Plant Genome最新文献

{"title":"Genome-wide association study reveals the genetic architecture of drought tolerance in maize using yield-based resistance indices and transcriptomic evidence.","authors":"Haoyang Li, Xurong Hu, Pengyan Zhang, Yueying Zhao, Yinglu Song, Zheng Zhang, Huahu Bu, Jianzhong Chang","doi":"10.1002/tpg2.70255","DOIUrl":"10.1002/tpg2.70255","url":null,"abstract":"<p><p>Drought is a major environmental factor limiting maize (Zea mays L.) production worldwide. Unraveling the genetic basis of drought tolerance and pinpointing key loci and candidate genes are fundamental to the molecular breeding of maize with enhanced drought resistance. In this research, 200 maize inbred lines were evaluated under two contrasting water conditions-water-stressed (WS) and well-watered-across two consecutive years (2022 and 2023). Grain yield and drought resistance index (DRI) at maturity were determined, with DRI serving as a yield-based index that provides a more comprehensive measure of drought tolerance. A genome-wide association study based on the FarmCPU model was conducted. A total of 126 significant single nucleotide polymorphisms were detected, including 43 associated with yield under WS conditions (phenotypic variance explained [PVE] = 4.64%-10.67%) and 55 related to DRI (PVE = 1.47%-10.67%). By combining two independent RNA-seq data collections, 43 core candidate genes were identified, 29 of which were functionally annotated. Gene Ontology enrichment and protein-protein interaction analyses demonstrated that these genes were primarily associated with biological processes related to metabolic regulation, signal transduction, and environmental stress responses. Notably, several transcription factors, including NAC35 (GRMZM5G813651), a MADS-box protein (GRMZM2G148693), and a C3HC4-type RING finger protein (GRMZM2G147319), have been previously implicated in drought response pathways. These findings provide new insights into the genetic architecture of drought tolerance in maize by using a mature-plant, yield-based DRI, which more directly reflects drought tolerance under field conditions.</p>","PeriodicalId":49002,"journal":{"name":"Plant Genome","volume":"19 2","pages":"e70255"},"PeriodicalIF":4.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13184411/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147976404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovering putative novel stem rust resistance loci in wheat genetic resources.","authors":"Jiaojiao Wang, Guoliang Li, Tim Kloppe, Kerstin Flath, Philipp Schulz, Mathias Wiegman, Thomas Miedaner, Yong Jiang, Jochen C Reif","doi":"10.1002/tpg2.70259","DOIUrl":"10.1002/tpg2.70259","url":null,"abstract":"<p><p>Stem rust (SR), caused by Puccinia graminis f. sp. tritici (Pgt), poses a threat to wheat production in Germany. This study evaluated 200 plant genetic resources (PGR) and 50 elite lines across four field environments. Multi-environment genome-wide association mapping based on PGR accessions revealed 1942 marker-trait associations (MTAs), grouped into eight quantitative trait loci (QTLs), which explained 0.84%-28.19% of phenotypic variance. Three of these QTL, QSr1B_1.47_67.79, QSr4A_716.31_716.32, and QSr7D_171.80_174.34, harbor five haplotypes that have likely not been used in breeding. Five representative MTAs (Chr1B:25506861, Chr1B:17349639, Chr1B:67755088, Chr4A:716319007, and Chr7D:171807054) represent these five putative novel haplotypes, and together they conservatively explained 2.40%-7.03% of the phenotypic variance. Four genotypes carrying all favorable alleles of the representative MTAs were identified as potential donor lines for future breeding. Within eight QTL regions, 44 candidate genes were prioritized. These findings provide new insights into the genetic architecture of SR resistance and highlight promising targets for marker-assisted prebreeding.</p>","PeriodicalId":49002,"journal":{"name":"Plant Genome","volume":"19 2","pages":"e70259"},"PeriodicalIF":4.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13492613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148158744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic dissection of plant architecture, development, and seed morphology in exotic × elite Upland cotton populations.","authors":"Hrithik Mangla, Ankush Sharma, Min Liu, Tariq Shehzad, Gary J Pierce, Rahul Chandnani, Sayan Das, Sameer Khanal, Jason G Wallace, John L Snider, Don C Jones, Peng W Chee, Andrew H Paterson","doi":"10.1002/tpg2.70256","DOIUrl":"10.1002/tpg2.70256","url":null,"abstract":"<p><p>Cotton (Gossypium hirsutum L.) breeding has primarily emphasized fiber yield and quality, yet plant architectural, developmental, and seed morphological traits also play key roles in productivity and management. Here, we dissect the genetic basis of four such traits: plant height (HT), number of nodes with one branch (N1B), days to flowering (DTF), and average surface area per seed (ASAS) using exotic × elite Upland cotton populations. Both population-specific and joint analyses of the populations identified 17 QTLs collectively, with ASAS having the largest number of detected QTLs. The exotic line T326 showed the highest potential for the improvement of ASAS. While HT, N1B, and DTF displayed limited genetic variance in these populations, gene ontology enrichment highlighted the CCR4-NOT complex as a potential shared regulatory hub connecting ASAS and DTF. In addition, ASAS showed a highly significant positive correlation and overlapping QTL regions with fiber strength and upper half mean length described in a companion paper, suggesting the possibility of strong linkage or pleiotropic effects of these genomic regions, which could be beneficial commercially. Overall, the data suggested no prevalence of negative allelic trade-offs or linkage drag from these secondary traits for the improvement of fiber quality traits, underscoring the value of exotic germplasm for simultaneous enhancement of fiber quality and seed morphology in Upland cotton breeding.