Ecological Informatics最新文献

{"title":"Body-part-based individual feral cat identification from camera trap images using deep learning","authors":"Rio Rifqi Syah Akbar ,&nbsp;Matthew W. Rees ,&nbsp;Patricia A. Fleming ,&nbsp;Ferdous Sohel","doi":"10.1016/j.ecoinf.2025.103258","DOIUrl":"10.1016/j.ecoinf.2025.103258","url":null,"abstract":"<div><div>Feral cats (<em>Felis catus</em>) are a significant threat to Australia's native wildlife, contributing to the decline and extinction of at least 20 native mammal species through predation impacts. To improve the identification and monitoring of populations, individual identification of cats is required. This study proposes a body-part-based computer algorithmic approach that uses deep learning for individual identification from photos that can address a common challenge associated with using camera trapping, where often only a partial or obscured view of the objects of interest is presented. We investigated the discriminatory attributes of the images of four body parts of the cats: flank (‘body’), back leg, front leg, and tail. We use a subset of a dataset of feral cats collected using camera traps deployed across the Glenelg and Otway regions of Victoria, Australia. Due to the skewed and imbalanced nature of images per individual in the dataset, we used a curated subset of 10 individuals, each with a relatively similar number of images, resulting in a total of 1644 images. We trained deep-learning models with a ResNet-50 backbone on these body parts indivdually as well as combinations of multiple body parts through feature concatenation. Results demonstrate that the body was the most discriminatory part for cat identification, with the back leg the next best part. Other parts added to the performance when they were combined. We conclude that individual cats can successfully be identified using partial body images captured using camera traps. While the body was the most distinctive part, the proposed method provides flexibility in cases where the body is obscured. This study shows that deep learning methods can meaningfully contribute to camera trap image analysis, and hence environmental conservation outcomes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103258"},"PeriodicalIF":5.8,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysing vocal complexity in relation to sociality in orcas of British Columbia: An application of long-term computational passive acoustics","authors":"Paul Best ,&nbsp;Marion Poupard ,&nbsp;Ricard Marxer ,&nbsp;Paul Spong ,&nbsp;Helena Symonds ,&nbsp;Hervé Glotin","doi":"10.1016/j.ecoinf.2025.103211","DOIUrl":"10.1016/j.ecoinf.2025.103211","url":null,"abstract":"<div><div>Orcas are both highly social and highly vocal animals. In coastal waters of the North-Eastern Pacific Ocean, the Northern Resident orca population is well monitored, providing a great opportunity to learn about their social and communicative behaviour. Here, we report a series of acoustic analyses that lead to the empirical assessment of factors that might impact vocal complexity.</div><div>Automatically processing long-term passive acoustic data, we detected and classified calls to transcribe vocal activity. Detailed post-hoc analyses show that the detection model is imperfect, especially in detecting calls of low energy. Also, diarisation is not possible with this data and transcriptions might gather a mixture of several emitters. Taking these limitations into account, we measured communicative complexity considering the groups’ vocal production as a whole. Acoustic and visual cues also enabled the identification of specific groups with estimated numbers of individuals.</div><div>Results highlight a positive correlation between vocal and social complexity, which could be due to the mere effect of having more potential emitters. Nonetheless, this brings a first demonstration of the non-trivial link between the number of emitters and complexity in the composition of sequences. We also demonstrate significant impacts of other proximate factors such as behaviour on vocal complexity measurements, and advocate for multi-factor considerations when evaluating communicative complexity.</div><div>This work demonstrates the pertinence of joint efforts between passive acoustics, visual observations and machine learning to enhance the scale of behavioural studies and assess the validity of evolutionary hypotheses of communication systems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103211"},"PeriodicalIF":5.8,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolutionary polynomial modeling for interpretable drought prediction and resilient resource management","authors":"Tulio J. Francisco ,&nbsp;Bruno da Silva Macêdo ,&nbsp;Zaher Mundher Yaseen ,&nbsp;Nikolay O. Nikitin ,&nbsp;Matteo Bodini ,&nbsp;Angela Gorgoglione ,&nbsp;Camila M. Saporetti ,&nbsp;L. Goliatt","doi":"10.1016/j.ecoinf.2025.103217","DOIUrl":"10.1016/j.ecoinf.2025.103217","url":null,"abstract":"<div><div>Droughts are natural hazards that exist in nature and can have a serious impact on the environment and society, which includes water shortages, crop failures, fires and, in some cases, soil manipulation. To assess and predict droughts, various methods, such as the Standardized Precipitation Index (SPI), were designed to segregate drought trends and excess rainfall over a period ranging from 3 to 48 months. This study proposes