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{"title":"Promoting Balanced Ecological-economic Development in Ecologically Vulnerable Regions: Spatio-temporal Variation and Driving Factors.","authors":"Dan Zhang, Jiapeng Xu, Kui Liu","doi":"10.1007/s00267-024-02105-x","DOIUrl":"10.1007/s00267-024-02105-x","url":null,"abstract":"<p><p>Formulating a consistent standard for ecosystem service value (ESV) estimation and incorporating it into government decision-making is an important way to achieve balanced ecological-economic development. Taking the ecologically vulnerable areas in Northwest China as an example, this paper uses the value transfer method to estimate the ESV of cropland, forest, grassland, waters, and unused land; analyzes the spatio-temporal characteristics of the increment of ESV (△ESV) and ecological-economic harmony (EEH) index in each city; as well as identifies their key influential factors. The results suggest that value transfer is a feasible approach to developing a consistent standard for ESV estimation. The ecological-economic system is limited by the natural environment, economic growth, local government, population, and the development of agriculture and livestock. The main factors that affect unit ESV, total ESV, and EEH are connected but vary across space. The findings can provide a reference for estimating ESV across regions, formulating policies for land management and ecological protection, and promoting sustainable development.</p>","PeriodicalId":543,"journal":{"name":"Environmental Management","volume":" ","pages":"1979-1993"},"PeriodicalIF":2.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142919033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Anthropogenic Activities Drive the Spatiotemporal Changes of Wetland Area in Tianjin, China.","authors":"Bin Wang, Wei Zhang, Lili Zhang, Shijia Xu, Xinyue Liu, Yidong Wang","doi":"10.1007/s00267-025-02182-6","DOIUrl":"10.1007/s00267-025-02182-6","url":null,"abstract":"<p><p>Countries and regions worldwide face varying degrees of wetland degradation risks due to economic development and climate changes. As a coastal megacity and economic powerhouse in northern China, Tianjin once boasted abundant wetland resources but has experienced significant ecosystem alteration. This study systematically investigated spatiotemporal dynamics and driving force of Tianjin wetlands (1990‒2020) using multi-source remote sensing data and statistical models (Partial Least Squares Structural Equation Modeling, PLS-SEM; Geographically Weighted Regression, GWR). Key findings reveal: (1) A net wetland loss of 655.64 km² with alternating phases of wetland loss and recovery; (2) The roles of climatic and soil factors have undergone a fundamental shift-transitioning from positive facilitation to significant suppression (path coefficients: 0.495 to -0.414 and 0.018 to -0.104, respectively), whereas the negative driving effects of urbanization have persisted throughout and shown intensifying trends (path coefficients: -0.330 to -0.372). Furthermore, urbanization indirectly exacerbates wetland degradation through its impacts on soil composition and topographic patterns, collectively establishing it as the central determinant of wetland area dynamics; (3) Urbanization dominates the dynamic changes in Tianjin wetland area through three mechanisms: direct encroachment, indirect ecological disturbances, and spatial reconfiguration; (4) Within the urban development axis and belts demarcated by Tianjin's Urban Master Plan, paddy fields and tidal flats wetlands have decreased by 70.82% and 99.33% respectively, with 43.11% and 64.88% of these wetlands respectively converted to built-up land. Conversely, three protected regions achieved a countervailing 21.84% wetland increase. These findings underscore anthropogenic urbanization processes and coupled ecological governance as the principal drivers of spatiotemporal wetland evolution. Our quantitative framework advances understanding of human-wetland interactions and provides a methodological basis for sustainable wetland management in rapidly urbanizing coastal regions globally.</p>","PeriodicalId":543,"journal":{"name":"Environmental Management","volume":" ","pages":"2059-2077"},"PeriodicalIF":2.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144075111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Three-dimensional Reservoir Modelling and Quantitative Microbial Risk Assessment of Recreational Access to a Drinking Water Reservoir.","authors":"K Cinque, D Deere, C Veal, A Ball, A Bath, J Frizenschaf, U Ryan","doi":"10.1007/s00267-025-02203-4","DOIUrl":"10.1007/s00267-025-02203-4","url":null,"abstract":"<p><p>The major drinking water reservoirs and catchments supplying many of Australia's capital cities have been protected from significant levels of public access since their construction (up to 140 years ago). In addition to the primary intended initial benefit, (protecting drinking water quality to prevent typhoid and similar disease outbreaks), additional benefits included reduced flooding, improved and more stable yield, reduced water treatment costs, and protection of native ecosystems. In relation to the latter benefit, over that period, much of the Australian landscape has been modified for various forms of development and recreational activities, leaving these water catchments as some of the last broad areas of remnant habitat for vulnerable and endangered ecosystems and species. Despite these widely appreciated and well-understood benefits, there has been continuous pressure from a diverse range of interest