Current Plant Biology最新文献

{"title":"Precision diagnosis of tomato diseases for sustainable agriculture through deep learning approach with hybrid data augmentation","authors":"Kamaldeep Joshi ,&nbsp;Sahil Hooda ,&nbsp;Archana Sharma ,&nbsp;Humira Sonah ,&nbsp;Rupesh Deshmukh ,&nbsp;Narendra Tuteja ,&nbsp;Sarvajeet Singh Gill ,&nbsp;Ritu Gill","doi":"10.1016/j.cpb.2025.100437","DOIUrl":"10.1016/j.cpb.2025.100437","url":null,"abstract":"<div><div>Tomato is a key crop in global agriculture, yet it faces yield and quality challenges due to various diseases. Traditional disease identification methods are slow and require expertise, limiting their practicality in large-scale farming. Integrating automated disease detection with precision agriculture provides a timely, accurate diagnosis, promoting sustainable practices. However, the scarcity of real-world data hampers effectiveness. To address this issue, data augmentation techniques simulate variations in farm images, enriching datasets for improved detection of diseases. This investigation aims to identify seven different tomato diseases, such as bacterial spot, early blight, late blight, and others, while also detecting healthy plant leaves. Unlike previous studies that relied on the controlled PlantVillage dataset, this study utilizes the real-world PlantDoc dataset. The study addresses different challenges faced throughout the model development process, like data scarcity and imbalances. A hybrid data augmentation technique is introduced to increase the dataset size from 737 images to 6696 images, which improves the accuracy and robustness of the computer vision model. The study employs the YOLOv8n deep convolutional neural network, achieving 96.5 % mAP, 97 % precision, 93.8 % recall, and 95 % F1 score. The results demonstrate a significant improvement in disease detection, addressing challenges from inadequate datasets and advancing AI-driven precision agriculture. The proposed YOLOv8n model has the potential to be applied beyond its current scope by training it on datasets of other crops. The model can learn and generalize the unique image features associated with various crop types, expanding its utility in agricultural applications. This flexibility allows the model to detect and classify plant characteristics, diseases, or pests across different crops, enabling its use in diverse agricultural environments. As a result, the YOLOv8n model could serve as a robust tool for precision farming, helping to optimize crop management and enhance productivity on a broader scale.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"41 ","pages":"Article 100437"},"PeriodicalIF":5.4,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143134635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcription factors – Insights into abiotic and biotic stress resilience and crop improvement","authors":"Roopali Bhoite ,&nbsp;Olive Onyemaobi ,&nbsp;Tanushree Halder ,&nbsp;Manisha Shankar ,&nbsp;Darshan Sharma","doi":"10.1016/j.cpb.2025.100434","DOIUrl":"10.1016/j.cpb.2025.100434","url":null,"abstract":"<div><div>Numerous crop traits are controlled by multiple gene-networks. These gene-networks play a crucial role in crop evolution, disease prevention, stress adaptation and other fundamental processes in different organisms. Transcription factors (TFs) are master regulators of gene-networks and therefore have been targets for genetic improvement in crops since the dawn of agriculture. Enhancement of quantitative traits through plant breeding often involves manipulation of several TF sites and altered RNA expression. Advancements in OMICS technology have significantly expanded our understanding of transcription factor (TF) binding sites in plants and their roles in various biological processes. This progress has facilitated the validation of TF-related mutations and alleles, offering breeders new opportunities to achieve rapid genetic gains in response to abiotic and biotic stresses. The crop improvements using TFs as master targets is irrespective of crop type, mode of inheritance, number of operative genes and their interactions. Here, we review some of the intensively studied families of TFs– <em>bZIP, bHLH, NAC, ATAF, AP2/ERF, MYB, and WRKY</em> for abiotic and biotic stress resilience in crops and their potential as targets for crop improvement. Breeders’ perspective on status and relevance of TFs in the current breeding programs, utilization of precision editing and prospects of using TFs as regular targets in future crop improvement is discussed.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"41 ","pages":"Article 100434"},"PeriodicalIF":5.