Computational Biology and Chemistry最新文献

{"title":"A novel regularization approach for loss functions to reduce instance imbalance in biomedical image segmentation","authors":"Muhammad Aqib Javed ,&nbsp;Muhammad Khuram Shahzad ,&nbsp;Hafiz Syed Muhammad Bilal Ali","doi":"10.1016/j.compbiolchem.2025.108555","DOIUrl":"10.1016/j.compbiolchem.2025.108555","url":null,"abstract":"<div><div>Biomedical Image Segmentation applications have witnessed mushroom growth in the last two decades. Current state-of-the-art approaches face challenges when dealing with instance imbalances in datasets. Various functions, such as Blob Loss, Lesion-wise Loss, and Dice Loss limitations, were addressed by Instance-wise loss and Center-of-Instance loss (ICI). ICI is the result of Instance loss, and the center of instance loss suffers from highly unregulated labels and outputs, resulting in low accuracy of aforementioned loss functions. We introduce a novel dual-coefficient regularization approach for loss functions that modifies both predicted outputs and labels before loss computation. This addresses instance imbalance more effectively than previous pixel-level or class-level weighting strategies. The proposed approach resulted in the enhancement of existing loss functions: (1) RIW (regularized instance-wise loss), (2) RCI (regularized center of instance loss), and (3) RPW (regularized pixel-wise loss). The simulation experiments on the ATLAS R2.0 (MICCAI, 2022) and BraTS’20 (MICCAI, 2020) datasets validated our approach in comparison with the state-of-the-art loss functions resulting in significant improvements in RIW (up to 69.16%), RCI ( up to 16.58%), RPW (67.82%), subsequently decreased false detection rate up to (97.78%), and number of missed instances.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"119 ","pages":"Article 108555"},"PeriodicalIF":3.1,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular insights into ATP-mediated NBD dimerization in an ABC transporter","authors":"Vinothini Santhakumar,&nbsp;Nahren Manuel Mascarenhas","doi":"10.1016/j.compbiolchem.2025.108600","DOIUrl":"10.1016/j.compbiolchem.2025.108600","url":null,"abstract":"<div><div>CmABCB1 is a Cyanidioschyzon merolae homolog of human ABCB1, which is a member of the ATP-binding cassette (ABC) transporter superfamily responsible for the efflux of a wide range of substrates from cells. The two major conformations of CmABCB1 are the inward-facing conformation that binds the substrate to be transported, and the outward-facing conformation that represents the state post the transport of the substrate. In this study, we have performed a 1000 ns all-atom MD simulation of CmABCB1 with and without ATP to understand how ATP binding influences the dynamics and conformation of the protein. Additionally, we have also performed two distinct methods of umbrella sampling (US) simulations to determine the free energy of binding of the nucleotide-binding domains (NBDs) both in the presence and absence of ATP. Our MD simulations reveal significant structural differences of the transporter depending on whether ATP is present or absent at the NBDs. Only when ATP was present at the NBDs, we discovered a specific salt-bridge interaction between the coupling helix (CH) and the nucleotide-binding domain (NBD), which we believe could play a potential role in substrate transport and the accompanying conformational change to the outward-facing state. We also observed a significant loss in the NBD-NBD interactions in the absence of ATP. Our umbrella sampling simulations showed that ATP binding stabilizes the NBD dimer by about ∼25 kJ/mol. Overall, our findings provide valuable insights into the conformational changes of CmABCB1 and the role of ATP in the transport cycle of ABC transporters.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"119 ","pages":"Article 108600"},"PeriodicalIF":2.6,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioinformatics analysis combined with experiments to unveil common hub genes, pathways, and transcription factor regulatory networks in ulcerative colitis and osteoporosis","authors":"Kun Li ,&nbsp;Tianshuang Xia ,&nbsp;Bin Peng ,&nbsp;Liyong Lai ,&nbsp;Weiqing Fan ,&nbsp;Yiping Jiang ,&nbsp;Jianyong Han ,&nbsp;Ruiqing Zhu ,&nbsp;Tao Jiang ,&nbsp;Ti Yang ,&nbsp;Xiaoqiang Yue ,&nbsp;Denghai Zhang ,&nbsp;Hailiang Xin","doi":"10.1016/j.compbiolchem.2025.108598","DOIUrl":"10.1016/j.compbiolchem.2025.108598","url":null,"abstract":"<div><h3>Background</h3><div>Osteoporosis (OP) is a common comorbidity in ulcerative colitis (UC) patients. However, the specific mechanism by which UC induces bone loss remains unclear.</div></div><div><h3>Methods</h3><div>Transcriptome data from the GSE87466 and GSE35958 datasets were analyzed to identify common differentially expressed genes (co-DEGs), construct protein-protein interaction network, and perform functional enrichment analysis. The R packages PROGENy, NetAct, and sRACIPE were used to infer the activity of the signaling pathways, construct transcription factor regulatory networks, and identify disease-promoting genes. A dextran sulfate sodium (DSS)-induced UC mouse model was established to validate the biological processes enriched among co-DEGs.