoid production. Finally, regulation and manipulation of the cannabinoid biosynthetic pathway using CRISPR- mediated metabolic engineering is discussed.
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Viswanathan P, Gosukonda JS, Sherman SH, Joshee N, Gosukonda RM. Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models. Heliyon 2022; 8:e11969. [PMID: 36544836 PMCID: PMC9761605 DOI: 10.1016/j.heliyon.2022.e11969] [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: 07/19/2022] [Revised: 09/28/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
This study was conducted to determine if artificial neural networks (ANN) can be used to accurately predict in vitro organogenesis of Bacopa monnieri compared with statistical regression. Prediction models were developed for shoot and root organogenesis (outputs) on two culture media (Murashige and Skoog and Gamborg B5) affected by two explant types (leaf and node) and two cytokinins (6-Benzylaminopurine and Thidiazuron at 1.0, 5.0, and 10.0 μM levels) with and without the addition of auxin (1-Naphthaleneacetic acid 0.1 μM) (inputs). Categorical data were encoded in numeric form using one-hot encoding technique. Backpropagation (BP) and Kalman filter (KF) learning algorithms were used to develop nonparametric models between inputs and outputs. Correlations between predicted and observed outputs (validation dataset) were similar in both ANN-BP (R values = 0.77, 0.71, 0.68, and 0.48), and ANN-KF (R values = 0.79, 0.68, 0.75, and 0.49), and were higher than regression (R values = 0.13, 0.48, 0.39, and 0.37) models for shoots and roots from leaf and node explants, respectively. Because ANN models have the ability to interpolate from unseen data, they could be used as an effective tool in accurately predicting the in vitro growth kinetics of Bacopa cultures.
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Affiliation(s)
- Pavitra Viswanathan
- Agricultural Research Station, Fort Valley State University, Fort Valley, GA 31030, USA,Department of Bioinformatics, Boston University, Boston, MA 02115, USA
| | - Jaabili S. Gosukonda
- Agricultural Research Station, Fort Valley State University, Fort Valley, GA 31030, USA,Houston County High School, Warner Robins, GA 31088, USA
| | - Samantha H. Sherman
- Agricultural Research Station, Fort Valley State University, Fort Valley, GA 31030, USA
| | - Nirmal Joshee
- Agricultural Research Station, Fort Valley State University, Fort Valley, GA 31030, USA
| | - Ramana M. Gosukonda
- Department of Agricultural Sciences, College of Agriculture, Family Sciences and Technology, Fort Valley State University, Fort Valley, GA 31030, USA,Corresponding author.
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Han B, Li Y, Bie Z, Peng C, Huang Y, Xu S. MIX-NET: Deep Learning-Based Point Cloud Processing Method for Segmentation and Occlusion Leaf Restoration of Seedlings. PLANTS (BASEL, SWITZERLAND) 2022; 11:plants11233342. [PMID: 36501381 PMCID: PMC9739940 DOI: 10.3390/plants11233342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/19/2022] [Accepted: 11/24/2022] [Indexed: 05/27/2023]
Abstract
In this paper, a novel point cloud segmentation and completion framework is proposed to achieve high-quality leaf area measurement of melon seedlings. In particular, the input of our algorithm is the point cloud data collected by an Azure Kinect camera from the top view of the seedlings, and our method can enhance measurement accuracy from two aspects based on the acquired data. On the one hand, we propose a neighborhood space-constrained method to effectively filter out the hover points and outlier noise of the point cloud, which can enhance the quality of the point cloud data significantly. On the other hand, by leveraging the purely linear mixer mechanism, a new network named MIX-Net is developed to achieve segmentation and completion of the point cloud simultaneously. Different from previous methods that separate these two tasks, the proposed network can better balance these two tasks in a more definite and effective way, leading to satisfactory performance on these two tasks. The experimental results prove that our methods can outperform other competitors and provide more accurate measurement results. Specifically, for the seedling segmentation task, our method can obtain a 3.1% and 1.7% performance gain compared with PointNet++ and DGCNN, respectively. Meanwhile, the R2 of leaf area measurement improved from 0.87 to 0.93 and MSE decreased from 2.64 to 2.26 after leaf shading completion.