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143134626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PhyIndBC: Development of a machine learning tool for screening of potential breast cancer inhibitors from phytochemicals","authors":"Agneesh Pratim Das ,&nbsp;Subhash M. Agarwal","doi":"10.1016/j.cpb.2025.100435","DOIUrl":"10.1016/j.cpb.2025.100435","url":null,"abstract":"<div><div>Breast cancer is the foremost contributor to cancer-related mortality among women on a global scale. However, its treatment encounters challenges compounded by the disease's complexity. A promising avenue in the quest for effective therapeutics lies within the realm of phytomolecules, which are characterized by their chemical diversity and biological potential. Thus, in the current study a machine learning (ML) model was created using phytomolecules having inhibitory activity against breast cancer cell lines. Multiple ML techniques viz., k-nearest neighbor (KNN), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) were combined with various molecular fingerprints (MACCS and Morgan2) to develop multiple predictive models. Among these models, the RF algorithm coupled with the MACCS fingerprint emerged as the best performing model. Mean decreases in impurity, t-SNE analysis, and k-means clustering was studied to determine the important features and understand chemical space diversity. Further, to predict potential breast cancer inhibitors, ADMET adherent Natural Products (NPs) of plant origin (identified from the COCONUT database) were screened using the developed ML model. NPs predicted as actives were further screened via ensemble virtual screening (eVS) technique against erb-b2 receptor tyrosine kinase 2 (HER2), to identify high-affinity molecules against this breast cancer drug target. In summary, the validated machine learning model developed in this study has been incorporated into a freely available standalone package named PhyIndBC (<span><span>https://github.com/subhashmagarwal/PhyIndBC</span><svg><path></path></svg></span>) which can be used for virtual screening and predicting breast cancer inhibitors of plant origin.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"42 ","pages":"Article 100435"},"PeriodicalIF":5.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Eliminating tissue culture from plant gene editing in the near future: A wish or reality?","authors":"Nadali Babaeianjelodar ,&nbsp;Jayati Trivedi ,&nbsp;Claudia Uhde-Stone","doi":"10.1016/j.cpb.2025.100433","DOIUrl":"10.1016/j.cpb.2025.100433","url":null,"abstract":"<div><div>Traditional plant breeding methods alone are insufficient to guarantee food security for a growing global population under a changing climate, necessitating more advanced approaches to develop productive and resilient crop varieties. The development of genome editing tools, particularly CRISPR/CAS, are significantly speeding up crop improvement by enabling targeted breeding in most crop species. However, for many crop species, the need for tissue culture remains a major bottle neck, slowing the progress of crop improvement. In this review, we are presenting and discussing approaches for delivering genome editing tools into a wide variety of crop plants, including perennials, and ideally without integration of transgenes. We suggest that efficient non-tissue culture delivery systems for high-performance genome editing are needed to fully reach the genome engineering potential in crop plants.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"41 ","pages":"Article 100433"},"PeriodicalIF":5.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering plant transcriptomes: Leveraging machine learning for deeper insights","authors":"Bahman Panahi ,&nbsp;Rasmieh Hamid ,&nbsp;Hossein Mohammad Zadeh Jalaly","doi":"10.1016/j.cpb.2024.100432","DOIUrl":"10.1016/j.cpb.2024.100432","url":null,"abstract":"<div><div>Plant transcriptomics is an important field for understanding the dynamics of gene expression, regulatory mechanisms and interactions underlying plant development and stress responses. Despite advances in high-throughput sequencing technologies, the vast amount of transcriptomic data poses significant challenges to traditional methods of analysis and limits the generation of meaningful biological insights. This review addresses the integration of machine learning (ML) techniques in plant transcriptomics and emphasizes their potential to transform data analysis and interpretation. We analyzed different ML methods and their applications in the identification of differentially expressed genes (DEGs), the elucidation of functional annotations and the reconstruction of regulatory networks. The main results show that ML approaches improve the accuracy of transcriptome analyses and facilitate the identification of novel gene functions and regulatory interactions that may be overlooked by conventional methods. The implications of this work are profound. The use of ML can lead to a deeper understanding of plant biology and significantly impact crop improvement strategies. By revealing the complexity of stress tolerance and developmental processes, ML applications can inform breeding programs and improve agricultural resilience. Future research should focus on refining ML algorithms, improving the accessibility of these tools for plant scientists, and fostering interdisciplinary collaborations to maximize the potential of ML in plant transcriptomics.