an innovative approach to predicting drought use, the Evolutionary Polynomial Expansion with Feature Selection (EPEFS) model, a hybrid method that integrates polynomial regression with feature selection to increase accuracy and interpretability. The methodology was applied to historical precipitation data from six meteorological stations in Türkiye, covering the period from 1971 to 2016. The drought index Standardized Precipitation Index (SPI) was used as the primary indicator, with predictions made for three different time scales: SPI-3, SPI-6 and SPI-12. Furthermore, a time series cross-validation strategy was employed to ensure performance assessment. The EPEFS model obtained R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> coefficients of 0.880, 0.903 and 0.929 for SPI-3, SPI-6 and SPI-12, respectively, surpassing the other models analyzed. Furthermore, the model presented less complexity in the generated expressions. The results suggest that the EPEFS model holds promise as a robust and interpretable tool for drought forecasting, with potential applications in early warning systems and mitigation strategies.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103217"},"PeriodicalIF":5.8,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decentralized federated learning using validation loss for model sharing in crop disease classification","authors":"Denis Mamba Kabala ,&nbsp;Adel Hafiane ,&nbsp;Laurent Bobelin ,&nbsp;Raphaël Canals","doi":"10.1016/j.ecoinf.2025.103205","DOIUrl":"10.1016/j.ecoinf.2025.103205","url":null,"abstract":"<div><div>Agriculture plays an essential role in the economies of many countries, as it provides numerous livelihoods. However, managing crop diseases is one of the major challenges in modern agriculture. Using artificial intelligence (AI) for the early detection and diagnosis of crop diseases is an interesting approach to tackle this problem. Several AI methods have been employed for this purpose, but despite achieving good results, many challenges remain, such as protecting farmers’ data, using machine learning on edge devices, and employing collaborative learning. In this context, federated learning (FL) has emerged as a promising machine learning approach that enables to build efficient models with a collaborative manner, while preserving data privacy and security. There exist two types of FL: centralized and decentralized. In this paper we employ the approach of decentralized FL for crop disease image classification that utilizes peer-to-peer communication for updating models for each client. To address the problem of the robustness of shared models, we propose a new strategy based on validation loss, where the aggregated models should satisfy a certain criterion of performances. We implemented and tested two types of deep learning architectures, convolutional neural networks (CNNs) and vision transformers (ViTs). The evaluation of model performance was based on four metrics: Accuracy, F1-Score, Precision, and Recall. However, for the presentation of results in this paper, we focus on Accuracy and F1-Score to highlight key aspects of model performance. We evaluated the impact of the number of shared models, communication cycles, number of clients involved, local iterations, and training data size on model performance. The results show that decentralized FL offers significant advantages over centralized FL approaches, improving rapid convergence to high and stable performance. These results highlight the potential of decentralized FL to advance crop disease management, thereby contributing to agricultural resilience and productivity.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103205"},"PeriodicalIF":5.8,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Projecting population dynamics and range expansion of reintroduced wild boar in Scotland using agent-based modelling","authors":"Connor Lovell ,&nbsp;Terence P. Dawson ,&nbsp;J. Gareth Polhill","doi":"10.1016/j.ecoinf.2025.103261","DOIUrl":"10.1016/j.ecoinf.2025.103261","url":null,"abstract":"<div><div>The number of species reintroductions is increasing globally via both legal and illegal routes. These reintroductions can be controversial with uncertain social-ecological outcomes, particularly for unsanctioned illegal releases, which risks causing conflict between stakeholders. Despite this, current reintroduction science is focused on short-term population establishment, with little long-term modelling of reintroduced populations. In this study, we develop an agent-based model (ABM) to simulate the controversial reintroduction of wild boar in Scotland. The ABM uses probabilistic birth, death, and movement rules from the literature to stochastically simulate boar population dynamics from their initial release to 50 years in the future. Model evaluation demonstrated that the ABM behaves in predictable and explainable ways, whilst reproducing real boar behaviours and aligning with the spatial distribution of boar sightings in Scotland. Projecting the ABM 50 years into the future suggests that current boar populations are likely viable and will continue to grow and expand, with the model confirming the existence and long-term persistence of four boar populations. We conclude by commenting on the potential future uses of the ABM.