工程技术最新文献

{"title":"Distance-dependent connectivity in the brain facilitates high dynamical and structural complexity.","authors":"Victor J Barranca","doi":"10.1007/s11571-025-10398-9","DOIUrl":"10.1007/s11571-025-10398-9","url":null,"abstract":"<p><p>Recent experiments have revealed that the inter-regional connectivity of the cerebral cortex exhibits strengths spanning over several orders of magnitude and decaying with distance. We demonstrate this to be a fundamental organizing feature that fosters high complexity in both connectivity structure and network dynamics, achieving an advantageous balance between integration and differentiation of information. This is verified through analysis of a multi-scale neuronal network model with nonlinear integrate-and-fire dynamics, incorporating inter-regional connection strengths decaying exponentially with spatial separation at the macroscale as well as small-world local connectivity at the microscale. Through numerical simulation and optimization over the model parameterspace, we show that inter-regional connectivity over intermediate spatial scales naturally facilitates maximally heterogeneous connection strengths, agreeing well with experimental measurements. In addition, we formulate complementary notions of structural and dynamical complexity, which are computationally feasible to calculate for large multi-scale networks, and we show that high complexity manifests for each over a similar parameter regime. We expect this work may help explain the link between distance-dependence in brain connectivity and the richness of neuronal network dynamics in achieving robust brain computations and effective information processing.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"23"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743046/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145849087","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 novel dilated Bi-LSTM framework for depression detection from speech signals through feature fusion.","authors":"Uma Jaishankar, Jagannath H Nirmal, Girish Gidaye","doi":"10.1007/s11571-026-10411-9","DOIUrl":"https://doi.org/10.1007/s11571-026-10411-9","url":null,"abstract":"<p><p>A crucial method for determining a person's mental health and assessing their degree of depression is depression detection. To identify depression through speech or conversation, a number of sophisticated methods and questionnaires have been created. The constraints of the current system are as follows: reduced effectiveness as a result of poor feature selection and extraction, problems with interpretability, and the difficulty of identifying depression in different languages. As a result, the proposed model is presented to offer improved accuracy and efficient performance. While adaptive threshold-based pre-processing (AdaT) is used to eliminate quiet and unnecessary information, the twinned Savitzky-Golay filter (TSaG) is used to minimize noise in the dataset. To turn the signal into an image, a Synchro-Squeezed Adaptive Wavelet Transform Algorithm (SSawT) is employed. The Singular Empirical Decomposition and Sparse Autoencoder (SiFE) model is used to extract linear and deep features. Input's deep, linear, and statistical properties are combined using the Weighted Soft Attention-based Fusion (WSAttF) model. From the fused features, the Chaotic Mud Ring Optimization algorithm (ChMR) chooses the best features. A Dilated Convolutional Neural Network (CNN) based Bidirectional-Long Short Term Memory-Bi-LSTM (DiCBiL) is used to detect different stages of depression, which lowers error rates and increases detection accuracy. The proposed method achieves 93.22% of F1-score, 93.11% precision, 93.12% recall, and 93.31% accuracy on the DAIC-WOZ original test set. During the testing time, two more datasets, namely AVEC 2019 and MELD, are used to validate the proposed performance, attaining an accuracy of 93.91% and 85.34% respectively.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-026-10411-9.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"44"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12868482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146123895","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":"EEG emotion recognition across subjects based on deep feature aggregation and multi-source domain adaptation.","authors":"Kunqiang Lin, Ying Li, Yiren He, Zihan Jiang, Renjie He, Xianzhe Wang, Hongxu Guo, Lei Guo","doi":"10.1007/s11571-025-10379-y","DOIUrl":"https://doi.org/10.1007/s11571-025-10379-y","url":null,"abstract":"<p><p>Electroencephalography (EEG) can objectively reflect an individual's emotional state. However, due to significant inter-subject differences, existing methods exhibit low generalization performance in emotion recognition across different individuals. Therefore, an EEG emotion classification framework based on deep feature aggregation and multi-source domain adaptation is proposed by us. First, we design a deep feature aggregation module that introduces a novel approach for extracting EEG hemisphere asymmetry features and integrates these features with the frequency and spatiotemporal characteristics of the EEG signals. Additionally, a multi-source domain adaptation strategy is proposed, where multiple independent feature extraction sub-networks