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"41 ","pages":"Article 100432"},"PeriodicalIF":5.4,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143134633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An overview of the regulation of specialized metabolism in tobacco","authors":"Xia Wu ,&nbsp;Sanjay Kumar Singh ,&nbsp;Barunava Patra ,&nbsp;Jing Wang ,&nbsp;Sitakanta Pattanaik ,&nbsp;Ling Yuan","doi":"10.1016/j.cpb.2024.100431","DOIUrl":"10.1016/j.cpb.2024.100431","url":null,"abstract":"<div><div><em>Nicotiana tabacum</em> (common tobacco) is an allotetraploid that presumably originated from the diploid ancestors of <em>N. sylvestris</em> and <em>N. tometosiformis</em> approximately 0.2 million years ago. Tobacco produces a diverse array of specialized metabolites (SM), including alkaloids, terpenoids, and flavonoids, and has been used as a model for studying the regulation of plant SM biosynthetic pathways. Nicotine, the primary pyridine alkaloid in tobacco, is synthesized in roots and transported through xylem to leaves. In addition to nicotine, tobacco produces three other pyridine alkaloids: anabasine, anatabine and nornicotine. Tobacco plants also accumulate various diterpenes, acylsugars, and phenolic compounds in the leaves, flowers and other tissues. Many genes encoding key enzymes involved in these SM pathways have been identified and characterized. Additionally, the transcription factors, such as those from the families of basic helix-loop-helix (bHLH), Apetala2/Ethylene Response Factor (AP2/ERF), MYB, and WRKY, have been identified as the major regulators of nicotine and flavonoid biosynthesis in tobacco. However, the regulation of diterpenes and acylsugar biosynthesis remains relatively underexplored. In addition to transcriptional regulation, SM pathways are also controlled by post-transcriptional and post-translational mechanisms, which have been less studied and discussed. In this review, we provide an overview of the molecular mechanisms governing biosynthesis of nicotine and phenolic compounds in tobacco, and we discuss future prospective and outstanding questions related to the regulation of these SM pathways. Understanding tobacco SM regulation has broad implications for plant biology, as it provides key insights into the regulation of metabolic pathways that produce important and structurally complex bioactive compounds.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"41 ","pages":"Article 100431"},"PeriodicalIF":5.4,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143134632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A yeast osmotic reporter system as a tool to identify biostimulant for plant drought stress tolerance","authors":"Hanae Makhokh ,&nbsp;Océane Busont ,&nbsp;Mélanie Larcher ,&nbsp;Frédéric Lamblin ,&nbsp;Françoise Chefdor ,&nbsp;Christiane Depierreux ,&nbsp;Émilie Destandau ,&nbsp;Sabine Carpin ,&nbsp;François Héricourt","doi":"10.1016/j.cpb.2024.100430","DOIUrl":"10.1016/j.cpb.2024.100430","url":null,"abstract":"<div><div>In the context of global warming, water availability is a major concern and strategies aiming at the protection of this vital resource without reduction of crop yield are of particular interest. In this regard, a system able to identify new biostimulants to improve plant drought tolerance could constitute a powerful tool. In poplar, previous studies have characterized a full signaling pathway that could be involved in osmosensing. This pathway corresponds to a multistep phosphorelay initiated by two transmembrane Histidine-aspartate Kinase receptors, HK1a and HK1b. We hypothesized that extracts or compounds that can modulate the activity of these receptors could trigger plant response to drought, acting as potential biostimulant to improve plant tolerance to this stress. To test this hypothesis, we created a yeast-based fluorescent reporter system for osmosensing. We demonstrated the functionality and the specificity of such a system, acting as an osmotic biosensor. As an example of application, the screening of 24 different extracts of poplar leaves was performed in this reporter system and two extracts were selected. These extracts were tested on two cultivated plant species under drought stress condition and showed a positive effect on plant growth, indicating a potential effect as biostimulant for drought stress tolerance. A chemical analysis of these extracts revealed interesting molecules to be further explored. Overall, this study provides a promising approach for a fast and cost-effective identification of potential biostimulant compounds able to enhance plant tolerance to drought.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"41 ","pages":"Article 100430"},"PeriodicalIF":5.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microevolution of longan flowering behaviors from primitive characteristic to KClO3 induction is driven by artificial selection for agricultural benefit","authors":"Suparat K. Lithanatudom ,&nbsp;Panurat Pipatchananan ,&nbsp;Natnapa Jaitan ,&nbsp;Pimonrat Tiansawat ,&nbsp;Pathrapol Lithanatudom","doi":"10.1016/j.cpb.2024.100427","DOIUrl":"10.1016/j.cpb.2024.100427","url":null,"abstract":"<div><div><em>Dimocarpus longan</em> is an economically important fruit tree primarily cultivated in various Asian countries and the Indochina region encompassing Thailand, China, Vietnam, Laos and Cambodia. Here, we report an extensive dataset of 606.72 Gb of the whole-genome resequencing data obtained from 31 <em>Dimocarpus longan</em> and 1 <em>Dimocarpus obtusus</em> samples performed on Illumina HiSeq PE150, providing the coverage over 30-fold of the reference genome (averaging 15 Gb per sample) with an addition of 12 longan accessions from China. A phylogenomic tree inferred from this analyzed dataset (19,270,513 SNPs) was constructed and results indicated that there are three separate monophyletic clades of China-USA (1), Thai (2) and Vietnam longan (3). Interestingly, however, Thai and Vietnam clades appear to be closely linked