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.

ADHD classification with cross-dataset feature selection for biomarker consistency detection.

Objective.Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children. While numerous intelligent methods are applied for its subjective diagnosis, they seldom consider the consistency problem of ADHD biomarkers. In practice, these data-driven approaches lead to varying learned features for ADHD classification across diverse ADHD datasets. This phenomenon significantly undermines the reliability of identified biomarkers and hampers the interpretability of these methods.Approach.In this study, we propose a cross-dataset feature selection (FS) module using a grouped SVM-based recursive feature elimination approach (G-SVM-RFE) to enhance biomarker consistency across multiple datasets. Additionally, we employ connectome gradient data for ADHD classification. In details, we introduce the G-SVM-RFE method to effectively concentrate gradient components within a few brain regions, thereby increasing the likelihood of identifying these regions as ADHD biomarkers. The cross-dataset FS module is integrated into an existing binary hypothesis testing (BHT) framework. This module utilizes external datasets to identify global regions that yield stable biomarkers. Meanwhile, given a dataset which waits for implementing the classification task as local dataset, we learn its own specific regions to further improve the performance of accuracy on this dataset.Main results.By employing this module, our experiments achieve an average accuracy of 96.7% on diverse datasets. Importantly, the discriminative gradient components primarily originate from the global regions, providing evidence for the significance of these regions. We further identify regions with the high appearance frequencies as biomarkers, where all the used global regions and one local region are recognized.Significance.These biomarkers align with existing research on impaired brain regions in children with ADHD. Thus, our method demonstrates its validity by providing enhanced biological explanations derived from ADHD mechanisms.

Subtype classification of attention deficit hyperactivity disorder with hierarchical binary hypothesis testing framework.

Objective. The diagnosis of attention deficit hyperactivity disorder (ADHD) subtypes is important for the refined treatment of ADHD children. Although automated diagnosis methods based on machine learning are performed with structural and functional magnetic resonance imaging (sMRI and fMRI) data which have full observation of brains, they are not satisfactory with the accuracy of less than80%for the ADHD subtype diagnosis.Approach. To improve the accuracy and obtain the biomarker of ADHD subtypes, we proposed a hierarchical binary hypothesis testing (H-BHT) framework by using brain functional connectivity (FC) as input bio-signals. The framework includes a two-stage procedure with a decision tree strategy and thus becomes suitable for the subtype classification. Also, typical FC is extracted in both two stages of identifying ADHD subtypes. That means the important FC is found out for the subtype recognition.Main results. We apply the proposed H-BHT framework to resting state fMRI datasets from ADHD-200 consortium. The results are achieved with the average accuracy97.1%and an average kappa score 0.947. Discriminative FC between ADHD subtypes is found by comparing the P-values of typical FC.Significance. The proposed framework not only is an effective structure for ADHD subtype classification, but also provides useful reference for multiclass classification of mental disease subtypes.

Altered brain function in patients with acrophobia: A voxel-wise degree centrality analysis.

AIM: To explore the local spontaneous neural activity and whole-brain functional connectivity patterns in the resting brain of acrophobia patients. METHODS: 50 patients with acrophobia and 47 healthy controls were selected for this study. All participants underwent resting-state MRI scans after enrollment. The imaging data were then analyzed using a voxel-based degree centrality (DC) method, and seed-based functional connectivity (FC) correlation analysis was used to explore the correlation between abnormal functional connectivity and clinical symptom scales in acrophobia. The severity of symptoms was evaluated using self-report and behavioral measures. RESULTS: Compared to controls, acrophobia patients showed higher DC in the right cuneus and left middle occipital gyrus and significantly lower DC in the right cerebellum and left orbitofrontal cortex (p < 0.01, GRF corrected). Additionally, there were negative correlations between the acrophobia questionnaire avoidance (AQ- Avoidance) scores and right cerebellum-left perirhinal cortex FC (r = -0.317, p = 0.025) and between scores of the 7-item generalized anxiety disorder scale and left middle occipital gyrus-right cuneus FC (r = -0.379, p = 0.007). In the acrophobia group, there was a positive correlation between behavioral avoidance scale and right cerebellum-right cuneus FC (r = 0.377, p = 0.007). CONCLUSIONS: The findings indicated that there are local abnormalities in spontaneous neural activity and functional connectivity in the visual cortex, cerebellum, and orbitofrontal cortex in patients with acrophobia.

