SAR and QSAR in Environmental Research最新文献

Multi-epitope vaccine construct against Staphylococcus aureus: insights from immunoinformatics and molecular dynamics simulations. 构建抗金黄色葡萄球菌的多表位疫苗：来自免疫信息学和分子动力学模拟的见解。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-10-01 DOI: 10.1080/1062936X.2025.2558784

K Nachammai, P Sangavi, K Abishek, K Langeswaran

{"title":"Multi-epitope vaccine construct against Staphylococcus aureus: insights from immunoinformatics and molecular dynamics simulations.","authors":"K Nachammai, P Sangavi, K Abishek, K Langeswaran","doi":"10.1080/1062936X.2025.2558784","DOIUrl":"https://doi.org/10.1080/1062936X.2025.2558784","url":null,"abstract":"The persistent challenge posed by multi-drug resistant Staphylococcus aureus infections worldwide necessitates new solutions. We describe the creation of a multi-epitope vaccine aimed at offering cross-strain immunity. Antigens α-haemolysin (Hla) and staphylococcal enterotoxin B (SEB) were chosen considering their high immunodominance and sequence conservation levels. B-cell and T-cell epitopes were combined into a multi-epitope vaccine with the proper adjuvant and linker sequences included to allow for maximum immunogenicity and structural stability. Physicochemical characterization demonstrated that the construct is non-allergenic, heat-stable, and immunogenic. Structural optimization and modelling were performed, with confirmation by Ramachandran plot analysis and ProSA z-score, which verified the correctness of the model. Molecular docking indicated robust and stable interactions between the vaccine and major immune receptors, such as TLR3, MHC class I, and MHC class II. In addition, 200 ns molecular dynamics simulations and binding free energy calculations indicated stability and longevity of these complexes. Codon optimization and in silico cloning indicated efficient expression in E. coli. Immune simulations also anticipated strong activation of humoral and cellular immune elements such as B-cells, cytotoxic T lymphocytes, and antigen-presenting cells, and rising Ig levels. The vaccine's ability to induce overall immune protection against S. aureus requires further experimental confirmation.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"1-31"},"PeriodicalIF":2.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145200568","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}

引用次数: 0

Structural characterization of length-varying peptide sequences for peptide quantitative structure-activity relationship. 长度变化多肽序列的结构表征，用于多肽定量构效关系。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-08-01 Epub Date: 2025-09-10 DOI: 10.1080/1062936X.2025.2552141

Y Zhang, K Li, Y Gan, P Zhou

{"title":"Structural characterization of length-varying peptide sequences for peptide quantitative structure-activity relationship.","authors":"Y Zhang, K Li, Y Gan, P Zhou","doi":"10.1080/1062936X.2025.2552141","DOIUrl":"https://doi.org/10.1080/1062936X.2025.2552141","url":null,"abstract":"Peptide quantitative structure-activity relationship (pQSAR) has been widely used in the computational peptidology community to model, predict and explain the activity and function of bioactive peptides. Various amino acid descriptors (AADs) have been developed to characterize the residue building blocks of peptides at sequence level. However, a significant issue is that the total number of AAD-characterized descriptors is proportional to peptide length, thus causing inconsistency in the resulting descriptor vector matrix for a panel of length-varying peptide sequences (LVPSs), which cannot be engaged in pQSAR modelling. Currently, only one AAD-based scaling approach, termed auto-cross covariance (ACC) that was proposed thirty years ago, is available for treating such issue. In this study, we described the second AAD-based multivariate method to do so, namely Residue Descriptor-Distance Vector (RDDV). The strategy characterizes a peptide sequence by using an inter-residue pseudo-interaction potential between different pre-assigned amino acid types involved in the sequence, which results in a given (invariable) number of descriptor parameters for different LVPSs. Here, the RDDV was tested, examined and validated in an in-house pQSAR-oriented bioactive peptide data cluster, which was explored systematically with combinations of different AADs and regression tools. We also compared RDDV with the traditional ACC in multiple aspects.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"36 8","pages":"727-751"},"PeriodicalIF":2.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030613","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}

引用次数: 0

Unravelling phosphorylation-induced impacts on inhibitor-CDK2 through multiple independent molecular dynamics simulations and deep learning. 通过多个独立的分子动力学模拟和深度学习揭示磷酸化诱导对cdk2抑制剂的影响。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-08-01 Epub Date: 2025-09-10 DOI: 10.1080/1062936X.2025.2552131