groups to open these areas. As government-owned organisations, the Australian water utilities consider all such requests from the community. Among the interest groups that make representations to access drinking water sources are a wide range of recreators. Pressure from such groups is increasing as populations grow. To help inform decisions on how to respond to such representations, this study predicted gastrointestinal disease burdens from recreation on a currently protected drinking water supply reservoir in Australia. This study considered a range of scenarios, described in terms understandable to the community, and predicted health implications using screening-level quantitative microbial risk assessment (QMRA). The assessment was limited to microbial risks to drinking water quality - risks from chemical or physical hazards were not considered, nor was ecosystem protection. The QMRA predicted that six reasonably foreseeable scenarios could result in microbial risks that exceeded the health-based target benchmark given in the Australian Drinking Water Guidelines (ADWG). Therefore, additional water treatment would be required to reduce those risks to acceptable levels. However, even with the introduction of additional treatment, permitting increases in the levels of recreational activity in the source water was found to be inconsistent with many of the Guiding Principles of the ADWG and with a landmark state Supreme Court planning decision that had interpreted how those principles should be applied in drinking water catchments. Therefore, the results did not support permitting recreational access to the reservoir, and the importance of source protection was reinforced.</p>","PeriodicalId":543,"journal":{"name":"Environmental Management","volume":" ","pages":"2046-2058"},"PeriodicalIF":2.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144300889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Integrating traditional QSAR and read-across-based regression models for predicting potential anti-leishmanial azole compounds.","authors":"Rajat Nandi, Anupama Sharma, Ananya Priya, Diwakar Kumar","doi":"10.1007/s11030-024-11070-w","DOIUrl":"10.1007/s11030-024-11070-w","url":null,"abstract":"<p><p>Leishmaniasis, a neglected tropical disease caused by various Leishmania species, poses a significant global health challenge, especially in resource-limited regions. Visceral Leishmaniasis (VL) stands out among its severe manifestations, and current drug therapies have limitations, necessitating the exploration of new, cost-effective treatments. This study utilized a comprehensive computational workflow, integrating traditional 2D-QSAR, q-RASAR, and molecular docking to identify novel anti-leishmanial compounds, with a focus on Glycyl-tRNA Synthetase (LdGlyRS) as a promising drug target. A feature selection process combining Genetic Function Approximation (GFA)-Lasso with Multiple Linear Regression (MLR) was used to characterize 99 azole compounds across ten structural classes. The baseline MLR model (MOD1), containing seven simple and interpretable 2D features, exhibited robust predictive capabilities, achieving an R²_train value of 0.82 and an R²_test value of 0.87. To further enhance prediction accuracy, three qualified single models (two MLR and one q-RASAR) were used to construct three consensus models (CMs), with CM2 (MAE_test = 0.127) demonstrating significantly higher prediction accuracy for test compounds than the MOD1. Subsequently, Support Vector Regression (SVR) and Boosting yielded 0.88 (R²_train), 0.86 (R²_test), 0.92 (R²_train), and 0.82 (R²_test), respectively. Molecular docking highlighted interactions of potent azoles within the QSAR dataset with critical residues in the LdGlyRS active site (Arg226 and Glu350), emphasizing their inhibitory potential. Furthermore, the pIC50 values of an accurate external set of 2000 azole compounds from the ZINC20 database were simultaneously predicted by CM2 + SVR + Boosting models and docked against the LdGlyRS, which identified Bazedoxifene, Talmetacin, Pyrvinium, Enzastaurin as leading FDA candidates, whereas three novel compounds with the database code ZINC000001153734, ZINC000011934652, and ZINC000009942262 displayed stable docked interactions and favourable ADMET assessments. Subsequently, Molecular Dynamics (MD) simulations for 100 ns were conducted to validate the findings further, offering enhanced insights into the stability and dynamic behaviour of the ligand-protein complexes. The integrated approach of this study underscores the efficacy of 2D-QSAR modelling. It identifies LdGlyRS as a promising leishmaniasis target, offering a robust strategy for discovering and optimizing anti-leishmanial compounds to address the critical need for improved treatments.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3207-3231"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142799066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bibliometric analysis of the Cheminformatics/QSAR literature (2000-2023) for predictive modeling in data science using the SCOPUS database.","authors":"Arkaprava Banerjee, Kunal Roy, Paola Gramatica","doi":"10.1007/s11030-024-11056-8","DOIUrl":"10.1007/s11030-024-11056-8","url":null,"abstract":"<p><p>A bibliometric analysis of the Cheminformatics/QSAR articles published in the present century (2000-2023) is presented based on a SCOPUS search made in October 2024 using a given set of search criteria. The obtained results of 52,415 documents against the specific query are analyzed based on the number of documents per year, contributions of different countries and Institutes in Cheminformatics/QSAR publications, the contributions of researchers based on the number of documents, appearance in the top-cited articles, h-index, composite c-score (ns), and the newly introduced q-score. Finally, a list of the top 50 Cheminformatics/QSAR researchers is presented. An analysis is also made for the content of the top-cited articles during the period 2000-2023 in comparison to those before 2000 to capture the trend of changes in the Cheminformatics/QSAR research. The limiting factors of any bibliometric analysis are also briefly presented.