The protective effect of vitamin D supplementation as adjunctive therapy to antidepressants on brain structural and functional connectivity of patients with major depressive disorder: a randomized controlled trial.

BACKGROUND: Growing evidence points to the pivotal role of vitamin D in the pathophysiology and treatment of major depressive disorder (MDD). However, there is a paucity of longitudinal research investigating the effects of vitamin D supplementation on the brain of MDD patients. METHODS: We conducted a double-blind randomized controlled trial in 46 MDD patients, who were randomly allocated into either VD (antidepressant medication + vitamin D supplementation) or NVD (antidepressant medication + placebos) groups. Data from diffusion tensor imaging, resting-state functional MRI, serum vitamin D concentration, and clinical symptoms were obtained at baseline and after an average of 7 months of intervention. RESULTS: Both VD and NVD groups showed significant improvement in depression and anxiety symptoms but with no significant differences between the two groups. However, a greater increase in serum vitamin D concentration was found to be associated with greater improvement in depression and anxiety symptoms in VD group. More importantly, neuroimaging data demonstrated disrupted white matter integrity of right inferior fronto-occipital fasciculus along with decreased functional connectivity between right frontoparietal and medial visual networks after intervention in NVD group, but no changes in VD group. CONCLUSIONS: These findings suggest that vitamin D supplementation as adjunctive therapy to antidepressants may not only contribute to improvement in clinical symptoms but also help preserve brain structural and functional connectivity in MDD patients.

Functional connectivity gradients of the insula to different cerebral systems.

The diverse functional roles of the insula may emerge from its heavy connectivity to an extensive network of cortical and subcortical areas. Despite several previous attempts to investigate the hierarchical organization of the insula by applying the recently developed gradient approach to insula-to-whole brain connectivity data, little is known about whether and how there is variability across connectivity gradients of the insula to different cerebral systems. Resting-state functional MRI data from 793 healthy subjects were used to discover and validate functional connectivity gradients of the insula, which were computed based on its voxel-wise functional connectivity profiles to distinct cerebral systems. We identified three primary patterns of functional connectivity gradients of the insula to distinct cerebral systems. The connectivity gradients to the higher-order transmodal associative systems, including the prefrontal, posterior parietal, temporal cortices, and limbic lobule, showed a ventroanterior-dorsal axis across the insula; those to the lower-order unimodal primary systems, including the motor, somatosensory, and occipital cortices, displayed radiating transitions from dorsoanterior toward both ventroanterior and dorsoposterior parts of the insula; the connectivity gradient to the subcortical nuclei exhibited an organization along the anterior-posterior axis of the insula. Apart from complementing and extending previous literature on the heterogeneous connectivity patterns of insula subregions, the presented framework may offer ample opportunities to refine our understanding of the role of the insula in many brain disorders.

Optimized Design of Touching Parts of Soil Disinfection Machine Based on Strain Sensing and Discrete Element Simulation.

With the increasing level in the intensification of agricultural production in China, continuous cropping obstacles have become a problem that needs to be solved. The use of vertical rotary tillage technology and soil disinfection technology is an effective solution. In this paper, a vertical rotary soil-tilling variable disinfection combine was developed and an on-board control system with STM32 as the control core was designed to realize the real-time acquisition of powder monopoly torque information and the variable application of soil disinfection chemicals. Based on the obtained experimental soil parameters, a discrete element soil particle model was established, and orthogonal experiments were conducted to analyze the single-blade roller tillage process, and the optimal operating parameters were finally selected as 500 mm powder monopoly depth, 320 r/min knife roller speed, and 0.26 m/s forward speed, respectively. The field experiment found that the average tillage depth of the implement was 489 mm, the stability coefficient of tillage depth was 94.50%, the uniformity coefficient of soil disinfection was 85.57%, and the applied amount and the speed ratio coefficient of the given flow were linearly related, respectively. This research provides a technical reference for the deep tillage and soil disinfection of the powder monopoly.

Transcriptional substrates underlying functional connectivity profiles of subregions within the human sensorimotor cortex.

