The human brain can be regarded as a complex network with interacting connections between brain regions. Complex brain network analyses have been widely applied to functional magnetic resonance ...imaging (fMRI) data and have revealed the existence of community structures in brain networks. The identification of communities may provide insight into understanding the topological functions of brain networks. Among various community detection methods, the modularity maximization (MM) method has the advantages of model conciseness, fast convergence and strong adaptability to large-scale networks and has been extended from single-layer networks to multilayer networks to investigate the community structure changes of brain networks. However, the problems of MM, suffering from instability and failing to detect hierarchical community structure in networks, largely limit the application of MM in the community detection of brain networks. In this study, we proposed the weighted modularity maximization (WMM) method by using the weight matrix to weight the adjacency matrix and improve the performance of MM. Moreover, we further proposed the two-step WMM method to detect the hierarchical community structures of networks by utilizing node attributes. The results of the synthetic networks without node attributes demonstrated that WMM showed better partition accuracy than both MM and robust MM and better stability than MM. The two-step WMM method showed better accuracy of community partitioning than WMM for synthetic networks with node attributes. Moreover, the results of resting state fMRI (rs-fMRI) data showed that two-step WMM had the advantage of detecting the hierarchical communities over WMM and was more insensitive to the density of the rs-fMRI networks than WMM.
Visual discomfort significantly limits the broader application of stereoscopic display technology. Hence, the accurate assessment of stereoscopic visual discomfort is a crucial topic in this field. ...Electroencephalography (EEG) data, which can reflect changes in brain activity, have received increasing attention in objective assessment research. However, inaccurately labeled data, resulting from the presence of individual differences, restrict the effectiveness of the widely used supervised learning methods in visual discomfort assessment tasks. Simultaneously, visual discomfort assessment methods should pay greater attention to the information provided by the visual cortical areas of the brain. To tackle these challenges, we need to consider two key aspects: maximizing the utilization of inaccurately labeled data for enhanced learning and integrating information from the brain's visual cortex for feature representation purposes. Therefore, we propose the weakly supervised graph convolution neural network for visual discomfort (WSGCN-VD). In the classification part, a center correction loss serves as a weakly supervised loss, employing a progressive selection strategy to identify accurately labeled data while constraining the involvement of inaccurately labeled data that are influenced by individual differences during the model learning process. In the feature extraction part, a feature graph module pays particular attention to the construction of spatial connections among the channels in the visual regions of the brain and combines them with high-dimensional temporal features to obtain visually dependent spatio-temporal representations. Through extensive experiments conducted in various scenarios, we demonstrate the effectiveness of our proposed model. Further analysis reveals that the proposed model mitigates the impact of inaccurately labeled data on the accuracy of assessment.
Sleep data are typically characterized by class imbalance, which can cause the model to be overly biased toward frequent classes, resulting in low accuracy of minority class classification. However, ...the minority class of sleep staging has important value in diagnosing certain disorders, such as an N1 Stage that is too short indicating possible hypersomnia or narcolepsy. To address this problem, we propose a multi-view CNN model based on adaptive margin-aware loss. A novel margin-aware factor that considers the relative sample sizes of both frequent and minority classes can improve the overfitting of minority classes by increasing the regularization strength of minority classes without changing the sample size to maximize the decision margins of minority classes. On this basis, we propose margin-aware cross-entropy and margin-aware complement entropy loss, respectively. Margin-aware complement entropy can be achieved to increase the regularization for minority classes while neutralizing errors for minority classes, thus improving the classification accuracy for minority classes. Finally, the synergy of margin-aware complement entropy and cross-entropy is realized in an adaptive way to improve the sleep staging classification accuracy. We tested on three sleep datasets and compared them with the state-of-the-art, and the results demonstrate that our proposed algorithm not only improves the accuracy of sleep staging in general, but also improves the minority classes to a greater extent.