W Zhang, G Xu, X Li, J Cong, P Wang, Y Xu, B Wei

{"title":"Unravelling phosphorylation-induced impacts on inhibitor-CDK2 through multiple independent molecular dynamics simulations and deep learning.","authors":"W Zhang, G Xu, X Li, J Cong, P Wang, Y Xu, B Wei","doi":"10.1080/1062936X.2025.2552131","DOIUrl":"https://doi.org/10.1080/1062936X.2025.2552131","url":null,"abstract":"Phosphorylation plays an important role in the activity of CDK2 and inhibitor binding, but the corresponding molecular mechanism is still insufficiently known. To address this gap, the current study innovatively integrates molecular dynamics (MD) simulations, deep learning (DL) techniques, and free energy landscape (FEL) analysis to systematically explore the action mechanisms of two inhibitors (SCH and CYC) when CDK2 is in a phosphorylated state and bound state of CyclinE. With the help of MD trajectory-based DL, key functional domains such as the loops L3 loop and L7 are successfully identified. The results of FEL analysis show that the binding of CyclinE significantly enhances conformational stability of key functional regions of CDK2 (such as the L3 loop, L7 loop, and αC helix), while phosphorylation modification increases conformational diversity of the CDK2-related system. Further verification by quantum mechanics/molecular mechanics-generalized Born surface area (QM/MM-GBSA) calculations shows that binding of CyclinE can enhance the binding ability of inhibitors, while phosphorylation weakens this binding effect. Residue-based free energy estimation reveals the hot spot regions of inhibitor-CDK2 binding, providing crucial target information for structure-based drug design. This study provides theoretical foundations for the development of highly selective CDK2 inhibitors and might be of great significance for cancer targeted therapy.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"36 8","pages":"673-700"},"PeriodicalIF":2.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030620","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}

引用次数: 0

Harnessing the potential of phytochemicals to design anti-filarial molecules targeting the MurE enzyme of Brugia malayi: a hierarchical virtual screening and molecular dynamics simulation study. 利用植物化学物质的潜力设计针对马来棕树MurE酶的抗丝虫分子：分层虚拟筛选和分子动力学模拟研究。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-08-01 Epub Date: 2025-09-10 DOI: 10.1080/1062936X.2025.2556512

D Prabhu, M Sureshan, S Rajamanikandan, J Jeyakanthan

{"title":"Harnessing the potential of phytochemicals to design anti-filarial molecules targeting the MurE enzyme of Brugia malayi: a hierarchical virtual screening and molecular dynamics simulation study.","authors":"D Prabhu, M Sureshan, S Rajamanikandan, J Jeyakanthan","doi":"10.1080/1062936X.2025.2556512","DOIUrl":"https://doi.org/10.1080/1062936X.2025.2556512","url":null,"abstract":"Brugia malayi, a causative agent of lymphatic filariasis, relies on its endosymbiont Wolbachia for survival. MurE ligase, a key enzyme in Wolbachia peptidoglycan biosynthesis, serves as a promising drug target for anti-filarial therapy. In this study, we employed a hierarchical virtual screening pipeline to identify phytochemical inhibitors targeting the MurE enzyme of the Wolbachia endosymbiont of B. malayi (wBmMurE). A validated high-quality model of wBmMurE was used to screen 17,967 phytochemicals, and the identified hits were subjected to toxicity profiling, and ADME filters to select potent drug-like candidates. Five phytochemicals such as biotin, quisqualic acid, succinic acid, 9,14-dihydroxyoctadecanoic acid, and N-isovaleroylglycine with permissible ADME profiles showed favourable binding affinities (GlideScore range: -12.86 to -10.57 kcal/mol), and stable interactions with catalytically important residues were selected from screened hits. Comparative analysis with reported MurE inhibitors validated the superior affinity and drug-like behaviour of our identified leads. Molecular dynamics simulations of 300 ns confirmed the conformational stability of ligand-bound complexes, while MM-GBSA analysis supported their favourable binding free energies. The results revealed that the identified compounds have the tendency of binding within substrate binding cavity of wBmMurE. These findings suggest that selected phytochemicals could serve as starting points for the development of novel anti-filarial agents.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"36 8","pages":"753-773"},"PeriodicalIF":2.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030666","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}

引用次数: 0

First report on machine learning based multiclass classification of Caco-2 permeability using different balancing strategies. 基于机器学习的Caco-2渗透率多类分类研究

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-08-01 Epub Date: 2025-09-08 DOI: 10.1080/1062936X.2025.2552134