The human sensorimotor cortex has multiple subregions showing functional commonalities and differences, likely attributable to their connectivity profiles. However, the molecular substrates underlying such connectivity profiles are unclear. Here, transcriptome-neuroimaging spatial correlation analyses were performed between transcriptomic data from the Allen human brain atlas and resting-state functional connectivity (rsFC) of 24 fine-grained sensorimotor subregions from 793 healthy subjects. Results showed that rsFC of six sensorimotor subregions were associated with expression measures of six gene sets that were specifically expressed in brain tissue. These sensorimotor subregions could be classified into the polygenic- and oligogenic-modulated subregions, whose rsFC were related to gene sets diverging on their numbers (hundreds vs. dozens) and functional characteristics. First, the former were specifically expressed in multiple types of neurons and immune cells, yet the latter were not specifically expressed in any cortical cell types. Second, the former were preferentially expressed during the middle and late stages of cortical development, while the latter showed no preferential expression during any stages. Third, the former were prone to be enriched for general biological functions and pathways, but the latter for specialized biological functions and pathways. Fourth, the former were enriched for neuropsychiatric disorders, whereas this enrichment was absent for the latter. Finally, although the identified genes were commonly associated with sensorimotor behavioral processes, the polygenic-modulated subregions associated genes were additionally related to vision and dementia. These findings may advance our understanding of the functional homogeneity and heterogeneity of the human sensorimotor cortex from the perspective of underlying genetic architecture.

SENGraph: A Self-Learning Evolutionary and Node-Aware Graph Network for Soft Sensing in Industrial Processes.

The last decade has witnessed the growing prevalence of deep models on soft sensing in industrial processes. However, most of the existing soft sensing models are developed to learn from regular data in the Euclidean space, ignoring the complex coupling relations among process variables. On the other hand, graph networks are gaining attraction in handling non-Euclidean relations in industrial data. However, the existing graph networks on soft sensing models still suffer from two major issues: 1) how to capture the intervariable structural relations and intravariable temporal dependencies from dynamic and strongly coupled industrial data and 2) how to learn from nodes with distinctive importance for the soft sensing task. To address these problems, we propose a self-learning evolutionary and node-aware graph network (SENGraph) for industrial soft sensing. We first develop a self-learning graph generation (SLG) module to combine the coarse-and fine-grained graphs to capture the global trend and local dynamics from process data. Then, we build a self-evolutionary graph module (EGM) to obtain diversified node features from the entire graph using mutation and crossover strategies. Finally, we design a node-aware module (NAM) to highlight the informative nodes and suppress the less significant ones to further improve the discriminative ability of the downstream soft sensing. Extensive experimental results and analysis on four real-world industrial datasets demonstrate that our proposed SENGraph model outperforms the existing state-of-the-art (SOTA) soft sensing methods.

Dimensional subtyping of first-episode drug-naïve major depressive disorder: A multisite resting-state fMRI study.

Major depressive disorder (MDD) is a heterogeneous syndrome, and understanding its neural mechanisms is crucial for the advancement of personalized medicine. However, conventional subtyping studies often categorize MDD patients into a single subgroup, neglecting the continuous interindividual variations. This implies a pressing need for a dimensional approach. 230 first-episode drug-naïve MDD patients and 395 healthy controls were obtained from 5 sites via the Rest-meta-MDD project. A Bayesian model was used to decompose the resting-state functional connectivity (RSFC) into multiple distinct RSFC patterns (refer to as "factors"), and each individual was allowed to express multiple factors to varying degrees (dimensional subtyping). The associations between demographic and clinical variables with the identified factors were calculated. We identified three latent factors with distinct but partially overlapping hypo- and hyper-RSFC patterns. Most participants co-expressed multiple latent factors. All factors shared abnormal RSFC involving the default mode network and frontoparietal network, but the directionality partially differed across factors. All factors were not significantly associated with demographic and clinical variables. These findings shed light on the interindividual variability in MDD and could form the basis for developing novel therapeutic approaches that capitalize on the heterogeneity of MDD.

Functional connectivity gradients of the cingulate cortex.

