Neural mechanism of non-adaptive cognitive emotion regulation in patients with non-suicidal self-injury.

BACKGROUND: The incidence of non-suicidal self-injury (NSSI) has been on the rise in recent years. Studies have shown that people with NSSI have difficulties in emotion regulation and cognitive control. In addition, some studies have investigated the cognitive emotion regulation of people with NSSI which found that they have difficulties in cognitive emotion regulation, but there was a lack of research on cognitive emotion regulation strategies and related neural mechanisms. METHODS: This study included 117 people with NSSI (age = 19.47 ± 5.13, male = 17) and 84 non-NSSI participants (age = 19.86 ± 4.14, male = 16). People with NSSI met the DSM-5 diagnostic criteria, and non-NSSI participants had no mental or physical disorders. The study collected all participants' data of Cognitive Emotion Regulation Questionnaire (CERQ) and functional magnetic resonance imaging (fMRI) to explore the differences in psychological performance and brain between two groups. Afterwards, Machine learning was used to select the found differential brain regions to obtain the highest correlation regions with NSSI. Then, Allen's Human Brain Atlas database was used to compare with the information on the abnormal brain regions of people with NSSI to find the genetic information related to NSSI. In addition, gene enrichment analysis was carried out to find the related pathways and specific cells that may have differences. RESULTS: The differences between NSSI participants and non-NSSI participants were as follows: positive refocusing (t = -4.74, p < 0.01); refocusing on plans (t = -4.11, p < 0.01); positive reappraisal (t = -9.22, p < 0.01); self-blame (t = 6.30, p < 0.01); rumination (t = 3.64, p < 0.01); catastrophizing (t = 9.10, p < 0.01), and blaming others (t = 2.52, p < 0.01), the precentral gyrus (t = 6.04, pFDR < 0.05) and the rolandic operculum (t = -4.57, pFDR < 0.05). Rolandic operculum activity was negatively correlated with blaming others (r = -0.20, p < 0.05). Epigenetic results showed that excitatory neurons (p < 0.01) and inhibitory neurons (p < 0.01) were significant differences in two pathways, "trans-synaptic signaling" (p < -log108) and "modulation of chemical synaptic transmission" (p < -log108) in both cells. CONCLUSIONS: People with NSSI are more inclined to adopt non-adaptive cognitive emotion regulation strategies. Rolandic operculum is also abnormally active. Abnormal changes in the rolandic operculum of them are associated with non-adaptive cognitive emotion regulation strategies. Changes in the excitatory and inhibitory neurons provide hints to explore the abnormalities of the neurological mechanisms at the cellular level of them. Trial registration number NCT04094623.

Preparation and application of a pseudo-templated multi-monomer aflatoxins imprinted polymer.

A functional material was developed with specific recognition properties for aflatoxins for pre-processing enrichment and separation in the detection of aflatoxins in Chinese herbal medicines. In the experiment, ethyl coumarin-3-carboxylate, which has a highly similar structure to the oxonaphthalene o-ketone of aflatoxin, was selected as a pseudo-template, zinc acrylate, neutral red derivative, and methacrylic acid, which have complementary functions, were selected as co-monomers to prepare a pseudo-template multifunctional monomer molecularly imprinted polymer (MIP). The MIP obtained under the optimal preparation conditions has a maximum adsorption capacity of 0.036 mg/mg and an imprinting factor of 3.67. The physical property evaluation of the polymers by Fourier infrared spectrometer, scanning electron microscopy, pore size analyzer, thermogravimetric analyzer, and diffuse reflectance spectroscopy showed that the MIP were successfully prepared and porous spherical-like particles were obtained. The synthesized polymer was used as a solid-phase extraction agent for the separation of aflatoxins from the extract of spina date seed. The linear range of the developed method was 10-1000 ng/mL, the limit of detection was 0.36 ng/mL, the limit of quantification was 1.19 ng/mL, and the recoveries of the extracts at the concentration level of 0.2 µg/mL were in the range 88.0-93.4%, with relative standard deviations (RSDs) of 1.97% (n). The results showed that the preparation of MIPs using ethyl coumarin-3-carboxylate as a template was simple, economical, and convenient. It is expected to become a promising functional material for the enrichment and separation aflatoxins from complex matrices.