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Affiliation(s)
- Binbin Han
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, Wuhan 430070, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shenzhen 518000, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
- School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
| | - Yaqin Li
- School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
| | - Zhilong Bie
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Horticultural Plant Biology, Ministry of Education, Wuhan 430070, China
| | - Chengli Peng
- Electronic Information School, Wuhan University, Wuhan 430072, China
| | - Yuan Huang
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Horticultural Plant Biology, Ministry of Education, Wuhan 430070, China
| | - Shengyong Xu
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, Wuhan 430070, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shenzhen 518000, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
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Modeling and optimizing in vitro percentage and speed callus induction of carrot via Multilayer Perceptron-Single point discrete GA and radial basis function. BMC Biotechnol 2022; 22:34. [PMCID: PMC9636657 DOI: 10.1186/s12896-022-00764-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/20/2022] [Indexed: 11/06/2022] Open
Abstract
Abstract
Background
Callus induction is the first step in optimizing plant regeneration. Fit embryogenesis and shooting rely on callus induction. In addition, using artificial intelligence models in combination with an algorithm can be helpful in the optimization of in vitro culture. The present study aimed to evaluate the percentage and speed of callus induction optimization in carrot with a Multilayer Perceptron-Single point discrete genetic algorithm (GA).
Materials and methods
In this study, the outputs included callus induction percentage and speed, while inputs were different types and concentrations of plant growth regulator (0. 5, 0.2 mg/l 2,4-D, 0.3, 0.2, 0.5 mg/l BAP, 1, 0.2 mg/l Kin, and 2 mg/l NAA), different explants (shoot, root, leaf, and nodal), a different concentration compound of MS medium (1 × MS, 4× MS, and 8× MS) and time of sampling. The data were obtained in the laboratory, and multilayer perceptron (MLP) and radial basis function (RBF), two well-known ANNs, were employed to model. Then, GA was used for optimization, and sensitivity analysis was performed to indicate the inputs’ importance.
Results
The results showed that MLP had better prediction efficiency than RBF. Based on the results, R2 in training and testing data was 95 and 95% for the percentage of callus induction, while it was 94 and 95% for the speed of callus induction, respectively. In addition, a concentration compound of MS had high sensitivity, while times of sampling had low sensitivity. Based on the MLP-Single point discrete GA, the best results were obtained for shoot explants, 1× MS media, and 0.5 mg/l 2, 4-D + 0.5 mg/l BAP. Further, a non-significant difference was observed between the test result and predicted MLP.
Conclusions
Generally, MLP-Single point discrete GA is considered a potent tool for predicting treatment and fit model results used in plant tissue culture and selecting the best medium for callus induction.
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Zarbakhsh S, Shahsavar AR. Artificial neural network-based model to predict the effect of γ-aminobutyric acid on salinity and drought responsive morphological traits in pomegranate. Sci Rep 2022; 12:16662. [PMID: 36198905 PMCID: PMC9534893 DOI: 10.1038/s41598-022-21129-z] [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: 01/31/2022] [Accepted: 09/22/2022] [Indexed: 11/20/2022] Open
Abstract
Recently, γ-Aminobutyric acid (GABA) has been introduced as a treatment with high physiological activity induction to enhance the ability of plants against drought and salinity stress, which led to a decline in plant growth. Since changes in morphological traits to drought and salinity stress are influenced by multiple factors, advanced computational analysis has great potential for computing nonlinear and multivariate data. In this work, the effect of four input variables including GABA concentration, pomegranate cultivars, days of treatment, and drought and salinity stress evaluated to predict and modeling of morphological traits using artificial neural network (ANN) models including multilayer perceptron (MLP) and radial basis function (RBF). Image processing technique was used to measure the LLI, LWI, and LAI parameters. Among the ANNs applied, the MLP algorithm was chosen as the best model based on the highest accuracy. Furthermore, to predict and estimate the optimal values of input variables for achieving the best morphological parameters, the MLP algorithm was linked to a non-dominated sorting genetic algorithm-II (NSGA-II). Based on the results of MLP-NSGA-II, the best values of crown diameter (18.42 cm), plant height (151.82 cm), leaf length index (5.67 cm), leaf width index (1.76 cm), and leaf area index (13.82 cm) could be achieved with applying 10.57 mM GABA on ‘Atabaki’ cultivar under control (non-stress) condition after 20.8 days. The results of modeling and optimization can be helpful to predict the morphological responses to drought and salinity conditions.