</div></div><div><h3>Results</h3><div>A total of 66 upregulated co-DEGs were identified, with ICAM1, ITGA5, THY1, ITGB2, TGFB1, MMP2, COL6A2, FLNA, CD5, and IL16 identified as hub genes. These upregulated co-DEGs were significantly enriched in processes, such as response to cytokine, leukocyte transendothelial migration, regulation of myeloid leukocyte mediated immunity, and osteoclast differentiation. Seven signaling pathways, NF-κB, TNF-α, MAPK, EGFR, TGF-β, hypoxia, and TRAIL, were consistently activated in both UC and OP. A total of 12 genes were identified as disease-suppressing and 22 as disease-promoting in UC, while 7 genes were found to be disease-suppressing and 5 disease-promoting in OP. Among these, RELA, NFKB1, and FOS were found to be common disease-promoting genes in both UC and OP. DSS administration in mice not only induced colitis, but also resulted in significant bone loss, likely driven by TNF-α–mediated enhancement of bone resorptive activity.</div></div><div><h3>Conclusions</h3><div>UC-related colonic inflammation triggers cytokine release and promotes immune cell activation and trafficking into the bone marrow microenvironment. Key inflammatory mediators, particularly TNF-α, act synergistically with receptor activator of nuclear factor kappa-Β ligand (RANKL) to enhance the differentiation of osteoclast precursors into mature osteoclasts. This inflammatory milieu accelerates bone resorption and ultimately leads to bone loss and structural degradation. These findings suggest three potential dual-targeted therapeutic strategies for UC and OP: inhibiting TNF-α, targeting leukocyte migration-related hub genes (ICAM1, ITGA5, ITGB2, and TGFB1), and inhibiting common disease-promoting genes (RELA, NFKB1, and FOS).</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"119 ","pages":"Article 108598"},"PeriodicalIF":2.6,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multiple Environmental Parameters and Molecular Fingerprints Contribution model for prediction of Gibbs free energy","authors":"Xin Zhao,&nbsp;Kang Li,&nbsp;Tao Zhang,&nbsp;Shuxin Cui,&nbsp;Yahui Cao,&nbsp;Xue Jia","doi":"10.1016/j.compbiolchem.2025.108583","DOIUrl":"10.1016/j.compbiolchem.2025.108583","url":null,"abstract":"<div><div>Accurate prediction of thermodynamic parameters in biochemical reactions is essential for understanding and designing metabolic systems. Most existing methods for predicting the Gibbs free energy of biochemical reactions often neglect the environmental influences on Gibbs free energy such as pH, temperature and ionic strength, and lack efficient feature selection mechanisms, resulting in suboptimal predictive accuracy. In this paper, a Convolutional Neural Network Based Model with Multiple Environmental Parameters and Molecular Fingerprint Contribution (MEFC-CNN) is proposed to address these problems. Firstly, an encoding method that incorporates environmental factors is proposed to improve the ability to represent features. Secondly, a convolutional neural network with multiple parallel feature inputs is designed to efficiently select the key features, thereby improving the accuracy of Gibbs free energy prediction of biochemical reactions. Experimental results demonstrate that the MEFC-CNN model achieves superior predictive accuracy compared to existing methods.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"119 ","pages":"Article 108583"},"PeriodicalIF":2.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"mCNN-GenEfflux: enhanced predicting Efflux protein and their super families by using generative proteins combined with multiple windows convolution neural networks","authors":"Muhammad Hussain ,&nbsp;Yu-Yen Ou ,&nbsp;Quang Thai Ho","doi":"10.1016/j.compbiolchem.2025.108595","DOIUrl":"10.1016/j.compbiolchem.2025.108595","url":null,"abstract":"<div><div>Efflux transporters play a critical role in bacterial antibiotic resistance by facilitating the removal of harmful substances. These are classified into five distinct families: ABC, MFS, MATE, RND, and SMR. The significant sequence variability among these families, coupled with insufficient functional annotation, presents considerable challenges for traditional categorization methods. We hypothesize that integrating of ProtGPT2-generated efflux protein sequences with a multi-window convolutional neural network (mCNN) is proposed to enhance classification accuracy by effectively capturing local motifs and broader evolutionary patterns often overlooked by previous methods. Generative models like ProtGPT2 are effective for this purpose, as they generate a variety of sequence variants that reflect patterns observed in natural efflux families, thereby minimizing issues related to data scarcity. The proposed GenEfflux framework, unlike alignment-based methods such as HHblits and single-feature CNNs, combines generative sequence expanding with multi-scale evolutionary feature extraction via Position-Specific Scoring Matrices (PSSMs), thereby enhancing the understanding of sequence-function relationships. In comparative evaluations, GenEfflux consistently outperformed the baseline deepEfflux model across all Efflux transporter classes. In Class B, sensitivity increased from 0.5385 to 0.9999, and the Matthews correlation coefficient (MCC) rose from 0.4397 to 0.9327. In Class C, accuracy improved from 0.8977 to 0.9668, alongside an increase in MCC from 0.7668 to 0.9331. The findings demonstrate that sequences generated by ProtGPT2 possess functional relevance and improve classification effectiveness. GenEfflux suggestions a comprehensive framework for enhancing efflux protein analysis and advancing research on antibiotic resistance.