IntroductionHigh frequency electroencephalogram (EEG) activity, particularly in the high gamma range, plays an important role in research on human emotions. However, the current understanding of high gamma EEG responses to emotional stimuli in virtual reality (VR) remains limited, especially regarding local activations and distributed network characteristics during different emotional states.MethodsIn this study, EEG responses to positive and negative VR stimuli were analyzed. EEG data were recorded from 19 participants as they viewed 4-second VR videos designed to elicit positive and negative responses. Two neural signatures were examined: high gamma band (53–80 Hz) spectral power and brain network features (nodal/local efficiency).Results and discussionSpectral power analysis revealed valence-specific spatial patterns in spectral power, with significantly higher frontal gamma activity during positive states and increased right temporal gamma power during negative states. Network analysis revealed elevated local efficiency during positive emotions, indicating enhanced modular connectivity. Machine learning classification demonstrated higher accuracy for spectral power features (73.57% ± 2.30%) compared to nodal efficiency (69.51% ± 2.62%) and local efficiency (65.03% ± 1.33%), with key discriminators identified in frontal, temporal, and occipital regions. These findings suggest that localized high gamma activity provides more direct discriminative information for emotion recognition in VR than network topology metrics, advancing the understanding of neurophysiological responses in immersive VR environments.

IntroductionAlzheimer’s Disease (AD) is a progressive neurodegenerative disorder, with Mild Cognitive Impairment (MCI) often serving as a prodromal stage. Early detection of MCI is critical for timely intervention.MethodsDynamic Functional Connectivity analysis reveals temporal dynamics obscured by static functional connectivity, making it valuable for analyzing and classifying psychiatric disorders. This study proposes a novel spatio-temporal approach for analyzing dynamic brain networks using resting-state fMRI. The method was evaluated on data from 85 subjects (33 healthy controls, 29 Early Mild Cognitive Impairment (EMCI), 23 AD) from the ADNI dataset.ResultsOur model outperformed existing techniques, achieving 83.9% accuracy and 83.1% AUC in distinguishing AD from healthy controls.DiscussionIn addition to improved classification performance, key affected regions such as left hippocampus, the right amygdala, the left inferior parietal lobe, the left olfactory cortex, the right precuneus, and the insula, were identified-areas known to be associated with memory function and early Alzheimer’s pathology. These findings suggest that dynamic connectivity analysis holds promise for non-invasive and interpretable early-stage diagnosis of AD.

Alzheimer’s disease (AD) is a significant neurological disorder characterized by progressive cognitive decline and memory loss. One essential task is understanding the molecular mechanisms underlying brain disorders of AD. Detecting biomarkers that contribute significantly to the classification of AD is an effective means to accomplish this essential task. However, most machine learning methods used to detect AD biomarkers require lengthy training and are unable to rapidly and effectively detect AD biomarkers. To detect biomarkers for AD accurately and efficiently, we proposed a novel approach using the Multi-Kernel Support Vector Machine (SVM) with Apriori algorithm to mine strongly associated feature sets from functional magnetic resonance imaging (fMRI) and gene expression profiles. Firstly, we downloaded the imaging data and genetic data of 121 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and transformed gene sequences into labeled sequences by encoding the four types of bases (A, T, C, and G) into distinct labels. Subsequently, we extracted the first 130 temporal sequences of brain regions and employed Pearson correlation analysis to construct “brain region gene pairs”. The integration of these data allowed us to explore the correlations between genes and brain regions. To improve classification accuracy and feature selection, we applied the Apriori algorithm to the multi-kernel SVM, dynamically building feature combinations and continuously validating classification results. By iteratively generating frequent itemsets, we obtained important brain region gene pairs. Experimental results show the effectiveness of our proposed approach. The Multi-Kernel SVM with Apriori model achieves an accuracy of 92.9%, precision of 95%, and an F1 score of 95% in classifying brain region-gene pairs within the AD–Late mild cognitive impairment (AD-LMCI) group. The amygdala, BIN1, RPN2, and IL15 associated with AD have been identified and demonstrate potential in identifying potential pathogenic factors of AD. The selected brain regions and associated genes may serve as valuable biomarkers for early AD diagnosis and better understanding of the disease’s molecular mechanisms. The integration of fMRI and gene data using the Multi-Kernel SVM–Apriori model holds great potential for advancing our knowledge of brain function and the genetic basis of neurological disorders. This approach provides a valuable tool for neuroscientists and researchers in the field of genomics and brain imaging studies.

