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Advances in Ginsenosides
| Advances in Ginsenosides |
| Autore | Chen Jen-Tsung |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (264 p.) |
| Soggetto topico | Medicine |
| Soggetto non controllato |
(20S)G-Rh2
allergy American ginseng anti-cancer anti-diabetic anti-inflammation Anxa2 Asian ginseng bioconversion bioreactor biotechnology biotransformation cell proliferation cell suspension cerebral ischemia and reperfusion injury clinical trials complete genome sequence compound M1 dammarane-type triterpene saponin EAE epithelial-mesenchymal transition ginseng Ginseng berries ginsenoside ginsenoside CK ginsenoside MT1 ginsenoside Rg3(S) ginsenosides gram unit production hairy root hepatoprotective HMGB1 homology modeling HR-MAS NMR spectroscopy immune system inflammation Lactobacillus ginsenosidimutans M2 polarization macrophage MAPK molecular docking analysis molecular interaction n/a neurogenesis neuroprotection NF-κB notoginseng leaf triterpenes novel glycoside hydrolases ocotillol type ginsenoside epimers Panax ginseng Panax quinquefolium L. Panax sp pharmacopuncture polyploidy polysaccharides primary metabolite pro-resolving protoplast recombinant enzyme Rg3 RORγt safety stereoselective ADME characteristics Th17 transglycosylation UPLC-QTOF/MS wild ginseng |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557766503321 |
Chen Jen-Tsung
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Bioinformatics for Plant Research and Crop Breeding
| Bioinformatics for Plant Research and Crop Breeding |
| Autore | Chen Jen-Tsung |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (608 pages) |
| Soggetto topico |
Plant breeding
Crop improvement |
| ISBN |
9781394209965
1394209967 9781394209941 1394209940 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Preface -- Chapter 1 Bioinformatics as a Powerful Tool to Foster Plant Science Research and Crop Breeding Through Its Involvement in a Multidisciplinary Research Activity -- 1.1 Introduction -- 1.2 Bioinformatics as a Powerful Tool for Big Data Analysis in Plant Science -- 1.3 Role of Bioinformatics in Trait Mapping -- 1.4 Bioinformatics in Molecular Biology -- 1.5 Role of Bioinformatics in Genetic Variation -- 1.6 Bioinformatics in Genome-wide Association Studies (GWAS) -- 1.7 Implication of Bioinformatics in “Omics” -- 1.8 Bioinformatics in Computational Biology and Evolutionary Studies -- 1.9 Role of Bioinformatics in Transcriptomics -- 1.10 Implication of Bioinformatics in Next-generation Sequencing (NGS) Analysis -- 1.11 Implication of Bioinformatics in Metabolomics -- 1.12 Bioinformatics and Epigenetics -- 1.13 Involvement of Bioinformatics in Synthetic Biology -- 1.14 How Can Bioinformatics Promote Plant Biotechnology? -- 1.15 Bioinformatics Use in Biotic and Abiotic Stress Management -- 1.16 Bioinformatics for the Investigation of Plant Resistance to Pathogens -- 1.17 Bioinformatics in Crop Breeding and Improvement -- 1.18 Bioinformatics Impacts on Plant Science -- 1.19 Application of Bioinformatics in Plant Breeding Programs -- 1.20 Conclusion -- References -- Chapter 2 Bioinformatics for Molecular Breeding and Enhanced Crop Performance: Applications and Perspectives -- 2.1 Introduction |
| Record Nr. | UNINA-9911018962903321 |
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Chen Jen-Tsung
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Cell Signaling in Model Plants
| Cell Signaling in Model Plants |
| Autore | Chen Jen-Tsung |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (278 p.) |
| Soggetto topico |
Biology, life sciences
Research & information: general |
| Soggetto non controllato |
ABA signaling