I Dasgupta, S Gayen

{"title":"First report on machine learning based multiclass classification of Caco-2 permeability using different balancing strategies.","authors":"I Dasgupta, S Gayen","doi":"10.1080/1062936X.2025.2552134","DOIUrl":"10.1080/1062936X.2025.2552134","url":null,"abstract":"Evaluating the permeability of different molecular structures across the Caco-2 cell line is crucial for drug discovery and development. The present study primarily focuses on developing machine learning-based multiclass classification models for predicting the permeability of molecules across the Caco-2 cell line. However, the class imbalance in permeability datasets poses a significant challenge for developing predictive models in the case of multiclass analysis. To address the class imbalance issue, we employed different balancing strategies, including oversampling, undersampling, and hybrid approaches, to balance the training set. A five-fold cross-validation approach was employed for optimizing the hyperparameters. After completion of the evaluation process, we concluded that the XGBoost multiclass classifier trained with ADASYN oversampling achieved the best performance (accuracy, 0.717; MCC, 0.512 on the test set). Additionally, extreme permeability classes were also classified separately, and the best model exhibited strong predictive performance (accuracy, 0.853; MCC, 0.703 on the test set). To enhance the interpretability of the best-performing models, we performed SHAP analysis to elucidate descriptor importance and provide explainability. Our findings demonstrate that appropriate data balancing strategies can significantly improve predictive performance in multiclass permeability classification, offering a valuable framework for drug permeability assessment.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"701-725"},"PeriodicalIF":2.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145016134","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}

引用次数: 0

ToxAI_assistant: a web platform for a comprehensive study of the acute toxicity of xenobiotics following oral and intravenous administration in rats. ToxAI_assistant：一个综合研究大鼠口服和静脉给药后外源性药物急性毒性的网络平台。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-07-01 Epub Date: 2025-08-05 DOI: 10.1080/1062936X.2025.2535606

O V Tinkov, V Y Grigorev

{"title":"ToxAI_assistant: a web platform for a comprehensive study of the acute toxicity of xenobiotics following oral and intravenous administration in rats.","authors":"O V Tinkov, V Y Grigorev","doi":"10.1080/1062936X.2025.2535606","DOIUrl":"10.1080/1062936X.2025.2535606","url":null,"abstract":"The existing QSAR approaches for mammalian acute toxicity have been limited in scope, often relying on small or narrowly focused datasets and on classification endpoints. In contrast, our work leverages a sufficiently large curated dataset (9843 rat oral and 2323 intravenous LD50 values) to build regression models of acute toxicity. The best-performing QSAR models developed using 2D RDKit descriptors and the Cat Boost method achieve Q2 test = 0.66 at a data coverage of at least 77% within the applicability domain (AD) during validation of test sets. All models were rigorously validated according to OECD QSAR principles with clearly defined endpoints, explicit algorithms, and a well-characterized AD. The best QSAR models are integrated into the ToxAI_assistant web platform (https://tox-ai-assistant.streamlit.app/), which includes toxicity-level prediction with allowance for AD in terms of World Health Organization (WHO). We also provide mechanistic insight by identifying key toxicophores - substructural features statistically associated with high toxicity - thereby offering a structural interpretation. In sum, these elements (large and diverse data, regression modelling, WHO-based categorization, detailed fragment analysis, and AD assessment) together address the gaps of earlier studies and constitute the core novelty of our approach.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"555-582"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144785180","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}

引用次数: 0

Targeting MAO-B selectivity: computational screening, docking, and molecular dynamics insights. 靶向MAO-B选择性：计算筛选，对接和分子动力学见解。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-07-01 Epub Date: 2025-08-12 DOI: 10.1080/1062936X.2025.2537248

K-M Thai, D-T Pham, T-M Ngo, H-T Nguyen, P-V Nguyen, T-Q Pham, D-N Nguyen, Q-T Nguyen, M-T Le

引用次数: 0

Machine learning-based q-RASAR modelling for the in silico identification of novel α7nAChR agonists for anti-Alzheimer's drug discovery. 基于机器学习的q-RASAR模型用于新型α7nAChR激动剂的计算机识别，用于抗阿尔茨海默病药物的发现。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-07-01 Epub Date: 2025-08-15 DOI: 10.1080/1062936X.2025.2540820

V Kumar, K Roy

{"title":"Machine learning-based q-RASAR modelling for the in silico identification of novel α7nAChR agonists for anti-Alzheimer's drug discovery.","authors":"V Kumar, K Roy","doi":"10.1080/1062936X.2025.2540820","DOIUrl":"10.1080/1062936X.2025.2540820","url":null,"abstract":"In this study, we employed a quantitative Read-Across Structure-Activity Relationship (q-RASAR) approach to develop a statistically robust machine learning (ML)-based q-RASAR model aimed at predicting the agonistic activity of compounds targeting the α7-nicotinic acetylcholine (α7nACh) receptor, a key therapeutic target in Alzheimer's disease (AD) due to its involvement in cognitive processes and neuroprotection. We developed a rigorously validated univariate q-RASAR linear regression (LR) model using an extensive dataset of 1,727 structurally diverse heterocyclic and aromatic hydrocarbon compounds sourced from the publicly available Binding Database (www.bindingdb.org). Additionally, we explored various other ML-based q-RASAR models to further enhance predictive performance. The established LR q-RASAR model was subsequently applied to predict the Mcule database (https://mcule.com/database/), containing 1,91,94,405 chemical compounds, to identify structurally relevant candidates with potential α7nACh receptor agonistic activity. Additionally, molecular docking analysis and molecular dynamics (MD) simulations for 100 ns were conducted to investigate interactions between the target protein and ligands. Overall, this investigation highlights the critical influence of hydrophobicity, electronic effects, ionization degree, and steric factors as key determinants in the design of potential anti-Alzheimer's agents.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"621-649"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856187","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}