ADHD classification using auto-encoding neural network and binary hypothesis testing.

Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental disease of school-age children. Early diagnosis is crucial for ADHD treatment, wherein its neurobiological diagnosis (or classification) is helpful and provides the objective evidence to clinicians. The existing ADHD classification methods suffer two problems, i.e., insufficient data and feature noise disturbance from other associated disorders. As an attempt to overcome these difficulties, a novel deep-learning classification architecture based on a binary hypothesis testing framework and a modified auto-encoding (AE) network is proposed in this paper. The binary hypothesis testing framework is introduced to cope with insufficient data of ADHD database. Brain functional connectivities (FCs) of test data (without seeing their labels) are incorporated during feature selection along with those of training data and affect the sequential deep learning procedure under binary hypotheses. On the other hand, the modified AE network is developed to capture more effective features from training data, such that the difference of inter- and intra-class variability scores between binary hypotheses can be enlarged and effectively alleviate the disturbance of feature noise. On the test of ADHD-200 database, our method significantly outperforms the existing classification methods. The average accuracy reaches 99.6% with the leave-one-out cross validation. Our method is also more robust and practically convenient for ADHD classification due to its uniform parameter setting across various datasets.

Purification and characterization of antioxidant peptides from hairtail surimi hydrolysates and their effects on beef color stability.

The antioxidant peptides extracted from plants or animals have shown great potential in preventing food quality deterioration caused by oxidization. Here, peptide fractions obtained from hairtail surimi hydrolysates (HSH) were investigated for structure and color-protective effect. The results showed the <3 kDa fraction obtained from HSH by ultrafiltration could be separated into five major fractions (A-E) by gel chromatography, among which fraction A possessed the highest antioxidant activities. This fraction A could be further separated into two fractions (A1 and A2 ) by the reversed-phase high-performance liquid chromatography, and fraction A2 with lower α-helix content exhibited the higher antioxidant activities. The amino acids sequence of fraction A2 was identified as DLYANTVLSGGTTMYPGIADR (2214.0627 Da). The synthetic peptide with this sequence was also found to exhibit obvious antioxidant activity. Moreover, both HSH, fractions A1 and A2 , and synthetic peptide demonstrated color-protective effects during the beef preservation. Taken together, the results obtained showed that the natural antioxidant peptides could be isolated from HSH, which can be used in meat preservation for inhibiting color deterioration. PRACTICAL APPLICATION: This study demonstrated the potential of hairtail surimi hydrolysates (HSH) as a source of antioxidant peptides. Furthermore, these antioxidant peptides purified from HSH exhibited the potential for prevention of beef color deterioration of beef, providing a potential application for meat preservation. Particularly, using the antioxidant peptides sourced from fish surimi for meat preservation may not only ease the safety concerns about artificial preservatives but also create a unique selling proposition, especially in far eastern Asian countries, since consumers in these countries believe "umami" is the combination of "fish" and "meat."

Preparation and photodynamic bactericidal effects of curcumin-ß-cyclodextrin complex.

To overcome the poor water solubility of curcumin, a curcumin-ß-cyclodextrin (Cur-ß-CD) complex was prepared as a novel photosensitizer. Fourier-transform infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), and X-ray diffraction (XRD) were used to verify the formation of Cur-ß-CD. Furthermore, the ROS generation capacity and photodynamic bactericidal effect were measured to confirm this Cur-ß-CD complex kept photodynamic activity of curcumin. The result showed Cur-ß-CD could effectively generate ROS upon blue-light irradiation. The plate count assay demonstrated Cur-ß-CD complex possess desirable photodynamic antibacterial effect against food-borne pathogens including Staphylococcus aureus, Listeria monocytogenes and Escherichia coli. The cell morphology determined by scanning electron microscope (SEM) and transmission electron microscope (TEM) showed Cur-ß-CD could cause cell deformation, surface collapse and cell structure damage of the bacteria, resulting in the leakage of cytoplasmic; while agarose gel electrophoresis and SDS-PAGE further illustrated the inactivation mechanisms by Cur-ß-CD involve bacterial DNA damage and protein degradation.

