Cingulo-Opercular Subnetworks Motivate Frontoparietal Subnetworks during Distinct Cognitive Control Demands.

Cognitive control is the ability to flexibly adapt behavior in a goal-directed manner when habit will not suffice. Control can be separated into distinct forms based on the timescale (present-future) and/or medium (external-internal) over which it operates. Both the frontoparietal network (FPN) and cingulo-opercular network (CON) are engaged during control, but their respective functions and interactions remain unclear. Here, we examined activations in the FPN and CON with fMRI in humans (male and female) during a task that manipulated control across timescales/mediums. The findings show that the CON can be distinguished into the following two separable subnetworks mirroring the FPN: a rostral/ventral subnetwork sensitive to future-oriented control involving internal representations, and a caudal/dorsal subnetwork sensitive to present-oriented control involving external representations. Relative to the FPN, activation in the CON was particularly pronounced during transitions into and out of particular control demands. Moreover, the relationship of each CON subnetwork to behavior was mediated by a respective FPN subnetwork. Such data are consistent with the idea that the CON motivates the FPN, which, in turn, drives behavior. Within the CON, the dorsomedial prefrontal cortex (dmPFC) mediated the relationship between the anterior insula and FPN, suggesting that the dmPFC acts as the crux that links the CON to the FPN. Collectively, these data indicate that parallel CON-FPN subnetworks mediate controlled behaviors at distinct timescales/mediums.SIGNIFICANCE STATEMENT The cingulo-opercular network (CON) and frontoparietal network (FPN) are engaged in diverse, demanding tasks. A functional model describing how areas within these networks can be distinguished, and also interact, would facilitate understanding of how the brain adapts to demanding situations. During a comprehensive control task, fMRI data revealed that the FPN and CON can be fractionated into subnetworks based on control demands that are either externally oriented for use in the present, or control demands that operate internally to guide future behavior. Moreover, we found evidence for a chain of relationships from the CON to FPN to behavior consistent with the idea that the CON drives the FPN to adapt behavior.

Comparative analysis of kinetic realizations of insulin signaling.

Several studies have developed dynamical models to understand the underlying mechanisms of insulin signaling, a signaling cascade that leads to the translocation of glucose, the human body's main source of energy. Fortunately, reaction network analysis allows us to extract properties of dynamical systems without depending on their model parameter values. This study focuses on the comparison of insulin signaling in healthy state (INSMS or INSulin Metabolic Signaling) and in type 2 diabetes (INRES or INsulin RESistance) using reaction network analysis. The analysis uses network decomposition to identify the different subsystems involved in insulin signaling (e.g., insulin receptor binding and recycling, GLUT4 translocation, and ERK signaling pathway, among others). Furthermore, results show that INSMS and INRES are similar with respect to some network, structo-kinetic, and kinetic properties. Their differences, however, provide insights into what happens when insulin resistance occurs. First, the variation in the number of species involved in INSMS and INRES suggests that when irregularities occur in the insulin signaling pathway, other complexes (and, hence, other processes) get involved, characterizing insulin resistance. Second, the loss of concordance exhibited by INRES suggests less restrictive interplay between the species involved in insulin signaling, leading to unusual activities in the signaling cascade. Lastly, GLUT4 losing its absolute concentration robustness in INRES may signify that the transporter has lost its reliability in shuttling glucose to the cell, inhibiting efficient cellular energy production. This study also suggests possible applications of the equilibria parametrization and network decomposition, resulting from the analysis, to potentially establish absolute concentration robustness in a species.

Imbalance in functional and structural connectivity underlying goal-directed and habitual learning systems in obsessive-compulsive disorder.

An imbalance between the goal-directed and habitual learning systems has been proposed to underlie compulsivity in obsessive-compulsive disorder (OCD). In addition, the overall balance between these systems may be influenced by stress hormones. We examined the multimodal networks underlying these dual learning systems. Both functional and structural measures indicated reduced connectivity within the goal-directed subnetwork (FC: P = 0.042; SC-FN: P = 0.014) and reduced connectivity between the goal-directed and habitual subnetworks (FC: P = 0.014; SC-FA: P = 0.052), but no differences within the habitual subnetwork in patients with OCD compared with controls. Path modeling indicated that anatomical connectivity in the goal-directed subnetwork influenced compulsive symptoms (R2 = 0.41), whereas functional connectivity within the habit subnetwork and between goal-directed and habitual subnetworks influenced obsessive symptoms (R2 = 0.63). In addition, the relationship between anatomical connectivity in the goal-directed subnetwork and compulsion was moderated by the stress hormone ACTH (adrenocorticotropic hormone), such that at low levels of ACTH greater connectivity resulted in lower compulsion, but at high levels of ACTH this relationship was reversed. These results provide new insights into the neural correlates of the imbalance between dual learning systems, and their relationship with symptom dimensions in patients with OCD. It may further support the reconceptualization of OCD as "compulsive-obsessive disorder," with a greater focus on the transdiagnostic dimension of compulsivity.

