Methods最新文献

{"title":"DP-site: A dual deep learning-based method for protein-peptide interaction site prediction","authors":"Shima Shafiee ,&nbsp;Abdolhossein Fathi ,&nbsp;Ghazaleh Taherzadeh","doi":"10.1016/j.ymeth.2024.06.001","DOIUrl":"10.1016/j.ymeth.2024.06.001","url":null,"abstract":"<div><h3>Background</h3><p>Protein-peptide interaction prediction is an important topic for several applications including various biological processes, understanding drug discovery, protein function abnormal cellular behaviors, and treating diseases. Over the years, studies have shown that experimental methods have improved the identification of this bio-molecular interaction. However, predicting protein-peptide interactions using these methods is laborious, time-consuming, dependent on third-party tools, and costly.</p></div><div><h3>Method</h3><p>To address these previous drawbacks, this study introduces a computational framework called DP-Site. The proposed framework concentrates on using a compound of a dual pipeline along with a combination predictor. A deep convolutional neural network for feature extraction and classification is embedded in pipeline 1. In addition, pipeline 2 includes a deep long-short-term memory<strong>-</strong>based and a random forest classifier for feature extraction and classification. In this investigation, the evolutionary, structure-based, sequence-based, and physicochemical information of proteins is utilized for identifying protein-peptide interaction at the residue level.</p></div><div><h3>Results</h3><p>The proposed method is evaluated on both the ten-fold cross-validation and independent test sets. The robust and consistent results between cross-validation and independent test sets confirm the ability of the proposed method to predict peptide binding residues in proteins. Moreover, experimental findings demonstrate that DP-Site has significantly outperformed other state-of-the-art sequence-based and structure-based methods. The proposed method achieves a remarkable balance between a specificity of 0.799 and a sensitivity of 0.770, along with the best f-measure of 0.661 and the highest precision of 0.580 using an independent test set.</p></div><div><h3>Conclusions</h3><p>The outcome of various experiments confirms the proficiency of the proposed method and outperforms state-of-the-art sequence-based and structure-based methods in terms of the mentioned criteria. DP-Site can be accessed at <span>https://github.com/shafiee</span><svg><path></path></svg> 95/shima.shafiee.DP-Site.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 17-29"},"PeriodicalIF":4.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141316367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSO-GP: 3-D segmentation of large and complex conjoined tree structures","authors":"Arijit De ,&nbsp;Nirmal Das ,&nbsp;Punam K. Saha ,&nbsp;Alejandro Comellas ,&nbsp;Eric Hoffman ,&nbsp;Subhadip Basu ,&nbsp;Tapabrata Chakraborti","doi":"10.1016/j.ymeth.2024.05.016","DOIUrl":"10.1016/j.ymeth.2024.05.016","url":null,"abstract":"<div><p>Robust segmentation of large and complex conjoined tree structures in 3-D is a major challenge in computer vision. This is particularly true in computational biology, where we often encounter large data structures in size, but few in number, which poses a hard problem for learning algorithms. We show that merging multiscale opening with geodesic path propagation, can shed new light on this classic machine vision challenge, while circumventing the learning issue by developing an unsupervised visual geometry approach (digital topology/morphometry). The novelty of the proposed MSO-GP method comes from the geodesic path propagation being guided by a skeletonization of the conjoined structure that helps to achieve robust segmentation results in a particularly challenging task in this area, that of artery-vein separation from non-contrast pulmonary computed tomography angiograms. This is an important first step in measuring vascular geometry to then diagnose pulmonary diseases and to develop image-based phenotypes. We first present proof-of-concept results on synthetic data, and then verify the performance on pig lung and human lung data with less segmentation time and user intervention needs than those of the competing methods.