Identifying ADHD Individuals From Resting-State Functional Connectivity Using Subspace Clustering and Binary Hypothesis Testing.

Objective: This study focused on the ADHD classification through functional connectivity (FC) analysis. Method: An ADHD classification method was proposed with subspace clustering and binary hypothesis testing, wherein partial information of test data was adopted for training. By hypothesizing the binary label (ADHD or control) for the test data, two feature sets of training FC data were generated during the feature selection procedure that employed both training and test data. Then, a multi-affinity subspace clustering approach was performed to obtain the corresponding subspace-projected feature sets. With the energy comparison of projected feature sets, we finally identified ADHD individuals for the test data. Results: Our method outperformed several state-of-the-art methods with the above 90% average identification accuracy. By the discriminative FC contribution analysis, it also proved the reliability of our method. Conclusion: Results demonstrate the remarkable classification performance of our method and reveal some useful brain circuits to identify ADHD.

Industrial kinetic resolution of d,l-pantolactone by an immobilized whole-cell biocatalyst.

Immobilized whole-cells of Pichia pastoris harboring recombinant d-lactonase were entrapped in calcium alginate gels and used as an efficient biocatalyst for catalytic kinetic resolution of d,l-pantolactone. The immobilized whole-cell biocatalyst exhibited good catalytic stability, which was applied for stereospecific hydrolysis of d-pantolactone for up to 56 repeated batch reactions without obvious loss in the catalytic activity and enantioselectivity.

A Novel EMD Mutation Identified by Whole-Exome Sequencing in Twins with Emery-Dreifuss Muscular Dystrophy.

This case reports a novel hemizygous frameshift EMD mutation (c.487delA, p.Ser163fs) in twins of an Emery-Dreifuss muscular dystrophy family with severe cardiac involvement and mild muscle weakness. Their mother carried the same heterozygous mutation.

Efficient CSP Algorithm With Spatio-Temporal Filtering for Motor Imagery Classification.

Common spatial pattern (CSP) is an efficient algorithm widely used in feature extraction of EEG-based motor imagery classification. Traditional CSP depends only on spatial filtering, that aims to maximize or minimize the ratio of variances of filtered EEG signals in different classes. Recent advances of CSP approaches show that temporal filtering is also preferable to extract discriminative features. In view of this perspective, a novel spatio-temporal filtering strategy is proposed in this paper. To improve computational efficiency and alleviate the overfitting issue frequently encountered in the case of small sample size, the same temporal filter is designed by EEG signals of the same class and shared by all the spatial channels. Spatial and temporal filters can be updated alternatively in practice. Furthermore, each of the resulting designs can still be cast as a CSP problem and tackled efficiently by the eigenvalue decomposition. To alleviate the adverse effects of outliers or noisy EEG channels, sparse spatial or temporal filters can also be achieved by incorporating an l1 -norm-based regularization term in our CSP problem. The regularized spatial or temporal filter design is iteratively reformulated as a CSP problem via the reweighting technique. Two sets of motor imagery EEG data of BCI competitions are used in our experiments to verify the effectiveness of the proposed algorithm.

ADHD classification by dual subspace learning using resting-state functional connectivity.