Comparative analysis of protein-protein interaction networks in neural differentiation mechanisms.

Neural differentiation as a major process during neural cell therapy is one of the main issues that is not fully characterized. This study focuses on the major deconstruction of the transcriptional networks that regulate cell fate determination during neural differentiation under the influence of RA signalling. In our studies, we used four different microarray datasets containing a total of 15,660 genes to determine which genes were differentially expressed during neural differentiation from pluripotent stem cells (P19), among the 17 samples from four different datasets that were integrated via meta-analysis approaches. Of the 15,660 gene expression in our data integration, 443 DEGs are induced during neural differentiation. Upstream dissection of these 443 DEGs revealed a network of protein-protein interactions (PPIs) from TFs and kinases, as well as intermediate proteins between them, which are indicated by three (POU51, NANOG, and FOXO1) down-expression genes and one PAX6 up-expression gene playing roles in up-stream of these 443 induced DEGs during neural differentiation. The constructed network from the PPIs database revealed that four novel sub-networks play major roles in neuron differentiation in cluster 3, retinol metabolism in cluster 4, Rap1 signalling pathways in cluster 2, and axonogenesis in cluster 6. These four clusters have revealed very useful information about how neural characterization will be created from pluripotent stem cells. This research reveals a plethora of information on the neural differentiation process, including cell commitment and neural differentiation, and lays the groundwork for future research into particular pathways involving protein-protein interactions in neurogenesis.

Identification of Differential Circular RNA Expression Profiles and Functional Networks in Human Macrophages Induced by Virulent and Avirulent Mycobacterium tuberculosis Strains.

Circular RNAs (circRNAs) are noncoding RNAs with diverse functions. However, most Mycobacterium tuberculosis (M.tb)-related circRNAs remain undiscovered. In this study, we infected THP-1 cells with virulent and avirulent M.tb strains and then sequenced the cellular circRNAs. Bioinformatic analysis predicted 58,009 circRNAs in all the cells. In total, 2035 differentially expressed circRNAs were identified between the M.tb-infected and uninfected THP-1 cells and 1258 circRNAs were identified in the virulent and avirulent M.tb strains. Further, the top 10 circRNAs were confirmed by Sanger sequencing, among which four circRNAs, namely circSOD2, circCHSY1, circTNFRSF21, and circDHTKD1, which were highly differentially expressed in infected cells compared with those in uninfected cells, were further confirmed by ring formation, specific primers, and RNase R digestion. Next, circRNA-miRNA-mRNA subnetworks were constructed, such as circDHTKD1/miR-660-3p/IL-12B axis. Some of the individual downstream genes, such as miR-660-3p and IL-12B, were previously reported to be associated with cellular defense against pathological processes induced by M.tb infection. Because macrophages are important immune cells and the major host cells of M.tb, these findings provide novel ideas for exploring the M.tb pathogenesis and host defense by focusing on the regulation of circRNAs during M.tb infection.

Combinatorial Network of Transcriptional and miRNA Regulation in Colorectal Cancer.

Colorectal cancer is one of the leading causes of cancer-associated mortality across the worldwide. One of the major challenges in colorectal cancer is the understanding of the regulatory mechanisms of biological molecules. In this study, we aimed to identify novel key molecules in colorectal cancer by using a computational systems biology approach. We constructed the colorectal protein-protein interaction network which followed hierarchical scale-free nature. We identified TP53, CTNBB1, AKT1, EGFR, HRAS, JUN, RHOA, and EGF as bottleneck-hubs. The HRAS showed the largest interacting strength with functional subnetworks, having strong correlation with protein phosphorylation, kinase activity, signal transduction, and apoptotic processes. Furthermore, we constructed the bottleneck-hubs' regulatory networks with their transcriptional (transcription factor) and post-transcriptional (miRNAs) regulators, which exhibited the important key regulators. We observed miR-429, miR-622, and miR-133b and transcription factors (EZH2, HDAC1, HDAC4, AR, NFKB1, and KLF4) regulates four bottleneck-hubs (TP53, JUN, AKT1 and EGFR) at the motif level. In future, biochemical investigation of the observed key regulators could provide further understanding about their role in the pathophysiology of colorectal cancer.

