工程技术最新文献

{"title":"Purinergic ecto-enzymes in human and ovine aortic valves: indicators of bacterial nanocellulose scaffold cellularization.","authors":"Barbara Kutryb-Zając, Ada Kawecka, Gabriela Harasim, Michał Bieńkowski, Klaudia Stawarska, Krzysztof Urbanowicz, Ryszard T Smoleński, Maciej M Kowalik, Magdalena Kołaczkowska, Piotr Siondalski","doi":"10.1080/21691401.2025.2502033","DOIUrl":"https://doi.org/10.1080/21691401.2025.2502033","url":null,"abstract":"<p><p>Purinergic signalling pathways play a vital role in the biological functions of the aortic valve (AV) through nucleotide and adenosine-dependent receptor effects. This study focused on characterizing a side-specific purinergic cascade in human non-stenotic and stenotic AVs, ovine native AVs and a novel bacterial nanocellulose (BNC) bio-prosthesis in an ovine model. Human stenotic AVs were collected during replacement surgeries, while non-stenotic AVs came from heart transplant patients. Ovine native AVs were sourced from domestic sheep, and the BNC prosthesis was implanted in the ovine aorta for six months, with hemodynamic monitoring throughout. Biochemical assessments revealed a beneficial ecto-enzyme pattern in non-stenotic and native AVs, contrasting with a detrimental pattern in stenotic valves. The BNC prosthesis demonstrated significantly lower nucleotide conversion activities than native valves and displayed increased peripheral blood mononuclear cell adhesion on its aortic surface. These findings suggest that nucleotide-converting ecto-enzymes could serve as markers for the biological activity of AV prostheses, highlighting the need for further studies to enhance the cellularization of BNC prostheses, potentially through adenosine-releasing scaffold modifications.</p>","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"53 1","pages":"219-230"},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958081","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":"Coherence resonance, parameter estimation and self-regulation in a thermalsensitive neuron.","authors":"Qun Guo, Ping Zhou, Xiaofeng Zhang, Zhigang Zhu","doi":"10.1007/s11571-025-10258-6","DOIUrl":"10.1007/s11571-025-10258-6","url":null,"abstract":"<p><p>In this work, two capacitors connected by a thermistor are used to explore the electrical property of double-layer membrane in a neuron, which the membrane property is sensitive to changes of temperature and two capacitive variables are used to measure the potentials of inner and outer membrane. The circuit characteristics and energy definition for the neural circuit and its equivalent neuron model in oscillator form are clarified from physical aspect. Considering the shape deformation of cell membrane under external physical stimuli and energy injection, intrinsic parameters of the neuron can be controlled with adaptive growth under energy flow, an adaptive control law is proposed to regulate the firing modes accompanying with energy shift. In presence of noisy excitation, coherence resonance can be induced and confirmed by taming the noise intensity carefully. The distributions of <i>CV</i> (coefficient variability) and average energy value < <i>H</i> > vs. noise intensity provide a feasible way to predict the coherence resonance and even stochastic resonance in the neural activities. Adaptive parameter observers are designed to identify the unknown parameters in this neuron model. The research findings of this study lay a foundation for the design of temperature-adaptive biomimetic neuromorphic devices and the research on multi-functional perception neural networks with temperature sensitivity.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"75"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep brain stimulation-induced two manners to eliminate bursting for Parkinson's diseases: synaptic current and bifurcation mechanisms.","authors":"Hui Zhou, Xianjun Wang, Huaguang Gu, Yanbing Jia","doi":"10.1007/s11571-025-10267-5","DOIUrl":"10.1007/s11571-025-10267-5","url":null,"abstract":"<p><p>Although deep brain stimulation (DBS) is effective in treating Parkinson's disease (PD) related to bursting, the underlying mechanisms remain unclear. In the present paper, the dynamical and synaptic mechanisms are studied in a basal ganglia-thalamus model. Firstly, slow and large oscillations of synaptic gating variables/currents are identified as the cause of the irregular and non-synchronous bursting for PD, indicating that interruption of these slow modulations may be a feasible measure to treat PD. Secondly, strong DBS with high frequency applied to subthalamic nucleus (STN) can induce fast synchronous spiking in both STN and external globus pallidus (GPe), then interrupt the slow gating variables, thereby eliminating the irregular bursting. Meanwhile, the gating variables of the excitatory and inhibitory synapses respectively from STN and GPe to the internal globus pallidus (GPi) become fast. Finally, competition between these two opposite synapses can induce two manners to eliminate the bursting of GPi and restore the normal state, appearing in vast majority of parameter space composed of multiple synaptic conductances. One