农学最新文献

{"title":"SNP density impact on kinship inference and IBS-machine learning optimization.","authors":"Lv Dai, Zi-Chen Tang, Zhen Jia, Li Jiang, Chuan-Tong Zhao, Zhi-Yuan Zhao, Wen-Ting Zhao, Cai-Xia Li","doi":"10.16288/j.yczz.25-287","DOIUrl":"https://doi.org/10.16288/j.yczz.25-287","url":null,"abstract":"<p><p>In recent years, multiple panels containing varying numbers of single nucleotide polymorphisms (SNPs) have been reported in forensic genetics for kinship inference. However, systematic exploration of the impact of SNP number on inference performance and the application of machine learning algorithms remains lacking. Therefore, we evaluated the impact of SNP number on kinship inference performance and the optimization effects of machine learning methods on the identity-by-state (IBS) algorithm. We constructed multiple SNP panels with SNP numbers ranging from 15,476 to 20,838, and evaluated the performance of the likelihood ratio (LR) method and the IBS algorithm for kinship inference under different SNP numbers based on simulated pedigrees. After selecting the optimal SNP panel, we validated it using real pedigrees and further combined the IBS algorithm with machine learning methods to enhance inference performance. Our results showed that for the LR method, the sensitivity in inferring sixth and seventh degree kinships exhibited a significant positive correlation with SNP number. For the IBS algorithm, although the sensitivity in inferring fourth to seventh degree kinships showed a significant positive correlation with SNP number, the actual improvement was limited (only 0.5%~2.2% increase). Based on these results, we determined the optimal panel containing 20,838 SNPs (21K panel). The 21K panel based on the LR method could accurately infer kinships within sixth degree (with a sensitivity of 93.65% for sixth degree kinship inference), and the 21K panel based on the IBS algorithm could accurately infer kinships within third degree (with a sensitivity of 86.79% for third degree kinship inference). After combining the IBS algorithm with machine learning, the sensitivity for fourth degree kinship inference improved from 69.10% to 87.66%, the sensitivities for fifth and sixth degree kinships improved from 38.03% and 21.41% to 48.75% and 37.80%, respectively.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"48 6","pages":"570-588"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148273287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"System construction for sui generis right of digital sequence information on genetic resources in China.","authors":"Wen-Fei Zhang, Fei Yang, Xian-Jie Zhou","doi":"10.16288/j.yczz.25-305","DOIUrl":"10.16288/j.yczz.25-305","url":null,"abstract":"<p><p>Digital sequence information on genetic resources plays an increasingly vital role in research on improving crop yields and reducing impacts of disease. However, there are still problems such as the imperfect rights' protection system, which hinders the protection and sustainable use of genetic resources and constrains the realization of the fair and equitable benefit-sharing objectives established by the Convention on Biological Diversity. To address these issues, we first define the connotation and legal nature of digital sequence information on genetic resources. Then, we elaborate how digital sequence information on genetic resources aligns with the modern intellectual property systems. For digital sequence information on genetic resources to be protected by intellectual property rights, we analyze the substantive conditions of novelty and practicality as well as the procedural conditions of registration. Building on this analysis, we identify the limitations of existing intellectual property frameworks in accommodating digital sequence information on genetic resources, and propose a normative structure of sui generis right of digital sequence information on genetic resources. The subjects of sui generis right are divided into two levels, i.e. static right attributed to the state, and dynamic right exercised by the farmer collective of specific communities and the uploaders of digital sequence information, exercising respectively in accordance with different functions. The contents of sui generis right include the informed consent right, the source indication right and benefit-sharing right based on licensing. Furthermore, we propose to establish the limitations on sui generis right, such as restriction on scope of application, compulsory licensing and protection periods. This framework established here is of great significance for achieving the balance of rights and interests, promoting innovation in plant breeding and protecting genetic diversity.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"48 6","pages":"557-569"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148273279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction and implementation of an ICF-based integrated teaching model for genetic disease severity assessment.","authors":"Qing-Li Quan, Mei-Xing Yu, Hai-Ou Jiang","doi":"10.16288/j.yczz.26-005","DOIUrl":"https://doi.org/10.16288/j.yczz.26-005","url":null,"abstract":"<p><p>To enhance the competency of clinical medicine students in evaluating the severity of genetic disorders, this study first modularized an assessment of genetic diseases severity based on the World Health Organization's International Classification of Functioning, Disability and Health (ICF) framework. Then, this modular teaching framework was applied in teaching instruction. During