Heterogeneity of the cingulate cortex is evident in multiple dimensions including anatomy, function, connectivity, and involvement in networks and diseases. Using the recently developed functional connectivity gradient approach and resting-state functional MRI data, we found three functional connectivity gradients that captured distinct dimensions of cingulate hierarchical organization. The principal gradient exhibited a radiating organization with transitions from the middle toward both anterior and posterior parts of the cingulate cortex and was related to canonical functional networks and corresponding behavioral domains. The second gradient showed an anterior-posterior axis across the cingulate cortex and had prominent geometric distance dependence. The third gradient displayed a marked differentiation of subgenual and caudal middle with other parts of the cingulate cortex and was associated with cortical morphology. Aside from providing an updated framework for understanding the multifaceted nature of cingulate heterogeneity, the observed hierarchical organization of the cingulate cortex may constitute a novel research agenda with potential applications in basic and clinical neuroscience.

A Novel Rain Removal Approach for Outdoor Dynamic Vision Sensor Event Videos.

As bio-inspired vision devices, dynamic vision sensors (DVS) are being applied in more and more applications. Unlike normal cameras, pixels in DVS independently respond to the luminance change with asynchronous output spikes. Therefore, removing raindrops and streaks from DVS event videos is a new but challenging task as the conventional deraining methods are no longer applicable. In this article, we propose to perform the deraining process in the width and time (W-T) space. This is motivated by the observation that rain steaks exhibits discontinuity in the width and time directions while background moving objects are usually piecewise smooth along with both directions. The W-T space can fuse the discontinuity in both directions and thus transforms raindrops and streaks to approximately uniform noise that are easy to remove. The non-local means filter is adopted as background object motion has periodic patterns in the W-T space. A repairing method is also designed to restore edge details erased during the deraining process. Experimental results demonstrate that our approach can better remove rain noise than the four existing methods for traditional camera videos. We also study how the event buffer depth and event frame time affect the performance investigate the potential implementation of our approach to classic RGB images. A new real-world database for DVS deraining is also created and shared for public use.

Ferroelectric control of pseudospin texture in CuInP2S6monolayer.

Spin-orbit coupling (SOC) plays an important role in condensed matter physics and has potential applications in spintronics devices. In this paper, we study the electronic properties of ferroelectric CuInP2S6(CIPS) monolayer through first-principles calculations. The result shows that CIPS monolayer is a potential for valleytronics material and we find that the in-plane helical and nonhelical pseudospin texture are induced by the Rashba and Dresselhaus effect, respectively. The chirality of helical pseudospin texture is coupled to the out-of-plane ferroelectric polarization. Furthermore, a large spin splitting due to the SOC effect can be found atKvalley, which can be regarded as the Zeeman effect under a valley-dependent pseudomagnetic field. The CIPS monolayer with Rashbaet aleffects provides a good platform for electrically controlled spin polarization physics.

Dynamic functional connectome predicts individual working memory performance across diagnostic categories.

Working memory impairment is a common feature of psychiatric disorders. Although its neural mechanisms have been extensively examined in healthy subjects or individuals with a certain clinical condition, studies investigating neural predictors of working memory in a transdiagnostic sample are scarce. The objective of this study was to create a transdiagnostic predictive working memory model from whole-brain functional connectivity using connectome-based predictive modeling (CPM), a recently developed machine learning approach. Resting-state functional MRI data from 242 subjects across 4 diagnostic categories (healthy controls and individuals with schizophrenia, bipolar disorder, and attention deficit/hyperactivity) were used to construct dynamic and static functional connectomes. Spatial working memory was assessed by the spatial capacity task. CPM was conducted to predict individual working memory from dynamic and static functional connectivity patterns. Results showed that dynamic connectivity-based CPM models successfully predicted overall working memory capacity and accuracy as well as mean reaction time, yet their static counterparts fell short in the prediction. At the neural level, we found that dynamic connectivity of the frontoparietal and somato-motor networks were negatively correlated with working memory capacity and accuracy, and those of the default mode and visual networks were positively associated with mean reaction time. Moreover, different feature selection thresholds, parcellation strategies and model validation methods as well as diagnostic categories did not significantly influence the prediction results. Our findings not only are coherent with prior reports that dynamic functional connectivity encodes more behavioral information than static connectivity, but also help advance the translation of cognitive "connectome fingerprinting" into real-world application.

Electric control of nearly free electron states and ferromagnetism in the transition-metal dichalcogenides monolayers.