De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet.

BACKGROUND: With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem. RESULTS: We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software. CONCLUSION: The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.

A novel subnetwork representation learning method for uncovering disease-disease relationships.

Analyzing disease-disease relationships plays an important role for understanding disease mechanisms and finding alternative uses for a drug. A disease is usually the result of abnormal state of multiple molecular process. Since biological networks can model the interplay of multiple molecular processes, network-based methods have been proposed to uncover the disease-disease relationships recently. Given a disease and a network, the disease could be represented as a subnetwork constructed by the disease genes involved in the given network, named disease subnetwork. Because it is difficult to learn the feature representation of disease subnetworks, most existing methods are unsupervised ones without using labeled information. To fill this gap, we propose a novel method named SubNet2vec to learn the feature vectors of diseases from their corresponding subnetwork in the biological network. By utilizing the feature representation of disease subnetwork, we can analyze disease-disease relationships in a supervised fashion. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on disease-disease/disease-drug association prediction. The source code and data are available athttps://github.com/MedicineBiology-AI/SubNet2vec.git.

Reaction Network Analysis of Metabolic Insulin Signaling.

Absolute concentration robustness (ACR) and concordance are novel concepts in the theory of robustness and stability within Chemical Reaction Network Theory. In this paper, we have extended Shinar and Feinberg's reaction network analysis approach to the insulin signaling system based on recent advances in decomposing reaction networks. We have shown that the network with 20 species, 35 complexes, and 35 reactions is concordant, implying at most one positive equilibrium in each of its stoichiometric compatibility class. We have obtained the system's finest independent decomposition consisting of 10 subnetworks, a coarsening of which reveals three subnetworks which are not only functionally but also structurally important. Utilizing the network's deficiency-oriented coarsening, we have developed a method to determine positive equilibria for the entire network. Our analysis has also shown that the system has ACR in 8 species all coming from a deficiency zero subnetwork. Interestingly, we have shown that, for a set of rate constants, the insulin-regulated glucose transporter GLUT4 (important in glucose energy metabolism), has stable ACR.

Genome-wide unraveling SNP pairwise epistatic effects associated with sheep body weight.

Epistatic effects are an important part of the genetic effect of complex traits in livestock. In this study, we used 218 synthetic ewes from the Xinjiang Academy of Agricultural Reclamation in China to identify interacting paired with genome-wide single nucleotide polymorphisms (SNPs) associated with birth weight, weaning weight, and one-yearling weight. We detected 2 and 66 SNP-SNP interactions of sheep birth weight and weaning weight, respectively. No significant epistatic interaction of one-year-old body weight was detected. The genetic interaction of sheep body weight is dynamic and time-dependent. Most significant interactions of weaning body weight contributed 1% or higher. In the weaning weight trait, 66 significant SNP pairs consisted of 98 single SNPs covering 23 chromosomes, 5 of which were nonsynonymous SNPs (nsSNPs), resulting in single amino acid substitution. We found that genes that interact with transcription factors (TFs) are target genes for the corresponding TFs. Four epitron networks affecting weaning weight, including subnetworks of HIVEP3 and BACH2 transcription factors, constructed using significant SNP pairs, were also analyzed and annotated. These results suggest that transcription factors may play an important role in explaining epistatic effects. It provides a new idea to study the genetic mechanism of weight developing.

Colorful Image Colorization with Classification and Asymmetric Feature Fusion.

An automatic colorization algorithm can convert a grayscale image to a colorful image using regression loss functions or classification loss functions. However, the regression loss function leads to brown results, while the classification loss function leads to the problem of color overflow and the computation of the color categories and balance weights of the ground truth required for the weighted classification loss is too large. In this paper, we propose a new method to compute color categories and balance the weights of color images. In this paper, we propose a new method to compute color categories and balance weights of color images. Furthermore, we propose a U-Net-based colorization network. First, we propose a category conversion module and a category balance module to obtain the color categories and to balance weights, which dramatically reduces the training time. Second, we construct a classification subnetwork to constrain the colorization network with category loss, which improves the colorization accuracy and saturation. Finally, we introduce an asymmetric feature fusion (AFF) module to fuse the multiscale features, which effectively prevents color overflow and improves the colorization effect. The experiments show that our colorization network has peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) metrics of 25.8803 and 0.9368, respectively, for the ImageNet dataset. As compared with existing algorithms, our algorithm produces colorful images with vivid colors, no significant color overflow, and higher saturation.

DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement.

Polar codes are closer to the Shannon limit with lower complexity in coding and decoding. As traditional decoding techniques suffer from high latency and low throughput, with the development of deep learning technology, some deep learning-based decoding methods have been proposed to solve these problems. Usually, the deep neural network is treated as a black box and learns to map the polar codes with noise to the original information code directly. In fact, it is difficult for the network to distinguish between valid and interfering information, which leads to limited BER performance. In this paper, a deep residual network based on information refinement (DIR-NET) is proposed for decoding polar-coded short packets. The proposed method works to fully distinguish the effective and interference information in the codewords, thus obtaining a lower bit error rate. To achieve this goal, we design a two-stage decoding network, including a denoising subnetwork and decoding subnetwork. This structure can further improve the accuracy of the decoding method. Furthermore, we construct the whole network solely on the basis of the attention mechanism. It has a stronger information extraction ability than the traditional neural network structure. Benefiting from cascaded attention modules, information can be filtered and refined step-by-step, thus obtaining a low bit error rate. The simulation results show that DIR-Net outperforms existing decoding methods in terms of BER performance under both AWGN channels and flat fading channels.

Maladaptive brain organization at 1 month into abstinence as an indicator for future relapse in patients with alcohol use disorder.

Abstinence is a lifelong endeavor, and the risk of a relapse is always present for patients with Alcohol Use Disorder (AUD). The aim of the study was to better understand specific characteristics of the intrinsic whole-brain-network architecture of 34 AUD patients that may support abstinence or relapse. We used Graph Theory Analysis (GTA) of resting-state fMRI data from treatment seekers at 1 month of abstinence and their follow-up data as abstainers or relapsers 3 months later, together with data from 30 light/non-drinking controls scanned at the same interval. We determined the group-specific intrinsic community configurations at both timepoints as well as the corresponding modularity Q, a GTA measure that quantifies how well individual network communities are separated from each other. Both AUD groups at both timepoints had community configurations significantly different from those of controls, but the three groups did not significantly differ in their Q values. However, relapsers showed a maladaptive community configuration at baseline, which became more similar to the controls' community organization after the relapsers had started consuming alcohol again during the study interval. Additionally, successful recovery from AUD was not associated with re-gaining the intrinsic brain organization found in light/non-drinkers, but with a re-configuration resulting in a new brain organization distinctly different from that of healthy controls. Resting-state fMRI provides useful measures reflecting neuroplastic adaptations related to AUD treatment outcome.

Post genome-wide association analysis: dissecting computational pathway/network-based approaches.

Over thousands of genetic associations to diseases have been identified by genome-wide association studies (GWASs), which conceptually is a single-marker-based approach. There are potentially many uses of these identified variants, including a better understanding of the pathogenesis of diseases, new leads for studying underlying risk prediction and clinical prediction of treatment. However, because of inadequate power, GWAS might miss disease genes and/or pathways with weak genetic or strong epistatic effects. Driven by the need to extract useful information from GWAS summary statistics, post-GWAS approaches (PGAs) were introduced. Here, we dissect and discuss advances made in pathway/network-based PGAs, with a particular focus on protein-protein interaction networks that leverage GWAS summary statistics by combining effects of multiple loci, subnetworks or pathways to detect genetic signals associated with complex diseases. We conclude with a discussion of research areas where further work on summary statistic-based methods is needed.

Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module.

Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze & excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.

Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma.

BACKGROUND: Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting. RESULTS: We built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories. We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets. CONCLUSIONS: This 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.

Identification of lncRNA-associated differential subnetworks in oesophageal squamous cell carcinoma by differential co-expression analysis.

Differential expression analysis has led to the identification of important biomarkers in oesophageal squamous cell carcinoma (ESCC). Despite enormous contributions, it has not harnessed the full potential of gene expression data, such as interactions among genes. Differential co-expression analysis has emerged as an effective tool that complements differential expression analysis to provide better insight of dysregulated mechanisms and indicate key driver genes. Here, we analysed the differential co-expression of lncRNAs and protein-coding genes (PCGs) between normal oesophageal tissue and ESCC tissues, and constructed a lncRNA-PCG differential co-expression network (DCN). DCN was characterized as a scale-free, small-world network with modular organization. Focusing on lncRNAs, a total of 107 differential lncRNA-PCG subnetworks were identified from the DCN by integrating both differential expression and differential co-expression. These differential subnetworks provide a valuable source for revealing lncRNA functions and the associated dysfunctional regulatory networks in ESCC. Their consistent discrimination suggests that they may have important roles in ESCC and could serve as robust subnetwork biomarkers. In addition, two tumour suppressor genes (AL121899.1 and ELMO2), identified in the core modules, were validated by functional experiments. The proposed method can be easily used to investigate differential subnetworks of other molecules in other cancers.