The hypothalamic paraventricular nucleus (PVN) plays a central role in regulating cardiovascular activity and blood pressure (BP). We administered hydroxylamine hydrochloride (HA), a cystathionine-β-synthase (CBS) inhibitor, into the PVN to suppress endogenous hydrogen sulfide (H2S) and investigate its effects on the mitogen-activated protein kinase (MAPK) pathway in high salt-induced hypertension. We randomly divided 40 male Dahl salt-sensitive rats into 4 groups: the NS+PVN vehicle group, the NS+PVN HA group, the HS+PVN vehicle group, and the HS+PVN HA group, with 10 rats in each group. The rats in the NS (normal salt) groups were fed a normal-salt diet containing 0.3% NaCl, while the HS (high salt) groups were fed a high-salt diet containing 8% NaCl. The mean arterial pressure (MAP) was calculated after noninvasive measurement using an automatic sphygmomanometer to occlude the tail cuff once a week. HA or vehicle was infused into the bilateral PVN using Alzet osmotic mini-pumps for 6 weeks after the hypertension model was successfully established. We measured the levels of H2S in the PVN and plasma norepinephrine (NE) using ELISA. Additionally, we assessed the parameters of the MAPK pathway, inflammation, and oxidative stress through western blotting, immunohistochemical analysis, or real-time PCR. In the current study, we discovered that decreased levels of endogenous hydrogen sulfide in the PVN contributed to the onset of high salt-induced hypertension. This was linked to the activation of the MAPK signaling pathway, proinflammatory cytokines, and oxidative stress in the PVN, as well as the activation of the sympathetic nervous system.

Alzheimer's disease (AD) is an advanced and incurable neurodegenerative disease. Genetic variations are intrinsic etiological factors contributing to the abnormal expression of brain function and structure in AD patients. A new multimodal feature fusion called "magnetic resonance imaging (MRI)-p value" was proposed to construct 3D fusion images by introducing genes as a priori knowledge. Moreover, a new deep joint learning diagnostic model was constructed to fully learn images features. One branch trained a residual network (ResNet) to learn the features of local pathological regions. The other branch learned the position information of brain regions with different changes in the different categories of subjects' brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. The feature and position information of the two branches were linearly interacted to acquire the diagnostic basis for classifying the different categories of subjects. The diagnoses of AD and health control (HC), AD and mild cognitive impairment (MCI), HC and MCI were performed with data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results showed that the proposed method achieved optimal results in AD-related diagnosis. The classification accuracy (ACC) and area under the curve (AUC) of the three experimental groups were 93.44% and 96.67%, 89.06% and 92%, and 84% and 81.84%, respectively. Moreover, a total of six novel genes were found to be significantly associated with AD, namely NTM, MAML2, NAALADL2, FHIT, TMEM132D and PCSK5, which provided new targets for the potential treatment of neurodegenerative diseases.

Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.

Voxel-based morphometry provides an opportunity to study Alzheimer’s disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10−6) and cell adhesion molecules (corrected p-value = 5.44 × 10−4). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.

Alzheimer's disease (AD) was associated with abnormal organization and function of large-scale brain networks. We applied group independent component analysis (Group ICA) to construct the triple-network consisting of the saliency network (SN), the central executive network (CEN), and the default mode network (DMN) in 25 AD, 60 mild cognitive impairment (MCI) and 60 cognitively normal (CN) subjects. To explore the dynamic functional network connectivity (dFNC), we investigated dynamic time-varying triple-network interactions in subjects using Group ICA analysis based on k-means clustering (GDA-k-means). The mean of brain state-specific network interaction indices (meanNII) in the three groups (AD, MCI, CN) showed significant differences by ANOVA analysis. To verify the robustness of the findings, a support vector machine (SVM) was taken meanNII, gender and age as features to classify. This method obtained accuracy values of 95, 94, and 77% when classifying AD vs. CN, AD vs. MCI, and MCI vs. CN, respectively. In our work, the findings demonstrated that the dynamic characteristics of functional interactions of the triple-networks contributed to studying the underlying pathophysiology of AD. It provided strong evidence for dysregulation of brain dynamics of AD.