the Medical Genetics course, the 2024 clinical medicine cohort at Hunan University of Medicine were clustered into a experimental group (6 classes, 203 students) and a control group (7 classes, 235 students) randomly. The experimental group engaged in the ICF-based module in the case analysis of genetic diseases, while students in the control group followed the traditional teaching methods. Learning outcomes were evaluated by analyzing the student-written genetic disease severity evaluation reports. Results demonstrated that students in the experimental group achieved significantly higher scores on their assessment reports (73.33±7.16) compared to the control group (64.79±5.45), with a statistically significant difference (<i>t</i>=13.87, <i>P</i><0.001). Furthermore, textual analysis further revealed that reports from the experimental group contained a significantly higher frequency of keywords related to patient psychology, social functioning, and environmental factors, indicating a broader focus on the patients and more comprehensive and in-depth understanding of the patient's situation. These findings suggest that the ICF-based modular teaching framework significantly improves medical students' ability to conduct individualized assessments of genetic diseases and effectively fosters their humanistic care. This study provides an actionable and scalable teaching practice pathway for cultivating clinical genetic counseling professionals.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"48 6","pages":"648-656"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148273216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The p53 R267W mutation intervenes p21-mediated cell cycle arrest and promotes proliferation and migration of lung cancer cells.","authors":"Jia-Ning Zhao, Jin-Zheng Wu, Shu-Bing Zhang","doi":"10.16288/j.yczz.25-222","DOIUrl":"https://doi.org/10.16288/j.yczz.25-222","url":null,"abstract":"<p><p>Pathogenic germline variants in <i>TP53</i> constitute the central etiological driver of hereditary tumor predisposition disorders syndromes. The oncogenic mechanisms of hotspot mutations in the DNA-binding domain of p53 are well-established. However, the functional consequences of non-hotspot missense mutations remain incompletely understood. In this study, we characterized the molecular pathogenesis and clinical significance of the p53 non-hotspot mutation p.Arg267Trp (p.R267W). In addition, evolutionary conservation analysis, structural prediction, and functional assays including CCK-8 cell proliferation, clonogenic assay, Transwell migration, wound healing assay, qPCR, Western blot, single luciferase reporter assay, and flow cytometry techniques were performed to assess the impact of R267W on <i>TP53</i> target gene (<i>CDKN1A</i>) regulation and tumor-suppressive phenotypes (proliferation, colony formation, migration) in non-small cell lung cancer models (A549/NCI-H1299). Experiments confirmed that the mutant does not affect the p53 protein stability. The impairment of protein function is hypothesized to result from the disruption of the DNA-binding domain conformation. Experimental evidence suggested that the mutant <i>TP53</i> exhibited significantly reduced transcriptional activity (<i>P</i><0.001), resulting in a concomitant reduction in <i>CDKN1A</i> mRNA expression and diminished cell cycle arrest capability when compared to wild type. At the tumor-suppressive functional level, the R267W mutant significantly reduced inhibition rates of non-small cell lung cancer cell proliferation, colony formation, and migration relative to wild type (<i>P</i><0.05). This study reveals that the R267W variant drives cell cycle dysregulation and malignant phenotypes in lung cancer by disrupting <i>TP53</i>'s transcriptional functions. These findings establish a molecular basis for the pathogenic classification of <i>TP53</i> variants of uncertain clinical significance.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"48 6","pages":"589-600"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148273292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data governance in smart farming: A systematic review of challenges and gaps","authors":"Radwan Rouzky ,&nbsp;Abdolhossein Sarrafzadeh ,&nbsp;Evelyn Sowells-Boone ,&nbsp;Hannaneh B. Pasandi ,&nbsp;Gregory Goins","doi":"10.1016/j.inpa.2025.11.006","DOIUrl":"10.1016/j.inpa.2025.11.006","url":null,"abstract":"<div><div>Smart Farming (SF) has emerged as a transformative approach in modern agriculture, leveraging advanced technologies to meet escalating global food demands. However, the extensive use of digital devices generates vast amounts of data, necessitating robust governance frameworks to ensure secure, ethical, and efficient management. This systematic review evaluates SF data governance literature from 2018 to 2024, analyzing 32 studies that collectively highlight critical challenges and opportunities. Key findings include the absence of standardized governance frameworks, limited exploration of security mechanisms, and insufficient farmer’s awareness regarding data privacy and security. Our novel taxonomy categorizes SF governance into data security, privacy, and sharing frameworks, offering actionable insights for stakeholders. By identifying gaps—such as the need for transparent governance systems and comprehensive regulatory policies—this paper provides a roadmap for advancing ethical and effective data governance in agriculture.