Ecological Informatics最新文献

{"title":"Bridging biodiversity gaps: Assessing R tools for harmonising vascular plant records","authors":"Diletta Santovito ,&nbsp;Alessandro Chiarucci ,&nbsp;Duccio Rocchini ,&nbsp;Francesco Santi ,&nbsp;Rocìo Beatriz Cortès Lobos ,&nbsp;Riccardo Testolin","doi":"10.1016/j.ecoinf.2025.103543","DOIUrl":"10.1016/j.ecoinf.2025.103543","url":null,"abstract":"<div><div>Biodiversity databases provide unprecedented opportunities for the use of species occurrence data for the development of large scale biodiversity analyses. However, these records often contain taxonomic uncertainties that can ultimately affect the outcomes of downstream analyses. Although several tools have been developed to address these issues, there is limited guidance on how to efficiently use and integrate them.</div><div>Here, we present a reproducible workflow for handling vascular plant occurrence data, and provide the first comparative analysis of R packages for the taxonomic harmonisation of vascular plant names. Our goal is to assess the differences in performance across the tested tools and to highlight best practices for leveraging large biodiversity databases.</div><div>We first downloaded occurrence data for vascular plants in Italy from the Botanical Information and Ecology Network (BIEN) and Global Biodiversity Information Facility (GBIF). We then compared seven R packages for taxonomic harmonisation, evaluating their ability to resolve names to accepted taxa and their overall performance.</div><div>Our results highlight heterogeneity in the number of names resolved by the different tools, with packages relying on plant-specific databases and implementing fuzzy matching outperforming those based on generalist databases and with no possibility of fuzzy matching. These findings underscore that the choice of both packages and taxonomic authorities can have a strong influence on data cleaning outcomes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103543"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790973","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":"Associations among park features, physical activities, and sensory perceptions from online reviews: A domain-specific named entity recognition model","authors":"Wenpei Li ,&nbsp;Jiarui Chi ,&nbsp;Jiaqian Wu ,&nbsp;Xin Zhang ,&nbsp;Jie Zhang ,&nbsp;Wenya Zhai ,&nbsp;Pengyuan Liu ,&nbsp;Christiane M. Herr ,&nbsp;Rudi Stouffs","doi":"10.1016/j.ecoinf.2025.103548","DOIUrl":"10.1016/j.ecoinf.2025.103548","url":null,"abstract":"<div><div>Research on human–environment interactions remains fragmented, with limited exploration of how diverse park features jointly relate to multiple physical activity (PA) levels and sensory perceptions. Large-scale textual data offer new opportunities to capture public experiences of parks. However, prevailing natural language processing approaches, such as lexicon-based and prompt-based methods, often lack contextual sensitivity and accuracy. To address these limitations, we developed a landscape character named entity recognition (LCNER) model fine-tuned on a manually curated dataset to simultaneously extract park features, sensory perceptions, and three PA levels from online reviews. Among six pre-trained language models evaluated, the DeBERTa-large–based LCNER achieved the highest mean F1 score (0.896 <span><math><mo>±</mo></math></span> 0.001) and outperformed domain-lexicon baselines, with the largest improvements observed for the best-performing entity categories: ＋0.558 in precision, ＋0.231 in recall, and ＋0.395 in F1 score. Quasi-binomial analyses revealed that facility-related features provided better model fit for moderate-to-vigorous PA (MVPA) and sound perception than for other activities and sensory types. Several features exhibited opposite associations with MVPA and sedentary behavior. Moreover, certain activity-oriented facilities were negatively associated with sensory perceptions, suggesting a potential trade-off between active engagement and sensory awareness. Overall, LCNER demonstrates the potential to unify the extraction of park features, activity levels, and sensory perceptions from online texts, advancing understanding of how park environments shape human experiences and behaviors. Code and prompts are available at <span><span>https://github.com/Wenpeimuzi/Landscape-NLP</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103548"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738130","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":"Background-invariant re-identification of dogs from camera-trap videos in non-controlled environments","authors":"Eugênio Dias Ribeiro Neto ,&nbsp;Cyril Barrelet ,&nbsp;Marc Chaumont ,&nbsp;Gérard Subsol ,&nbsp;Muhammad Nur Faiz Mahfudz ,&nbsp;Muhammad Najib Arung Petana Raja Bone ,&nbsp;Barandi Sapta Widartono ,&nbsp;Hery Wijayanto ,&nbsp;Dyah Ayu Widiasih ,&nbsp;Mia Nur Farida ,&nbsp;Wayan Tunas Artama ,&nbsp;Thibaut Langlois ,&nbsp;Hélène Guis ,&nbsp;Etienne Loire ,&nbsp;Michel de Garine-Wichatitsky","doi":"10.1016/j.ecoinf.2025.103547","DOIUrl":"10.1016/j.ecoinf.2025.103547","url":null,"abstract":"<div><div>This paper addresses the general problem of re-identification in natural conditions with multiple camera traps, poor video quality and small datasets. We focus on generalizable re-identification of dogs in cross-camera setups, adapting from