引用次数: 0

Unveiling the biophysical basis of DYRK kinase family isoform selectivity mechanism of Abemaciclib using computational approaches. 利用计算方法揭示Abemaciclib的DYRK激酶家族亚型选择性机制的生物物理基础。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-07-01 Epub Date: 2025-09-04 DOI: 10.1080/1062936X.2025.2552133

K D Ursal, P Kar

{"title":"Unveiling the biophysical basis of DYRK kinase family isoform selectivity mechanism of Abemaciclib using computational approaches.","authors":"K D Ursal, P Kar","doi":"10.1080/1062936X.2025.2552133","DOIUrl":"https://doi.org/10.1080/1062936X.2025.2552133","url":null,"abstract":"Dual-specificity tyrosine phosphorylation-regulated kinases (DYRKs) play crucial roles in regulating cell growth and brain development. Dysregulation of these kinases is linked to disorders like Down syndrome and cancers. The selective inhibition of DYRK1A over other isoforms remains a significant challenge due to their high structural similarity. This study investigates the selectivity of Abemaciclib, an FDA-approved CDK4/6 inhibitor known to target DYRK1A, against other DYRK family isoforms. We employed molecular docking and molecular dynamics simulations, coupled with the Molecular Mechanics Poisson-Boltzmann Surface Area method, to evaluate the selectivity profile of Abemaciclib. Results showed that it binds strongest to DYRK1B, followed by DYRK1A, DYRK4, DYRK3 and DYRK2, which is validated with the statistical analysis. Enhanced selectivity for DYRK1B arises from stronger van der Waals and electrostatic interactions. Hydrophobic contacts and hydrogen bonds, especially within the kinase's hinge region, help stabilize the complex. Notably, Leu241 in DYRK1A and its identical residues in other isoforms play a pivotal role in these stabilizing interactions. Key residue differences, like Phe170, Glu239 and His285 in DYRK1A, contribute to specific interactions that underpin the molecular binding pattern. By identifying conserved and isoform-specific interactions, our study provides valuable insights for the rational design of potent and selective DYRK inhibitors.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"36 7","pages":"651-671"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144993457","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}

引用次数: 0

An effective machine learning model for rat acute oral toxicity prediction of emerging chemicals: multi-domain applications and structure-activity relationships. 新兴化学物质大鼠急性口服毒性预测的有效机器学习模型：多领域应用和构效关系。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-06-01 Epub Date: 2025-07-31 DOI: 10.1080/1062936X.2025.2531172

J Yan, Z Shen

{"title":"An effective machine learning model for rat acute oral toxicity prediction of emerging chemicals: multi-domain applications and structure-activity relationships.","authors":"J Yan, Z Shen","doi":"10.1080/1062936X.2025.2531172","DOIUrl":"https://doi.org/10.1080/1062936X.2025.2531172","url":null,"abstract":"Given the widespread presence of emerging contaminants in the environment, assessing and ensuring their biosafety is urgent. Under the Globally Harmonized System (GHS), the LD50 parameter of acute oral toxicity (AOT) is crucial for chemical safety classification. Animal testing limitations have highlighted the need for alternative methods, and machine learning offers a new approach to predict LD50 through quantitative structure-activity relationship (QSAR) models. This study developed and optimized a machine learning model for LD50 classification of emerging contaminants based on data from more than 6000 known AOT. Using molecular descriptors and fingerprints, the model achieves an accuracy above 0.86 and a recall score over 0.84, outperforming previous models. The model's robustness was confirmed across various types of emerging contaminants. Shapley additive explanations (SHAP) identified key descriptors like BCUTp_1h, ATSC1pe, and SLogP_VSA4, while the information gain (IG) method highlighted alert substructures [P-O, P-S]. These findings suggest that compounds with high polarizability, mean electronegativity and significant surface area may adversely affect rats. This model enhances understanding of acute toxicity mechanisms and serves as a tool for early screening of safer compounds, promoting the design of greener chemicals.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"36 6","pages":"537-554"},"PeriodicalIF":2.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144754109","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}

引用次数: 0