Analyzing brain structural differences among undergraduates with different grades of self-esteem using multiple anatomical brain network.

BACKGROUND: Self-esteem is the individual evaluation of oneself. People with high self-esteem grade have mental health and can bravely cope with the threats from the environment. With the development of neuroimaging techniques, researches on cognitive neural mechanisms of self-esteem are increased. Existing methods based on brain morphometry and single-layer brain network cannot characterize the subtle structural differences related to self-esteem. METHOD: To solve this issue, we proposed a multiple anatomical brain network based on multi-resolution region of interest (ROI) template to study the brain structural connections of self-esteem. The multiple anatomical brain network consists of ROI features and hierarchal brain network features that are extracted from structural MRI. For each layer, we calculated the correlation relationship between pairs of ROIs. In order to solve the high-dimensional problem caused by the large amount of network features, feature selection methods (t-test, mRMR, and SVM-RFE) are adopted to reduce the number of features while retaining discriminative information to the maximum extent. Multi-kernel SVM is employed to integrate the various types of features by appropriate weight coefficient. RESULT: The experimental results show that the proposed method can improve classification accuracy to 97.26% compared with single-layer brain network. CONCLUSIONS: The proposed method provides a new perspective for the analysis of brain structural differences of self-esteem, which also has potential guiding significance in other researches involved brain cognitive activity and brain disease diagnosis.

[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.

[Study on classification and identification of depressed patients and healthy people among adolescents based on optimization of brain characteristics of network].

To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately ( P < 0.05). Next, using the relationship between multiple thresholds and network parameters, the area under the curve (AUC) of each network parameter was extracted as new features ( P < 0.05). Finally, support vector machine (SVM) was used to classify the two groups with the network parameters and their AUC as features. The study results show that with strength, average characteristic path length, and average clustering coefficient as features, the classification accuracy in the θ band is increased from 69% to 71%, 66% to 77%, and 50% to 68%, respectively. In the α band, the accuracy is increased from 72% to 79%, 69% to 82%, and 65% to 75%, respectively. And from overall view, when AUC of network parameters was used as a feature in the α band, the classification accuracy is improved compared to the network parameter feature. In the θ band, only the AUC of average clustering coefficient was applied to classification, and the accuracy is improved by 17.6%. The study proved that based on graph theory, the method of feature optimization of brain function network could provide some theoretical support for the computer-aided diagnosis of adolescent depression.

[Classification of Children with Attention-Deficit/Hyperactivity Disorder and Typically Developing Children Based on Electroencephalogram Principal Component Analysis and k-Nearest Neighbor].

This paper aims to assist the individual clinical diagnosis of children with attention-deficit/hyperactivity disorder using electroencephalogram signal detection method.Firstly,in our experiments,we obtained and studied the electroencephalogram signals from fourteen attention-deficit/hyperactivity disorder children and sixteen typically developing children during the classic interference control task of Simon-spatial Stroop,and we completed electroencephalogram data preprocessing including filtering,segmentation,removal of artifacts and so on.Secondly,we selected the subset electroencephalogram electrodes using principal component analysis(PCA)method,and we collected the common channels of the optimal electrodes which occurrence rates were more than 90%in each kind of stimulation.We then extracted the latency(200~450ms)mean amplitude features of the common electrodes.Finally,we used the k-nearest neighbor(KNN)classifier based on Euclidean distance and the support vector machine(SVM)classifier based on radial basis kernel function to classify.From the experiment,at the same kind of interference control task,the attention-deficit/hyperactivity disorder children showed lower correct response rates and longer reaction time.The N2 emerged in prefrontal cortex while P2 presented in the inferior parietal area when all kinds of stimuli demonstrated.Meanwhile,the children with attention-deficit/hyperactivity disorder exhibited markedly reduced N2 and P2amplitude compared to typically developing children.KNN resulted in better classification accuracy than SVM classifier,and the best classification rate was 89.29%in StI task.The results showed that the electroencephalogram signals were different in the brain regions of prefrontal cortex and inferior parietal cortex between attention-deficit/hyperactivity disorder and typically developing children during the interference control task,which provided a scientific basis for the clinical diagnosis of attention-deficit/hyperactivity disorder individuals.

