Alterations in the brain functional network of abstinent male individuals with methamphetamine use disorder.

Methamphetamine is a highly addictive psychostimulant drug that is abused globally and is a serious threat to health worldwide. Unfortunately, the specific mechanism underlying addiction remains unclear. Thus, this study aimed to investigate the characteristics of functional connectivity in the brain network and the factors influencing methamphetamine use disorder in patients using magnetic resonance imaging. We included 96 abstinent male participants with methamphetamine use disorder and 46 age- and sex-matched healthy controls for magnetic resonance imaging. Compared with healthy controls, participants with methamphetamine use disorder had greater impulsivity, fewer small-world attributes of the resting-state network, more nodal topological attributes in the cerebellum, greater functional connectivity strength within the cerebellum and between the cerebellum and brain, and decreased frontoparietal functional connectivity strength. In addition, after controlling for covariates, the partial correlation analysis showed that small-world properties were significantly associated with methamphetamine use frequency, psychological craving, and impulsivity. Furthermore, we revealed that the small-word attribute significantly mediated the effect of methamphetamine use frequency on motor impulsivity in the methamphetamine use disorder group. These findings may further improve our understanding of the neural mechanism of impulse control dysfunction underlying methamphetamine addiction and assist in exploring the neuropathological mechanism underlying methamphetamine use disorder-related dysfunction and rehabilitation.

Integrated biomarker profiling of the metabolome associated with type 2 diabetes mellitus among Tibetan in China.

INTRODUCTION: Metabolomic signatures of type 2 diabetes mellitus (T2DM) in Tibetan Chinese population, a group with high diabetes burden, remain largely unclear. Identifying the serum metabolite profile of Tibetan T2DM (T-T2DM) individuals may provide novel insights into early T2DM diagnosis and intervention. METHODS: Hence, we conducted untargeted metabolomics analysis of plasma samples from a retrospective cohort study with 100 healthy controls and 100 T-T2DM patients by using liquid chromatography-mass spectrometry. RESULTS: The T-T2DM group had significant metabolic alterations that are distinct from known diabetes risk indicators, such as body mass index, fasting plasma glucose, and glycosylated hemoglobin levels. The optimal metabolite panels for predicting T-T2DM were selected using a tenfold cross-validation random forest classification model. Compared with the clinical features, the metabolite prediction model provided a better predictive value. We also analyzed the correlation of metabolites with clinical indices and found 10 metabolites that were independently predictive of T-T2DM. CONCLUSION: By using the metabolites identified in this study, we may provide stable and accurate biomarkers for early T-T2DM warning and diagnosis. Our study also provides a rich and open-access data resource for optimizing T-T2DM management.

Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor.

Background: Essential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging is a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity change mechanisms and build the potential diagnostic biomarker in ET patients. Methods: The histogram features based on the Resting-state functional magnetic resonance imaging (Rs-fMRI) data were extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs, and classification performance of the established models was evaluated by the mean area under the curve (AUC). Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics. Results: Each classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; and 92.27%, 0.939, respectively. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were two histogram features negatively and one positively correlated with tremor severity. Conclusion: Our findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients.

Intracellular trafficking of HLA-E and its regulation.

Interest in MHC-E-restricted CD8+ T cell responses has been aroused by the discovery of their efficacy in controlling simian immunodeficiency virus (SIV) infection in a vaccine model. The development of vaccines and immunotherapies utilizing human MHC-E (HLA-E)-restricted CD8+ T cell response requires an understanding of the pathway(s) of HLA-E transport and antigen presentation, which have not been clearly defined previously. We show here that, unlike classical HLA class I, which rapidly exits the endoplasmic reticulum (ER) after synthesis, HLA-E is largely retained because of a limited supply of high-affinity peptides, with further fine-tuning by its cytoplasmic tail. Once at the cell surface, HLA-E is unstable and is rapidly internalized. The cytoplasmic tail plays a crucial role in facilitating HLA-E internalization, which results in its enrichment in late and recycling endosomes. Our data reveal distinctive transport patterns and delicate regulatory mechanisms of HLA-E, which help to explain its unusual immunological functions.

Identification of essential tremor based on resting-state functional connectivity.