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103261"},"PeriodicalIF":5.8,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SegmentR: Deep learning for automated segmentation with an R interface","authors":"James D. Boyko","doi":"10.1016/j.ecoinf.2025.103259","DOIUrl":"10.1016/j.ecoinf.2025.103259","url":null,"abstract":"<div><div>The increasing digitization of biological data has generated biodiversity data at an unprecedented scale. However, extracting phenotypic information from these images poses unique challenges for biologists. Manual image segmentation is time-consuming and can be subjective, while existing automated solutions often require extensive coding experience or utilize coding languages not typically used by practicing ecologists and evolutionary biologists. Here, I present SegmentR, a user-friendly software package that leverages two state-of-the-art deep learning models – GroundinDINO and an efficient version of the Segment Anything Model (SAM). The SegmentR package provides an R-based interface, making it more accessible to biologists without coding experience. SegmentR allows users to load images, automatically segment them based on text prompts, and extract regions of interest for downstream analysis. The package includes basic visualization and data processing functions to facilitate interpretation of the results and integration with existing analytical workflows. This paper introduces SegmentR's features and demonstrates its utility through examples including isolating fish anatomy, batch processing flower images for color analysis, and segmenting museum specimens.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103259"},"PeriodicalIF":5.8,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tricho-Vision: The use of computer vision in trichotaxonomy for enhancing wildlife conservation of priority species","authors":"Alloy Das ,&nbsp;Priyanka Banerjee ,&nbsp;Sanket Biswas ,&nbsp;Manokaran Kamalakannan ,&nbsp;Joydev Chattopadhyay ,&nbsp;Dhriti Banerjee ,&nbsp;Tanoy Mukherjee","doi":"10.1016/j.ecoinf.2025.103161","DOIUrl":"10.1016/j.ecoinf.2025.103161","url":null,"abstract":"<div><div>Mammalian hair serves as a critical biological marker, aiding species identification essential for wildlife conservation and crime control. This study introduces the first extensive benchmark for classifying microscopic images of mammal hair from species prioritized for conservation. Our goal is to develop standardized methods, metrics, and best practices for utilizing advanced computer vision techniques, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Swin Transformers, to classify hair samples across Order, Family, Genus and Species taxonomic levels. We present a novel dataset of 76 species, including critically endangered and endangered species, curated specifically for this classification challenge. The methodology integrates automated feature extraction of cuticle patterns and medulla structures, enabling high-precision species differentiation. Our findings demonstrate that Swin Transformer-based models outperform traditional CNNs and ViTs across taxonomic levels, with techniques like image cropping further improving classification accuracy by diversifying the training set. The proposed Tricho-Vision framework offers significant applications in biodiversity monitoring and wildlife crime investigation, facilitating accurate species identification from forensic hair samples. Additionally, we introduce a interactive tool for real-time taxonomic classification, showcasing the practical utility of our research and fostering broader interdisciplinary engagement in conservation science and forensic applications.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103161"},"PeriodicalIF":5.8,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A lightweight context-aware framework for toxic mushroom detection in complex ecological environments","authors":"Zhanchen Wei ,&nbsp;Jiali Wang ,&nbsp;Haohai You ,&nbsp;Ruiqing Ji ,&nbsp;Fude Wang ,&nbsp;Lei Shi ,&nbsp;Helong Yu","doi":"10.1016/j.ecoinf.2025.103256","DOIUrl":"10.1016/j.ecoinf.2025.103256","url":null,"abstract":"<div><div>The accidental proliferation of toxic mushrooms in natural ecosystems poses risks to both biodiversity and human activities in forested regions. Existing detection methods struggle with three key challenges in environmental monitoring: (1) poor discrimination of morphologically similar species in wild habitats, (2) high computational costs limiting deployment in resource-constrained field settings, and (3) performance degradation under ecological variations such as weather changes and terrain complexity. To address these challenges, we propose PM-YOLO which integrates the Contextual and Spatial Feature Calibration Network (CSFCN) and Contextual Anchor Attention (CAA) mechanisms, and is specifically designed for poisonous mushroom recognition. With the help of knowledge distillation technology, our model achieves an [email protected] with 92.64 %, which is 2.06 % higher than that of YOLOv8s. Meanwhile, the number of parameters is only 31.25 % of that of YOLOv8s (3.5 M vs. 11.2 M). Rigorous 10-fold cross-validation demonstrates its excellent robustness, with performance differences of less than 2 % across various test scenarios. PM-YOLO achieves multi-scale feature alignment through hierarchical context fusion, performs adaptive attention weighting for morphological variations, and maintains a low computational cost while significantly improving accuracy. This breakthrough enables the practical application of AI-assisted mushroom identification, effectively bridging the critical gap between academic research and field applications in the field.