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Affiliation(s)
- Saeedeh Zarbakhsh
- Department of Horticultural Science, College of Agriculture, Shiraz University, Shiraz, Iran
| | - Ali Reza Shahsavar
- Department of Horticultural Science, College of Agriculture, Shiraz University, Shiraz, Iran.
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Brunetto G, Stefanello LO, Kulmann MSDS, Tassinari A, de Souza ROS, Rozane DE, Tiecher TL, Ceretta CA, Ferreira PAA, de Siqueira GN, Parent LÉ. Prediction of Nitrogen Dosage in ‘Alicante Bouschet’ Vineyards with Machine Learning Models. PLANTS 2022; 11:plants11182419. [PMID: 36145819 PMCID: PMC9501305 DOI: 10.3390/plants11182419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/09/2022] [Accepted: 09/11/2022] [Indexed: 12/02/2022]
Abstract
Vineyard soils normally do not provide the amount of nitrogen (N) necessary for red wine production. Traditionally, the N concentration in leaves guides the N fertilization of vineyards to reach high grape yields and chemical composition under the ceteris paribus assumption. Moreover, the carryover effects of nutrients and carbohydrates stored by perennials such as grapevines are neglected. Where a well-documented database is assembled, machine learning (ML) methods can account for key site-specific features and carryover effects, impacting the performance of grapevines. The aim of this study was to predict, using ML tools, N management from local features to reach high berry yield and quality in ‘Alicante Bouschet’ vineyards. The 5-year (2015–2019) fertilizer trial comprised six N doses (0–20–40–60–80–100 kg N ha−1) and three regimes of irrigation. Model features included N dosage, climatic indices, foliar N application, and stem diameter of the preceding season, all of which were indices of the carryover effects. Accuracy of ML models was the highest with a yield cutoff of 14 t ha−1 and a total anthocyanin content (TAC) of 3900 mg L−1. Regression models were more accurate for total soluble solids (TSS), total titratable acidity (TTA), pH, TAC, and total phenolic content (TPC) in the marketable grape yield. The tissue N ranges differed between high marketable yield and TAC, indicating a trade-off about 24 g N kg−1 in the diagnostic leaf. The N dosage predicted varied from 0 to 40 kg N ha−1 depending on target variable, this was calculated from local features and carryover effects but excluded climatic indices. The dataset can increase in size and diversity with the collaboration of growers, which can help to cross over the numerous combinations of features found in vineyards. This research contributes to the rational use of N fertilizers, but with the guarantee that obtaining high productivity must be with adequate composition.
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Affiliation(s)
- Gustavo Brunetto
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
- Correspondence: ; Tel.: +55-32208108
| | | | | | - Adriele Tassinari
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
| | | | - Danilo Eduardo Rozane
- Fruticulture Department, State University of Paulista “Julio Mesquita Filho”, Registro 11900-000, Brazil
| | - Tadeu Luis Tiecher
- Rio Grande do Sul Federal Institute, Campus Restinga, Porto Alegre 91791-508, Brazil
| | - Carlos Alberto Ceretta
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
| | | | | | - Léon Étienne Parent
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
- Department of Soil and Agri-Food Engineering, Laval University, Québec City, QC G1V 0A6, Canada
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Latif G, Abdelhamid SE, Mallouhy RE, Alghazo J, Kazimi ZA. Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model. PLANTS 2022; 11:plants11172230. [PMID: 36079612 PMCID: PMC9460897 DOI: 10.3390/plants11172230] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 08/19/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022]
Abstract
Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.