Alzheimer's disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer's Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.

In the studies of Alzheimer’s disease (AD), jointly analyzing imaging data and genetic data provides an effective method to explore the potential biomarkers of AD. AD can be separated into healthy controls (HC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD. In the meantime, identifying the important biomarkers of AD progression, and analyzing these biomarkers in AD provide valuable insights into understanding the mechanism of AD. In this paper, we present a novel data fusion method and a genetic weighted random forest method to mine important features. Specifically, we amplify the difference among AD, LMCI, EMCI and HC by introducing eigenvalues calculated from the gene p-value matrix for feature fusion. Furthermore, we construct the genetic weighted random forest using the resulting fused features. Genetic evolution is used to increase the diversity among decision trees and the decision trees generated are weighted by weights. After training, the genetic weighted random forest is analyzed further to detect the significant fused features. The validation experiments highlight the performance and generalization of our proposed model. We analyze the biological significance of the results and identify some significant genes (CSMD1, CDH13, PTPRD, MACROD2 and WWOX). Furthermore, the calcium signaling pathway, arrhythmogenic right ventricular cardiomyopathy and the glutamatergic synapse pathway were identified. The investigational findings demonstrate that our proposed model presents an accurate and efficient approach to identifying significant biomarkers in AD.

Alzheimer's disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD.

How to effectively extract features with high representation ability has always been a research topic and a challenge for classification tasks. Most of the existing methods mainly solve the problem by using deep convolutional neural networks as feature extractors. Although a series of excellent network structures have been successful in the field of Chinese ink-wash painting classification, but most of them adopted the methods of only simple augmentation of the network structures and direct fusion of different scale features, which limit the network to further extract semantically rich and scale-invariant feature information, thus hindering the improvement of classification performance. In this paper, a novel model based on multi-level attention and multi-scale feature fusion is proposed. The model extracts three types of feature maps from the low-level, middle-level and high-level layers of the pretrained deep neural network firstly. Then, the low-level and middle-level feature maps are processed by the spatial attention module, nevertheless the high-level feature maps are processed by the scale invariance module to increase the scale-invariance properties. Moreover, the conditional random field module is adopted to fuse the optimized three-scale feature maps, and the channel attention module is followed to refine the features. Finally, the multi-level deep supervision strategy is utilized to optimize the model for better performance. To verify the effectiveness of the model, extensive experimental results on the Chinese ink-wash painting dataset created in this work show that the classification performance of the model is better than other mainstream research methods.

人工神经网络通过模拟生物神经网络处理信息的过程和机制已经成功地解决了一些比较复杂的非线性问题,如分类问题.传统训练人工神经网络的方法容易陷入局部最优,并且模型可解释性差.因此用进化计算替代传统的训练方式来训练树突状神经网络模型.为了验证进化计算能够训练神经网络,采用了4个不同的分类数据集和3个不同的进化计算算法进行实验,结果表明了进化计算能够更好地训练神经网络.

本文首先阐述了应用型高校应立足自身,不断地探索和研究如何提高软件工程专业学生的实践创新能力的重要性.接着介绍了从培养模式、教学改革、创新能力平台等一系列的具体措施和做法,然后论述了多元化实践创新能力人才培养模式、多层次的实践教学体系、人才培养的管理和质量保障体系等在实践创新能力培养中的应用.最后对本文中提出的学生创新能力培养体系研究进行了总结.

As the well-known McCulloch–Pitts neuron model has long been criticized to be oversimplified, different algebra to formulate a single neuron model has received increasing interests. The dendritic neuron model (DNM) which considers the nonlinear information processing capacity of both synapses and dendrites has shown its effectiveness on classification problems. However, an effective learning method for DNM is still highly desired and challenging because the traditional error back-propagation (BP) algorithm usually suffers from issues originated from the proliferation of saddle points and local minima trapping. In this study, a novel non-BP learning algorithm based on the differential evolution algorithm is proposed. By fully incorporating the feedback information from evolving population and individuals, the proposed algorithm significantly outperforms other ten state-of-the-art non-BP algorithms and BP in terms of the classification accuracy. Additionally, the neural morphology and hardware realization are also confirmed. These lead us to believe that the well-learned DNM is considerably more powerful on computations than the traditional McCulloch–Pitts type, and can be possibly used as a fundamental unit in the next-generation deep learning techniques.