is the synchronous silence of GPi, and the other the synchronous regular fast spiking, which occurs for large conductance of the inhibitory and excitatory synapse, respectively. Both result in regular spiking of thalamus, via interrupting slow gating variables of synapse projected to thalamus. In addition, as the two conductances approach each other, the synaptic current to GPi oscillates around zero slowly, resulting in irregular firings of GPi and thalamus for PD in a narrow parameter space. Furthermore, the bursting observed in PD before DBS and three types of electrical activities of GPi during DBS are explained, using a saddle-node bifurcation of limit cycles and oscillation patterns of synaptic current. The distinction from the post inhibitory rebound bursting reported in previous studies is discussed. The results present the mechanisms for DBS to treat PD via eliminating bursting in wide parameter region.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"78"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient system for classifying cyclic alternating pattern phases in sleep.","authors":"Megha Agarwal, Amit Singhal","doi":"10.1007/s11571-025-10261-x","DOIUrl":"10.1007/s11571-025-10261-x","url":null,"abstract":"<p><p>Electroencephalogram (EEG) signals are a popular tool to analyze sleep patterns. Cyclic alternating patterns (CAP) can be observed in EEG signals during unconscious periods of sleep. Detailed study of CAP can help in early diagnosis of many sleep disorders. Firstly, the CAP cycles need to be segregated into their constituents, phase A and phase B periods. In this work, we develop an accurate and easy-to-implement system to distinguish between the two CAP phases. The EEG signals are denoised and divided into smaller segments for an easier processing. These segments are decomposed into different frequency sub-bands using zero-phase filtering. Thereafter, statistical features are extracted from the sub-band components, and significant features are selected using the Kruskal-Wallis test. We consider four different algorithms for classification, namely, k-nearest neighbour (kNN), support vector machine (SVM), bagged tree (BT) and neural network (NN). The classification results are compiled for the datasets that include healthy subjects and those suffering from insomnia. The BT classifier produces the best results for the combined balanced dataset, with 83.29% accuracy and 83.58% F-1 score. The proposed method is more accurate and efficient than the existing schemes and can be considered for widespread deployments in real-world scenarios.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"79"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-patient seizure prediction via continuous domain adaptation and similar sample replay.","authors":"Ziye Zhang, Aiping Liu, Yikai Gao, Ruobing Qian, Xun Chen","doi":"10.1007/s11571-024-10216-8","DOIUrl":"10.1007/s11571-024-10216-8","url":null,"abstract":"<p><p>Seizure prediction based on electroencephalogram (EEG) for people with epilepsy, a common brain disorder worldwide, has great potential for life quality improvement. To alleviate the high degree of heterogeneity among patients, several works have attempted to learn common seizure feature distributions based on the idea of domain adaptation to enhance the generalization ability of the model. However, existing methods ignore the inherent inter-patient discrepancy within the source patients, resulting in disjointed distributions that impede effective domain alignment. To eliminate this effect, we introduce the concept of multi-source domain adaptation (MSDA), considering each source patient as a separate domain. To avoid additional model complexity from MSDA, we propose a continuous domain adaptation approach for seizure prediction based on the convolutional neural network (CNN), which performs sequential training on multiple source domains. To relieve the model catastrophic forgetting during sequential training, we replay similar samples from each source domain, while learning common feature representations based on subdomain alignment. Evaluated on a publicly available epilepsy dataset, our proposed method attains a sensitivity of 85.0% and a false alarm rate (FPR) of 0.224/h. Compared to the prevailing domain adaptation paradigm and existing domain adaptation works in the field, the proposed method can efficiently capture the knowledge of different patients, extract better common seizure representations, and achieve state-of-the-art performance.