Currently, machine-learning algorithms have been considered the most promising approach to reach a clinical diagnosis at the individual level. This study aimed to investigate whether the whole-brain resting-state functional connectivity (RSFC) metrics combined with machine-learning algorithms could be used to identify essential tremor (ET) patients from healthy controls (HCs) and further revealed ET-related brain network pathogenesis to establish the potential diagnostic biomarkers. The RSFC metrics obtained from 127 ET patients and 120 HCs were used as input features, then the Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) methods were applied to reduce feature dimensionality. Four machine-learning algorithms were adopted to identify ET from HCs. The accuracy, sensitivity, specificity and the area under the curve (AUC) were used to evaluate the classification performances. The support vector machine, gradient boosting decision tree, random forest and Gaussian naïve Bayes algorithms could achieve good classification performances with accuracy at 82.8%, 79.4%, 78.9% and 72.4%, respectively. The most discriminative features were primarily located in the cerebello-thalamo-motor and non-motor circuits. Correlation analysis showed that two RSFC features were positively correlated with tremor frequency and four RSFC features were negatively correlated with tremor severity. The present study demonstrated that combining the RSFC matrices with multiple machine-learning algorithms could not only achieve high classification accuracy for discriminating ET patients from HCs but also help us to reveal the potential brain network pathogenesis in ET.

Hippocampal adaptation to high altitude: a neuroanatomic profile of hippocampal subfields in Tibetans and acclimatized Han Chinese residents.

The hippocampus is highly plastic and vulnerable to hypoxia. However, it is unknown whether and how it adapts to chronic hypobaric hypoxia in humans. With a unique sample of Tibetans and acclimatized Han Chinese individuals residing on the Tibetan plateau, we aimed to build a neuroanatomic profile of the altitude-adapted hippocampus by measuring the volumetric differences in the whole hippocampus and its subfields. High-resolution T1-weighted magnetic resonance imaging was performed in healthy Tibetans (TH, n = 72) and healthy Han Chinese individuals living at an altitude of more than 3,500 m (HH, n = 27). In addition, healthy Han Chinese individuals living on a plain (HP, n = 72) were recruited as a sea-level reference group. Whereas the total hippocampal volume did not show a significant difference across groups when corrected for age, sex, and total intracranial volume, subfield-level differences within the hippocampus were found. Post hoc analyses revealed that Tibetans had larger core hippocampal subfields (bilateral CA3, right CA4, right dentate gyrus); a larger right hippocampus-amygdala transition area; and smaller bilateral presubiculum, right subiculum, and bilateral fimbria, than Han Chinese subjects (HH and/or HP). The hippocampus and all its subfields were found to be slightly and non-significantly smaller in HH subjects than in HP subjects. As a primary explorational study, our data suggested that while the overall hippocampal volume did not change, the core hippocampus of Tibetans may have an effect of adaptation to chronic hypobaric hypoxia. However, this adaptation may have required generations rather than mere decades to accumulate in the population.

Combined brain network topological metrics with machine learning algorithms to identify essential tremor.

Background and objective: Essential tremor (ET) is a common movement syndrome, and the pathogenesis mechanisms, especially the brain network topological changes in ET are still unclear. The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET. Methods: Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 101 ET and 105 HCs. The topological properties were analyzed by using GT analysis, and the topological metrics under every single threshold and the area under the curve (AUC) of all thresholds were used as features. Then a Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) were conducted to feature dimensionality reduction. Four ML algorithms were adopted to identify ET from HCs. The mean accuracy, mean balanced accuracy, mean sensitivity, mean specificity, and mean AUC were used to evaluate the classification performance. In addition, correlation analysis was carried out between selected topological features and clinical tremor characteristics. Results: All classifiers achieved good classification performance. The mean accuracy of Support vector machine (SVM), logistic regression (LR), random forest (RF), and naïve bayes (NB) was 84.65, 85.03, 84.85, and 76.31%, respectively. LR classifier achieved the best classification performance with 85.03% mean accuracy, 83.97% sensitivity, and an AUC of 0.924. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with tremor severity. Conclusion: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET from HCs but also help us to reveal the potential topological pathogenesis of ET.

Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor.

Background: Although depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), the diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET. Methods: Based on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance. Results: The MGPC algorithm was able to classify the three groups (depressed ET, non-depressed ET, and HCs) with a total accuracy of 84.5%. The BSVM algorithm achieved a better classification performance with total accuracy of 90.7, 88.64, and 90.48% for depressed ET vs. HCs, non-depressed ET vs. HCs, and depressed ET vs. non-depressed ET, and the sensitivity for them at 80.49, 76.64, and 80.49%, respectively. The significant discriminative features of depressed ET vs. HCs were primarily located in the cerebellar-motor-prefrontal gyrus-anterior cingulate cortex pathway, and for depressed ET vs. non-depressed ET located in the cerebellar-prefrontal gyrus-anterior cingulate cortex circuits. The partial correlation showed that the ReHo values in the bilateral middle prefrontal gyrus (positive) and the bilateral cerebellum XI (negative) were significantly correlated with clinical depression severity. Conclusion: Our findings suggested that combined individual ReHo maps with MVPA not only could be used to identify depressed ET but also help to reveal the intrinsic brain activity changes and further act as the potential diagnosis biomarker in depressed ET patients.