ABF2 abiotic stress Arabidopsis Botrytis cinerea brassinosteroid signaling cascade brassinosteroids calcite calcium cAMP catalytic activity cellular signalization CPS CRISPR/Cas9 crops cyclic nucleotides-gated channels DELLA DELLA/TVHYNP development disease resistance drought drought tolerance Dwarf dwarfism endocytosis enzymatic anti-oxidative system flowering FLOWERING LOCUS C GA GA signaling GA20OX2 gasotransmitter gene cloning GSK3-like kinases herbivore Hydrogen sulfide ion channels iprodione juvenility kinase Malus domestica Medicago truncatula metabolism microRNAs miPEPs MNP1 mutant n/a peptides phytohormone plant biotic stress plant defense plant hormone plant innate immunity plant signalling priming protein phosphatases protein-protein interaction reactive oxygen species reactive oxygen species (ROS) receptor receptor-like kinases Rosaceae S-sulfhydration salinity selenium (Se) somatic embryogenesis receptor-like kinases stress stress adaptation stress memory tobacco tomato transcriptome analysis |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557518203321 |
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Chen Jen-Tsung
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Ethnopharmacology and Drug Discovery for COVID-19: Anti-SARS-CoV-2 Agents from Herbal Medicines and Natural Products / / edited by Jen-Tsung Chen
| Ethnopharmacology and Drug Discovery for COVID-19: Anti-SARS-CoV-2 Agents from Herbal Medicines and Natural Products / / edited by Jen-Tsung Chen |
| Autore | Chen Jen-Tsung |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (591 pages) |
| Disciplina | 614.5924144 |
| Soggetto topico |
Pharmacology
Natural products Nanobiotechnology Bioinformatics Diseases - Causes and theories of causation Molecular biology Natural Products Pathogenesis Molecular Biology |
| ISBN | 981-9936-64-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. COVID-19: An overview of virology, mutations, pathology, epidemiology, diagnosis, preventions and treatments -- 2. The recent development of therapeutic strategies against COVID-19 -- 3. Plant immunoenhancers: Promising ethnopharmacological candidates for anti-SARS-CoV-2 activity -- 4. Herbal formulations in fighting against the SARS-CoV-2 infection -- 5. Rejuvenation of traditional medicine in the 21st century against SARS-CoV-2 -- 6. Traditional herbal medicines and their active constituents in combating SARS-CoV-2 infection -- 7. Antiviral phytocompounds against animal-to-human transmittable SARS-CoV-2 -- 8. Plants-derived bioactive compounds as potential ACE-2 inhibitors against SARS-CoV-2 infection -- 9. Insights into in silico methods to explore plant bioactive substances in combating SARS-CoV-2 -- 10. Dietary plants, spices and fruits in curbing SARS-CoV-2 virulence -- 11. Therapeutic potential of selected medicinal plants for neurological disorders after the infection of COVID-19 -- 12. Glycyrrhizae Radix et Rhizoma (Gan Cao) for the management of COVID-19 -- 13. COVID-19-induced kidney disease: Ethnopharmacological intervention to ameliorate kidney damage and improve kidney function -- 14. Phytochemicals and nutraceuticals targeting SARS-CoV-2: An in silico analysis -- 15. Therapeutic and prophylactic effects of plant derivatives against SARS-CoV-2 -- 16. Therapeutic potential of essential oils against SARS-CoV-2 infection -- 17. Antiviral properties of South Indian plants against SARS-CoV-2 -- 18. Immune-boosting plants used in Turkish folk medicine and their potential against COVID-19 -- 19. A comparison study of medicinal plants used against SARS-CoV-2 and those recommended against malaria in Africa -- 20. Exploring the potential antiviral properties of Nigella sativa L. against SARS-CoV-2: Mechanisms and prospects -- . |
| Record Nr. | UNINA-9910746084103321 |
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Chen Jen-Tsung
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Genome and Epigenome Editing for Stress-Tolerant Crops