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103256"},"PeriodicalIF":5.8,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How spatial resolution mediates canopy spectral diversity as a proxy for marsh plant diversity","authors":"Yi Fu ,&nbsp;Yunlong Yao ,&nbsp;Lei Wang ,&nbsp;Huaihu Yi ,&nbsp;Yuanqi Shan","doi":"10.1016/j.ecoinf.2025.103253","DOIUrl":"10.1016/j.ecoinf.2025.103253","url":null,"abstract":"<div><div>Spectral reflectance variations comprehensively capture differences in the biochemical composition and morphological characteristics among plant species, making them a promising approach for monitoring and estimating plant diversity. However, the relationship between spectral reflectance and plant diversity is influenced by multiple factors and remains inherently unstable. Spatial resolution is one of the key factors driving the spatial heterogeneity of spectral information. Currently, it remains unclear how spatial resolution influences the spectral-plant diversity relationship in marshes and what the optimal resolution is for establishing significant correlations. This study focuses on typical marshes in Northeast China, using multispectral data acquired from unmanned aerial vehicle (UAV) at spatial resolutions ranging from 5 cm to 40 cm. Downsampling and upsampling algorithms were applied to resample the spectral data at 5 cm and 40 cm resolutions, generating datasets that cover the entire range from 5 cm to 40 cm. Spectral diversity (SD) indices, including the mean and standard deviation of KNDVI, MTCI, NDREI, and NDVI, were evaluated for their ability to predict plant species diversity across varying spatial resolutions and data sources. Results show that the predictive ability of vegetation indices (VIs) significantly declines as spatial resolution decreases to 40 cm. The optimal spatial resolution for predicting plant diversity varies among different VIs, but VIs calculated from the same spectral bands consistently show similar predictive trends. Notably, MTCI at a 10 cm resolution achieved the highest predictive accuracy for species richness (R²_adj = 0.48), the Shannon-Wiener index (R²_adj = 0.46), and the Gini-Simpson index (R²_adj = 0.43). Furthermore, resampling methods were found to produce lower accuracy in estimating species diversity compared to UAV data acquired on-site. These findings emphasize the importance of selecting appropriate spatial resolutions and SD metrics to enhance the accuracy of remote sensing-based biodiversity prediction models.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103253"},"PeriodicalIF":5.8,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microbiome dynamics and multiscale environmental response patterns of later-diverging coral clade across latitudes, reefs and geomorphological zones in the South China Sea","authors":"Biao Chen ,&nbsp;Lin Liang ,&nbsp;Kefu Yu ,&nbsp;Yuxin Wei ,&nbsp;Xinyue Liang ,&nbsp;Zeming Bao ,&nbsp;Zhiheng Liao ,&nbsp;Xiaopeng Yu ,&nbsp;Zhenjun Qin ,&nbsp;Lijia Xu ,&nbsp;Yongzhi Wang ,&nbsp;Yaru Kang","doi":"10.1016/j.ecoinf.2025.103244","DOIUrl":"10.1016/j.ecoinf.2025.103244","url":null,"abstract":"<div><div>The climatic adaptability and resilience of coral-associated microbiomes are pivotal under the global change. However, the environmental responses and acclimation patterns of microbiome within corals from the latest clades across multiple spatial scales remain unclear. This study analyzed the community and function characteristics of Symbiodiniaceae and bacteria in <em>Lithophyllon scabra</em> (latest-diverging clade of Fungiidae) across latitudes, reefs and geomorphological zones in the South China Sea. The results showed that <em>L.scabra</em> acclimated to environmental variation at multiple spatial scales by establishing specific symbioses with C27 sub-clade. The deterministic assembly of Symbiodiniaceae was associated with nutrient declines at latitudinal scales, while at reefal and geomorphological scales, it is driven by climatic factors and their interactions with local effects, respectively. However, the stochastic process of Symbiodiniaceae was shaped by symbionts dispersal across multiple spatial scales. Notably, environment filtration entirely governed the bacterial assembly process. At latitudinal and reefal scales, the environmental effects and responses pattern of bacterial community aligned with “Pierre Cardin principle” and “Anna Karenina principle”, respectively. Interestingly, bacterial community was enriched with nitrogen-metabolizing taxa and photoautotrophic functions in the lagoon, while exhibiting a higher abundance of heterotrophic functions and antibacterial taxa on the reef slope, which suggests that changes in nutritional patterns and composition of the bacterial community were crucial for the acclimation of <em>L. scabra</em> to distinct geomorphological zones. These results provide novel insights into the environmental interactions and adaptive strategies of the microbiome associated with younger clades of coral across multiple spatial scales in the context of climate change.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103244"},"PeriodicalIF":5.8,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}