The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer's disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups with high accuracy is meaningful. In this study, by jointly analyzing the multimodal data, we propose a novel method to construct fusion features and a classification method based on the random forest for identifying the important features. Specifically, we construct the fusion features using the gene sequence and subregions correlation to reduce the diversity in same group. Moreover, samples and features are selected randomly to construct a random forest, and genetic algorithm and clustering evolutionary are used to amplify the difference in initial decision trees and evolve the trees. The features in resulting decision trees that reach the peak classification are the important "subregion gene pairs". The findings verify that our method outperforms well in classification performance and generalization. Particularly, we identified some significant subregions and genes, such as hippocampus amygdala transition area (HATA), fimbria, parasubiculum and genes included RYR3 and PRKCE. These discoveries provide some new candidate genes for AD and demonstrate the contribution of hippocampal subregions and genes to AD.

近些年来,江苏省大力开展中外合作办学教育模式.把"发展中外合作办学,积极推进办学投资体制多元化"作为实施"科教兴省"发展战略的重要举措之一.目前,江苏省在中外合作办学方面成效显著,共有4个中英合作办学机构,占全省中外合作机构总规模的16.7％;48个中英合作办学项目,占全省中外合作项目总数的17.6％.通过数据发现,我校的办学规模已经初见成效.文章从江苏省中英合作办学的现状出发,针对办学层次、区域分布情况、战略合作等方面提出有针对性的对策,旨在促进中英合作办学的教学质量.

为了提升医学诊断的预测精度,设计了增强稀疏自编码器和Softmax回归的特征学习和分类阶段组合方法.在稀疏自编码器(SAE)网络的特征学习中,通过惩罚网络的权重实现稀疏性,结合反向传播学习方法将变化向后传递并迭代优化成本函数.在Softmax回归分类阶段中,利用带动量的小批量梯度下降法来优化Softmax分类器的交叉熵,结合小批数据计算模型误差更新模型参数并实现收敛性.将所提出方法用于心脏病、宫颈癌和慢性肾病(CKD)数据集实验,其预测精度分别为91％、97％和98％,并且表现出较高的特征学习和鲁棒的分类性能.

Talent cultivation is the primary task of universities. Local general undergraduate colleges and universities should adhere to the basic guidelines of systematization, practicality and integration, continuously explore the concept of "student-centered" talent cultivation, and build a three-dimensional practical teaching system from three aspects: strengthening the planning and design of the three-dimensional practical teaching system; building an internal and external practical teaching platform; and improving the evaluation and guarantee system of practical teaching quality. The system of practical teaching quality evaluation and guarantee is improved. In order to improve the cultivation ability of applied talents in all aspects.

Single nucleotide polymorphisms (SNPs) play a significant role in microRNA (miRNA) generation, processing, and function and contribute to multiple phenotypes and diseases. Therefore, whole-genome analysis of how SNPs affect miRNA maturation mechanisms is important for precision medicine. The present study established an SNP-associated pre-miRNA (SNP-pre-miRNA) database, named miRSNPBase, and constructed SNP-pre-miRNA sequences. We also identified phenotypes and disease biomarker-associated isoform miRNA (isomiR) based on miRFind, which was developed in our previous study. We identified functional SNPs and isomiRs. We analyzed the biological characteristics of functional SNPs and isomiRs and studied their distribution in different ethnic groups using whole-genome analysis. Notably, we used individuals from Great Britain (GBR) as examples and identified isomiRs and isomiR-associated SNPs (iso-SNPs). We performed sequence alignments of isomiRs and miRNA sequencing data to verify the identified isomiRs and further revealed GBR ethnographic epigenetic dominant biomarkers. The SNP-pre-miRNA database consisted of 886 pre-miRNAs and 2640 SNPs. We analyzed the effects of SNP type, SNP location, and SNP-mediated free energy change during mature miRNA biogenesis and found that these factors were closely associated to mature miRNA biogenesis. Remarkably, 158 isomiRs were verified in the miRNA sequencing data for the 18 GBR samples. Our results indicated that SNPs affected the mature miRNA processing mechanism and contributed to the production of isomiRs. This mechanism may have important significance for epigenetic changes and diseases.