| Genome and Epigenome Editing for Stress-Tolerant Crops |
| Autore | Chen Jen-Tsung |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (0 pages) |
| Disciplina | 581.3/5 |
| Soggetto topico |
Plant genetics
Plants - Effect of stress on Epigenetics |
| ISBN |
1-394-28004-1
1-394-28003-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911020204303321 |
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Chen Jen-Tsung
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning for Plant Biology
| Machine Learning for Plant Biology |
| Autore | Chen Jen-Tsung |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2026 |
| Descrizione fisica | 1 online resource (371 pages) |
| ISBN | 1-394-32963-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Table of Contents -- Preface -- List of Contributors -- Chapter 1: Edge-Based Machine Learning for Computer Vision in Smart Plant Biology Imaging -- 1.1 Introduction -- 1.2 Electronic Devices for Embedded AI-driven Computer Vision -- 1.3 Light Deep Learning Strategies -- 1.4 Benchmark of Light Embedded Deep Learning on a Plant Imaging Use Case -- 1.4.1 Image Acquisition and Segmentation -- 1.4.2 Model Adaptation -- 1.4.3 Knowledge Distillation -- 1.4.4 Kolmogorov-Arnold Network -- 1.5 Discussion -- 1.6 Conclusion -- Chapter 2: Machine Learning for Studying Plant Evolutionary Developmental Biology -- 2.1 Introduction to Plant Evolutionary Developmental Biology -- 2.1.1 Overview of Plant Evolutionary Developmental Biology -- 2.1.2 Key Concepts in Plant Evolution and Development -- 2.1.3 Importance of Evo-Devo in Understanding Plant Adaptations -- 2.1.4 Role of Computational and AI Tools in Evo-Devo Studies -- 2.2 Basics of ML in Biological Research -- 2.2.1 Fundamentals of ML in Biology -- 2.2.2 Supervised, Unsupervised, and Reinforcement Learning -- 2.2.3 Deep Learning and Neural Networks in Evo-Devo -- 2.2.4 ML Workflow: Data Collection, Processing, Model Selection, and Interpretation -- 2.2.5 Challenges of Applying ML to Evo-Devo Research -- 2.3 ML Applications in Plant Morphological Evolution -- 2.3.1 ML for Analyzing Fossilized Plant Structures -- 2.3.2 Shape and Trait Evolution Using CNNs and Autoencoders -- 2.3.3 3D Reconstruction of Plant Organs Through ML-based Image Processing -- 2.3.4 Quantitative Trait Analysis Using SVM -- 2.3.5 Integrating Phylogenetics and ML for Morphological Adaptation Studies -- 2.4 Genomic and Transcriptomic Insights Through ML -- 2.4.1 Evolutionary Genomics: Identifying Selection Signatures with ML -- 2.4.2 GRN Prediction Using Graph Neural Networks.
2.4.3 ML for Comparative Genomics in Evolutionary Studies -- 2.4.4 Understanding Non-coding RNA Evolution with NLP-based ML Models -- 2.4.5 Unraveling Epigenetic Modifications in Plant Evolution Using ML -- 2.5 Inferring Evolutionary Developmental Pathways Using ML -- 2.5.1 ML Models for Predicting Gene Expression Patterns -- 2.5.2 Identifying Key Developmental Genes via Feature Selection Algorithms -- 2.5.3 Evolution of Transcription Factor Networks with ML -- 2.5.4 Bayesian ML for Inferring Ancestral Gene Interactions -- 2.5.5 Evolution of Polyploidy and Hybridization Analyzed Through ML Model -- 2.6 Phylogenetics and Evolutionary Tree Reconstruction Using ML -- 2.6.1 Phylogenetic Tree Prediction via Deep Learning -- 2.6.2 Bayesian ML for Inferring Evolutionary Relationships -- 2.6.3 Predicting Adaptive Radiation Events with ML Models -- 2.6.4 Network-based Approaches for Studying Horizontal Gene Transfer -- 2.6.5 Automating Phylogenomic Inference Using AI-based Pipelines -- 2.7 ML for Studying Developmental Plasticity and Environmental Adaptation -- 2.7.1 Predicting Phenotypic Plasticity with ML Algorithms -- 2.7.2 Climate-responsive Developmental Evolution Using ML -- 2.7.3 Adaptive Traits Discovery Using ML in Dynamic