Manipulation of Subwavelength Periodic Structures Formation on 4H-SiC Surface with Three Temporally Delayed Femtosecond Laser Irradiations.

Controlling laser-induced periodic surface structures on semiconductor materials is of significant importance for micro/nanophotonics. We here demonstrate a new approach to form the unusual structures on 4H-SiC crystal surface under irradiation of three collinear temporally delayed femtosecond laser beams (800 nm wavelength, 50 fs duration, 1 kHz repetition), with orthogonal linear polarizations. Different types of surface structures, two-dimensional arrays of square islands (670 nm periodicity) and one-dimensional ripple structures (678 nm periodicity) are found to uniformly distribute over the laser-exposed areas, both of which are remarkably featured by the low spatial frequency. By altering the time delay among three laser beams, we can flexibly control the transition between the two surface structures. The experimental results are well explained by a physical model of the thermally correlated actions among three laser-material interaction processes. This investigation provides a simple, flexible, and controllable processing approach for the large-scale assembly of complex functional nanostructures on bulk semiconductor materials.

Combined multivariate pattern analysis with frequency-dependent intrinsic brain activity to identify essential tremor.

Essential tremor (ET) is the most common tremor disorder, and the intrinsic brain activity changes and diagnostic biomarkers of ET remain unclear. Combined multivariate pattern analysis (MVPA) with resting-state functional MRI (Rs-fMRI) data provides the most promising way to identify individual subjects, reveal brain activity changes, and further establish diagnostic biomarkers in neurological diseases. Using voxel-level amplitude of low-frequency fluctuations (ALFF) and local (regional homogeneity, ReHo) and global (degree centrality, DC) brain connectivity mappings based on three frequency bands (classical band: 0.01-0.10 Hz; slow-5: 0.01-0.023 Hz; slow-4: 0.023-0.073 Hz) of 162 ET patients and 153 well-matched healthy controls (HCs) as input features, MVPA (binary support vector machine, SVM) was performed to differentiate ET from HCs. Each modality achieved good classification performance, except for ReHo based on the slow-4 band with a sensitivity, specificity and total accuracy of 58.64%, 65.36%, 61.90%, respectively (P < 0.05). The classification performance with slow-4 bands was poorer than that with slow-5 and classical bands, but slow-4 bands could be used to reveal the spatial distribution changes in subcortical structures, especially the thalamus. The significant discriminative features were mostly located in the cerebello-thalamo-cortical pathway, and partial correlation analyses showed that significant discriminative features in the cerebello-thalamo-cortical pathway could be used to explain the clinical features of tremor in ET patients. Our findings revealed that voxel-level frequency-dependent ALFF, ReHo and DC could be used to discriminate ET from HCs and help to reveal intrinsic brain activity changes, further acting as potential diagnostic biomarkers.

Impairments in intrinsic functional networks in type 2 diabetes: A meta-analysis of resting-state functional connectivity.

Type 2 diabetes mellitus (T2DM) is associated with abnormal communication among large-scale brain networks, revealed by resting-state functional connectivity (rsFC), with inconsistent results between studies. We performed a meta-analysis of seed-based rsFC studies to identify consistent network connectivity alterations. Thirty-three datasets from 30 studies (1014 T2DM patients and 902 healthy controls [HC]) were included. Seed coordinates and between-group effects were extracted, and the seeds were divided into networks based on their location. Compared to HC, T2DM patients showed hyperconnectivity and hypoconnectivity within the DMN, DMN hypoconnectivity with the affective network (AN), ventral attention network (VAN) and frontal parietal network, and DMN hyperconnectivity with the VAN and visual network. T2DM patients also showed AN hypoconnectivity with the somatomotor network and hyperconnectivity with the VAN. T2DM illness durations negatively correlated with within-DMN rsFC. These DMN-centered impairments in large-scale brain networks in T2DM patients may help to explain the cognitive deficits associated with T2DM.

Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate Pattern Analysis.