</p>","PeriodicalId":49002,"journal":{"name":"Plant Genome","volume":"19 2","pages":"e70256"},"PeriodicalIF":4.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13492605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147976305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A chromosome-level genome assembly of Solanum brevicaule (PI 473011) Y1-5, a wild potato relative with robust resistance to potato cyst nematodes.","authors":"Senthilkumar Shanmugavel, Shengwei Hu, Huijun Yang, Shiyan Chen, John Bamberg, Dongyan Zhao, Bhoja R Basnet, Craig T Beil, Moira Sheehan, Vidyasagar Sathuvalli, Xiaohong Wang","doi":"10.1002/tpg2.70265","DOIUrl":"10.1002/tpg2.70265","url":null,"abstract":"<p><p>Potato cyst nematodes (PCNs), Globodera rostochiensis and Globodera pallida, are globally recognized quarantine pests that can cause severe yield losses in potatoes (Solanum tuberosum L.) if left uncontrolled. Deploying host resistance remains the most effective, economical, and environmentally sustainable strategy for PCN control. The US Potato Genebank (USPG) maintains a rich collection of wild potato species, providing a valuable resource for discovering novel resistance to PCN. In this study, we identified a Solanum brevicaule clone (PI 473011; designated Y1-5) from the USPG that exhibits robust resistance to both PCN species. We sequenced the whole genome of Y1-5 using PacBio high-fidelity sequencing technology and generated a haplotype-resolved assembly, with genome sizes of 763.49 and 764.93 Mb for haplotypes 1 and 2, respectively. Repeat elements accounted for approximately 65% of the genome, while gene prediction identified 80,021 protein-coding genes. A comprehensive set of genes encoding nucleotide-binding and leucine-rich repeat (NLR) proteins-intracellular receptors that mediate disease resistance-was identified in the annotated genome. NLR genes were particularly enriched on chromosomes 4, 6, and 11. Importantly, Y1-5 was found to encode more coiled-coil-type NLRs than several previously sequenced wild Solanum clones. These findings suggest that Y1-5 may serve as a promising new source of resistance to potato diseases. Development of this genomic resource is valuable for understanding the mechanisms of PCN resistance and for supporting potato breeding efforts to develop varieties with durable nematode resistance.</p>","PeriodicalId":49002,"journal":{"name":"Plant Genome","volume":"19 2","pages":"e70265"},"PeriodicalIF":4.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13261804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148228889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of deep learning in crop research: From genomics to phenomics.","authors":"Zefeng Wu, Yali Sun, Qian Luo, Jiaping Wei, Junmei Cui, Yan Fang, Yining Niu, Zhaohong Li, Xiaolin Wang, Zigang Liu","doi":"10.1002/tpg2.70268","DOIUrl":"10.1002/tpg2.70268","url":null,"abstract":"<p><p>Deep learning, as a pivotal branch of machine learning, has demonstrated remarkable potential in advancing crop science by effectively integrating genomics and phenomics. This review systematically outlines the application of diverse deep learning architectures-such as convolutional neural networks, recurrent neural networks, and transformers-across key crop genomic tasks, including gene expression prediction, alternative splicing analysis, cis-regulatory element identification, epigenomic profiling, and genome-based trait prediction. In phenomics, these models facilitate high-throughput extraction of crop phenotypic traits from multispectral, unmanned aerial vehicle, and ground-based imagery, supporting yield forecasting, disease diagnosis, and stress response monitoring. We critically evaluate the performance and limitations of each model type across tasks, considering trade-offs between complexity, accuracy, and interpretability, to offer practical guidance for crop researchers. Additionally, the review addresses major challenges in deploying deep learning-such as data scarcity, model transparency, and computational demands-and proposes future pathways to enhance model generalizability, multimodal data integration, and applications in intelligent breeding and sustainable agriculture.</p>","PeriodicalId":49002,"journal":{"name":"Plant Genome","volume":"19 2","pages":"e70268"},"PeriodicalIF":4.