Environments -- 2.7.4 ML-based Prediction of Plant Evolution Under Climate Change -- 2.8 High-throughput Image-based ML Approaches in Evo-Devo -- 2.8.1 ML for Automated Plant Organ Recognition and Classification -- 2.8.2 Deep Learning for Leaf, Flower, and Root Morphological Evolution -- 2.8.3 CNNs for Large-scale Evolutionary Trait Analysis -- 2.8.4 Time-series ML for Tracking Developmental Transitions -- 2.8.5 Integrating ML with Phenotyping Platforms for Evo-Devo Research -- 2.9 Single-cell and Multi-omics ML Integration in Plant Evo-Devo -- 2.9.1 ML for Single-cell RNA Sequencing Data in Evolutionary Studies. 2.9.2 Integrating Proteomics, Transcriptomics, and Metabolomics with ML -- 2.9.3 Deep Learning for Cell Fate and Differentiation Analysis in Evo-Devo -- 2.9.4 Predicting Evo-Devo Pathways Through Multi-omics Data Fusion -- 2.9.5 ML for Analyzing Spatial and Temporal Omics Data -- 2.10 Ethical, Computational, and Experimental Challenges -- 2.11 Conclusion -- Acknowledgement -- Data Availability -- Chapter 3: Machine Learning for Plant High-Throughput Phenotyping -- 3.1 Introduction -- 3.2 Overview of HTP -- 3.3 ML for Plant Phenotyping -- 3.3.1 What Is ML -- 3.3.2 ML in Handling Big Data -- 3.4 Overview of ML Algorithms in Phenotyping -- 3.4.1 DL in Plant Phenotyping -- 3.4.2 Computer Vision in Phenotyping -- 3.5 Applications of ML in Plant Phenotyping -- 3.5.1 ML for Plant Recognition and Disease Detection -- 3.5.2 Spectral Analysis and Optical Imaging for Stress Detection -- 3.5.3 Hyperspectral and Multispectral Imaging -- 3.5.4 Thermal and Fluorescence Imaging for Stress Analysis -- 3.5.5 ML Approaches for Plant Stress Classification -- 3.5.6 Automated Image Analysis for Trait Extraction -- 3.5.6.1 Morphological Trait Measurement -- 3.5.6.2 Image-based Feature Extraction -- 3.5.7 Prediction Models for Yield Forecasting -- 3.6 Integration of ML with Emerging Technologies -- 3.7 Future Directions and Potential of ML in Agriculture -- 3.8 Case Studies and Real-world Applications -- 3.9 Conclusion -- Chapter 4: Machine Learning for Studying Plant Secondary Metabolites -- 4.1 Introduction -- 4.2 ML Techniques in Metabolite Research -- 4.2.1 Supervised Learning Techniques -- 4.2.2 Unsupervised Learning Techniques -- 4.2.3 Deep Learning Techniques -- 4.2.4 Ensemble Learning Techniques -- 4.2.5 Reinforcement Learning in Metabolomics -- 4.2.6 Feature Selection and Dimensionality Reduction -- 4.2.7 Hybrid Models -- 4.2.8 Emerging Techniques in Metabolomics. 4.3 Applications of ML in PSM Research -- 4.3.1 Predicting Metabolic Pathways -- 4.3.2 Identification of Key Biosynthetic Genes -- 4.3.3 Metabolite Profiling and Classification -- 4.3.4 Enhancing Plant Stress Response Through Metabolite Analysis -- 4.3.5 Chemotaxonomy and Species Identification -- 4.3.6 Pharmacological and Nutraceutical Research -- 4.3.7 Metabolic Engineering for Enhanced PSM Production -- 4.3.8 PSM-based Environmental Monitoring -- 4.3.9 Predictive Modeling for Agricultural Improvement -- 4.4 Challenges and Future Directions -- 4.4.1 Challenges in ML for PSM Research -- 4.4.1.1 Data-related Challenges -- 4.4.1.2 Algorithmic Challenges -- 4.4.1.3 Biological Complexity and Unknown Pathways -- 4.4.1.4 Computational Challenges -- 4.4.2 Future Directions in ML for PSM Research -- 4.4.2.1 Integrating Multi-omics Data -- 4.4.2.2 Advancing Explainable Artificial Intelligence -- 4.4.2.3 Improving Data Augmentation Strategies -- 4.4.2.4 Automation and High-throughput Analysis -- 4.4.2.5 Advancing Computational Infrastructure -- 4.4.2.6 Developing Crop-specific ML Models -- 4.4.3 Ethical Considerations and Regulatory Frameworks -- 4.5 Conclusion -- Chapter 5: Machine Learning for Plant Ecological Research -- 5.1 Introduction to Machine Learning in Ecology -- 5.1.1 Overview of Machine Learning in Ecological Studies -- 5.1.2 Importance of ML in Plant Ecological Research -- 5.1.3 Comparison of Traditional vs. ML-based Ecological Analysis -- 5.2 Data Sources and Preprocessing for Plant Ecology -- 5.2.1 Types of Ecological Data -- 5.2.2 Data Collection Techniques -- 5.2.3 Data Cleaning, Normalization, and Feature Engineering -- 5.3 Machine Learning Techniques for Plant Ecology -- 5.3.1 Supervised Learning -- 5.3.2 Unsupervised Learning -- 5.3.3 Deep Learning -- 5.3.4 Reinforcement Learning -- 5.4 Applications of Machine Learning in Plant Ecology. 