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"26"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A stacking classifier for distinguishing stages of Alzheimer's disease from a subnetwork perspective.","authors":"Gaoxuan Li, Bo Chen, Weigang Sun, Zhenbing Liu","doi":"10.1007/s11571-025-10221-5","DOIUrl":"10.1007/s11571-025-10221-5","url":null,"abstract":"<p><p>Accurately distinguishing stages of Alzheimer's disease (AD) is crucial for diagnosis and treatment. In this paper, we introduce a stacking classifier method that combines six single classifiers into a stacking classifier. Using brain network models and network metrics, we employ <i>t</i>-tests to identify abnormal brain regions, from which we construct a subnetwork and extract its features to form the training dataset. Our method is then applied to the ADNI (Alzheimer's Disease Neuroimaging Initiative) datasets, categorizing the stages into four categories: Alzheimer's disease, mild cognitive impairment (MCI), mixed Alzheimer's mild cognitive impairment (ADMCI), and healthy controls (HCs). We investigate four classification groups: AD-HCs, AD-MCI, HCs-ADMCI, and HCs-MCI. Finally, we compare the classification accuracy between a single classifier and our stacking classifier, demonstrating superior accuracy with our stacking classifier from a subnetwork-based viewpoint.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"38"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural dynamics of deception: insights from fMRI studies of brain states.","authors":"Weixiong Jiang, Lin Li, Yulong Xia, Sajid Farooq, Gang Li, Shuaiqi Li, Jinhua Xu, Sailing He, Xiangyu Wu, Shoujun Huang, Jing Yuan, Dexing Kong","doi":"10.1007/s11571-025-10222-4","DOIUrl":"10.1007/s11571-025-10222-4","url":null,"abstract":"<p><p>Deception is a complex behavior that requires greater cognitive effort than truth-telling, with brain states dynamically adapting to external stimuli and cognitive demands. Investigating these brain states provides valuable insights into the brain's temporal and spatial dynamics. In this study, we designed an experiment paradigm to efficiently simulate lying and constructed a temporal network of brain states. We applied the Louvain community clustering algorithm to identify characteristic brain states associated with lie-telling, inverse-telling, and truth-telling. Our analysis revealed six representative brain states with unique spatial characteristics. Notably, two distinct states-termed <i>truth-preferred</i> and <i>lie-preferred</i>-exhibited significant differences in fractional occupancy and average dwelling time. The truth-preferred state showed higher occupancy and dwelling time during truth-telling, while the lie-preferred state demonstrated these characteristics during lie-telling. Using the average z-score BOLD signals of these two states, we applied generalized linear models with elastic net regularization, achieving a classification accuracy of 88.46%, with a sensitivity of 92.31% and a specificity of 84.62% in distinguishing deception from truth-telling. These findings revealed representative brain states for lie-telling, inverse-telling, and truth-telling, highlighting two states specifically associated with truthful and deceptive behaviors. The spatial characteristics and dynamic attributes of these brain states indicate their potential as biomarkers of cognitive engagement in deception.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10222-4.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"42"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11842687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the impact of improved maize varieties on agricultural productivity and technical efficiency among smallholder farmers in the Eastern Cape, South Africa: an empirical analysis.","authors":"Lelethu Mdoda, Nthabeleng Tamako, Lungile S Gidi, Denver Naidoo","doi":"10.1080/21645698.2025.2476667","DOIUrl":"10.1080/21645698.2025.2476667","url":null,"abstract":"<p><p>Agriculture is essential to South Africa's economy, and maize is a crucial crop for smallholder farmers in the Eastern Cape. Traditional maize varieties face challenges related to productivity and resilience, prompting the promotion of Improved Maize Varieties (IMVs) to enhance yields and efficiency. This study investigates the impact of IMV adoption on agricultural productivity and technical efficiency in the region, addressing a gap in empirical evidence. Using a multistage random sampling approach, data was collected from 150 smallholder maize farmers and analyzed using stochastic production frontier, endogenous switching regression models, and the stochastic meta-frontier model. The study results reveal that 62% of the farmers are male, averaging 53 years old, and manage about four hectares with a mean monthly income of ZAR 3,562.13. Challenges, such as rainfall shortages and limited access to credit, hinder IMV adoption, although high access to extension services and diverse input use positively affect productivity. The adopted IMVs by farmers, including open-pollinated, hybrid, and genetically modified (GM) varieties, significantly boost maize yields and farm returns - yielding an average increase of 1.92 kg/ha and returns of ZAR 468.01 per hectare. Key adoption factors are education, farm size, and access to seeds and extension services, whereas barriers include market distance and family size. Technical efficiency is generally high at 74%, with farm size, seed, pesticides, agrochemicals, and fertilizers positively impacting maize production, whereas family labor negatively affects it. Factors such as age, education, and access to services significantly reduce technical inefficiency, while herd size, off-farm income, and distance to the market have mixed effects. The stochastic meta-frontier approach reveals that smallholder farmers adopting improved technologies show higher mean technical efficiency, indicating that advanced methods contribute to better resource use and productivity than traditional systems. This study suggests that targeted support is needed for farmers, enhancing access to extension services, affordable seeds, financial support, and investing in infrastructure and education can further improve adoption rates, technical efficiency, and overall productivity. Promoting improved technologies such as maize varieties will enhance the technical efficiency of farms, regardless of their adoption status. It would be key to improving overall agricultural productivity and farm household incomes.