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3703-3715"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142783787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-DPAPT: a machine learning framework for predicting PROTAC activity.","authors":"Amr S Abouzied, Bahaa Alshammari, Hayam Kari, Bader Huwaimel, Saad Alqarni, Shaymaa E Kassab","doi":"10.1007/s11030-024-11011-7","DOIUrl":"10.1007/s11030-024-11011-7","url":null,"abstract":"<p><p>Proteolysis Targeting Chimeras are part of targeted protein degradation (TPD) techniques, which are significant for pharmacological and therapy development. Small-molecule interaction with the targeted protein is a complicated endeavor and a challenge to predict the proteins accurately. This study used machine learning algorithms and molecular fingerprinting techniques to build an AI-powered PROTAC Activity Prediction Tool that could predict PROTAC activity by examining chemical structures. The chemical structures of a diverse set of PROTAC drugs and their corresponding activities are selected as a dataset for training the tool. The processes used in this study included data preparation, feature extraction, and model training. Further, evaluation was done for the performance of the various classifiers, such as AdaBoost, Support Vector Machine, Random Forest, Gradient Boosting, and Multi-Layer Perceptron. The findings show that the methods selected here depict accurate PROTAC activities. All the models in this study showed an ROC curve better than 0.9, while the random forest on the test set of the AI-DPAPT had an area under the curve score of 0.97, thus showing accurate results. Furthermore, the study revealed significant insights into the molecular features that can influence the functions of the PROTAC. These findings can potentially increase the understanding of the structure-activity correlations involved in the TPD. Overall, the investigation contributes to computational drug development by introducing this platform powered by artificial intelligence that predicts the function of PROTAC. In addition, it sped up the processes of identifying and improving previously unknown medications. The AI-DPAPT platform can be accessed online using a web server at https://ai-protac.streamlit.app/ .</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"2995-3007"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transit time mean and variance are markers of vascular network structure, wall shear stress distribution and oxygen extraction fraction.","authors":"Stephen J Payne, Yidan Xue, Jen-Feng Kuo, Wahbi K El-Bouri","doi":"10.1007/s10237-025-01959-2","DOIUrl":"10.1007/s10237-025-01959-2","url":null,"abstract":"<p><p>Perfusion measurements provide information about flow magnitude, but more detailed information is found from transit time distributions (TTD). Whether TTDs provide intrinsic (flow-independent) information about vascular geometry or just flow field remains unknown. We propose a new approach to calculate TTD, based on wall shear stress (WSS). We show that constant WSS yields zero-variance TTD. Simulations in statistical networks show that mean transit time (MTT) and capillary transit time heterogeneity (CTH) are primarily determined by pathway number distribution rather than pressure drop distribution. Using 1000 statistically generated cortical columns, we show that (1) the central volume theorem provides a very good approximation for MTT, hence is a measure of tissue permeability; (2) CTH/MTT ratio, RTH (relative transit time heterogeneity), is a marker of WSS variability; and (3) RTH is inversely related to network oxygen extraction fraction (OEF) but only weakly related to MTT. RTH is below one in animal models, but above one in humans, indicating that WSS distribution is tighter in small animals (lower RTH and higher OEF), due to higher metabolic rate. Human WSS distribution appears to be an inherent property, since simulations show much larger RTH. Finally, WSS distribution is unaffected in ageing, but altered in pathology.</p>","PeriodicalId":489,"journal":{"name":"Biomechanics and Modeling in Mechanobiology","volume":" ","pages":"1155-1167"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Covariation between rotator cuff muscle quality and shoulder morphometric bony features in B-glenoids: a statistical modeling approach.","authors":"Nazanin Daneshvarhashjin, Philippe Debeer, Harold Matthews, Peter Claes, Filip Verhaegen, Lennart Scheys","doi":"10.1007/s10237-025-01947-6","DOIUrl":"10.1007/s10237-025-01947-6","url":null,"abstract":"<p><p>Rotator cuff muscle (RCM) degeneration, bone morphology, and humeral head subluxation (HHS) are known risk factors for failure of anatomic total shoulder arthroplasty in patients with B-glenoid shoulder osteoarthritis. Yet, the understanding of RCM asymmetry in these patients remains an area of active investigation, including its relation with other risk factors. We therefore aimed to characterize the variability of RCM degeneration in B-glenoids and