5.5 Remote Sensing and AI in Plant Ecology -- 5.5.1 Use of Satellite and Drone Imagery for Plant Monitoring -- 5.5.2 Image Segmentation and Object Detection for Vegetation Analysis -- 5.5.3 AI Models for Automated Plant Health Assessment -- 5.6 Biodiversity Conservation and Ecosystem Monitoring -- 5.6.1 Machine Learning for Biodiversity Pattern Analysis -- 5.6.2 AI-driven Monitoring of Endangered Plant Species -- 5.6.3 Predicting Ecosystem Resilience and Response to Environmental Stressors -- 5.7 Predictive Modeling for Ecological Trends -- 5.7.1 Time-series Forecasting of Ecological Parameters -- 5.7.2 ML Models for Predicting Drought and Deforestation Impact -- 5.7.3 Climate-Plant Interaction Modeling -- 5.8 Challenges and Limitations of Machine Learning in Plant Ecology -- 5.9 Future Perspectives and Emerging Technologies -- 5.10 Conclusion -- Acknowledgement -- Data Availability -- Chapter 6: Machine Learning for Modeling Plant Abiotic Stress Responses -- 6.1 Introduction -- 6.2 Definition of Abiotic Stress -- 6.3 Effect of Abiotic Stress on Crops -- 6.4 Key Applications of Machine Learning in Abiotic Stress Research -- 6.4.1 Phenotypic Prediction -- 6.4.2 Gene Expression Modeling -- 6.4.3 High-throughput Image Analysis -- 6.4.4 Omics Data Integration -- 6.4.5 Stress Response Prediction and Simulation -- 6.5 ML Techniques Commonly Used for Abiotic Stress Modeling -- 6.5.1 Supervised Learning -- 6.5.2 Unsupervised Learning -- 6.5.3 Deep Learning -- 6.6 Challenges and Future Directions -- 6.6.1 Data Availability and Quality -- 6.6.2 Generalization -- 6.6.3 Interpretability -- 6.7 Conclusion -- Chapter 7: Machine Learning for Modeling Plant-Pathogen Interactions -- 7.1 Introduction -- 7.2 Basics of PPI -- 7.3 Basics of ML and Its Integration into Biological Systems -- 7.3.1 Supervised Learning -- 7.3.2 Unsupervised Learning -- 7.3.3 Random Forests. 7.3.4 Naive Bayes. |
| Record Nr. | UNINA-9911042411903321 |
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Chen Jen-Tsung
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| Newark : , : John Wiley & Sons, Incorporated, , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Molecular and Biotechnological Tools for Plant Disease Management / / edited by Jen-Tsung Chen, Masudulla Khan, Aiman Parveen, Jayanta Kumar Patra
| Molecular and Biotechnological Tools for Plant Disease Management / / edited by Jen-Tsung Chen, Masudulla Khan, Aiman Parveen, Jayanta Kumar Patra |
| Autore | Chen Jen-Tsung |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (533 pages) |
| Disciplina | 580 |
| Altri autori (Persone) |
KhanMasudulla
ParveenAiman PatraJayanta Kumar |
| Collana | Interdisciplinary Biotechnological Advances |
| Soggetto topico |
Botany
Plant diseases Plants Stress (Physiology) Plant biotechnology Biotechnology Plant Science Plant Pathology Plant Signalling Plant Stress Responses Plant Biotechnology |
| ISBN |
9789819775101