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"119 ","pages":"Article 108595"},"PeriodicalIF":2.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144666003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GShC-Net: Hybrid deep learning with DCTLAP feature extraction for brain tumor detection","authors":"Zulaikha Beevi S ,&nbsp;Vanitha L ,&nbsp;Shoba B ,&nbsp;K. Prabhu Chandran","doi":"10.1016/j.compbiolchem.2025.108577","DOIUrl":"10.1016/j.compbiolchem.2025.108577","url":null,"abstract":"<div><div>A brain tumor is an abnormal cell growth in a brain, which is not detected early. Initial detection of brain tumors is extremely critical for treatment planning as well as the survival of a patient. Brain tumors come in different forms, have unique properties, and require tailored therapies. Thus, detecting brain tumors physically is a laborious, complex, as well as error-prone process. Hence, an automated computer-assisted diagnosis with better correctness is presently in high demand. Here, this paper developed a hybrid GoogleNet-Shepard Convolutional Networks (GShC-Net) method that is employed for detecting brain tumors. The process of this approach is as illustrated follows. Firstly, an input image is carried out from the database that is given to a pre-processing module. After that, brain tumor segmentation is performed, as well as features such as Haralick texture features, Statistical features, and Discrete Cosine Transform with Local Arc Pattern (DCTLAP) are extracted. Finally, brain tumor is detected based on GShC-Net. Moreover, the GoogleNet and Shepard Convolutional Neural Networks (ShCNN) models are fused to create GShC-Net, in which the layers are modified. The proposed GShC-Net method effectively improves the early detection and classification of brain tumors, potentially aiding in more accurate and timely medical diagnoses. Furthermore, the GShC-Net is assessed by using True Positive Rate (TPR), True Negative Rate (TNR), as well as accuracy and the values attained are0.940, 0.930, and 0.932, respectively.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"119 ","pages":"Article 108577"},"PeriodicalIF":2.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling key hub genes in E. coli biofilm formation: An in silico approach integrating differential gene expression, biosurfactant targeting, MD simulation and MM-PBSA free energy calculations","authors":"Rohit Pritam Das,&nbsp;Arun Kumar Pradhan","doi":"10.1016/j.compbiolchem.2025.108596","DOIUrl":"10.1016/j.compbiolchem.2025.108596","url":null,"abstract":"<div><div>Biofilm formation by <em>Escherichia coli</em> is a critical factor in antibiotic resistance and persistent infections, posing significant challenges to public health. In this study, we evaluated the biofilm inhibition potential of two novel biosurfactants, BG2A and BG2B, targeting differentially expressed genes (DEGs) during the maturation stages of biofilm development. Differential gene expression (DGE) analysis revealed significant transcriptional changes in biofilm-associated pathways, with pathway enrichment and Gene Ontology (GO) analyses identifying key biological processes. Protein-protein interaction (PPi) network analysis and hub gene identification pinpointed critical regulatory nodes, such as <em>ibpA</em>, <em>ybeD</em>, and <em>ycjF</em>, which play pivotal roles in biofilm maturation and stability. Molecular docking studies demonstrated strong binding affinities, due to its higher binding energy and stable hydrogen bonding networks. These findings were further corroborated by molecular dynamics (MD) simulations, which demonstrated complex stability through low RMSD and RMSF values. Binding free energy calculations using the Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) approach highlighted substantial van der Waals and electrostatic contributions to binding. Additionally, principal component analysis (PCA) and free energy landscape (FEL) analyses provided insights into the conformational dynamics of the ligand–protein complexes. Taken together, this in silico study suggests that BG2A and BG2B hold promise as potential inhibitors of <em>E. coli</em> biofilm maturation. However, further in vitro and in vivo studies are necessary to experimentally validate their therapeutic potential and establish their efficacy in clinical settings.