analyze its covariation with scapular morphology and HHS. First, computed tomography images were used to quantify 3D RCM degeneration, including muscle atrophy and fatty infiltration, in sixty B-glenoids referenced against twenty-five healthy controls. Next, the 3D scapular shape of B-glenoids was quantified using a previously published statistical shape model. Thirdly, 3D HHS was quantified. Using dedicated correlation analyses covariation patterns were modeled between each of these risk factors. Results indicated that RCM degeneration in B-glenoids is primarily characterized by fatty infiltration, without any sign of asymmetric impact on the anterior versus posterior RCM. However, B-glenoids with asymmetric bone loss were found to have more RCM atrophy and fatty infiltration of the infraspinatus. We identified four significant patterns of RCM degeneration and scapular shape, explaining 90.3% of their correlation. The primary mode indicates an association between combined posterior glenoid erosion and coracoid rotation with an increased infraspinatus' fatty infiltration. Interestingly, this mode was also positively correlated with posterior HHS (r = 0.46, P < 0.01). Identification of such patterns can improve the accuracy of musculoskeletal models in predicting postoperative implant failure risks.</p>","PeriodicalId":489,"journal":{"name":"Biomechanics and Modeling in Mechanobiology","volume":" ","pages":"1141-1153"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The great dispersal: The fall and rise of global environmental governance.","authors":"Sverker Sörlin, Paul Warde, Isobel Akerman, Jasmin Höglund Hellgren, Sabine Höhler, Erik Isberg, Eric Paglia, Gloria Samosír, Thomas Harbøll Schrøder","doi":"10.1007/s13280-025-02177-x","DOIUrl":"10.1007/s13280-025-02177-x","url":null,"abstract":"<p><p>This article presents a new way of understanding Global Environmental Governance (GEG), historically and functionally. We outline a revised analytical framing, which connects the post-WWII moment of early globalizing conservation with the intensifying attempts to govern the human-earth relationship through an ever-growing assemblage of governable environmental objects and their quantifiable indicators as proxies. Our argument is as follows: (1) GEG has followed a trajectory of dispersal of actors, institutions, conceptual tools and responsibilities from the micro- and local scales to the planetary. We analyze how these trajectories unfold in three essential domains: Earth System science, sovereignty, and neoliberalization. (2) GEG is performative. The governance itself has created the dynamic environmental objects under governance. (3) In this way, GEG has normalized the environment as a policy object.</p>","PeriodicalId":461,"journal":{"name":"Ambio","volume":" ","pages":"1267-1288"},"PeriodicalIF":5.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12214105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 4D tensor-enhanced multi-dimensional convolutional neural network for accurate prediction of protein-ligand binding affinity.","authors":"Dingfang Huang, Yu Wang, Yiming Sun, Wenhao Ji, Qing Zhang, Yunya Jiang, Haodi Qiu, Haichun Liu, Tao Lu, Xian Wei, Yadong Chen, Yanmin Zhang","doi":"10.1007/s11030-024-11044-y","DOIUrl":"10.1007/s11030-024-11044-y","url":null,"abstract":"<p><p>Protein-ligand interactions are the molecular basis of many important cellular activities, such as gene regulation, cell metabolism, and signal transduction. Protein-ligand binding affinity is a crucial metric of the strength of the interaction between the two, and accurate prediction of its binding affinity is essential for discovering drugs' new uses. So far, although many predictive models based on machine learning and deep learning have been reported, most of the models mainly focus on one-dimensional sequence and two-dimensional structural characteristics of proteins and ligands, but fail to deeply explore the detailed interaction information between proteins and ligand atoms in the binding pocket region of three-dimensional space. In this study, we introduced a novel 4D tensor feature to capture key interactions within the binding pocket and developed a three-dimensional convolutional neural network (CNN) model based on this feature. Using ten-fold cross-validation, we identified the optimal parameter combination and pocket size. Additionally, we employed feature engineering to extract features across multiple dimensions, including one-dimensional sequences, two-dimensional structures of the ligand and protein, and three-dimensional interaction features between them. We proposed an efficient protein-ligand binding affinity prediction model MCDTA (multi-dimensional convolutional drug-target affinity), built on a multi-dimensional convolutional neural network framework. Feature ablation experiments revealed that the 4D tensor feature had the most significant impact on model performance. MCDTA performed exceptionally well on the PDBbind v.2020 dataset, achieving an RMSE of 1.231 and a PCC of 0.823. In comparative experiments, it outperformed five other mainstream binding affinity prediction models, with an RMSE of 1.349 and a PCC of 0.795. Moreover, MCDTA demonstrated strong generalization ability and practical screening performance across multiple benchmark datasets, highlighting its reliability and accuracy in predicting protein-ligand binding affinity. The code for MCDTA is available at https://github.com/dfhuang-AI/MCDTA .</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3041-3058"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}