9819775108 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Recent strategies in the management of bacterial diseases for cereals -- Diagnostics and detection tools for pathogens in food crops -- Molecular diagnostics and management of phyto-parasitic nematodes -- Advances in the management of bacterial diseases of vegetable crops -- Emerging approaches based on genome-wide association studies (GWAS) for crop disease tolerance -- Recent diagnostics, detection and monitoring tools: Implications for plant pathogens and their management -- Molecular diagnostics of plant viruses, viroids, and phytoplasma: An updated overview -- Detection and identification of plant viruses, viroids, and phytoplasma based on high-throughput molecular approaches -- Advances in Contemporary Tools for Detecting and Diagnosing Plant Pathogens -- Advanced molecular techniques in the identification of phytopathogenic fungi -- Plant microRNAs: Identification and their application in disease management -- RNAi as a potential tool for control and management of plant disease: An updated overview -- RNA interference for plant disease management: Updated methods, current applications and future directions -- Emerging molecular tools and breeding strategies for plant bacterial disease management -- Disease-resistant genes and signal transduction pathways and their applications in disease management -- Genome editing technologies for resistance against phytopathogens -- CRISPR/Cas9 system of crop improvement: Understanding the underlying machinery -- CRISPR-edited plants for plant-disease management. |
| Record Nr. | UNINA-9910983048703321 |
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Chen Jen-Tsung
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Molecular and Physiological Insights into Plant Stress Tolerance and Applications in Agriculture
| Molecular and Physiological Insights into Plant Stress Tolerance and Applications in Agriculture |
| Autore | Chen Jen-Tsung |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Sharjah : , : Bentham Science Publishers, , 2023 |
| Descrizione fisica | 1 online resource (235 pages) |
| Disciplina | 581.7 |
| Soggetto topico |
Plants - Effect of stress on
Plant physiology |
| ISBN |
9789815136562
9815136569 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title -- Copyright -- End User License Agreement -- Contents -- Foreword -- Preface -- List of Contributors -- Influence of Abiotic Stress on Molecular Responses of Flowering in Rice -- Chanchal Kumari1, Shobhna Yadav1 and Ramu S. Vemanna1,* -- 1. INTRODUCTION -- 1.1. Receptor for Light and Temperature -- 1.2. Reproduction and Maintenance of Shoot Apical Meristem -- 1.3. Molecular Mechanisms of Flowering -- 1.4. Adaptation of Rice to Different Climatic Conditions -- 1.5. Development Made at Molecular Level to Combat Abiotic Stress in Plants -- CONCLUSION -- REFERENCE -- A Peep into the Tolerance Mechanism and the Sugar Beet Response to Salt Stress -- Varucha Misra1,* and Ashutosh Kumar Mall1 -- 1. INTRODUCTION -- 1.1. Characteristics of Halophytes for Salt Stress Condition -- 1.2. Salt Stress Tolerance Mechanism in Sugar Beet -- 1.3. Salt Overly Sensitive (SOS) Pathway for Salt Tolerance -- 1.4. The Response of Sugar Beet under Salt Stress -- CONCLUSION -- REFERENCES -- The Role of Functional Genomics to Fight the Abiotic Stresses for Better Crop Quality and Production -- Neha Sharma1,*, Bharti Choudhary1 and Nimisha Sharma2 -- 1. INTRODUCTION -- 1.1. The Use of Functional Genomics in Studying Plant Physiology under Abiotic Stresses -- 1.1.1. Microarrays and MicroRNAs -- 1.1.2. Serial Analysis of Gene Expression (SAGE) -- 1.1.3. RNA Sequencing -- 1.1.4. RNAi -- 1.1.5. CRISPR/Cas9 -- 1.1.6. Tilling and ECO Tilling -- CONCLUSION -- REFERENCES -- Genetic Enhancement for Salt Tolerance in Rice -- Morphological and Physiological Responses of Plants Under Temperature Stress and Underlying Mechanisms -- Asma Shakeel1,*, Syed Andleeba Jan1, Shakeel A Mir2, Z. Mehdi1, Inayat M. Khan1 and Mehnaz Shakeel1 -- 1. INTRODUCTION -- 2. TEMPERATURE STRESS -- 3. PLANT RESPONSES TO HIGH TEMPERATURE (HT) STRESS: AN OVERVIEW -- 3.1. Germination Stage.