Broad autism phenotype features of Chinese parents with autistic children and their associations with severity of social impairment in probands.

BACKGROUND: Parents of children with autism have higher rates of broad autism phenotype (BAP) features than parents of typically developing children (TDC) in Western countries. This study was designed to examine the rate of BAP features in parents of children with autism and the relationship between parental BAP and the social impairment of their children in a Chinese sample. METHODS: A total of 299 families with autistic children and 274 families with TDC participated in this study. Parents were assessed using the Broad Autism Phenotype Questionnaire (BAPQ), which includes self-report, informant-report, and best-estimate versions. Children were assessed using the Chinese version of the Social Responsiveness Scale (SRS). RESULTS: Parents of children with autism were significantly more likely to have BAP features than were parents of TDC; mothers and fathers in families with autistic children had various BAP features. The total scores of the informant and best-estimate BAPQ versions for fathers were significantly associated with their children's SRS total scores in the autism group, whereas the total scores of the three BAPQ versions for mothers were significantly associated with their children's SRS total scores in the TDC group. In the autism group, the total SRS scores of children with "BAP present" parents (informant and best-estimate) were higher than the total SRS scores of children with"BAP absent" parents. In the TDC group, the total SRS scores of children with "BAP present" parents were higher than the total SRS scores of children with"BAP absent" parents (best-estimate). CONCLUSIONS: Parents of autistic children were found to have higher rates of BAP than parents of TDC in a sample of Chinese parents. The BAP features of parents are associated with their children's social functioning in both autism families and TDC families, but the patterns of the associations are different.

Extraction of brain function pattern with visual-capture-task fMRI using dynamic time-window method in ADHD children.

Attention deficit/Hyperactivity disorder (ADHD) has a great impact on children's development. This paper uses a novel adaptive brain state extraction algorithm to construct a dynamic time-window brain network, which captures the brain function pattern characteristics of ADHD children with higher temporal resolution. The test data were acquired by functional magnetic resonance imaging (fMRI) obtained from 23 children with ADHD during the visual-capture-task [age: (8.27 ± 2.77)]. A spatial standard deviation method is used after the initial data processing, to extract the brain activity pattern state; An improved clustering algorithm is constructed to verify the changes made to the dynamic time-window brain network model. There can be seen clear differences between each state within 0.05 s after the test. The results show that our improved new framework can effectively obtain the characteristics of dynamic brain functional connection strength changes during the task. In addition, the new algorithm is able to capture the dynamic changes of the brain network, with an 80 % improvement compared to traditional methods for the average modularity value Q. This work demonstrates a novel approach to find out the pattern changes between dynamic brain function connections, which can be of great significance for the adjuvant treatment of children with ADHD.

The Relationship Between Perceived Stress, Impulsivity, Executive Dysfunction and Non-Suicidal Self-Injury Thoughts Among Chinese College Students: A Gender Difference Study.

Background: The present study aimed to investigate the potential role of perceived stress, impulsivity trait, executive dysfunction in non-suicidal self-injury (NSSI) thoughts among college students, as well as the gender differences. Methods: A sample of 890 university students completed self-report measures of NSSI thoughts in the past month, the level of perceived stress, impulsivity traits, and executive dysfunction. Results: Compared to those with low level of perceived stress, participants with high level of perceived stress reported significant higher levels of impulsivity trait and executive dysfunction, and higher frequency of NSSI thoughts, and there were no gender differences. Male participants with NSSI thoughts, compared to males without NSSI thoughts, reported significant higher levels of perceived stress and executive dysfunction. Female participants with NSSI thoughts, compared to females without NSSI thoughts, reported significant higher levels of perceived stress, impulsivity trait, and executive dysfunction. Hierarchical regression analysis revealed only executive dysfunction was associated with NSSI thoughts in males, while only perceived stress was associated with NSSI thoughts in females. Conclusion: This study revealed different influence factors for NSSI thoughts in male and female college students. NSSI thoughts in males were more likely associated with executive dysfunction while in females were due to recently perceived stress.