are employed to process each domain separately, extracting discriminative features and thereby alleviating the feature shift problem between domains. Then, a domain adaptation strategy is employed to align multiple source domains with the target domain, thereby reducing inter-domain distribution discrepancies and facilitating effective cross-domain knowledge transfer. Simultaneously, to enhance the learning ability of target samples near the decision boundary, pseudo-labels are dynamically generated for the unlabeled samples in the target domain. By leveraging predictions from multiple classifiers, we calculate the average confidence of each pseudo-label group and select the pseudo-label set with the highest confidence as the final label for the target sample. Finally, the mean of the outputs from multiple classifiers is used as the model's final prediction. A comprehensive set of experiments was performed using the publicly available SEED and SEED-IV datasets. The findings indicate that the method we proposed outperforms alternative methods.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"8"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12644276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145630828","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":"[Formula: see text]AC-Dep: dynamic adaptive feature fusion and domain adaptation collaboration for cross-subject depression detection.","authors":"Huang Huang, Xinhui Li, Minchao Wu, Rui Ouyang, JingJing Li, Zhao Lv","doi":"10.1007/s11571-026-10474-8","DOIUrl":"https://doi.org/10.1007/s11571-026-10474-8","url":null,"abstract":"<p><p>Depression is a major global public health issue that profoundly deteriorates the quality of life for patients and increases the risk of mortality. Electroencephalogram (EEG) signals, as objective physiological markers, have emerged as a focal point in research for depression detection. However, existing methods suffer from inadequate feature representation and fusion, as well as poor model generalization performance due to individual variability. Inspired by the above observations, we propose a cross-subject depression detection method based on the synergy of dynamic adaptive feature fusion and domain adaptation. Specifically, we design a CNN-Transformer dual-branch structure to separately capture local and global EEG features, and further introduce a lightweight dynamically adaptive attention fusion module to efficiently integrate multi-branch features. Moreover, we propose an end-to-end collaborative optimization framework that unifies feature learning and domain adaptation. By jointly optimizing the source domain classification loss, pseudo-label loss, and domain-adversarial loss, the model extracts discriminative representations while aligning cross-domain distributions, thereby achieving deep coupling between feature discriminability and domain invariance and effectively improving cross-subject generalization. Experiments are conducted on two public datasets. Our method achieves 94.39% and 90.77% accuracy, outperforming the state-of-the-art baselines by 5.94% and 2.75%, respectively.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"104"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13230424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148161769","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":"JDA-RSDB: a multimodal domain adaptation method for cross-session emotion recognition from EEG and eye movement signals.","authors":"Magdiel Jiménez-Guarneros, Jonas Grande-Barreto, Gibran Fuentes-Pineda","doi":"10.1007/s11571-026-10447-x","DOIUrl":"10.1007/s11571-026-10447-x","url":null,"abstract":"<p><p>Multimodal emotion recognition has shown growing interest in affective computing, as combining Electroencephalogram (EEG) and eye movement (EM) signals enables the capture of complex emotional processes. However, EEG and EM signals are exposed to joint distribution differences across different days and recorded sessions, reducing the recognition performance. Currently, domain adaptation has been developed to address such distribution differences. Unfortunately, existing domain adaptation solutions still show suboptimal classification results, since ambiguous and non-discriminative decision boundaries are still learned during distribution matching. This paper presents Joint Distribution Alignment with Refined and Separable Decision Boundaries (JDA-RSDB), a multimodal domain adaptation method for cross-session emotion recognition from EEG and EM signals. Our proposed method assumes that a more discriminative feature representation must be ensured on new sessions during joint distribution matching. For this, JDA-RSDB produces similar marginal and conditional distributions between domains, first aligning feature statistics at modality and domain levels, and then, motivating consistent similarity between fused samples from different domains that produce the same class prediction. Simultaneously, this similarity is enhanced by learning a separable feature space on target data, placing decision boundaries on low-density regions. More importantly, decision boundaries are refined by achieving an agreement between target predictions from a principal classifier and those from an auxiliary classifier. Experiments were conducted on three public datasets, SEED-GER, SEED-IV, and SEED-V, in a cross-session setting. The proposed framework achieves an average accuracy of 83.33%, 80.89%, and 75.17% across the three available sessions on SEED-GER, SEED-IV, and SEED-V, outperforming state-of-the-art solutions.