3.2. Photosynthesis -- 3.3. Reproductive Growth -- 3.4. Transpiration -- 3.5. Water Relation -- 3.6. Oxidative Stress -- 3.7. Yield -- 4. MITIGATION STRATEGIES FOR HIGH-TEMPERATURE STRESS -- 4.1. The Function of Modified Membrane in Heat Tolerance -- 4.2. The Function of Antioxidative Defense in Heat Tolerance -- 4.3. The Function of Heat Stress Proteins (Hsps) in Heat Tolerance -- 4.4. The Function of Exogenous Phyto-protectants in Heat Tolerance -- 4.5. Genetic Engineering Approach For Heat Tolerance -- 5. PLANT RESPONSE TO LOW-TEMPERATURE STRESS: AN OVERVIEW -- 5.1. Chilling Injury -- 5.2. Cytological Changes Caused by Chilling Injury -- 5.3. Physiological Changes Caused by Chilling Injury -- 5.4. Water Regimes -- 5.5. Mineral Nutrition -- 5.6. Respiration Rate -- 5.7. Photosynthesis Rate -- 6. MECHANISM FOR CHILLING TOLERANCE -- 6.1. Thermal Effect -- 6.2. Chemical Treatment -- 6.3. Cellular and Genetic Engineering -- 6.4. Freezing Injury -- 7. MECHANISM FOR FREEZING TOLERANCE -- 7.1. Adaptation -- 7.2. Avoidance -- 7.3. Tolerance -- CONCLUSION -- REFERENCES -- Molecular Studies and Metabolic Engineering of Phytohormones for Abiotic Stress Tolerance -- Sekhar Tiwari1 and Ravi Rajwanshi2,* -- 1. INTRODUCTION -- 2. PHYTOHORMONES MEDIATED ABIOTIC STRESS TOLERANCE -- 2.1. Abscisic Acid (ABA) -- 2.2. Auxins (IAA) -- 2.3. Cytokinins (CKs) -- 2.4. Ethylene (ET) -- 2.5. Gibberellins (GAs) -- 2.6. Brassinosteroids (BRs) -- 2.7. Jasmonates (JAs) -- 2.8. Salicylic Acid (SA) -- 2.9. Strigolactones (SL) -- 3. MOLECULAR STUDIES AND METABOLIC ENGINEERING OF PHYTOHORMONES -- CONCLUSION AND PERSPECTIVES -- REFERENCES -- Living with Abiotic Stress from a Plant Nutrition Perspective in Arid and Semi-arid Regions -- Nesreen H. Abou-Baker1,* -- 1. INTRODUCTION -- 2. BACKGROUND AND REVIEW OF LITERATURE -- 2.1. The Ecological Factors Related to Plant Production. 2.2. The Abiotic Stressors Under Arid And Semi-Arid Regions -- 2.2.1. Salinity -- 2.2.2. Drought -- 2.2.3. Heat -- 2.2.4. Pollution -- 2.2.5. The Impact of Abiotic Stressors on Plant -- 2.3. Ordinary Management and Rehabilitation of Soils and Plants under Stress -- 2.3.1. Soil Management -- 2.3.2. Water Management -- 2.3.3. Crop Management -- 2.4. Modern Techniques to Combate Abiotic Stress -- 2.4.1. Nano-technology -- 2.4.2. Intelligent-green Composites -- 2.4.3. Genetic Engineering -- 2.5. Economic Aspects -- 3. A FUTURE VISION/ CONCLUSION -- REFERENCES -- Understanding Molecular Mechanisms of Plant Physiological Responses Under Drought and Salt Stresses -- Abhishek Kanojia1, Ayushi Jaiswal1 and Yashwanti Mudgil1,* -- 1. INTRODUCTION -- 1.1. Signaling Mechanisms Under Salt Stress -- 1.2. Salt Stress Regulation in Plants -- 1.3. Signaling in Drought Stress -- 1.4. Pathways in Details -- 1.5. The Core ABA-Signalling Pathway -- 1.6. PP2C: Regulator of ABA Signalling in Plants -- 1.7. ABA Receptors -- 1.8. SnRK2 -- 1.9. ABA-Dependent Signalling Pathway -- 1.10. ABA-Independent Pathway -- 1.11. Early Osmotic Stress Signalling Pathway -- 1.12. Calcium Dependent Signalling -- 1.13. MAPK-mediated Signalling Pathway -- 1.14. Proteolysis -- 1.15. Phospholipid Signalling -- 1.16. ROS-mediated Signalling -- 1.17. Ethylene (ET) Signalling -- 1.18. Jasmonic Acid (JA) Signalling -- 1.19. Salicylic Acid (SA) Signalling -- 1.20. Brassinosteroids (BRs) Signalling -- CONCLUSION -- REFERENCES -- Salt Stress and its Mitigation Strategies for Enhancing Agricultural Production -- Priyanka Saha1,*, Jitendra Singh Bohra2, Anamika Barman1 and Anurag Bera2 -- 1. INTRODUCTION -- 2. BACKGROUND -- 3. PROBLEM SOILS AND THEIR FEATURES -- 3.1. Acid Soil -- 3.2. Salt-affected Soils -- 4. DIAGNOSTIC CRITERIA AND CLASSIFICATION -- 5. MANAGEMENT STRATEGIES. 5.1. Management Strategies for Reclaiming Acid Soil -- 5.2. Management Strategies for Reclaiming Sodic Soil -- 5.3. Management of Saline Soil -- CONCLUSION -- PATH AHEAD -- REFERENCES -- Impact of Heat Coupled with Drought Stress on Plants -- Battana Swapna1,*, Srinivasan Kameswaran1, Mandala Ramakrishna1 and Thummala Chandrasekhar2 -- 1. INTRODUCTION -- 1.1. Morpho-physiological Responses to Drought Coupled with Heat Stress -- 1.2. Plant Growth -- 1.3. Root System -- 1.4. Photosynthesis -- 1.5. Metabolites -- 1.6. Antioxidants -- 1.7. Yield -- 1.8. Molecular Responses to Heat Coupled with Drought Stress -- 1.9. New Approaches for Developing Tolerance to Heat Coupled with Drought Stress -- CONCLUSION AND FUTURE PERPSPECTIVES -- REFERENCES -- Subject Index -- Back Cover. |