The recent emergence of mobile cloud computing has enabled mobile users to offload computing tasks from mobile devices to nearby cloudlets, so as to reduce energy consumption and improve application ...performance. In this paper, we consider the problem of maximizing the profit of the cloudlets' managing platform that receives computing requests from mobile users and fulfils these requests by leveraging computing service of participating cloudlets. However, it is very challenging to maximize the operating profit for such a managing platform, due to unpredictable arrival of user requests, dynamic participation of mobile cloudlets, and complexity in computing resource allocations. Based on the Lyapunov optimization technique combined with the technique of weight perturbation, we introduce a new stochastic control algorithm that makes online decisions on computing request admission and dispatching, computing service purchasing, and computing resource allocation. Different from traditional techniques, this algorithm does not require any statistical knowledge of relevant system dynamics, and is efficient for implementation in practice. Theoretical analysis and simulation results have demonstrated both the profit optimality and the system stability achieved by the proposed control algorithm.
Iris germanica, a species with very high ornamental value, exhibits the strongest drought resistance among the species in the genus Iris, but the molecular mechanism underlying its drought resistance ...has not been evaluated. To investigate the gene expression profile changes exhibited by high-drought-resistant I. germanica under drought stress, 10 cultivars with excellent characteristics were included in pot experiments under drought stress conditions, and the changes in the chlorophyll (Chl) content, plasma membrane relative permeability (RP), and superoxide dismutase (SOD), malondialdehyde (MDA), free proline (Pro), and soluble protein (SP) levels in leaves were compared among these cultivars. Based on their drought-resistance performance, the 10 cultivars were ordered as follows: 'Little Dream' > 'Music Box' > 'X'Brassie' > 'Blood Stone' > 'Cherry Garden' > 'Memory of Harvest' > 'Immortality' > 'White and Gold' > 'Tantara' > 'Clarence'. Using the high-drought-resistant cultivar 'Little Dream' as the experimental material, cDNA libraries from leaves and rhizomes treated for 0, 6, 12, 24, and 48 h with 20% polyethylene glycol (PEG)-6000 to simulate a drought environment were sequenced using the Illumina sequencing platform. We obtained 1, 976, 033 transcripts and 743, 982 unigenes (mean length of 716 bp) through a hierarchical clustering analysis of the resulting transcriptome data. The unigenes were compared against the Nr, Nt, Pfam, KOG/COG, Swiss-Prot, KEGG, and gene ontology (GO) databases for functional annotation, and the gene expression levels in leaves and rhizomes were compared between the 20% PEG-6000 stress treated (6, 12, 24, and 48 h) and control (0 h) groups using DESeq2. 7849 and 24,127 differentially expressed genes (DEGs) were obtained from leaves and rhizomes, respectively. GO and KEGG enrichment analyses of the DEGs revealed significantly enriched KEGG pathways, including ribosome, photosynthesis, hormone signal transduction, starch and sucrose metabolism, synthesis of secondary metabolites, and related genes, such as heat shock proteins (HSPs), transcription factors (TFs), and active oxygen scavengers. In conclusion, we conducted the first transcriptome sequencing analysis of the I. germanica cultivar 'Little Dream' under drought stress and generated a large amount of genetic information. This study lays the foundation for further exploration of the molecular mechanisms underlying the responses of I. germanica to drought stress and provides valuable genetic resources for the breeding of drought-resistant plants.
Using a single light source to simulate tunable spectra of various light sources, such as International Commission on Illumination (CIE) standard illuminants, has attracted great interest. This can ...be realized through the spectral mixing of multi-channel LEDs. However, there is currently a lack of a general and comprehensive spectral-mixing method that can effectively achieve the target spectra with a minimized number of LED channels. In this work, we propose such a method. It begins by determining the desired wavelength range according to an eye-sensitivity threshold based on the V(λ) function. Subsequently, a strategy for LED component selection is introduced. Lastly, detailed spectral mixing strategies are presented: (i) when using a large number of LED channels, spectral mixing optimization for direct spectral-shape matching ensures favorable outcomes; while (ii) with a limited number of LED channels, solely relying on the effort of matching the spectral shape may lead to a significant deviation in correlated color temperature (CCT). To solve this problem, an additional step of color mixing is applied. Our proposed method demonstrates significant improvements over prior methods in terms of spectral and CCT matching quality, as well as using a reduced number of LED channels.