Background and Objective: Although depression is one of the most common non-motor symptoms in essential tremor (ET), its pathogenesis and diagnosis biomarker are still unknown. Recently, machine learning multivariate pattern analysis (MVPA) combined with connectivity mapping of resting-state fMRI has provided a promising way to identify patients with depressed ET at the individual level and help to reveal the brain network pathogenesis of depression in patients with ET. Methods: Based on global brain connectivity (GBC) mapping from 41 depressed ET, 49 non-depressed ET, 45 primary depression, and 43 healthy controls (HCs), multiclass Gaussian process classification (GPC) and binary support vector machine (SVM) algorithms were used to identify patients with depressed ET from non-depressed ET, primary depression, and HCs, and the accuracy and permutation tests were used to assess the classification performance. Results: While the total accuracy (40.45%) of four-class GPC was poor, the four-class GPC could discriminate depressed ET from non-depressed ET, primary depression, and HCs with a sensitivity of 70.73% (P < 0.001). At the same time, the sensitivity of using binary SVM to discriminate depressed ET from non-depressed ET, primary depression, and HCs was 73.17, 80.49, and 75.61%, respectively (P < 0.001). The significant discriminative features were mainly located in cerebellar-motor-prefrontal cortex circuits (P < 0.001), and a further correlation analysis showed that the GBC values of significant discriminative features in the right middle prefrontal gyrus, bilateral cerebellum VI, and Crus 1 were correlated with clinical depression severity in patients with depressed ET. Conclusion: Our findings demonstrated that GBC mapping combined with machine learning MVPA could be used to identify patients with depressed ET, and the GBC changes in cerebellar-prefrontal cortex circuits not only posed as the significant discriminative features but also helped to understand the network pathogenesis underlying depression in patients with ET.

One-step fabrication and electromagnetic wave absorption of graphene/Ag@polyaniline ternary nanocomposites.

Based on in situ intercalation polymerization of aniline, a one-step synthesis of a graphene/Ag@PANI ternary composite is proposed. The results show that together with sunlight exposure, Ag+ induces the polymerization of aniline accompanied by self-reduction to form a Ag@PANI core-shell nanostructure, and consequently, exfoliates the graphite sheet into graphene. Through a PANI shell, Ag@PANI nanoparticles all anchor onto the surface of graphene, forming a stable ternary structure. The performance of graphene/Ag@PANI is closely related to its micro-morphology, which depends on the selected Ag+/aniline ratio during the synthesis. Double-layer absorbers with graphene/Ag@PANI as the absorbing layer present excellent absorption performance. The effective absorbing bandwidths of DB-10, DB-5, and DB-1 all exceed 3 GHz with a thickness of 1 mm and the reflection loss of 1.3 mm DB-10 reaches -44.5 dB at 10.5 GHz. The as-proposed facile and eco-friendly preparation of a graphene-based ternary composite is also of great significance for sensors, supercapacitor electronics, degradation of polymers, and other applications.

Synergic effect of graphene and core-shells structured Au NR@SiO2@TiO2 in dye-sensitized solar cells.

Graphene and Au nanorods (AuNRs) coated with SiO2@TiO2 double shells (AuNR@SiO2@TiO2) were incorporated to form novel composite photoanodes in dye sensitized solar cells (DSSCs). The performances of the photoanodes and DSSCs are studied systematically. The short circuit current density (J sc) and power conversion efficiency (PCE) of these composited DSSCs were greatly enhanced and the influences of the graphene, AuNRs and the SiO2@TiO2 double shells were revealed. The optimal properties with the maximal J sc of 16.26 mA cm-2 and PCE of 8.08% are obtained in the DSSC co-doped with graphene and AuNR@SiO2@TiO2, significantly higher than those of the conventional DSSC with pure TiO2 photoanode by 37.7% and 32.9%, respectively. These significant enhancements in J sc and PCE are attributed to the synergistic effect of graphene, the local surface plasma resonance of AuNRs, as well as the outer SiO2@TiO2 double shells, which result in the increased specific surface area and dye adsorption, the increased light absorption, the decreased charge transfer resistance R 2 and electron recombination and thus the increased J sc and PCE of the DSSCs.

Formation of uniform two-dimensional subwavelength structures by delayed triple femtosecond laser pulse irradiation.

The fabrication of subwavelength two-dimensional (2D) structures on metals is of paramount importance to modern nanophotonics. Here we report a method to fabricate 2D conic structures on nickel surfaces using a single beam with three temporally delayed pulses. The 2D structures are fabricated over the entire irradiated region with relatively high uniformity. By controlling the delay between the three pulses, we control the effect of each pulse in creating laser-induced periodic surface structures which enables the control of the 2D structure features, namely, the period and structure dimensions. We explain the results based on the surface plasmon polariton-femtosecond laser interference model.