Shrub leaf area and leaf vein trait trade-offs in response to the light environment in a vegetation transitional zone.

The leaf is an important site for energy acquisition and material transformation in plants. Leaf functional traits and their trade-off mechanisms reflect the resource utilisation efficiency and habitat adaptation strategies of plants, and contribute to our understanding of the mechanism by which the distribution pattern of plant populations in arid and semi-arid areas influences the evolution of vegetation structure and function. We selected two natural environments, the tree-shrub community canopy area and the shrub-grass community open area in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau. We studied the trade-off relationships of leaf area with leaf midvein diameter and leaf vein density in Cotoneaster multiflorus using the standardised major axis (SMA) method. The results show that the growth pattern of C. multiflorus , which has small leaves of high density and extremely small vein diameters, in the open area. The water use efficiency and net photosynthetic rate of plants in the open area were significantly greater than those of plants growing in the canopy area. The adaptability of C. multiflorus to environments with high light and low soil water content reflects its spatial colonisation potential in arid and semiarid mountains.

Changes in Community Structure of Brain Dynamic Functional Connectivity States in Mild Cognitive Impairment.

Recent researches have noted many changes of short-term dynamic modalities in mild cognitive impairment (MCI) patients' brain functional networks. In this study, the dynamic functional brain networks of 82 MCI patients and 85 individuals in the normal control (NC) group were constructed using the sliding window method and Pearson correlation. The window size was determined using single-scale time-dependent (SSTD) method. Subsequently, k-means was applied to cluster all window samples, identifying three dynamic functional connectivity (DFC) states. Collective sparse symmetric non-negative matrix factorization (cssNMF) was then used to perform community detection on these states and quantify differences in brain regions. Finally, metrics such as within-community connectivity strength, community strength, and node diversity were calculated for further analysis. The results indicated high similarity between the two groups in state 2, with no significant differences in optimal community quantity and functional segregation (p < 0.05). However, for state 1 and state 3, the optimal community quantity was smaller in MCI patients compared to the NC group. In state 1, MCI patients had lower within-community connectivity strength and overall strength than the NC group, whereas state 3 showed results opposite to state 1. Brain regions with statistical difference included MFG.L, ORBinf.R, STG.R, IFGtriang.L, CUN.L, CUN.R, LING.R, SOG.L, and PCUN.R. This study on DFC states explores changes in the brain functional networks of patients with MCI from the perspective of alterations in the community structures of DFC states. The findings could provide new insights into the pathological changes in the brains of MCI patients.

The clinicopathological significance and prognostic impact of 14-3-3σ/stratifin expression on patients with surgically resectable intrahepatic cholangiocarcinoma.

INTRODUCTION: Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver cancer after hepatocellular carcinoma. Through data mining of publicly available iCCA transcriptomic datasets from the Gene Expression Omnibus, we identified SFN as the most significantly up-regulated gene in iCCA compared to normal tissue, focusing on the Gene Ontology term "cell proliferation" (GO:0008283). SFN encodes the 14-3-3σ protein, also known as stratifin, which plays crucial roles in various cellular processes. MATERIALS AND METHODS: Immunohistochemistry was used to assess stratifin expression in 182 patients with localized iCCAs undergoing surgical resection. Patients were divided into low and high expression groups, and the association between stratifin expression and clinicopathological features was analyzed. Univariate and multivariate survival analyses were performed to assess overall survival (OS), disease-specific survival (DSS), local recurrence-free survival (LRFS), and metastasis-free survival (MeFS). RESULTS: Elevated stratifin expression in iCCAs was significantly associated with the absence of hepatitis, positive surgical margins, advanced primary tumor stages, and higher histological grades (all p ≤ 0.011). Survival analyses demonstrated a significant negative association between stratifin expression and all prognostic indicators, including OS, DSS, LRFS, and MeFS (all p ≤ 0.0004). Multivariate analysis revealed that stratifin overexpression was significantly correlated with poorer outcomes in terms of DSS, LRFS, and MeFS (all p < 0.001). CONCLUSIONS: These findings suggest that stratifin may play a crucial role in iCCA oncogenesis and tumor progression, serving as a potential novel prognostic biomarker.