</p>","PeriodicalId":54282,"journal":{"name":"Gm Crops & Food-Biotechnology in Agriculture and the Food Chain","volume":"16 1","pages":"272-304"},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11925116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images.","authors":"B Nageswara Rao, U Rajendra Acharya, Ru-San Tan, Pratyusa Dash, Manoranjan Mohapatra, Sukanta Sabut","doi":"10.1007/s11571-025-10259-5","DOIUrl":"10.1007/s11571-025-10259-5","url":null,"abstract":"<p><p>Intracerebral haemorrhage (ICH) is a common form of stroke that affects millions of people worldwide. The incidence is associated with a high rate of mortality and morbidity. Accurate diagnosis using brain non-contrast computed tomography (NCCT) is crucial for decision-making on potentially life-saving surgery. Limited access to expert readers and inter-observer variability imposes barriers to timeous and accurate ICH diagnosis. We proposed a hybrid deep learning model for automated ICH diagnosis using NCCT images, which comprises a convolutional autoencoder (CAE) to extract features with reduced data dimensionality and a dense neural network (DNN) for classification. In order to ensure that the model generalizes to new data, we trained it using tenfold cross-validation and holdout methods. Principal component analysis (PCA) based dimensionality reduction and classification is systematically implemented for comparison. The study dataset comprises 1645 (\"ICH\" class) and 1648 (\"Normal\" class belongs to patients with non-hemorrhagic stroke) labelled images obtained from 108 patients, who had undergone CT examination on a 64-slice computed tomography scanner at Kalinga Institute of Medical Sciences between 2020 and 2023. Our developed CAE-DNN hybrid model attained 99.84% accuracy, 99.69% sensitivity, 100% specificity, 100% precision, and 99.84% F1-score, which outperformed the comparator PCA-DNN model as well as the published results in the literature. In addition, using saliency maps, our CAE-DNN model can highlight areas on the images that are closely correlated with regions of ICH, which have been manually contoured by expert readers. The CAE-DNN model demonstrates the proof-of-concept for accurate ICH detection and localization, which can potentially be implemented to prioritize the treatment using NCCT images in clinical settings.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"77"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer's disease diagnosis and biomarker identification.","authors":"Shan Huang, Yixin Liu, Jingyu Zhang, Yiming Wang","doi":"10.1080/21691401.2025.2506591","DOIUrl":"https://doi.org/10.1080/21691401.2025.2506591","url":null,"abstract":"<p><p>The unknown pathogenic mechanisms of Alzheimer's disease (AD) make treatment challenging. Neuroimaging genetics offers a method for identifying disease biomarkers for early diagnosis, but traditional approaches struggle with complex non-linear, multimodal and multi-expression data. However, traditional association analysis methods face challenges in handling nonlinear, multimodal and multi-expression data. Therefore, a multimodal attention fusion deep self-restructuring presentation (MAFDSRP) model is proposed to solve the above problem. First, multimodal brain imaging data are processed through a novel histogram-matching multiple attention mechanisms to dynamically adjust the weight of each input brain image data. Simultaneous, the genetic data are preprocessed to remove low-quality samples. Subsequently, the genetic data and fused neuroimaging data are separately input into the self-reconstruction network to learn the nonlinear relationships and perform subspace clustering at the top layer of the network. Finally, the learned genetic data and fused neuroimaging data are analysed through expression association analysis to identify AD-related biomarkers. The identified biomarkers underwent systematic multi-level analysis, revealing biomarker roles at molecular, tissue and functional levels, highlighting processes like inflammation, lipid metabolism, memory and emotional processing linked to AD. The experimental results show that MAFDSRP achieved 0.58 in association analysis, demonstrating its great potential in accurately identifying AD-related biomarkers.</p>","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"53 1","pages":"231-243"},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135996","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}