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 303-317"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of main strains of ARC inoculant via machine learning-assisted single-cell Raman spectroscopy","authors":"Jie Kang ,&nbsp;Feng Wei ,&nbsp;Xi Shen ,&nbsp;Ao Liu ,&nbsp;Jingjing Dou ,&nbsp;Shujie Zhang ,&nbsp;Jinli Hu ,&nbsp;Liangxiao Zhang ,&nbsp;Qi Zhang ,&nbsp;Peiwu Li","doi":"10.1016/j.ocsci.2026.02.001","DOIUrl":"10.1016/j.ocsci.2026.02.001","url":null,"abstract":"<div><div>ARC inoculant (A, aflatoxin prevention and control; R, Rhizobia nodulation induction; C, Coupling) is a brand-new inoculant with coupling function that enhances legume quality and nitrogen fixation. Comprehensive characterization of its key functional strains is critical for establishing a quality-control framework for the inoculant's formulation. Here, we constructed a characteristic spectral dataset comprising over 63,000 single-cell Raman spectra of the constituent strains by employing Ramanome technology. Six machine learning-based predictive models were developed and compared for the constituent strains, while the Linear Discriminant Analysis (LDA) model demonstrated the best performance, with a classification accuracy exceeding 92.4%. This work provides a unique spectral fingerprint for ARC inoculant and will directly aid its application in sustainable agricultural production.</div></div>","PeriodicalId":34095,"journal":{"name":"Oil Crop Science","volume":"11 2","pages":"Pages 113-116"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148141183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ICIS: Intelligent and Label-Free crop identifying using Multi-Source time series and model transfer","authors":"Su Rina ,&nbsp;Wu Nile ,&nbsp;Na Mula ,&nbsp;Sicheng Wei ,&nbsp;Ying Guo ,&nbsp;Jiquan Zhang ,&nbsp;Zhijun Tong ,&nbsp;Xingpeng Liu ,&nbsp;Chunli Zhao ,&nbsp;Cha Ersi","doi":"10.1016/j.inpa.2025.11.005","DOIUrl":"10.1016/j.inpa.2025.11.005","url":null,"abstract":"<div><div>Crop cultivation is intrinsically linked to human well-being, and the timely identification of crop types is critical for agricultural monitoring and food security assessment. However, without extensive field surveys, achieving timely, large-scale, high-resolution crop identification remains challenging. This study proposes an intelligent crop identification strategy (ICIS) that integrates crop phenological development information with a model transfer strategy based on machine learning, generating 10-meter resolution crop distribution maps of major crop types from 2020 to 2023 in Northeast China (NE). ICIS utilizes time series data from Sentinel-1 (S1) and Sentinel-2 (S2) to extract multi-source temporal features covering the full growth cycles of maize, rice, and soybeans. A Random Forest (RF) classifier was trained on representative sample sites, and model transfer was employed to predict crop distribution across different years and regions. The experimental results show that the models achieved F1-scores above 0.92 across provinces, peaking at 0.97. For rice, the F1-scores for Jilin, Heilongjiang, and Liaoning remained stable at 0.94. Maize F1-scores consistently exceeded 0.92, and although the soybean accuracy varied spatially, it reached 0.97 in certain areas. Comparison with crop area statistics from the China Statistical Yearbook at the city level revealed that the average root mean square error (RMSE) across four years for the three crops was 8.6 × 10⁴ ha, with coefficients of determination (R²) consistently above 0.90, reaching up to 0.96. These results demonstrated the strong generalizability and robustness of the proposed ICIS Strategy for crop mapping across both temporal and spatial scales. Moreover, this study reveals the complementary strengths and key contributions of optical and Synthetic Aperture Radar (SAR) time series features in modeling crop growth dynamics. Overall, the ICIS provides an efficient and reliable technical means for the rapidly acquiring large-scale crop information. It provides robust support for agricultural monitoring and food security assessment, demonstrates significant potential for large-scale agricultural applications, and offers valuable insights and technical support for remote sensing data processing, image analysis, and machine learning-based agricultural automation and decision-making.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 283-302"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"YOLO-MEST: A re-parameterized multi-scale fusion model with enhanced detection head for high-accuracy tea bud detection","authors":"Chuanyang Yu ,&nbsp;Yi Xue ,&nbsp;Liuyang Zhang ,&nbsp;Xue An ,&nbsp;Ce Liu ,&nbsp;Liqing Chen","doi":"10.1016/j.inpa.2025.10.001","DOIUrl":"10.1016/j.inpa.2025.10.001","url":null,"abstract":"<div><div>Accurate recognition of tea buds is essential for automated harvesting. However, conventional image-based methods have difficulty with complex field conditions, such as varying illumination, occlusion, and cluttered backgrounds. To overcome these challenges, we introduce YOLO-MEST, a new detection model that incorporates multi-scale feature fusion. This model introduced the RepNCSELAN4 module to enhance feature extraction capabilities and the SPPELAN module to improve feature fusion. The efficient detection head, LiteShiftHead, was added to the network output to improve the accuracy of bounding boxes and classification regression. An improved loss function dDIoU based on the difference in the width-to-height ratio of bounding boxes was designed to enhance the accuracy of bounding box localization further. To form a complete detection-to-picking pipeline, a morphological algorithm-based tea bud picking point estimation algorithm was further proposed, which effectively determined the tea bud picking points based on the detection results. We conducted performance tests on tea bud recognition based on a self-built dataset of high-quality tea. Compared to the original YOLOv8 model, the YOLO-MEST model increased mAP50 by 1.7% and mAP by 4.4%, respectively. Ablation studies confirm the contribution of each component. The proposed method significantly improves detection accuracy and supports practical intelligent tea harvesting.