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 9-16"},"PeriodicalIF":4.8,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141260624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Copper catalyzed cycloaddition for the synthesis of non isomerisable 2′ and 3′-regioisomers of arg-tRNAarg","authors":"Yusif Afandizada ,&nbsp;Thilini Abeywansha ,&nbsp;Vincent Guerineau ,&nbsp;Yi Zhang ,&nbsp;Bruno Sargueil ,&nbsp;Luc Ponchon ,&nbsp;Laura Iannazzo ,&nbsp;Mélanie Etheve-Quelquejeu","doi":"10.1016/j.ymeth.2024.05.017","DOIUrl":"10.1016/j.ymeth.2024.05.017","url":null,"abstract":"<div><p>In this report, non-isomerisable analogs of arginine tRNA (Arg-triazole-tRNA) have been synthesized as tools to study tRNA-dependent aminoacyl-transferases. The synthesis involves the incorporation of 1,4 substituted-1,2,3 triazole ring to mimic the ester bond that connects the amino acid to the terminal adenosine in the natural substrate. The synthetic procedure includes (i) a coupling between 2′- or 3′-azido-adenosine derivatives and a cytidine phosphoramidite to access dinucleotide molecules, (ii) Cu-catalyzed cycloaddition reactions between 2′- or 3′-azido dinucleotide in the presence of an alkyne molecule mimicking the arginine, providing the corresponding Arg-triazole-dinucleotides, (iii) enzymatic phosphorylation of the 5′-end extremity of the Arg-triazole-dinucleotides with a polynucleotide kinase, and (iv) enzymatic ligation of the 5′-phosphorylated dinucleotides with a 23-nt RNA micro helix that mimics the acceptor arm of arg-tRNA or with a full tRNA^arg. Characterization of nucleoside and nucleotide compounds involved MS spectrometry, ¹H, ¹³C and ³¹P NMR analysis. This strategy allows to obtain the pair of the two stable regioisomers of arg-tRNA analogs (2′ and 3′) which are instrumental to explore the regiospecificity of arginyl transferases enzyme. In our study, a first binding assay of the arg-tRNA micro helix with the Arginyl-tRNA-protein transferase 1 (ATE1) was performed by gel shift assays.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 94-107"},"PeriodicalIF":4.2,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141246889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RDscan: Extracting RNA-disease relationship from the literature based on pre-training model","authors":"Yang Zhang ,&nbsp;Yu Yang ,&nbsp;Liping Ren ,&nbsp;Lin Ning ,&nbsp;Quan Zou ,&nbsp;Nanchao Luo ,&nbsp;Yinghui Zhang ,&nbsp;Ruijun Liu","doi":"10.1016/j.ymeth.2024.05.012","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.05.012","url":null,"abstract":"<div><p>With the rapid advancements in molecular biology and genomics, a multitude of connections between RNA and diseases has been unveiled, making the efficient and accurate extraction of RNA-disease (RD) relationships from extensive biomedical literature crucial for advancing research in this field. This study introduces RDscan, a novel text mining method developed based on the pre-training and fine-tuning strategy, aimed at automatically extracting RD-related information from a vast corpus of literature using pre-trained biomedical large language models (LLM). Initially, we constructed a dedicated RD corpus by manually curating from literature, comprising 2,082 positive and 2,000 negative sentences, alongside an independent test dataset (comprising 500 positive and 500 negative sentences) for training and evaluating RDscan. Subsequently, by fine-tuning the Bioformer and BioBERT pre-trained models, RDscan demonstrated exceptional performance in text classification and named entity recognition (NER) tasks. In 5-fold cross-validation, RDscan significantly outperformed traditional machine learning methods (Support Vector Machine, Logistic Regression and Random Forest). In addition, we have developed an accessible webserver that assists users in extracting RD relationships from text. In summary, RDscan represents the first text mining tool specifically designed for RD relationship extraction, and is poised to emerge as an invaluable tool for researchers dedicated to exploring the intricate interactions between RNA and diseases. Webserver of RDscan is free available at <span>https://cellknowledge.com.cn/RDscan/</span><svg><path></path></svg>.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 48-54"},"PeriodicalIF":4.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141090649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mobility of sodium ions in agarose gels probed through combined single- and triple-quantum NMR","authors":"Evgeny Nimerovsky ,&nbsp;Daniel Sieme ,&nbsp;Nasrollah Rezaei-Ghaleh","doi":"10.1016/j.ymeth.2024.05.015","DOIUrl":"10.1016/j.ymeth.2024.05.015","url":null,"abstract":"<div><p>Metal ions, including biologically prevalent sodium ions, can modulate electrostatic interactions frequently involved in the stability of condensed compartments in cells. Quantitative characterization of heterogeneous ion dynamics inside biomolecular condensates demands new experimental approaches. Here we develop a ²³Na NMR relaxation-based integrative approach to probe dynamics of sodium ions inside agarose gels as a model system. We exploit the electric quadrupole moment of spin-3/2 ²³Na nuclei and, through combination of single-quantum and triple-quantum-filtered ²³Na NMR relaxation methods, disentangle the relaxation contribution of different populations of sodium ions inside gels. Three populations of sodium ions are identified: a population with bi-exponential relaxation representing ions within the slow motion regime and two populations with mono-exponential relaxation but at different rates. Our study demonstrates the dynamical heterogeneity of sodium ions inside agarose gels and presents a new experimental approach for monitoring dynamics of sodium and other spin-3/2 ions (e.g. chloride) in condensed environments.