[Altered patterns of functional connectivity of posterior cingulate cortex on resting-state magnetic resonance imaging in children with attention-deficit or hyperactivity disorder].

OBJECTIVE: To explore the pathophysiological changes in the functional connectivity of posterior cingulate cortex (PCC) with other brain regions in children with attention-deficit or hyperactivity disorder (ADHD) on resting-state functional magnetic resonance imaging(fMRI) and explore the neural mechanisms of ADHD at the point of relationships between brain regions. METHODS: Thirty children with ADHD from the Third Affiliated Hospital of Soochow University from June 2008 to April 2010 and another 30 age-and-gender-matched controls from a normal primary school over the same period underwent resting-state fMRI scans. And blood oxygenation level dependent (BOLD) signal was acquired to calculate the functional connectivity of PCC with other brain regions controls. Significant differences of connectivity between groups were analyzed with REST software. RESULTS: The pattern of functional connectivity of PCC for the ADHD group was similar to that of the control group. Significant positive functional connectivity with PCC was observed in the default mode of network (DMN) while negative functional connectivity was present in dorsolateral prefrontal cortex, anterior cingulate, parietal cortex and basal ganglia(all P < 0.05, corrected). Compared to the controls, the ADHD group exhibited decreased positive connectivity with PCC in bilateral medial prefrontal cortex (0.07 ± 0.20 vs 0.33 ± 0.23, t = -5.47), right posterior cingulate gyrus(0.25 ± 0.28 vs 0.48 ± 0.30, t = -3.44), right inferior temporal gyrus (-0.05 ± 0.19 vs 0.22 ± 0.22, t = -4.61) and cerebellar posterior lobe (-0.04 ± 0.21 vs 0.17 ± 0.16, t = -3.99), while decreased negative functional connectivity with PCC was observed in left insula (-0.10 ± 0.26 vs -0.30 ± 0.19, t = 3.71), right inferior parietal lobule (0.02 ± 0.18 vs -0.23 ± 0.17, t = 5.20), left postcentral gyrus (0.08 ± 0.26 vs -0.17 ± 0.25, t = 4.06), left superior temporal gyrus (-0.04 ± 0.25 vs -0.27 ± 0.17, t = 4.27), right superior temporal gyrus (-0.08 ± 0.25 vs -0.31 ± 0.21, t = 3.80) and left fusiform gyrus (-0.01 ± 0.25 vs -0.18 ± 0.17, t = 3.57)(all P < 0.05, corrected). CONCLUSIONS: The connectivity of DMN between brain regions is abnormal in ADHD group. And the strengthen of negative relationship between DMN and task activated network becomes reduced. It is surmised that the decreased internal synchronization of default network and disrupted balance between DMN and prefrontal-parietal attentional networks may be important neural mechanisms of ADHD.

[Mathematical cognitive function in children with attention deficit hyperactivity disorder: a behavior and event-related potential study].