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 163-175"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of quality characteristics, composition, antioxidant activity, and volatile profiles of seven Camellia oleifera seed oils","authors":"Zhenxia Xu ,&nbsp;Shuaijun Lu ,&nbsp;Yufei Liu ,&nbsp;Weijuan Su ,&nbsp;Xiaoyang Xia ,&nbsp;Nanjie Jiang ,&nbsp;Xia Xiang","doi":"10.1016/j.ocsci.2026.05.005","DOIUrl":"10.1016/j.ocsci.2026.05.005","url":null,"abstract":"<div><div><em>Camellia oleifera</em> seed oil (CSO) is widely appreciated for its nutritional and functional properties, yet its quality is strongly affected by processing and storage conditions. In this study, seven CSO samples originating from different sources were systematically evaluated for physicochemical properties, bioactive compounds, lipid composition, antioxidant capacity, and volatile flavor profiles. Pronounced differences were observed among the samples. Sample S5 exhibited the highest oxidative deterioration due to long-term storage, with elevated acid value (2.27 mg KOH/g) and peroxide value (2.98 g/100 g). In contrast, sample S2 showed the greatest oxidative stability (induction period of 10.88 h) and superior in vitro antioxidant capacity (DPPH and ABTS), which was closely associated with its higher contents of carotenoids, tocopherols, and total phenolics. All CSO samples were characterized by a high oleic acid content (76.19%-81.99%). The lipidomic analysis revealed 5 classes and 32 subclasses of lipids among 354 species, with triacylglycerols and fatty acids as the dominant types. Volatile analysis using HS-SPME-GC-MS identified 81 aroma compounds, with aldehydes, alcohols, acids, lipid-derived compounds, and furans as the main contributors. This study elucidated the intrinsic quality differences among various CSO and provided a robust theoretical foundation for a comprehensive understanding of their nutritional attributes and sensory diversity.</div></div>","PeriodicalId":34095,"journal":{"name":"Oil Crop Science","volume":"11 2","pages":"Pages 166-178"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148183314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of irrigation doses and planting density of hemp (Cannabis sativa, L.) in a temperate climate through CRITIC-TOPSIS-OWA","authors":"J.M. Cámara-Zapata ,&nbsp;S. Pardo-Pina ,&nbsp;R. Abadía ,&nbsp;J.M. Brotons-Martínez","doi":"10.1016/j.inpa.2025.11.003","DOIUrl":"10.1016/j.inpa.2025.11.003","url":null,"abstract":"<div><div>Hemp is a high interest crop in the agri-food sector due to its large number of applications and its beneficial effects. However, there are no clear recommendations to optimize its production, perhaps due to its strict regulation. In order to optimize the management of the crop, the present work analyzes the effect of irrigation doses and planting density on the growth, production, and quality of the Kompolti variety of hemp, with an irrigation dose equal to the total water requirements of the plant and two deficit treatments, corresponding to 75 and 50 % of these requirements, and two planting densities, 7.0 and 3.1 plants m⁻². For this, a multi-criteria analysis was performed that proposes the use of the Ordered Weighted Averaging (OWA) operator combined with the CRiteria Importance Through Inter-criteria Correlation (CRITIC) to obtain the weights, where the correlation is obtained as a function of the Pearson-OWA, to determine the aggregation of the quadratic differences by assigning different levels of importance as a function of their size. This application allows for the objective estimation of the weights starting with the attitudinal character of the decisor and its integration to the TOPSIS method. The strength of the innovation proposal has been evaluated, demonstrating that it does not exhibit a rank reversal phenomenon. Furthermore, the resulting ranking has been compared with those obtained using direct assignment methods, such as TOPSIS itself, Multicriteria Optimization and Compromise Solution (VIKOR), and Simple Additive Weighting (SAW), revealing minimal differences. This allows us to regard the proposed method as a generalization of these approaches. In the conditions utilized, the irrigation dose had a greater effect on growth, production, and the content of long fibers, rather than the planting density. In turn, planting density had a greater influence on the cannabidiol (CBD) content as compared to the irrigation dose. The classification obtained was led by the treatments with a higher planting density. Among them, the decrease in the irrigation dose worsened the assessment of the treatments. However, when the planting dose is low, a moderate water deficit improves the results with respect to the complete irrigation treatment or with intense deficit irrigation.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 253-262"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}