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13287554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148303622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimum recombination rates for genetic gains in simulated recurrent selection in empirical maize populations.","authors":"Chandrappa Anilkumar, Rex Bernardo","doi":"10.1002/tpg2.70229","DOIUrl":"10.1002/tpg2.70229","url":null,"abstract":"<p><p>Meiotic recombination creates new allelic combinations, but it also disrupts favorable parental haplotypes. Our objective was to assess if optimum recombination rates exist in elite maize (Zea mays L.) populations undergoing simulated short-term and long-term recurrent selection. Genomewide marker effects were calculated for eight populations genotyped with 3072 single nucleotide polymorphism markers and phenotyped in up to 18 environments for yield, moisture, test weight, and plant and ear height. The recombination rate was altered by multiplying the sizes of linkage maps by 0.25-10. The regression of genetic gain on the number of crossovers was often curvilinear, and an optimum number of crossovers (CO_Opt) was found in nearly 90% of the trait-population combinations. The CO_Opt was higher in long-term than in short-term selection, and all linear regression coefficients that were significant (p ≤ 0.05) and negative in short-term selection became positive or nonsignificant in long-term selection. This result indicated that increased recombination initially disrupted favorable parental haplotypes but later facilitated the accumulation of favorable haplotypes. The variation in CO_Opt was greater among populations than among traits within the same population, and CO_Opt also increased as the size of the linkage map increased. Overall, our results showed that a higher recombination rate is not always beneficial and that each trait-population combination has an optimum recombination rate that maximizes genetic gain. The practical application of these results awaits the development of methods to both suppress and boost genomewide recombination rates.</p>","PeriodicalId":49002,"journal":{"name":"Plant Genome","volume":"19 2","pages":"e70229"},"PeriodicalIF":4.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13032164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147533839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic architecture of angular leaf spot resistance in cultivated strawberry shaped by epistasis and genotype-by-environment interactions.","authors":"Shai Torgeman, Dominique D A Pincot, Marta Bjornson, Randi A Famula, Paul Skillin, Allison Krill-Brown, Mitchell J Feldmann","doi":"10.1002/tpg2.70246","DOIUrl":"10.1002/tpg2.70246","url":null,"abstract":"<p><p>Angular leaf spot (ALS), caused by Xanthomonas fragariae, is a bacterial disease that limits strawberry (Fragaria × ananassa) productivity worldwide. Although major resistance loci have been identified in wild Fragaria species, their introgression into elite germplasm remains constrained by linkage drag and inconsistent inheritance. To dissect the genetic architecture of ALS resistance, we evaluated a diverse panel of n = 241 greenhouse-tested and n = 468 field-tested strawberry accessions representing elite cultivars, breeding materials, and heirloom varieties of cultivated strawberry (F. × ananassa). Plants were evaluated under controlled inoculations and natural field infection using a standardized phenotypic scale and genotyped with 50K single nucleotide polymorphism array. Broad-sense heritability was moderate (H² = 0.45), whereas narrow-sense heritability was low (h² = 0.10), reflecting the contribution of nonadditive genetic effects such as epistasis. Genome-wide association studies (GWAS) identified two loci on chromosomes 1D and 2D, explaining 4%-5% of phenotypic variance, respectively. A segregating F1 population validated the 2D locus, confirmed by co-localization with the GWAS signal explaining 11.3% of the phenotypic variance. Two-dimensional genome wide scan in this F1 population revealed a significant epistatic interaction between locus on chromosome 2D and an additional locus on chromosome 6B, collectively explaining 27% of the phenotypic variance. Together, these results demonstrate that nonadditive effects in these populations control ALS resistance. Understanding this complexity provides a foundation for developing elite cultivars with durable resistance, such as UCD Royal Royce. Our findings underscore the need for predictive breeding strategies that capture epistatic and environmental variance to accelerate genetic gain for ALS resistance in strawberry.</p>","PeriodicalId":49002,"journal":{"name":"Plant Genome","volume":"19 2","pages":"e70246"},"PeriodicalIF":4.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13150400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147844901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A benchmarking of genomic selection models for predicting grain-yield related traits using haplotype-based and genome-wide association study-based markers in rice.","authors":"Xiankang Hu, Liu Xian, Guangping Cao, Shuoran Liu, Di Wang, Yanru Che, Weichao Huang, Yanhui Lin, Liqiang He","doi":"10.1002/tpg2.70226","DOIUrl":"10.1002/tpg2.70226","url":null,"abstract":"<p><p>Rice (Oryza sativa) is an important staple food, feeding more than half of the global population. A feasible improvement of rice yield is necessary to meet the ever-growing food demands. Genomic selection (GS), as an advanced breeding technique, enables the prediction of phenotypes solely based on genotypic data using a constructed genomic prediction model. A benchmarking study is required to evaluate the prediction performance of various available GS models for accelerating the breeding process. To address this concern, a diverse population containing 688 rice accessions with 66,456 haplotype-based single nucleotide polymorphisms (SNPs) (HA-SNPs) were used to construct 17 GS models for grain length, grain width, and 