OBJECTIVE: To explore the mathematics cognitive function of children with attention deficit hyperactivity disorder and explore neural mechanisms with event-related potential(ERP) and behaviors. METHODS: Behavior data and ERP elicited by performing mental calculation tasks were recorded in 27 children with ADHD and 29 normal controls from July to October 2012 at Third Affiliated Hospital of Soochow University.The differences of behaviors and N2 component of ERP were compared and analyzed. RESULTS: The reaction time of the children with ADHD were longer than the control group in addition, subtraction and multiplication ((949 ± 144) vs (829 ± 166) ms, (981 ± 129) vs (856 ± 170) ms, (944 ± 136) vs (825 ± 172) ms, all P < 0.05). While the correct rate were less than normal control in all three arithmetic operations (0.80% (0.72%, 0.88%) vs 0.90% (0.85%,0.96%), 0.78% (0.64%,0.85%) vs 0.90% (0.84%,0.93%), 0.86% (0.74%,0.92%) vs 0.93%(0.90%,0.98%), all P < 0.05). N2 component could be elicited by all subjects in forehead. The amplitude of N2 of children with ADHD were significantly lower than control group in all three arithmetic operations at left frontal (F3: (-3.5 ± 5.2) vs (-6.7 ± 3.5)µV, (-3.8 ± 4.0) vs (-7.4 ± 4.5)µV, -5.8 (-7.6,1.6) vs -6.4(-10.3, -4.9) µV, all P < 0.05) and Fz ((-4.3 ± 6.4) vs ( -7.4 ± 4.2) µV, (-5.0 ± 5.4) vs (-7.9 ± 4.6)µV, -5.2(-9.7, -0.6) vs -7.9 (-10.5, -5.1)µV, all P < 0.05), the latency of ADHD group were prolonger than controls in subtraction operations at right and left frontal ((328 ± 36) vs (307 ± 27)ms, 325 (307,354)vs 309 (280, 330)ms) and frontal electrodes ((331 ± 35) vs (311 ± 30) ms, all P < 0.05). In addition and multiplication operations, there was no significant difference in latency (all P > 0.05). CONCLUSIONS: The children with ADHD have weak capacities of inhibition irrelevant information and paying attention to control. Their deficits in mental arithmetics may be due to the difficulties of selecting the best strategy during cognitive tasks.

Association of non-HDL-C and depression: a cross-sectional analysis of the NHANES data.

Objectives: Non-high-density lipoprotein cholesterol (non-HDL-C) has attracted attention because it is associated with a variety of diseases and is easy to measure. However, the relationship between non-HDL-C and depression is still unclear. Our aim was to assess the relationship between non-HDL-C and depression using the cross-sectional NHANES survey from 2005 to 2018. Methods: We examined the association between non-HDL-C and depression using weighted multivariable logistic regression models and subgroup analysis. Sensitivity analysis demonstrated the robustness of the results. Results: There were 42,143 participants in this study and 8.6% had depression (weighted 7.53%). Non-HDL-C was higher in participants with depression compared to those without depression (weighted mean 3.64 vs. 3.73, p < 0.01). There was a positive association between non-HDL-C and depression with a 95% OR of 1.22 adjusted for multifactorial (95% CI,1.03-1.45). In subgroup analyses, non-HDL-C was positively associated with depression in men (OR, 1.31; 95% CI, 1.01-1.70), normal BMI (OR: 0.93; 95% CI: 0.66-1.32) and in participants without hypertension (OR, 1.29; 95% CI, 1.01-1.66). Conclusion: Non-HDL-C positively correlated with depression, and further research may be better for clinical service.

Dynamic functional connectivity changes of resting-state brain network in attention-deficit/hyperactivity disorder.

Patients with attention-deficit/hyperactivity disorder (ADHD) have shown abnormal functional connectivity and network disruptions at the whole-brain static level. However, the changes in brain networks in ADHD patients from dynamic functional connectivity (DFC) perspective have not been fully understood. Accordingly, we executed DFC analysis on resting-state fMRI data of 25 ADHD patients and 27 typically developing (TD) children. A sliding window and Pearson correlation were used to construct the dynamic brain network of all subjects. The k-means+ + clustering method was used to recognize three recurring DFC states, and finally, the mean dwell time, the fraction of time spent for each state, and graph theory metrics were quantified for further analysis. Our results showed that ADHD patients had abnormally increased mean dwell time and the fraction of time spent in state 2, which reached a significant level (p < 0.05). In addition, a weak correlation between the default mode network was associated in three states, and the positive correlations between visual network and attention network were smaller than TD in three states. Finally, the integration of each network node of ADHD in state 2 is more potent than that of TD, and the degree of node segregation is smaller than that of TD. These findings provide new evidence for the DFC study of ADHD; dynamic changes may better explain the developmental delay of ADHD and have particular significance for studying neurological mechanisms and adjuvant therapy of ADHD.

Emotion Regulation and Depressive Symptoms Mediate the Association Between Schizotypal Personality Traits and Suicidality in Chinese College Students.