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Affiliation(s)
- Ghazanfar Latif
- Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia
- Department of Computer Sciences and Mathematics, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Québec, QC G7H 2B1, Canada
- Correspondence:
| | - Sherif E. Abdelhamid
- Department of Computer and Information Sciences, Virginia Military Institute, Lexington, VA 24450, USA
| | - Roxane Elias Mallouhy
- Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia
| | - Jaafar Alghazo
- Department of Computer Engineering, Virginia Military Institute, Lexington, VA 24450, USA
| | - Zafar Abbas Kazimi
- Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia
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Aasim M, Katirci R, Baloch FS, Mustafa Z, Bakhsh A, Nadeem MA, Ali SA, Hatipoğlu R, Çiftçi V, Habyarimana E, Karaköy T, Chung YS. Innovation in the Breeding of Common Bean Through a Combined Approach of in vitro Regeneration and Machine Learning Algorithms. Front Genet 2022; 13:897696. [PMID: 36092939 PMCID: PMC9451102 DOI: 10.3389/fgene.2022.897696] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 06/03/2022] [Indexed: 11/13/2022] Open
Abstract
Common bean is considered a recalcitrant crop for in vitro regeneration and needs a repeatable and efficient in vitro regeneration protocol for its improvement through biotechnological approaches. In this study, the establishment of efficient and reproducible in vitro regeneration followed by predicting and optimizing through machine learning (ML) models, such as artificial neural network algorithms, was performed. Mature embryos of common bean were pretreated with 5, 10, and 20 mg/L benzylaminopurine (BAP) for 20 days followed by isolation of plumular apice for in vitro regeneration and cultured on a post-treatment medium containing 0.25, 0.50, 1.0, and 1.50 mg/L BAP for 8 weeks. Plumular apice explants pretreated with 20 mg/L BAP exerted a negative impact and resulted in minimum shoot regeneration frequency and shoot count, but produced longer shoots. All output variables (shoot regeneration frequency, shoot counts, and shoot length) increased significantly with the enhancement of BAP concentration in the post-treatment medium. Interaction of the pretreatment × post-treatment medium revealed the need for a specific combination for inducing a high shoot regeneration frequency. Higher shoot count and shoot length were achieved from the interaction of 5 mg/L BAP × 1.00 mg/L BAP followed by 10 mg/L BAP × 1.50 mg/L BAP and 20 mg/L BAP × 1.50 mg/L BAP. The evaluation of data through ML models revealed that R2 values ranged from 0.32 to 0.58 (regeneration), 0.01 to 0.22 (shoot counts), and 0.18 to 0.48 (shoot length). On the other hand, the mean squared error values ranged from 0.0596 to 0.0965 for shoot regeneration, 0.0327 to 0.0412 for shoot count, and 0.0258 to 0.0404 for shoot length from all ML models. Among the utilized models, the multilayer perceptron model provided a better prediction and optimization for all output variables, compared to other models. The achieved results can be employed for the prediction and optimization of plant tissue culture protocols used for biotechnological approaches in a breeding program of common beans.