short-term to long-term scenarios. Long-term re-identification across multiple cameras presents challenges due to variations in background, camera angles, and lighting conditions. While realistic, few animal re-identification methods are tested under such settings, mainly due to the lack of datasets and high complexity of annotation. Short-term datasets are often used to train re-identification networks, since they can be simply generated through web scraping algorithms. We introduce two publicly available datasets: the YT-BB-Dog, a short-term dataset with 2723 dogs from YouTube videos, and the Sibetan, a long-term dataset featuring 59 dogs recorded over 5 days and 12 cameras placed in Sibetan, Bali, Indonesia. Our goal is to use the YT-BB-Dog to train a feature extractor robust to covariate shifts, enabling better generalization in unknown domains. Our experiments revealed that state-of-the-art (SOTA) methods trained on the YT-BB-Dog are heavily influenced by background variations and perform poorly on complex scenarios like Sibetan. To address this, we propose Background Invariant Feature extractOR (BIFOR), a three-step method that leverages a novel mini-batch sampling technique with triplet loss and online hard mining. BIFOR achieves SOTA performance on Sibetan, improving rank-1 accuracy of the baseline by more than 9%. We also present a complete pipeline combining detection, tracking, and re-identification based on BIFOR.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103547"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737572","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":"Elevated extinction risk of sea moths under climate change","authors":"Shuaishuai Liu ,&nbsp;Ying Liu ,&nbsp;Stefano Mammola ,&nbsp;Songxi Yuan ,&nbsp;Junmei Qu ,&nbsp;Xin Wang ,&nbsp;Qiang Lin ,&nbsp;Zhixin Zhang","doi":"10.1016/j.ecoinf.2025.103578","DOIUrl":"10.1016/j.ecoinf.2025.103578","url":null,"abstract":"<div><div>Climate change is increasingly associated with global biodiversity loss; therefore, it is essential to account for the threat of climate change when assessing species conservation status. Neglecting the effects of climate change may lead to a biased assessment of the extinction risk of focal species and misguided conservation strategies. In this study, we evaluated the extinction risk of five marine sea moth species under climate change by integrating the IUCN Red List Assessment criterion A3c and redistribution projection via species distribution models. These models showed relatively good predictive abilities and accurately described the spatial distributions of sea moths. Model projections indicated that future climate change would lead to the redistribution of sea moths and considerable range contractions especially in the Indo-Pacific. Because of climate-driven range shifts, sea moths were expected to face an increased extinction risk in the future. Our findings indicate that neglecting the threat of climate change might lead to underestimating the extinction risk faced by marine species. This has important implications for assessing or updating the extinction risk status of marine species and designing conservation measures.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103578"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925399","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":"SALMA: A machine learning tool for precise leaf morphology measurements","authors":"Ilya Shabanov ,&nbsp;Julie R Deslippe ,&nbsp;Andrew Lensen","doi":"10.1016/j.ecoinf.2025.103592","DOIUrl":"10.1016/j.ecoinf.2025.103592","url":null,"abstract":"<div><div>Leaf area is a critical plant functional trait, widely used for understanding plant responses to climate change, ecosystem productivity, and species' adaptive strategies. Inaccurate leaf area measurements compromise the accuracy of other traits normalised by area, such as foliar chemical traits, respiration, and photosynthesis. However, existing measurement methods are ineffective for small-leaved plants and often necessitate manual processing, which limits sample sizes and potentially obscures subtle trait-environment relationships. We developed SALMA (Semi-Automated Leaf Morphological Analysis), which employs logistic regression trained on one to four human-generated examples per species to delineate leaf boundaries for that species accurately. SALMA's training step adapts to species-specific features by integrating multiple characteristics, such as colour variations and edge details. The approach is validated on an extensive dataset (64 species, 3332 images) that covers 91.4 % of the worldwide leaf area variation, as well as two smaller datasets comprising low-quality photographs of morphologically complex or damaged leaves. SALMA consistently achieved leaf area errors 2 to 15 times lower than existing algorithms and a theoretical upper bound of any grayscale intensity-based method. Critically, we identify a previously overlooked power-law relationship between leaf area and measurement error, demonstrating that existing methods may overestimate leaf area by at least 5 % for 43 % of global species, whereas SALMA achieves comparable errors for only 2.1 % of species. SALMA is a standalone software with an intuitive interface that supports parallel processing, making it accessible for large-scale ecological studies globally. It can potentially enhance the quality of trait datasets and facilitate large-scale sampling, thereby advancing our understanding of plant-environment interactions. Our published dataset contains manually created binary segmentations of leaves and background, providing a baseline for future leaf measurement algorithms.