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"76"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13066199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147671064","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":"Multi-scroll generation mechanism, dynamic analysis, and DSP implementation of a dual-memristor-coupled Sprott-C system.","authors":"Yichen Bi, Shuhan Yang, Xianying Xu, Santo Banerjee, Jun Mou","doi":"10.1007/s11571-026-10430-6","DOIUrl":"https://doi.org/10.1007/s11571-026-10430-6","url":null,"abstract":"<p><p>A five-dimensional multi-scroll chaotic system is presented by introducing two memristive elements into a three-dimensional chaotic system. The resulting model generates multi-scroll attractors whose scroll count can be regulated by tuning the memristors' internal parameters. We analyze the equilibria and then quantify the dynamic behaviors using phase portraits, Poincaré sections, bifurcation diagrams, and Lyapunov exponents. Coexisting multi-scroll attractors are observed, and their attraction basins are mapped to visualize the corresponding spatial domains. Parameter-driven adjustment of local amplitude is also demonstrated, enabling flexible modulation of the system output. A DSP-based implementation is further provided to validate the realizability of the proposed design. The study advances memristor-assisted multi-scroll construction and supports engineering-oriented hardware realization of high-dimensional chaotic systems.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"63"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12988073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147466737","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 new BCI paradigm based on biological brain - digital twin brain dialogue.","authors":"Ting Zhang, Rongxin Zhang, Xin Zeng, Min Zeng, Yuanhang Xu, Yue Xiong, Ge Zhang, Daqing Guo, Dezhong Yao","doi":"10.1007/s11571-026-10439-x","DOIUrl":"10.1007/s11571-026-10439-x","url":null,"abstract":"<p><p>Brain-computer interface (BCI) establishes a bidirectional pathway between the brain and external devices. Its applications fall into two main categories: utilizing the brain as a controller (e.g., for prosthetics) or as a modulation target (e.g., for cognitive regulation). Progress in BCI is constrained by two core bottlenecks: in brain control, limited understanding of neural coding mechanisms restricts improvements in the accuracy and robustness of encoding/decoding algorithms; in brain regulation, one-size-fits-all regulatory strategies struggle to address significant individual variability, resulting in heterogeneous therapeutic responses. Inspired by neuroscience advances, this perspective proposes a new biological brain - digital twin brain based BCI (BDBCI) paradigm. Here, the biological brain acts as an empirical anchor and ultimate validation platform, while a high-fidelity digital twin brain (DTB) serves as a theoretical inference engine and virtual testbed. Specifically, experimental induction is applied to the biological brain to distill preliminary conclusions, such as brain-behavior mappings and brain-stimulation causal relationships, which are then used to construct and calibrate the DTB model. Subsequently, on the DTB platform, large-scale model deduction is conducted to validate and deepen these preliminary insights mechanistically, thereby optimizing control/regulation parameters or informing the parameter ranges for the next round of experimental induction and model deduction. Through this BDBCI paradigm, we aim to advance BCI research from empirical trial-and-error toward a new era of model-driven, predictable, and explainable precision science.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"70"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13009331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147510091","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":"Antibacterial and antibiofilm evaluation of biogenic ternary CuO-AgO-MgO nanocomposite against clinical isolates of carbapenem-resistant <i>Klebsiella pneumoniae</i>.","authors":"Mohammed S Abdulrahman, Nasir A Ibrahim, Fathy M Elkady, Amr H Hashem, Faisal Alsenani, Nosiba S Basher, Faisal H Altemani, Abdel-Aty E Alfeky, Ayman Salama, Adnan Alharbi, Sulaiman A Alsalamah, Fahd A Nasr, Mohammed Aufy, Ebrahim Saied","doi":"10.1080/21691401.2026.2665582","DOIUrl":"https://doi.org/10.1080/21691401.2026.2665582","url":null,"abstract":"<p><p>Escalation of antimicrobial resistance amongst <i>Klebsiella pneumoniae</i> (<i>K. pneumoniae</i>) represents a remarkable worldwide health concern. This study reports the green biosynthesis of copper, silver and magnesium oxide trimetallic nanocomposites (CuO-AgO-MgO TNCs) using banana peel extract as a safe and sustainable reducing and stabilizing agent and emphasizes its inhibitory activity against carbapenem-resistant <i>K. pneumoniae</i> (CRKP) strains. The TNCs were characterized by ultraviolet-visible (UV-vis) spectroscopy, Fourier transform infra-red (FTIR) spectroscopy, X-ray diffraction (XRD) analysis, transmission electron microscope (TEM), scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX) analysis, confirming their crystalline structure (average size 125 nm) and elemental composition (Cu, Ag, Mg, O). Cytotoxicity assessment using WI-38 normal cells showed minimal toxic effect with an IC₅₀ of 193.49 ± 2.85 µg/mL. Antibacterial activity was evaluated against genotypically diverse CRKP strains. Well diffusion assay (WDA) showed inhibition zones of 18-38 mm, while the broth microdilution assay provided minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values ranging from 8 to 1024 µg/mL and 128 to 2048 µg/mL, respectively. Time-kill assays indicated rapid bacterial growth reduction within 2 h, and antibiofilm assays showed 25-91% inhibition of biofilm formation. The image provided by SEM revealed pronounced morphological alterations in treated bacterial cells. Our findings exhibited biogenic CuO-AgO-MgO TNCs with potent antibacterial and antibiofilm activities against CRKP while showing negligible toxicity towards normal cells, highlighting their potential as a safe and effective alternative antimicrobial strategy.</p>","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"54 1","pages":"223-247"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148238618","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":"Prefrontal cortex-extracellular vesicle RNAs coupling in alcohol use disorder: towards non-invasive biomarkers and targeted therapeutics.","authors":"Jie-Quan Wang, Qing-Rong Xia, Xuan Zhou, Chun-Yu Yan, Wen-Xiu Hong, Yi-Yun Deng, Feng Shan, Ya-Ru Liu, Jun Liang","doi":"10.1080/21691401.2026.2617703","DOIUrl":"10.1080/21691401.2026.2617703","url":null,"abstract":"<p><p>Alcohol use disorder (AUD) poses a substantial global health burden, with its molecular mechanism remaining elusive. This study integrated datasets (GSE253155, GSE180722, GSE182173 and GSE176122) and clinical specimens to identify non-invasive biomarkers and therapeutic candidates for AUD. Transcriptomic analysis of post-mortem prefrontal cortex identified 34 differentially expressed genes (DEGs), significantly enriched in TNF/IL-17 signalling, ribosome, cytokine-cytokine receptor interaction and endoplasmic reticulum protein processing. Among multiple machine learning algorithms, the support vector machine demonstrated superior classification performance, prioritizing <i>MDK</i>, <i>FAM225B</i> and <i>SERPINA3</i> as critical biomarkers. These were incorporated into a highly predictive diagnostic nomogram. Intersection with plasma extracellular vesicle (EV) RNAs showed that all three biomarkers were detectable and upregulated in AUD patients versus controls, which was confirmed by quantitative real-time PCR (qRT-PCR) in a clinical validation cohort. <i>SERPINA3</i> exhibited a particularly robust association with AUD, maintaining significance after adjustment for confounders and suggesting direct effects via mediation analysis. Cross-species validation confirmed a conserved dysregulation of murine Serpina3n. Computational drug repurposing identified loratadine, doxepin, citalopram and imipramine as promising therapeutic candidates targeting <i>SERPINA3</i>. Collectively, this work delineated PFC signature genes, proposed a EVs-supported brain-periphery molecular crosstalk, and provided translatable biomarkers and repurposable candidates for AUD precision medicine.</p>","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"54 1","pages":"132-149"},"PeriodicalIF":5.6,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148148333","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":"Melatonin-enabled omics: understanding plant responses to single and combined abiotic stresses for climate-smart agriculture.","authors":"Ali Raza, Yiran Li, Sidra Charagh, Chunli Guo, Mengkai Zhao, Zhangli Hu","doi":"10.1080/21645698.2026.2614130","DOIUrl":"10.1080/21645698.2026.2614130","url":null,"abstract":"<p><p>Climate change-driven single and combined abiotic stresses pose escalating threats to sustainable, climate-smart agriculture and global food security. Melatonin (MLT, a powerful plant biostimulant) has established noteworthy potential in improving stress tolerance by regulating diverse physiological, biochemical, and molecular responses. Therefore, this review delivers a comprehensive synopsis of MLT-enabled omics responses across genomics, transcriptomics, proteomics, metabolomics, miRNAomics, epigenomics, phenomics, ionomics, and microbiomics levels that collectively regulate plant adaptation to multiple abiotic stresses. We also highlight the crosstalk between these omics layers and the power of integrated multi-omics (panomics) approaches to harness the complex regulatory networks underlying MLT-enabled stress tolerance. Lastly, we argue for translating these omics insights into actionable strategies through advanced genetic engineering and synthetic biology platforms to develop MLT-enabled, stress-smart crop plants.</p>","PeriodicalId":54282,"journal":{"name":"Gm Crops & Food-Biotechnology in Agriculture and the Food Chain","volume":"17 1","pages":"2614130"},"PeriodicalIF":4.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12851399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146055237","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}