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 55-64"},"PeriodicalIF":4.8,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1046202324001348/pdfft?md5=53d99b6b5eec66db96e7a8940a8829df&pid=1-s2.0-S1046202324001348-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141086378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules","authors":"Zhihao Su ,&nbsp;Yejian Wu ,&nbsp;Kaiqiang Cao ,&nbsp;Jie Du ,&nbsp;Lujing Cao ,&nbsp;Zhipeng Wu ,&nbsp;Xinyi Wu ,&nbsp;Xinqiao Wang ,&nbsp;Ying Song ,&nbsp;Xudong Wang ,&nbsp;Hongliang Duan","doi":"10.1016/j.ymeth.2024.05.013","DOIUrl":"10.1016/j.ymeth.2024.05.013","url":null,"abstract":"<div><p>Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 38-47"},"PeriodicalIF":4.8,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141074700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intelligent model for prediction of abiotic stress-responsive microRNAs in plants using statistical moments based features and ensemble approaches","authors":"Ansar Naseem ,&nbsp;Yaser Daanial Khan","doi":"10.1016/j.ymeth.2024.05.008","DOIUrl":"10.1016/j.ymeth.2024.05.008","url":null,"abstract":"<div><p>This study proposed an intelligent model for predicting abiotic stress-responsive microRNAs in plants. MicroRNAs (miRNAs) are short RNA molecules regulates the stress in genes. Experimental methods are costly and time-consuming, as compare to in-silico prediction. Addressing this gap, the study seeks to develop an efficient computational model for plant stress response prediction. The two benchmark datasets for MiRNA and Pre-MiRNA dataset have been acquired in this study. Four ensemble approaches such as bagging, boosting, stacking, and blending have been employed. Classifiers such as Random Forest (RF), Extra Trees (ET), Ada Boost (ADB), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM). Stacking and Blending employed all stated classifiers as base learners and Logistic Regression (LR) as Meta Classifier. There have been a total of four types of testing used, including independent set, self-consistency, cross-validation with 5 and 10 folds, and jackknife. This study has utilized evaluation metrics such as accuracy score, specificity, sensitivity, Mathew's correlation coefficient (MCC), and AUC. Our proposed methodology has outperformed existing state of the art study in both datasets based on independent set testing. The SVM-based approach has exhibited accuracy score of 0.659 for the MiRNA dataset, which is better than the previous study. The ET classifier has surpassed the accuracy of Pre-MiRNA dataset as compared to the existing benchmark study, achieving an impressive score of 0.67. The proposed method can be used in future research to predict abiotic stresses in plants.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 65-79"},"PeriodicalIF":4.8,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141069833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational prediction of phosphorylation sites of SARS-CoV-2 infection using feature fusion and optimization strategies","authors":"Mumdooh J. Sabir ,&nbsp;Majid Rasool Kamli ,&nbsp;Ahmed Atef ,&nbsp;Alawiah M. Alhibshi ,&nbsp;Sherif Edris ,&nbsp;Nahid H. Hajarah ,&nbsp;Ahmed Bahieldin ,&nbsp;Balachandran Manavalan ,&nbsp;Jamal S.M. Sabir","doi":"10.1016/j.ymeth.2024.04.021","DOIUrl":"10.1016/j.ymeth.2024.04.021","url":null,"abstract":"<div><p>SARS-CoV-2′s global spread has instigated a critical health and economic emergency, impacting countless individuals. Understanding the virus's phosphorylation sites is vital to unravel the molecular intricacies of the infection and subsequent changes in host cellular processes. Several computational methods have been proposed to identify phosphorylation sites, typically focusing on specific residue (S/T) or Y phosphorylation sites. Unfortunately, current predictive tools perform best on these specific residues and may not extend their efficacy to other residues, emphasizing the urgent need for enhanced methodologies. In this study, we developed a novel predictor that integrated all the residues (STY) phosphorylation sites information. We extracted