Predicting escitalopram monotherapy response in depression: The role of anterior cingulate cortex.

Neuroimaging biomarkers of treatment efficacy can be used to guide personalized treatment in major depressive disorder (MDD). Escitalopram is recommended as first-line therapy for MDD and severe depression. An interesting hypothesis suggests that the reconfiguration of dynamic brain networks might provide important insights into antidepressant mechanisms. The present study assesses whether the spatiotemporal modulation across functional brain networks could serve as a predictor of effective antidepressant treatment with escitalopram. A total of 106 first-episode, drug-naïve patients and 109 healthy controls from three different multicenters underwent resting-state functional magnetic resonance imaging. Patients were considered as responders if they had a reduction of at least 50% in Hamilton Rating Scale for Depression scores at endpoint (>2 weeks). Multilayer modularity framework was applied on the whole brain to construct features in relation to network dynamic characters that were used for multivariate pattern analysis. Linear soft-threshold support vector machine models were used to separate responders from nonresponders. The permutation tests demonstrated the robustness of discrimination performances. The discriminative regions formed a spatially distributed pattern with anterior cingulate cortex (ACC) as the hub in the default mode subnetwork. Interestingly, a significantly larger module allegiance of ACC was also found in treatment responders compared to nonresponders, suggesting high interactivities of ACC to other regions may be beneficial for the recovery after treatment. Consistent results across multicenters confirmed that ACC could serve as a predictor of escitalopram monotherapy treatment outcome, implying strong likelihood of replication in the future.

Microarray analysis of preterm preeclampsia.

Premature preeclampsia is the second cause of maternal mortalities around the world. To investigate its potential driving mechanism(s), we constructed a multi-regulatory-mediated preeclampsia dysfunction module. Through combining differential expression analysis, co-expression analysis, and enrichment analysis, we obtained 23 sets of preeclampsia expression disorder modules in the disease, which involve the modular aggregations of 3016 genes. The modules were subjected to be analyzed for GO and KEGG paths for enrichment analysis. Based on these pivotal regulators, it is possible to manipulate the essential parts of the modular subnetwork and study their cooperative acts to mediate the driving mechanism of the preeclampsia. Simultaneously, they mainly cause the onset of the disease through the regulation of the apoptotic signaling pathway, down-regulation of an inflammatory response and retinol metabolism. This may present a potential driving mechanism for the disease. The predictor analysis of the regulators showed a series of non-coding RNAs that have potentially significant regulatory effects on the disease, including miR-182-5p, miR-200b-3p, miR-23a-3p, miR -429, miR-590-3p, and transcription factors. These pivotal regulators might mediate the potential driving processes. Based on a comprehensive multivariate analysis, we found a possible driving mechanism in which significant pivotal regulators were used as distinct functional segments in the preterm preeclampsia-driven process.

[Comparative research on brain networks of children with attention deficit hyperactivity disorder and normal children based on visual cognitive tasks].

Aiming at the difference between the brain networks of children with attention deficit hyperactivity disorder (ADHD) and normal children in the task-executing state, this paper conducted a comparative study using the network features of the visual function area. Functional magnetic resonance imaging (fMRI) data of 23 children with ADHD ï¼»age: (8.27 ± 2.77) yearsï¼½ and 23 normal children ï¼»age: (8.70 ± 2.58) yearsï¼½ were obtained by the visual capture paradigm when the subjects were performing the guessing task. First, fMRI data were used to build a visual area brain function network. Then, the visual area brain function network characteristic indicators including degree distribution, average shortest path, network density, aggregation coefficient, intermediary, etc. were obtained and compared with the traditional whole brain network. Finally, support vector machines (SVM) and other classifiers in the machine learning algorithm were used to classify the feature indicators to distinguish ADHD children from normal children. In this study, visual brain function network features were used for classification, with a classification accuracy of up to 96%. Compared with the traditional method of constructing a whole brain network, the accuracy was improved by about 10%. The test results show that the use of visual area brain function network analysis can better distinguish ADHD children from normal children. This method has certain help to distinguish the brain network between ADHD children and normal children, and is helpful for the auxiliary diagnosis of ADHD children.