工程技术最新文献

{"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}

{"title":"A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.","authors":"Ilknur Sercek, Niranjana Sampathila, Irem Tasci, Tuba Ekmekyapar, Burak Tasci, Prabal Datta Barua, Mehmet Baygin, Sengul Dogan, Turker Tuncer, Ru-San Tan, U R Acharya","doi":"10.1007/s11571-025-10249-7","DOIUrl":"https://doi.org/10.1007/s11571-025-10249-7","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop a computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: We retrospectively analyzed the EEG records of 134 AD and 113 non-AD patients. To generate multilevel features, a multilevel discrete wavelet transform was used to decompose the input EEG-signals. We devised a novel quantum-inspired EEG-signal feature extraction function based on 7-distinct different subgraphs of the Goldner-Harary pattern (GHPat), and selectively assigned a specific subgraph, using a forward-forward distance-based fitness function, to each input EEG signal block for textural feature extraction. We extracted statistical features using standard statistical moments, which we then merged with the extracted textural features. Other model components were iterative neighborhood component analysis feature selection, standard shallow k-nearest neighbors, as well as iterative majority voting and greedy algorithm to generate additional voted prediction vectors and select the best overall model results. With leave-one-subject-out cross-validation (LOSO CV), our model attained 88.17% accuracy. Accuracy results stratified by channel lead placement and brain regions suggested P4 and the parietal region to be the most impactful. Comparison with existing methods: The proposed model outperforms existing methods by achieving higher accuracy with a computationally efficient quantum-inspired approach, ensuring robustness and generalizability. Cortex maps were generated that allowed visual correlation of channel-wise results with various brain regions, enhancing model explainability.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"71"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143981499","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":"MicroRNA-214-3p Delivered by Bone Marrow Mesenchymal Stem Cells-Secreted Exosomes Affects Oxidative Stress in Alzheimer's Disease Rats by Targeting CD151.","authors":"Luzy Zhang","doi":"10.1080/15476278.2025.2489673","DOIUrl":"https://doi.org/10.1080/15476278.2025.2489673","url":null,"abstract":"<p><strong>Objective: </strong>This study probed the effect of targeted regulation of CD151 by microRNA-214-3p (miR-214-3p) delivered by bone marrow mesenchymal stem cells-secreted exosomes (BMSCs-exo) on oxidative stress and apoptosis of neurons in Alzheimer's disease (AD).</p><p><strong>Methods: </strong>Rat BMSCs were isolated, from which MSCs-exo were extracted and identified. The AD rat model was established and injected with MSC-exo suspension. Meanwhile, miR-214-3p and CD151 interfering lentivirus were transfected in MSCs. After injection, learning and cognitive ability of the rats were assessed, as well as neuronal apoptosis and oxidative stress injury. miR-214-3p and CD151 levels were determined, and their relationship was explored.</p><p><strong>Results: </strong>AD rats had prolonged escape latency, weakened learning and cognitive ability, increased neuronal apoptosis in the hippocampal CA3 region, and aggravated oxidative stress. After MSC-exo injection, these changes in AD rats were partially rescued. CD151 was targeted by miR-214-3p, and MSC-exo improved AD in rats through the miR-214-3p/CD151 axis.</p><p><strong>Conclusion: </strong>MSC-exo down-regulates CD151 by targeting miR-214-3p to enhance antioxidant capacity, thereby improving the pathological injury of AD rats.</p>","PeriodicalId":19596,"journal":{"name":"Organogenesis","volume":"21 1","pages":"2489673"},"PeriodicalIF":1.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12036478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143991800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized Individualized Nursing Improves Recovery and Reduces Complications in ICU Patients with Severe Pneumonia.","authors":"Linjuan Wu, Jingchuan Lin","doi":"10.1080/15476278.2025.2489670","DOIUrl":"10.1080/15476278.2025.2489670","url":null,"abstract":"<p><strong>Objective: </strong>This study evaluates the effectiveness of optimized individualized nursing interventions on clinical outcomes in intensive care unit (ICU) patients with severe pneumonia.</p><p><strong>Methods: </strong>In this randomized controlled trial, 76 patients with severe pneumonia were randomized into a control group and an experimental group. Both groups received routine nursing care. On this basis, the experimental group received optimized individualized nursing. After the nursing intervention, clinical outcomes, respiratory function, coagulation function, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and St. George's Respiratory Problems Questionnaire (SGRQ) score were assessed, and the complication and mortality rates were counted.</p><p><strong>Results: </strong>After the intervention, compared with the control group, the experimental group exhibited shorter times of fever reduction, white blood cell count recovery, and off-boarding and ICU stay, higher oxygenation index, lower rapid shallow breathing index, respiratory rate, activated partial thromboplastin time, prothrombin time, fibrinogen, and D-Dimer levels, lower APACHE II scores and SGRQ scores (<i>p</i> < 0.05). Additionally, the experimental group possessed a lower complication rate and mortality rate than the control group (<i>p</i> < 0.05).</p><p><strong>Conclusion: </strong>Implementing optimized individualized nursing can significantly enhance recovery and reduce complications in ICU patients with severe pneumonia.</p>","PeriodicalId":19596,"journal":{"name":"Organogenesis","volume":"21 1","pages":"2489670"},"PeriodicalIF":1.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11980446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of macrophage-associated diagnostic biomarkers and molecular subtypes in gestational diabetes mellitus based on machine learning.","authors":"Kai Wei, Liyun Yuan, Yongsheng Ge, Han Yu, Guoping Zhao, Guoqing Zhang, Guohua Liu","doi":"10.1080/21691401.2025.2513893","DOIUrl":"https://doi.org/10.1080/21691401.2025.2513893","url":null,"abstract":"<p><p>Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy, involving multiple immune and inflammatory factors. Macrophages play a crucial role in its development. This study integrated scRNA-seq and RNA-seq data to explore macrophage-related diagnostic genes and GDM subtypes. For scRNA-seq data, cell clusters were annotated using the SingleR package and validated with marker gene expression profiles, while hdWGCNA analysis identified three gene modules related to macrophages. A diagnostic model for GDM derived from endothelial cell transcriptomes was constructed by employing a variety of machine learning ensemble algorithms, achieving an AUC of 0.887. The model identified five differentially expressed genes (ZEB2, MALAT1, HEBP1, AHSA1, and TTC3) as potential diagnostic biomarkers. The CB-DSNMF algorithm was proposed to identify two distinct GDM subtypes from RNA-seq data, revealing significant differences in biological behaviours. This algorithm outperformed other baselines in multiple clustering metrics. Mendelian randomisation analysis identified ZEB2 as a gene causally related to GDM risk. A transcription factor (TF)-gene regulatory network was constructed for these genes using the ENCODE database. The study highlights the importance of macrophages in GDM, provides a high-precision diagnostic model, and offers new insights into personalised treatment strategies, contributing to a better understanding of GDM pathophysiology.</p>","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"53 1","pages":"20-33"},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233033","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}