ten different feature descriptors, primarily derived from composition, evolutionary, and position-specific information, and assessed their discriminative power through five classifiers. Our results indicated that Light Gradient Boosting (LGB) showed superior performance, and five descriptors displayed excellent discriminative capabilities. Subsequently, we identified the top two integrated features have high discriminative capability and trained with LGB to develop the final prediction model, LGB-IPs. The proposed approach shows an excellent performance on 10-fold cross-validation with an ACC, MCC, and AUC values of 0.831, 0.662, 0.907, respectively. Notably, these performances are replicated in the independent evaluation. Consequently, our approach may provide valuable insights into the phosphorylation mechanisms in SARS-CoV-2 infection for biomedical researchers.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"229 ","pages":"Pages 1-8"},"PeriodicalIF":4.8,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141069834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data","authors":"Shenglun Chen ,&nbsp;Jia Meng ,&nbsp;Yuxin Zhang","doi":"10.1016/j.ymeth.2024.05.009","DOIUrl":"10.1016/j.ymeth.2024.05.009","url":null,"abstract":"<div><p>With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devoted to those popular types such as m6A and pseudouridine. To address current limitations on studying the crucial regulator, m1A modification, we conceived this study. We have developed an integrated computational workflow designed for the detection of m1A modifications from direct RNA sequencing data. This workflow comprises a feature extractor responsible for capturing signal characteristics (such as mean, standard deviations, and length of electric signals), a single molecule-level m1A predictor trained with features extracted from the IVT dataset using classical machine learning algorithms, a confident m1A site selector employing the binomial test to identify statistically significant m1A sites, and an m1A modification rate estimator. Our model achieved accurate molecule-level prediction (Average AUC = 0.9689) and reliable m1A site detection and quantification. To show the feasibility of our workflow, we conducted a study on in vivo transcribed human HEK293 cell line, and the results were carefully annotated and compared with other techniques (i.e., Illumina sequencing-based techniques). We believed that this tool will enabling a comprehensive understanding of the m1A modification and its functional mechanisms within cells and organisms.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 30-37"},"PeriodicalIF":4.8,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141069927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OphthalMimic: A new alternative apparatus without animal tissue for the evaluation of topical ophthalmic drug products","authors":"Geisa N. Barbalho ,&nbsp;Manuel A. Falcão ,&nbsp;Venâncio Alves Amaral ,&nbsp;Jonad L.A. Contarato ,&nbsp;Aliucha M. Barbalho ,&nbsp;Gabriela Kaori Diógenes ,&nbsp;Melyssa Mariana Gomes Silva ,&nbsp;Beatriz Carvalho de Barros do Vale Rochelle ,&nbsp;Guilherme M. Gelfuso ,&nbsp;Marcilio Cunha-Filho ,&nbsp;Tais Gratieri","doi":"10.1016/j.ymeth.2024.05.005","DOIUrl":"10.1016/j.ymeth.2024.05.005","url":null,"abstract":"<div><p>The necessity of animal-free performance tests for novel ophthalmic formulation screening is challenging. For this, we developed and validated a new device to simulate the dynamics and physical–chemical barriers of the eye for in vitro performance tests of topic ophthalmic formulations. The OphthalMimic is a 3D-printed device with an artificial lacrimal flow, a cul-de-sac area, a support base, and a simulated cornea comprised of a polymeric membrane containing poly-vinyl alcohol 10 % (w/v), gelatin 2.5 % (w/v), and different proportions of mucin and poloxamer, i.e., 1:1 (M1), 1:2 (M2), and 2:1 (M3) w/v, respectively. The support base is designed to move between 0° and 50° to replicate the movement of an eyelid. We challenged the model by testing the residence performance of poloxamer®407 16 % and poloxamer®407 16 % + chitosan 1 % (PLX16CS10) gels containing fluconazole. The test was conducted with a simulated tear flow of 1.0 mL.min⁻¹ for 5 min. The OphthalMimic successfully distinguished PLX16 and PLX16C10 formulations based on their fluconazole drainage (M1: 65 ± 14 % and 27 ± 10 %; M2: 58 ± 6 % and 38 ± 9 %; M3: 56 ± 5 % and 38 ± 18 %). In conclusion, the OphthalMimic is a promising tool for comparing the animal-free performance of ophthalmic formulations.</p></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"228 ","pages":"Pages 1-11"},"PeriodicalIF":4.8,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}