| Record Nr. | UNINA-9911008994203321 |
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Chen Jen-Tsung
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| Sharjah : , : Bentham Science Publishers, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Molecular Aspects of Plant Salinity Stress and Tolerance
| Molecular Aspects of Plant Salinity Stress and Tolerance |
| Autore | Chen Jen-Tsung |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (286 p.) |
| Soggetto topico |
Biology, life sciences
Research & information: general |
| Soggetto non controllato |
abiotic stress
ACC deaminase amino acids antioxidation aquaporin aquaporins Arabidopsis thaliana barley biotechnology breeding calmodulin-like cell wall integrity cell wall sensor crosstalk CrRLK1Ls drought stress endocytosis exogenous jasmonate applications GmbZIP15 GWAS halophiles halophyte halophytic wild barley heterologous expression high-affinity potassium transporter (HKT) high-throughput sequencing Hordeum marinum IAA ion homeostasis ion transport ionome jasmonate signaling pathway jasmonates LRXs metabolome Millettia pinnata n/a Na+ transporter oocytes osmoregulation osmotic stress PGPR plant growth plant growth-promoting rhizobacteria (PGPR) plasma membrane intrinsic proteins (PIPs) QTLs quantitative reverse transcriptase PCR (qRT-PCR) rice RNA sequence analysis (RNA-seq) RNA-seq salinity salt stress salt tolerance seed priming sensing signaling soybean Sporobolus virginicus stress adaptation transcription factor transcription factors transcriptome watermelon Zygophyllum xanthoxylum |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557350603321 |
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Chen Jen-Tsung
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Nanobiosensors for Crop Monitoring and Precision Agriculture / / edited by Jen-Tsung Chen
| Nanobiosensors for Crop Monitoring and Precision Agriculture / / edited by Jen-Tsung Chen |
| Autore | Chen Jen-Tsung |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (427 pages) |
| Disciplina |
620.5
660.6 |
| Collana | Agroecosystem Dynamics and Sustainable Practices |
| Soggetto topico |
Nanobiotechnology
Agricultural biotechnology Nanotechnology Microbiology - Technique Agricultural Biotechnology Microbiology Techniques |
| ISBN | 9789819683352 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Nanobiosensors for Precision Agrobiotechnology -- Chapter 2. Optical Nanobiosensors in Precision Agriculture -- Chapter 3. Calorimetric Nanobiosensors: Revolutionizing Precision Agriculture for Sustainable Crop Management -- Chapter 4. Immuno-Nanobiosensors in Precision Agriculture: Methods and Applications -- Chapter 5. Enzymatic Nanobiosensors in Precision Agriculture: Methods and Applications -- Chapter 6. Apta-Nanobiosensors in Precision Agriculture: Methods and Applications -- Chapter 7. Nanobiosensors for Precision Plant Disease Management -- Chapter 8. Nanobiosensors in Plant Environmental Stress Management: Methods and Current Achievements -- Chapter 9. Nanobiosensors: Advanced Detection and Monitoring of Plant Hormones -- Chapter 10. Nanobiosensors for Detecting and Monitoring Soil Conditions -- Chapter 11. Nanobiosensors for Precision Fertilizer Management -- Chapter 12. Nanobiosensors for Precision Pesticide Management -- Chapter 13. Nanobiosensors for Real-Time and High-Resolution Crop Monitoring -- Chapter 14. Regulations and Ethical Considerations for Nanotechnology in Agri-Food Applications. |
| Record Nr. | UNINA-9911018661603321 |
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Chen Jen-Tsung
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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