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"119 ","pages":"Article 108596"},"PeriodicalIF":2.6,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systems approach to pigmentation control by ascorbic acid esterified with coconut oil-derived medium-chain fatty acids: Investigated via network pharmacology, molecular dynamics, elastic network and Markov state models","authors":"Modhi O. Alotaibi","doi":"10.1016/j.compbiolchem.2025.108594","DOIUrl":"10.1016/j.compbiolchem.2025.108594","url":null,"abstract":"<div><div>Tyrosinase family enzymes (TYR, TRP1, TRP2) play pivotal roles in melanogenesis, making them targets for pigmentation modulation. Ascorbic acid (ASC) and coconut oil have shown promise in skin whitening. Inspired by these ASC esterified with coconut oil-derived medium-chain fatty acids (MCFAs) such as capric acid (ASC-CAP), caproic acid (ASC-CAPRO), caprylic acid (ASC-CAPRY) and lauric acid (ASC-LAU) are investigated via <em>in silico</em> analysis such as network pharmacology (NP), molecular docking and molecular dynamics (MD) simulation to understand their interaction with tyrosinase family enzymes. This study introduces a novel approach to skin depigmentation by examining the combined effects of ascorbic acid and coconut oil derivatives on the regulation of tyrosinase family enzymes involved in melanogenesis. NP analysis identified a key hub of enzymes-LRRC8A (cell regulation), MITF (melanogenesis), and CD8A (immune signalling)-emphasizing MITF's central role in activating tyrosinase-mediated pathways. Toxicity predictions revealed minimal risk with low LD₅₀ values of the compounds studied. MD simulations showed strong ligand stability at enzyme active sites, supported by RMSD, RMSF, DCCM, RTA, ENM, PCA, FEL, MSM and MM-GBSA binding free energy analyses. Energy landscape analyses identified metastable low-energy states, identifying stable ligand-enzyme interactions. These findings highlight the potential of ASC esterified with MCFAs as safe, and effective agents for pigmentation regulation. The present study reveals that ASC-CAPRO for TRP1 and TRP2; and ASC-LAU for TYR are the most promising candidates, offering insights into therapeutic approaches for hyperpigmentation treatment.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"119 ","pages":"Article 108594"},"PeriodicalIF":2.6,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative review of intelligent nuchal translucency for genetic disorder","authors":"Smita Satish Pawar ,&nbsp;Mangesh D. Nikose","doi":"10.1016/j.compbiolchem.2025.108597","DOIUrl":"10.1016/j.compbiolchem.2025.108597","url":null,"abstract":"<div><div>Nuchal Translucency (NT) screening is a critical prenatal diagnostic tool used to detect chromosomal abnormalities and congenital heart defects, yet it has limitations in accuracy and reliability. Despite its importance, research in this area, particularly involving Deep Learning (DL) techniques, remains limited. This survey addresses this gap by collecting and analyzing 53 research papers related to NT screening and detection. The study starts with a systematic paper selection process and followed by a literature review. Additionally, it outlines the general steps involved in conventional NT screening approaches. The analysis and discussion section includes a chronological review of the studies, an examination of the datasets used, and a detailed analysis of the performance of conventional NT approaches, which is further broken down into performance metrics and statistical tests. The findings reveal significant research gaps and challenges in traditional NT screening methods, underscoring the need for more efficient Machine Learning (ML) and DL-based NT detection approaches. This study highlights the importance of advancing DL techniques to improve the detection and diagnosis of chromosomal abnormalities and congenital heart defects through NT screening.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"119 ","pages":"Article 108597"},"PeriodicalIF":2.6,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive study on topological indices and entropy measures for terbium niobate using logarithmic regression models","authors":"W. Eltayeb Ahmed ,&nbsp;Muhammad Farhan Hanif ,&nbsp;Mazhar Hussain ,&nbsp;Muhammad Kamran Siddiqui","doi":"10.1016/j.compbiolchem.2025.108558","DOIUrl":"10.1016/j.compbiolchem.2025.108558","url":null,"abstract":"<div><div>In this article, we establish a detailed mathematical investigation of the molecular graph of terbium niobate (TbNbO<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>) from a chemical graph theory point of view. A series of degree-based topological indices, such as Randić, ABC, GA, Zagreb, and their redefined versions, are calculated to define molecular structure. In addition, related entropy values from these indices are found to determine structural complexity and information content. Numerical and graphical studies illustrate how indices and entropies are related to molecular size, showing unique growth trends and sensitivities. Logarithmic SPSS regression models are formulated to investigate how topological indices are related to entropy measures, providing significant correlations. The findings show how various indices are complementary to each other in representing local and global structures and are useful in molecular characterization, drug discovery, and computational chemistry.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"119 ","pages":"Article 108558"},"PeriodicalIF":2.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}