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103592"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925403","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":"Detection algorithm for pine wilt disease in complex environments","authors":"Siyuan Chen,&nbsp;Qing Yang,&nbsp;Jiawen Zhang,&nbsp;Feng Xu","doi":"10.1016/j.ecoinf.2025.103572","DOIUrl":"10.1016/j.ecoinf.2025.103572","url":null,"abstract":"<div><div>Pine wilt disease (PWD), caused by the pine wood nematode, is a highly destructive forest disease with severe ecological and economic consequences worldwide. Early and accurate detection of PWD is therefore crucial for effective prevention and control. To address the challenge of detecting small, sparsely distributed infected pine trees in complex environments, this study utilized RGB orthophotos captured by DJI drones flying at altitudes of 50–100 m across two representative pine forest regions in China - Jiangning District, Jiangsu Province (southern region) and Xinbin County, Liaoning Province (northern region).</div><div>We propose an improved detection model, termed YOLO-ESCF, designed to enhance detection performance under challenging conditions. The model integrates a C3ECA fused attention module and a SimCSPSPPF module into the backbone network, which effectively reduces the interference caused by overlapping tree crowns and environmental complexity while improving sensitivity to early-stage symptoms. In the neck structure, an enhanced coordinate convolution is introduced to enable the network to exploit spatial positional information, thereby improving its ability to learn target distribution patterns. Experimental results demonstrate that the proposed YOLO-ESCF model achieves an average detection accuracy of 82.9 % and an F1-score of 0.919 across both early and late PWD stages, outperforming conventional detection models. With a model size of only 18.8 MB and an FPS (frames per second, f/s) of 121.9, YOLO-ESCF offers a strong balance between accuracy and efficiency. These results highlight its potential for real-time monitoring and automated early warning systems, providing valuable support for timely intervention to minimize ecological and economic losses.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103572"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925486","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":"Corrigendum to “Projecting population dynamics and range expansion of reintroduced wild boar in Scotland using agent-based modelling” [Ecological Informatics, Volume 90 (2025), 103261, https://doi.org/10.1016/j.ecoinf.2025.103261]","authors":"Connor Lovell ,&nbsp;Terence P. Dawson ,&nbsp;J. Gareth Polhill","doi":"10.1016/j.ecoinf.2025.103385","DOIUrl":"10.1016/j.ecoinf.2025.103385","url":null,"abstract":"","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103385"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385301","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":"Deep tracks: Using deep learning and procedurally simulated data for automated vertebrate footprints classification","authors":"Carolina S. Marques ,&nbsp;Afonso Mota ,&nbsp;Matteo Belvedere ,&nbsp;Diego Castanera ,&nbsp;Ignacio Díaz-Martínez ,&nbsp;Elisabete Malafaia ,&nbsp;Soraia Pereira ,&nbsp;Luís Miguel Rosalino ,&nbsp;Vanda F. Santos ,&nbsp;Lara Sciscio ,&nbsp;Emmanuel Dufourq","doi":"10.1016/j.ecoinf.2025.103523","DOIUrl":"10.1016/j.ecoinf.2025.103523","url":null,"abstract":"<div><div>The study of vertebrate footprints provides useful information on animal behavior, locomotion, and ecology. However, automatically classifying these records using photographs is difficult due to the significant morphological variation in footprints and the lack of readily available labeled datasets. To address this issue, this study developed Deep Tracks, a novel Unity application to procedurally create a dataset of simulated footprint images. Two datasets were used to evaluate the influence and impact of the simulated dataset on real footprint classification: (1) a dataset comprising 40,000 simulated footprints, (2) approximately 1,500 real vertebrate footprints from 10 different vertebrate groups. Both simulated and real footprints belong to the following clades: Mammalia (coyotes, foxes, bears, otters, squirrels, raccoons, deer), avian Dinosauria (turkeys) and non-avian Dinosauria (theropods, sauropods). Convolutional Neural Networks (CNNs) were used to classify the different datasets either from the simulated or real footprints. An initial comparison of five different architectures (DenseNet-121, ResNet-18, ResNet-50, EfficientNet-b0, and InceptionNet-v3) was done using the simulated dataset, with EfficientNet-b0 presenting better metrics results. Seven experimental configurations were designed to evaluate different strategies for incorporating the real data into the model development. The first configuration involved training and testing exclusively on real footprints, without any simulated data. The second configuration trained the model on real data, but tested it on simulated footprints. The third configuration used transfer learning to fine-tune a CNN, initially trained on simulated data, for classifying real footprint images. The remaining four configurations incorporated simulated data into the training process alongside a fixed percentage of real data — 20%, 50%, 80%, or 100%. The application of fine-tuning led to an accuracy improvement of over 30% in classifying real footprints, compared to a CNN trained solely on real data. These results highlight the significance of advanced data augmentation techniques in improving both accuracy and reliability in vertebrate footprint classification, particularly in scenarios with limited real data availability.