Obtaining a fine bainitic ferrite for high carbon steel at low temperature requires a long period. Thus, creating strategies to accelerate bainite transformation is important. This investigation aims ...to explore the introduction of vanadium carbide at the bay region on bainitic transformation kinetics and mechanical properties by comparing isothermal bainitic transformation treatment directly and in combination with bay treatment prior to bainite transformation. Results show that introduced fine VC precipitation during bay treatment notably accelerates the initial bainite transformation with the incubation period being reduced by 91%. Decrease in activation energy for bainite nucleation at vanadium carbide/austenite interfaces and generation of preferred nucleation sites lead to a favorable acceleration of bainitic formation kinetics. However, indirect refinement of grain size for intragranular bainitic ferrite nucleation and consumption of available vanadium carbide/austenite interfaces result in overall rate of the subsequent bainite transformation. For the sample with prior bay treatment, the lath directions of bainitic ferrite are more complex and the size of block RA is smaller than that of the conventional sample. Moreover, the introduction of vanadium carbide at the bay region can simultaneously enhance the tensile strength and impact toughness of the steel.
Working memory is important for a wide range of high-level cognitive activities. Previous studies have shown that the dorsal lateral prefrontal cortex (DLPFC) plays a critical role in working memory ...and that behavioral training of working memory can alter the activity of DLPFC. However, it is unclear whether the activation in the DLPFC can be self-regulated and whether any self-regulation can affect working memory behavior. The recently emerged real-time functional magnetic resonance imaging (rtfMRI) technique enables the individuals to acquire self-control of localized brain activation, potentially inducing desirable behavioral changes. In the present study, we employed the rtfMRI technique to train subjects to up-regulate the activation in the left DLPFC, which is linked to verbal working memory. After two rtfMRI training sessions, activation in the left DLPFC was significantly increased, whereas the control group that received sham feedback did not show any increase in DLPFC activation. Pre- and post-training behavioral tests indicated that performance of the digit span and letter memory task was significantly improved in the experimental group. Between-group comparison of behavioral changes showed that the increase of digit span in the experimental group was significantly greater than that in the control group. These findings provide preliminary evidence that working memory performance can be improved through learned regulation of activation in associated brain regions using rtfMRI.
The anthocyanin biosynthetic pathway is the main pathway regulating floral coloration in Iris germanica, a well-known ornamental plant. We investigated the transcriptome profiles and targeted ...metabolites to elucidate the relationship between genes and metabolites in anthocyanin biosynthesis in the bitone flower cultivar ‘Clarence’, which has a deep blue outer perianth and nearly white inner perianth. In this study, delphinidin-, pelargonidin-, and cyanidin-based anthocyanins were detected in the flowers. The content of delphinidin-based anthocyanins increased with the development of the flower. At full bloom (stage 3), delphinidin-based anthocyanins accounted for most of the total anthocyanin metabolites, whereas the content of pelargonidin- and cyanidin-based anthocyanins was relatively low. Based on functional annotations, a number of novel genes in the anthocyanin pathway were identified, which included early biosynthetic genes IgCHS, IgCHI, and IgF3H and late biosynthetic genes Ig F3′5′H, IgANS, and IgDFR. The expression of key structural genes encoding enzymes, such as IgF3H, Ig F3′5′H, IgANS, and IgDFR, was significantly upregulated in the outer perianth compared to the inner perianth. In addition, most structural genes exhibited their highest expression at the half-color stage rather than at the full-bloom stage, which indicates that these genes function ahead of anthocyanins synthesis. Moreover, transcription factors (TFs) of plant R2R3-myeloblastosis (R2R3-MYB) related to the regulation of anthocyanin biosynthesis were identified. Among 56 R2R3-MYB genes, 2 members belonged to subgroup 4, with them regulating the expression of late biosynthetic genes in the anthocyanin biosynthetic pathway, and 4 members belonged to subgroup 7, with them regulating the expression of early biosynthetic genes in the anthocyanin biosynthetic pathway. Quantitative real-time PCR (qRT-PCR) analysis was used to validate the data of RNA sequencing (RNA-Seq). The relative expression profiles of most candidate genes were consistent with the FPKM of RNA-seq. This study identified the key structural genes encoding enzymes and TFs that affect anthocyanin biosynthesis, which provides a basis and reference for the regulation of plant anthocyanin biosynthesis in I. germanica.
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