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Affiliation(s)
- Muhammad Aasim
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Ramazan Katirci
- Department of Metallurgical and Materials Engineering, Faculty of Engineering and Natural Sciences, Sivas University of Science and Technology, Sivas, Turkey
| | - Faheem Shehzad Baloch
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
- *Correspondence: Faheem Shehzad Baloch, ; Yong Suk Chung,
| | - Zemran Mustafa
- Department of Plant Production and Technologies, Faculty of Agricultural Science and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Allah Bakhsh
- Center of Excellence in Molecular Biology, University of the Punjab, Lahore, Pakistan
| | - Muhammad Azhar Nadeem
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Seyid Amjad Ali
- Department of Information Systems and Technologies, Bilkent University, Ankara, Turkey
| | - Rüştü Hatipoğlu
- Department of Field Crops, Faculty of Agriculture, University of Çukurova, Adana, Turkey
| | - Vahdettin Çiftçi
- Department of Field Crops, Faculty of Agriculture, Bolu Abant Izzet Baysal University, Bolu, Turkey
| | - Ephrem Habyarimana
- International Crops Research Institute for the Semi-Arid Tropics, Patancheru, India
| | - Tolga Karaköy
- Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Yong Suk Chung
- Department of Plant Resources and Environment, Jeju National University, Jeju, South Korea
- *Correspondence: Faheem Shehzad Baloch, ; Yong Suk Chung,
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Zhu X, Du C, Mohsin A, Yin Q, Xu F, Liu Z, Wang Z, Zhuang Y, Chu J, Guo M, Tian X. An Efficient High-Throughput Screening of High Gentamicin-Producing Mutants Based on Titer Determination Using an Integrated Computer-Aided Vision Technology and Machine Learning. Anal Chem 2022; 94:11659-11669. [PMID: 35942642 DOI: 10.1021/acs.analchem.2c02289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The "design-build-test-learn" (DBTL) cycle has been adopted in rational high-throughput screening to obtain high-yield industrial strains. However, the mismatch between build and test slows the DBTL cycle due to the lack of high-throughput analytical technologies. In this study, a highly efficient, accurate, and noninvasive detection method of gentamicin (GM) was developed, which can provide timely feedback for the high-throughput screening of high-yield strains. First, a self-made tool was established to obtain data sets in 24-well plates based on the color of the cells. Subsequently, the random forest (RF) algorithm was found to have the highest prediction accuracy with an R2 value of 0.98430 for the same batch. Finally, a stable genetically high-yield strain (998 U/mL) was successfully screened out from 3005 mutants, which was verified to improve the titer by 72.7% in a 5 L bioreactor. Moreover, the verified new data sets were updated on the model database in order to improve the learning ability of the DBTL cycle.
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Affiliation(s)
- Xiaofeng Zhu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China.,School of Biotechnology, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China
| | - Congcong Du
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China.,School of Biotechnology, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China
| | - Ali Mohsin
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China.,School of Biotechnology, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China
| | - Qian Yin
- College of Biological & Medical Engineering, South-Central University for Nationalities, Minzu Road 182, Wuhan, Hubei 430070, China
| | - Feng Xu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China.,School of Biotechnology, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China
| | - Zebo Liu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China.,School of Biotechnology, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China
| | - Zejian Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China.,School of Biotechnology, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China.,School of Biotechnology, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China.,School of Biotechnology, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China
| | - Meijin Guo
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China.,School of Biotechnology, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China
| | - Xiwei Tian
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China.,School of Biotechnology, East China University of Science and Technology, 130 Meilong Rd., Shanghai 200237, China
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A Machine-Learning Method to Assess Growth Patterns in Plants of the Family Lemnaceae. PLANTS 2022; 11:plants11151910. [PMID: 35893614 PMCID: PMC9332063 DOI: 10.3390/plants11151910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 11/17/2022]
Abstract
Numerous new technologies have been implemented in image analysis methods that help researchers draw scientific conclusions from biological phenomena. Plants of the family Lemnaceae (duckweeds) are the smallest flowering plants in the world, and biometric measurements of single plants and their growth rate are highly challenging. Although the use of software for digital image analysis has changed the way scientists extract phenomenological data (also for studies on duckweeds), the procedure is often not wholly automated and sometimes relies on the intervention of a human operator. Such a constraint can limit the objectivity of the measurements and generally slows down the time required to produce scientific data. Herein lies the need to implement image analysis software with artificial intelligence that can substitute the human operator. In this paper, we present a new method to study the growth rates of the plants of the Lemnaceae family based on the application of machine-learning procedures to digital image analysis. The method is compared to existing analogical and computer-operated procedures. The results showed that our method drastically reduces the time consumption of the human operator while retaining a high correlation in the growth rates measured with other procedures. As expected, machine-learning methods applied to digital image analysis can overcome the constraints of measuring growth rates of very small plants and might help duckweeds gain worldwide attention thanks to their strong nutritional qualities and biological plasticity.