1000-grain weight separately. The effect of population size on the GS prediction accuracy was slightly greater than that of marker size. Among these models, ridge regression best linear unbiased prediction (RRBLUP) offered several advantages in terms of robustness, simplicity, and computational efficiency. Regarding the predictive ability, machine learning and deep learning models are also viable alternatives. Additionally, the prediction accuracy of genome-wide association study-detected SNP based GS was significantly higher than that of HA-SNP based GS. Furthermore, a subset including 63 selected accessions was used to construct the RRBLUP model, and followed by the evaluation of elite germplasm for practical breeding. Taken together, these findings have the potential to reduce the rice yield breeding cycle and enhance the genetic gain.</p>","PeriodicalId":49002,"journal":{"name":"Plant Genome","volume":"19 2","pages":"e70226"},"PeriodicalIF":4.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13028923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147533774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ConvCGP: A convolutional neural network to predict genetic values of agronomic traits from compressed genome-wide polymorphisms.","authors":"Tanzila Raihan, Chyon Hae Kim, Hiroyuki Shimono, Akio Kimura, Hiroyoshi Iwata","doi":"10.1002/tpg2.70223","DOIUrl":"10.1002/tpg2.70223","url":null,"abstract":"<p><p>The growing size of genome-wide polymorphism data in animal and plant breeding has raised concerns regarding computational load and time, particularly when predicting genetic values for target traits using genomic prediction. Several deep learning and conventional methods, including dimensionality reduction techniques such as principal component analysis (PCA) and autoencoders, have been proposed to address these challenges by selecting subsets of polymorphisms or compressing high-dimensional data for predictive analysis. However, these methods are often computationally intensive and time-consuming. A major challenge in applying deep-learning models directly to high-dimensional genomic data is the substantial computational cost and time required for hyperparameter tuning and model training. To address these limitations, we propose a novel deep learning framework, Compression-based Genomic Prediction using Convolutional Neural Networks (ConvCGP), that integrates autoencoder-based nonlinear compression with convolutional neural network-based prediction in an end-to-end trainable pipeline. This method reduces data to a compact latent representation that retains meaningful information for prediction, thereby significantly reducing storage needs and computational load. We applied ConvCGP to high-dimensional rice datasets for agronomic trait prediction and further tested it on maize, which is large in scale. The results show that ConvCGP maintained prediction accuracy comparable to models trained on uncompressed data, even under extreme compression where only 2% of the original features were retained. This demonstrates that ConvCGP not only scales effectively to massive datasets but also preserves predictive information under drastic dimensionality reduction. Moreover, ConvCGP consistently outperformed PCA-based models, genomic best linear unbiased prediction, LASSO (least absolute shrinkage and selection operator), support vector machine, and other methods, establishing it as a powerful, efficient, and scalable solution for modern genomic prediction.</p>","PeriodicalId":49002,"journal":{"name":"Plant Genome","volume":"19 2","pages":"e70223"},"PeriodicalIF":4.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13092881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in gene cloning and functional genomics approaches for wheat (Triticum aestivum L.) improvement.","authors":"Santosh Gudi, Khizar Razzaq, Jatinder Singh, Luis Del Rio Mendoza, Mohamed Khan, Rajeev Gupta, Upinder Gill","doi":"10.1002/tpg2.70261","DOIUrl":"10.1002/tpg2.70261","url":null,"abstract":"<p><p>Wheat (Triticum aestivum L.) is a globally important cereal crop which provides ∼20% calories in human diet. Identifying genes and elucidating their functions is still challenging in wheat due to its large genome (∼16 Gb) with significant amount of repetitive elements. However, the gene cloning and functional characterization efforts are gradually picking up over the last decade. In this review article, we discussed the recent advances in wheat functional genomics including strategies (beyond the map-based cloning) based on next-generation sequencing, association analysis, and bulk segregant analysis along with its modifications. We also discussed how functional genomics approaches including conventional, genetic engineering, genome editing, and transient gene expression can be utilized in wheat improvement. Besides recent developments, we discussed the possible opportunities available for improving the field of gene cloning and functional genomics in wheat. Introducing the innovative and advanced functional genomics tools helps in identification and functional validation of target genes in wheat for faster genetic gain.</p>","PeriodicalId":49002,"journal":{"name":"Plant Genome","volume":"19 2","pages":"e70261"},"PeriodicalIF":4.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13261100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148228958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}