in genetic relationship based on analyses of karyotype, genomic data and certain morphological characteristics. <em>De novo</em> transcriptome assembly further revealed variations in gene sequences, divergent gene expression and candidate genes associated with different flowering behaviors. The greatest change in differential gene expression (51.87 %) observed between natural independent and seasonal flowering implied the microevolutionary shift observed in longan is influenced by artificial selection, resulting in gradual changes in flowering behaviors from natural independent flowering to artificial flowering induction using potassium chlorate (KClO₃). In summary, the data obtained from this study serves as the essential evidence for elucidating the microevolution of longan and shedding new light on an agronomical application of artificial flowering induction via modulation of KClO₃ responsive genes in longan or other fruits in the future.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"40 ","pages":"Article 100427"},"PeriodicalIF":5.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcription factors-regulated leaf senescence in major crops: Insights, applications, and challenges","authors":"Sen Li ,&nbsp;Shichun Li ,&nbsp;Shuya Tan,&nbsp;Zhonghua Liu,&nbsp;Zhonghai Li","doi":"10.1016/j.cpb.2024.100428","DOIUrl":"10.1016/j.cpb.2024.100428","url":null,"abstract":"<div><div>Leaf senescence, the final stage of leaf development, is a complex biological process characterized by the degradation of macromolecules and nutrient redistribution. This process significantly contributes to plant fitness and adaptability. Senescence is regulated at multiple levels, including chromatin remodeling, transcription, post-transcription, translation, and post-translational modifications. Transcription factors play a pivotal role in regulating leaf senescence, with NAC and WRKY families being the most extensively studied. This review comprehensively summarizes recent advancements in understanding the regulatory roles of transcription factors in leaf senescence in important crops such as rice, wheat, maize, and soybean, and also briefly summarized significant findings in other crops such as sorghum, tomato, cotton, and cabbage. Additionally, we discuss molecular breeding strategies for optimizing crop performance through the manipulation of senescence by genetically engineering transcription factors.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"40 ","pages":"Article 100428"},"PeriodicalIF":5.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rhodiola rosea L. extract ameliorates ethanol-induced gastric ulcer in rats by alleviating oxidative stress and inflammation via NF-κB pathway inhibition","authors":"Rahamat Unissa Syed ,&nbsp;Mohd.Abdul Hadi ,&nbsp;Aisha Mofareh Almarir ,&nbsp;Amal Mohammad Alahmari ,&nbsp;Yusra Hasan Alremthi ,&nbsp;Asia Abdulrahman A. Alsagri ,&nbsp;Danah Laimooniah ,&nbsp;Mohammed Khaled Bin Break","doi":"10.1016/j.cpb.2024.100421","DOIUrl":"10.1016/j.cpb.2024.100421","url":null,"abstract":"<div><div><em>Rhodiola rosea L</em>. is a traditional plant that has been found to exhibit a wide range of biological activities, however, there is a lack of research regarding its potential anti-ulcer activity. In this study, the plant’s anti-ulcer activity has been evaluated in detail for the first time. <em>R. rosea</em> methanolic extract anti-ulcer activity was investigated against ethanol-induced ulcer rats. The results showed that administering the extract to ulcerated rats reduced gastric acidity and ulcer index while improving nitric oxide (NO) and cytoprotective prostaglandin E2 (PGE2), as well as anti-oxidants glutathione (GSH), catalase (CAT), and superoxide dismutase (SOD). On the other hand, the extract decreased malondialdehyde (MDA) and myeloperoxidase (MPO) levels. Further, ELISA assays showed that <em>R. rosea</em> extract decreased pro-inflammatory cytokines IL-1β, IL-6, IL-8, and TNF-α levels in ulcerated rats. Western blot analysis confirmed the ELISA results, indicating that the extract decreased IL-6 and TNF-α protein expression and inhibited the NF-κB signalling pathway. Macroscopic and histological investigations on ulcerated rats verified the extract's anti-ulcer effects. The extract’s anti-ulcer activity was dose-dependent with the 600 mg/kg/day dose showing superior activity across all assays. GCMS analysis identified the extract’s major constituents as bicyclo [4.1.0] heptane, 7-pentyl (50.784 %) followed by 2-heptadecenal (33.2 %), and it is thought that these compounds play a crucial role in the extract’s bioactivity. Finally, <em>in silico</em> studies showed that the most abundant molecule, bicyclo [4.1.0] heptane, 7-pentyl, demonstrated the highest binding affinity in its interaction with H⁺, K⁺-ATPase. Taken together, the extract’s mode of action might include the inhibition of H⁺, K⁺-ATPase by bicyclo [4.1.0] heptane, 7-pentyl, followed by NF-κB pathway inhibition and subsequent regulation of cytokines and other inflammatory biomarkers. This innovative finding has the potential to lead to a successful anti-ulcer medicine, which might be followed by additional clinical trials or bioguided isolation research.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"40 ","pages":"Article 100421"},"PeriodicalIF":5.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}