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Krzemińska M, Owczarek A, Olszewska MA, Grzegorczyk-Karolak I. In Vitro Strategy for the Enhancement of the Production of Bioactive Polyphenols in Transformed Roots of Salvia bulleyana. Int J Mol Sci 2022; 23:ijms23147771. [PMID: 35887119 PMCID: PMC9322094 DOI: 10.3390/ijms23147771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/13/2022] [Accepted: 07/13/2022] [Indexed: 11/21/2022] Open
Abstract
The underground parts of Salvia bulleyana, a rare Chinese plant species, have long been used in traditional Chinese medicine. The Rhizobium rhizogenes-transformed root culture obtained from this plant might be a promising novel source of valuable phenolics, including rosmarinic acid. The present study identifies for the first time, the optimal growth conditions of S. bulleyana hairy roots regarding production efficiency. The comprehensive optimization comprised cultivation in different basal media (B5, SH, MS, and WP) with full- and half-strength macro- and microelements, different vitamin contents (full, half, one-quarter part, and without) and sucrose concentrations (2, 3, 4, 5%), and under different light conditions: in dark, under blue LED (λ = 430 nm), red LED (λ = 670 nm), mixed blue and red LED (30%:70%), and white LED (390–670 nm). Hairy root growth and bioactive compound accumulation were also detailed every five days over the 50-day culture cycle. The optimal conditions were determined using a technique for order preference by similarity to the ideal solution (TOPSIS). The most efficient combination for root growth and polyphenol content was found to be ½SH liquid medium with half vitamin concentration and 3% sucrose when grown in the dark. The biomass yield during the growth cycle was 6.1 g (fresh weight—FW) and 0.92 g (dry weight—DW) on one Erlenmeyer flask: a 14.3-fold increase in FW and 16.1-fold increase in DW in relation to the inoculum. The highest mean total phenolic content was 93.6 mg/g DW including about 70 mg/g DW rosmarinic acid, reached on day 40 of culture; compared to roots of two-year-old plants grown under field conditions, the total phenolic acid content was four times higher and rosmarinic acid eight times higher. The obtained results place the investigated culture among the best hair root cultures for rosmarinic acid production.
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Affiliation(s)
- Marta Krzemińska
- Department of Biology and Pharmaceutical Botany, Medical University of Lodz, Muszynskiego 1, 90-151 Lodz, Poland;
| | - Aleksandra Owczarek
- Department of Pharmacognosy, Medical University of Lodz, Muszynskiego 1, 90-151 Lodz, Poland; (A.O.); (M.A.O.)
| | - Monika A. Olszewska
- Department of Pharmacognosy, Medical University of Lodz, Muszynskiego 1, 90-151 Lodz, Poland; (A.O.); (M.A.O.)
| | - Izabela Grzegorczyk-Karolak
- Department of Biology and Pharmaceutical Botany, Medical University of Lodz, Muszynskiego 1, 90-151 Lodz, Poland;
- Correspondence:
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Gammoudi N, Nagaz K, Ferchichi A. Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron-multi-objective genetic algorithm. BMC PLANT BIOLOGY 2022; 22:324. [PMID: 35790933 PMCID: PMC9254583 DOI: 10.1186/s12870-022-03674-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/31/2022] [Indexed: 05/26/2023]
Abstract
BACKGROUND Contamination-free culture is a prerequisite for the success of in vitro - based plant biotechnology. Aseptic initiation is an extremely strenuous stride, particularly in woody species. Meanwhile, over-sterilization is potentially detrimental to plant tissue. The recent rise of machine learning algorithms in plant tissue culture proposes an advanced interpretive tool for the combinational effect of influential factors for such in vitro - based steps. RESULTS A multilayer perceptron (MLP) model of artificial neural network (ANN) was implemented with four inputs, three sterilizing chemicals at various concentrations and the immersion time, and two outputs, disinfection efficiency (DE) and negative disinfection effect (NDE), intending to assess twenty-seven disinfection procedures of Pistacia vera L. seeds. Mercury chloride (HgCl2; 0.05-0.2%; 5-15 min) appears the most effective with 100% DE, then hydrogen peroxide (H2O2; 5.25-12.25%; 10-30 min) with 66-100% DE, followed by 27-77% DE for sodium hypochlorite (NaOCl; 0.54-1.26% w/v; 10-30 