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103523"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738132","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":"Navigating the blue frontier: A review of machine learning approaches for sustainable marine bioresource utilization","authors":"Oraléou Sangué Djandja ,&nbsp;XiaoMei Zhong ,&nbsp;Jie Yang ,&nbsp;Hugh McIntyre ,&nbsp;Quan Sophia He ,&nbsp;Usman Ali","doi":"10.1016/j.ecoinf.2025.103549","DOIUrl":"10.1016/j.ecoinf.2025.103549","url":null,"abstract":"<div><div>The sustainable management and utilization of marine bioresources faces increasing challenges due to environmental variability, data scarcity, and the complexity of marine ecosystems. Addressing these issues demands advanced technological methods that enhance efficiency, precision, and environmental management. This review aims to examine how machine learning (ML) is transforming the field of marine bioresources by enabling precise species tracking, early detection of harmful algal blooms, rapid identification of bioactive compounds, and innovations in biofuels and sustainable fisheries. The novelty of this review lies in synthesizing recent developments in ML applications across these domains while critically analyzing emerging paradigms of hybrid and interpretable ML models. It highlights key algorithms, including artificial neural networks, random forests, gradient boosting, support vector machines, and adaptive neuro-fuzzy inference systems, emphasizing their potential to improve scalability and prediction performance. The review provides discussions on unresolved challenges, ethical integration pathways, and future directions for sustainable marine bioeconomy practices. Besides technological progress, the review highlights a governance and ethics perspective, emphasizing the need to align ML applications with ocean governance frameworks, environmental laws, and principles of social and ecological justice. By connecting technological innovation with institutional responsibility, this work provides a comprehensive roadmap for developing ML-driven systems that support rather than undermine ocean stewardship.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103549"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685094","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 novel statistical workflow using pollen records and regression kriging to reconstruct the spatially and temporally explicit demographic history of tree species","authors":"Azzurra Pistone ,&nbsp;Denis Allard ,&nbsp;Christoph Schwörer ,&nbsp;César Morales-Molino ,&nbsp;Willy Tinner ,&nbsp;Katalin Csilléry","doi":"10.1016/j.ecoinf.2025.103561","DOIUrl":"10.1016/j.ecoinf.2025.103561","url":null,"abstract":"<div><div>Understanding the effects of past climate shifts on the demography of forest tree species is crucial to assessing their response to ongoing climate change. However, little effort has so far been made to quantify past demographic changes in a spatially and temporally explicit manner at a continental scale. We have developed a novel statistical workflow that integrates two regression kriging models to reconstruct the demographic history of tree species across Europe. Our workflow anticipates spatially the probability of species occurrence (PoO), and interpolates their relative abundances (RelAb) spatially and temporally. Climate variables can be included as covariates. Our approach can accommodate non-stationary species responses to climate, and incorporates the presence of source populations, colonization constraints, and population trends as factors influencing species RelAb. We applied this workflow to European fir species (<em>Abies</em> spp.) since the Last Glacial Maximum (LGM), using fossil pollen records from 241 sites, and simulated paleoclimate data on a 0.41-degree grid and 500-year time bins. Model performance, assessed with cross-validation, demonstrates that including climate as a covariate enhances the spatial heterogeneity. Climate has a positive effect on RelAb interpolation under millennial static spatial distribution structure conditions, while the presence of source populations plays a more important role during rapid demographic processes. Additionally, we applied our workflow to assess future regional changes in the RelAb of <em>Abies</em> spp. under the main future climate scenarios. Our workflow is particularly suited for temperate and boreal tree species and can be used in various downstream analyses.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"93 ","pages":"Article 103561"},"PeriodicalIF":7.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925320","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}