As one of the most common neurobehavioral diseases in school-age children, Attention Deficit Hyperactivity Disorder (ADHD) has been increasingly studied in recent years. But it is still a challenge problem to accurately identify ADHD patients from healthy persons. To address this issue, we propose a dual subspace classification algorithm by using individual resting-state Functional Connectivity (FC). In detail, two subspaces respectively containing ADHD and healthy control features, called as dual subspaces, are learned with several subspace measures, wherein a modified graph embedding measure is employed to enhance the intra-class relationship of these features. Therefore, given a subject (used as test data) with its FCs, the basic classification principle is to compare its projected component energy of FCs on each subspace and then predict the ADHD or control label according to the subspace with larger energy. However, this principle in practice works with low efficiency, since the dual subspaces are unstably obtained from ADHD databases of small size. Thereby, we present an ADHD classification framework by a binary hypothesis testing of test data. Here, the FCs of test data with its ADHD or control label hypothesis are employed in the discriminative FC selection of training data to promote the stability of dual subspaces. For each hypothesis, the dual subspaces are learned from the selected FCs of training data. The total projected energy of these FCs is also calculated on the subspaces. Sequentially, the energy comparison is carried out under the binary hypotheses. The ADHD or control label is finally predicted for test data with the hypothesis of larger total energy. In the experiments on ADHD-200 dataset, our method achieves a significant classification performance compared with several state-of-the-art machine learning and deep learning methods, where our accuracy is about 90 % for most of ADHD databases in the leave-one-out cross-validation test.

Targeted next-generation sequencing identified a known EMD mutation in a Chinese patient with Emery-Dreifuss muscular dystrophy.

Emery-Dreifuss muscular dystrophy (EDMD) is a rare X-linked recessive disease characterized by the clinical triad of early childhood joint contractures, progressive weakness in muscles and cardiac involvement and can result in sudden death. Targeted next-generation sequencing was performed for a Chinese patient with EDMD and the previously reported mutation [NM_000117.2: c.251_255del (p.Leu84Profs*7)] in exon 3 of the emerin gene (EMD) was identified.

Nanopipette-assisted single cell metabolic glycan labeling.

Here, we report a single cell glycan labeling strategy by combining nanoscale intracellular glass electrodes with bioorthogonal reaction. With the tip diameter less than 100 nm, the nanopipette electrode can be spatially controlled to inject artificial monosaccharides into single living cells with minimal invasion. The injection process can be precisely regulated by electroosmotic flow inside the nanopipette, and fluorescence labeling of sialic acid at single cell level is achieved.

The Prognostic Factors of Alcoholic Cardiomyopathy: A single-center cohort study.

BACKGROUND: Alcoholic cardiomyopathy (ACM) is considered one of the main causes of left ventricular dysfunction and is the leading cause of nonischemic dilated cardiomyopathy (DCM) in developed countries. However, very few studies have investigated the relationship between clinical characteristics and prognosis in ACM. AIMS: This study aimed to identify risk factors related to a poor outcome in ACM patients. STUDY DESIGN: Retrospective cohort study. METHODS: This study included 321 patients with ACM admitted to our hospital between 2003 and 2013. This study aimed to investigate the clinical characteristics and outcomes of the patients with ACM, and the primary endpoint of the study was all-cause mortality, which was assessed through patient medical records (review of patient hospital records and periodic examination of patients in the outpatient clinic) and medical follow-up calls with trained personnel. All-cause mortality was assessed using Kaplan-Meier survival curves, and the risk factors were assessed using Cox regression. A receiver operating characteristic (ROC) curve analysis was performed to optimize the cutoff point for discriminating between the 2 risk groups. RESULTS: After a median follow-up period of 3.78 years (interquartile range: 2.08-6.52 years), 83 (27.7%) patients were dead. The independent predictors of all-cause mortality due to ACM were the QRS duration (HR: 1.014; 95% CI: 1.004-1.019; P = .003), systolic blood pressure (HR: 0.980; 95% CI: 0.963- 0.997; P = .020), and New York Heart Association classification (HR: 1.595; 95% CI: 1.110-2.290; P = .011) at admission. CONCLUSION: Our study indicated that the QRS duration, systolic blood pressure, and New York Heart Association classification at admission provided independent prognostic information in patients with ACM.