min). Concurrently, NDE was detected, including chlorosis, hard embryo germination, embryo deformation, and browning tissue, namely, a low repercussion with NaOCl (0-14%), a moderate impact with H2O2 (6-46%), and pronounced damage with HgCl2 (22-100%). Developed ANN showed R values of 0.9658, 0.9653, 0.8937, and 0.9454 for training, validation, testing, and all sets, respectively, which revealed the uprightness of the model. Subsequently, the model was linked to multi-objective genetic algorithm (MOGA) which proposed an optimized combination of 0.56% NaOCl, 12.23% H2O2, and 0.068% HgCl2 for 5.022 min. The validation assay reflects the high utility and accuracy of the model with maximum DE (100%) and lower phytotoxicity (7.1%). CONCLUSION In one more case, machine learning algorithms emphasized their ability to resolve commonly encountered problems. The current successful implementation of MLP-MOGA inspires its application for more complicated plant tissue culture processes.
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Affiliation(s)
- Najet Gammoudi
- Arid and Oases Cropping Laboratory, Arid Lands Institute (IRA), 4119, Medenine, Tunisia.
| | - Kamel Nagaz
- Arid and Oases Cropping Laboratory, Arid Lands Institute (IRA), 4119, Medenine, Tunisia
| | - Ali Ferchichi
- National Institute of Agronomy of Tunis, 43 Charles Nicolle, 1082, Tunis, Tunisia
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Zhou Y. Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6558512. [PMID: 35694587 PMCID: PMC9187448 DOI: 10.1155/2022/6558512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/07/2022] [Accepted: 05/10/2022] [Indexed: 11/17/2022]
Abstract
The change of urban cultural space layout is a multi-variable, multi-objective, and restricted research process. The optimization of urban cultural space construction and layout is a multi-objective decision-making problem that needs to be solved urgently. Based on the forward three-layer neural network theory, this paper constructs an optimization model for the construction and layout of urban cultural space evaluation of the layout of cultural space. This paper first analyzes the feasibility of combining the forward three-layer neural network model with the optimization and adjustment of cultural space layout structure. Taking the three-layer feedforward network as an example, the structure optimization model based on the forward three-layer neural network is selected, and the established model is used to reflect the internal environment of the objective world. Structure and perform dynamic simulation. In the process of simulation modeling, from the aspects of system description, model structure, logical analysis, reasoning, and interpretation, two effective computer dynamic simulation methods, namely, forward three-layer neural network model and system dynamics SD model, were carried out for theoretical comparison and identification. The experimental results show that the feasibility and calculation error of the application of the optimization model are relatively good, reaching 0.897 and 6.21%, respectively. The number of newly added cultural spaces and the expansion speed show an increasing trend, expanding at an average annual speed of about 35 km2, effectively increasing the quality of regional planning and construction layout.
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Affiliation(s)
- Ye Zhou
- Zhejiang University City College, Zhejiang, Hangzhou 310015, China
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Hesami M, Alizadeh M, Jones AMP, Torkamaneh D. Machine learning: its challenges and opportunities in plant system biology. Appl Microbiol Biotechnol 2022; 106:3507-3530. [PMID: 35575915 DOI: 10.1007/s00253-022-11963-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 03/14/2022] [Accepted: 05/07/2022] [Indexed: 12/25/2022]
Abstract
Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.
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Affiliation(s)
- Mohsen Hesami
- Department of Plant Agriculture, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - Milad Alizadeh
- Department of Botany, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | | | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC, G1V 0A6, Canada. .,Institut de Biologie Intégrative Et Des Systèmes (IBIS), Université Laval, Québec City, QC, G1V 0A6, Canada.
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