OBJECTIVE: The psychological mechanisms underlying the relationship between schizotypal personality traits and suicidality are not understood. This study investigated the association of schizotypal personality traits with suicidality and explored the mediating role of cognitive appraisal and depression in the relationship between those two variables in a sample of Chinese college students. METHOD: Participants (N = 2457) completed the Schizotypal Personality Questionnaire, the Emotional Regulation Questionnaire, the Zung Self-rating Depression Scale, and three questions related to suicidality. RESULTS: The cognitive reappraisal score was lower in the students with suicidality than the students without suicidality, whereas scores for depression and schizotypal personality traits were higher in the students with suicidality than the students without suicidality. Schizotypal personality traits and depression were risk factors for suicidality. Depression mediated the association between schizotypal personality traits and suicidality. Cognitive reappraisal negatively affected symptoms of depression and had a significant mediating effect on the association between schizotypal personality traits and suicidality. CONCLUSIONS: Schizotypal personality traits and depression are risk factors for suicidality. Cognitive reappraisal and depression mediate the association between schizotypal personality traits and suicidality.

One-Step Hydrothermal Synthesis, Thermochromic and Infrared Camouflage Properties of Vanadium Dioxide Nanorods.

Vanadium dioxide (VO2) has attracted interest from researchers because it undergoes a metal-insulator phase transition (MIT), which is accompanied by a reversible and remarkable change in both electrical and optical properties. VO2 exhibits numerous polymorphs and thus it is essential to control the growth of specific monoclinic VO2 (M) and rutile VO2 (R) phases. In this study, we developed a cost-effective and facile method for preparing VO2 nanorods with a highly crystalline monoclinic phase by one-step hydrothermal synthesis, in which only V2O5 and H2C2O4 are used as raw materials. The phase evolution of VO2 during the hydrothermal process was studied. The obtained VO2 nanorods were thoroughly mixed with fluorocarbon resin and homogeneous emulsifier in an ethanol solution to obtain a VO2 dispersion. To prepare VO2 films, screen printing was performed with a stainless steel screen mesh mask on glasses or fabric substrate. The VO2 coating had good thermochromic performance; the infrared transmittance change was greater than 20% @1.5 µm whilst keeping the visible transmittance greater than 50%. Meanwhile, the polyester base coating on the fabric had an emissivity change of up to 22%, which provides a solution for adaptive IR camouflage.

A highly efficient heterogeneous catalyst for selective oxidation of sulfides derived from an open-framework copper borovanadate containing a unique crown-shaped anion.

A novel 3D open-framework copper borovanadate with a unique crown-shaped anion [(VIVO)8(VVO)4B32O64(OH)8]12- and the largest ratio of Cu2+/borovanadate anions (6/1) has been successfully synthesized and systematically studied. The compound not only possesses high stability in a wide pH range of 3.2-10.8 (DMF solution), but also exhibits excellent catalytic activities for selective oxidation of sulfides.

A Domain Adaptation Sparse Representation Classifier for Cross-Domain Electroencephalogram-Based Emotion Classification.

The brain-computer interface (BCI) interprets the physiological information of the human brain in the process of consciousness activity. It builds a direct information transmission channel between the brain and the outside world. As the most common non-invasive BCI modality, electroencephalogram (EEG) plays an important role in the emotion recognition of BCI; however, due to the individual variability and non-stationary of EEG signals, the construction of EEG-based emotion classifiers for different subjects, different sessions, and different devices is an important research direction. Domain adaptation utilizes data or knowledge from more than one domain and focuses on transferring knowledge from the source domain (SD) to the target domain (TD), in which the EEG data may be collected from different subjects, sessions, or devices. In this study, a new domain adaptation sparse representation classifier (DASRC) is proposed to address the cross-domain EEG-based emotion classification. To reduce the differences in domain distribution, the local information preserved criterion is exploited to project the samples from SD and TD into a shared subspace. A common domain-invariant dictionary is learned in the projection subspace so that an inherent connection can be built between SD and TD. In addition, both principal component analysis (PCA) and Fisher criteria are exploited to promote the recognition ability of the learned dictionary. Besides, an optimization method is proposed to alternatively update the subspace and dictionary learning. The comparison of CSFDDL shows the feasibility and competitive performance for cross-subject and cross-dataset EEG-based emotion classification problems.