BrCl+ elimination from Coulomb explosion of 1,2-bromochloroethane induced by intense femtosecond laser fields.

By using a dc-slice imaging technique, photodissociation of 1,2-C2H4BrCl was investigated at 800 nm looking for heteronuclear unimolecular ion elimination of BrCl+ in an 80 fs laser field. The occurrence of fragment ion BrCl+ in the mass spectrum verified the existence of a unimolecular decomposition channel of BrCl+ in this experiment. The relative quantum yield of the BrCl+ channel was measured to be 0.8%. By processing and analyzing the velocity and angular distributions obtained from the corresponding sliced images of BrCl+ and its partner ion C2H4 +, we concluded that BrCl+ came from Coulomb explosion of the 1,2-bromochloroethane dication 1,2-C2H4BrCl2+. With the aid of quantum chemical calculations at the M06-2X/def2-TZVP level, the potential energy surface for BrCl+ detachment from 1,2-C2H4BrCl2+ has been examined in detail. According to the ab initio calculations, two transition state structures tended to correlate with the reactant 1,2-C2H4BrCl2+ and the products BrCl+ + C2H4 +. In this entire dissociation process, the C-Br and C-Cl bond lengths were observed to elongate asymmetrically, that is, the C-Br chemical bond broke firstly, and subsequently a new Br-Cl chemical bond started to emerge while the C-Cl bond continued to exist for a while. Hence, an asynchronous concerted elimination mechanism was favored for BrCl+ detachment.

Controlling periodic ripple microstructure formation on 4H-SiC crystal with three time-delayed femtosecond laser beams of different linear polarizations.

The control of laser-induced periodic ripple microstructures on 4H-SiC crystal surface is studied using temporally delayed collinear three femtosecond laser pulse trains linearly polarized in different directions. The ripple orientation appears to develop independent of the individual laser polarizations and exhibits non-monotonical change with variable time delays, whose variation tendency is also affected by the polarization intersection angles. Remarkably, the ripple period is observed to transfer from high- to low-spatial-frequency regions, accompanied by distinctly improved morphological uniformity and clearness. The results are satisfactorily interpreted based on a physical model of the surface wave excitation on a transient index metasurface, which is confirmed by further experiments. Our investigations indicate that transient noneqilibrium dynamics of the material surface provides an effective way to manipulate the laser-induced microstructures.

Optimization of molecular docking scores with support vector rank regression.

This work introduces the support vector rank regression (SVRR) algorithm for the optimization of molecular docking scores. Seven original docking scores reported by two docking software were integrated by the SVRR algorithm. The resulting SVRR scores showed an average of 12.1% improvement (59.5-66.7%) in binding conformation prediction tests to rank the correctly computed conformation in the first place, along with 16.7% RMSD improvement (2.5414 vs. 2.1162 Å) for the top ranked conformations. In compound library screening (LS) tests, an average of 46.3% improvement (18.2-26.6%) was also observed to rank the correct ligand in the first place. Furthermore, it was shown that SVRR scores trained with different example datasets, using different training strategies, all exhibited exceedingly consistent accuracies, suggesting that the SVRR algorithm is highly robust and generalizable. In contrast, using the same training datasets, traditional support vector classification and regression algorithms failed to improve comparably the accuracy of LS and conformation prediction. These results suggested that, with additional features to indicate the comparative fitness between computed binding conformations, the SVRR algorithm holds the potential to create a new category of more accurate integrative docking scores.

The interprotein scoring noises in glide docking scores.

Small molecule drugs are rarely selective enough to interact solely with their designated targets. Unintended "off-target" interactions often lead to side effects, but also serendipitously lead to new therapeutic uses. Identification of the off-targets of a compound is therefore of significant value to the evaluation of its developmental potential. In computational biology, the strategy of "reverse docking" has been introduced to predict the targets of a compound, which uses a compound to virtually screen a library of proteins, reversing the bait and prey in "normal" docking screenings. The present study shows that, in reverse docking, additional optimization of the scoring function may help to improve the target prediction accuracy. In a case study with the Glide scores, we found that only 57% of the ligand-protein relationships could be correctly identified in a library of 58 complexes whose crystal binding conformations were all able to be accurately reproduced. This was likely a result of the constant over- or under-estimation of the scores for specific proteins. In other words, there were interprotein noises in the Glide scores. Introducing a correction term based on protein characteristics improved the target-prediction accuracy by 27% (57-72%). It is our hope that this focused discussion on the Glide scores would invite further efforts to characterize and normalize this type of interprotein noises in all docking scores, so that better target prediction accuracy can be achieved with the strategy of reverse docking.