Using the first-principles calculations, we explore the nearly free electron (NFE) states in the transition-metal dichalcogenidesMX2(M= Mo, W;X= S, Se, Te) monolayers. It is found that both the external electric field and electron (not hole) injection can flexibly tune the energy levels of the NFE states, which can shift down to the Fermi level and result in novel transport properties. In addition, we find that the valley polarization can be induced by both electron and hole doping in MoTe2monolayer due to the ferromagnetism induced by the charge injection, which, however, is not observed in other five kinds ofMX2monolayers. We carefully check band structures of all theMX2monolayers, and find that the exchange splitting in the top of the valence band and the bottom of conduction band plays the key role in the ferromagnetism. Our researches enrich the electronic, spintronic, and valleytronic properties ofMX2monolayers.

Lithium intercalation into bilayer graphene.

The real capacity of graphene and the lithium-storage process in graphite are two currently perplexing problems in the field of lithium ion batteries. Here we demonstrate a three-dimensional bilayer graphene foam with few defects and a predominant Bernal stacking configuration, and systematically investigate its lithium-storage capacity, process, kinetics, and resistances. We clarify that lithium atoms can be stored only in the graphene interlayer and propose the first ever planar lithium-intercalation model for graphenic carbons. Corroborated by theoretical calculations, various physiochemical characterizations of the staged lithium bilayer graphene products further reveal the regular lithium-intercalation phenomena and thus fully illustrate this elementary lithium storage pattern of two-dimension. These findings not only make the commercial graphite the first electrode with clear lithium-storage process, but also guide the development of graphene materials in lithium ion batteries.

Synthesizing 1T-1H Two-Phase Mo1-xWxS2 Monolayers by Chemical Vapor Deposition.

1T-1H metal-semiconductor interfaces in two-dimensional (2D) transition-metal dichalcogenides (TMDs) play a crucial role in utilizing the band gaps of TMDs for applications in electronic devices. Although the 1T-1H two-phase structure has been observed in exfoliated 2D nanosheets and chemically or physically treated TMDs, it cannot in principle be achieved in large-scale TMD monolayers grown by chemical vapor deposition (CVD), which is a fabrication method for electronic device applications, because of the metastable nature of the 1T phase. In this study we report CVD growth of 1T-1H two phase TMD monolayers by controlling thermal strains and alloy compositions. It was found that in-plane thermal strains arising from the difference in thermal expansion coefficients between TMD monolayers and substrates can drive the 1H to 1T transition during cooling after CVD growth. Moreover, grain boundaries in the 2D crystals act as the nucleation sites of the 1T phase and the lattice strain perturbations from alloying noticeably promote the formation of the metastable 1T phase. This work has an important implication in tailoring structure and properties of CVD grown 2D TMDs by phase engineering.

Synergistic Effect of MoS2 Nanosheets and VS2 for the Hydrogen Evolution Reaction with Enhanced Humidity-Sensing Performance.

As a typical transition-metal dichalcogenides, MoS2 has been a hotspot of research in many fields. In this work, the MoS2 nanosheets were compounded on 1T-VS2 nanoflowers (VS2@MoS2) successfully by a two-step hydrothermal method for the first time, and their hydrogen evolution properties were studied mainly. The higher charge-transfer efficiency benefiting from the metallicity of VS2 and the greater activity due to more exposed active edge sites of MoS2 improve the hydrogen evolution reaction performance of the nanocomposite electrocatalyst. Adsorption and transport of an intermediate hydrogen atom by VS2 also enhances the hydrogen evolution efficiency. The catalyst shows a low onset potential of 97 mV, a Tafel slope as low as 54.9 mV dec-1, and good stability. Combining the electric conductivity of VS2 with the physicochemical stability of MoS2, VS2@MoS2 also exhibits excellent humidity properties.

Chemical Selectivity at Grain Boundary Dislocations in Monolayer Mo1-xWxS2 Transition Metal Dichalcogenides.

Grain boundaries (GBs) are unavoidable crystal defects in polycrystalline materials and significantly influence their properties. However, the structure and chemistry of GBs in 2D transition metal dichalcogenide alloys have not been well established. Here we report significant chemical selectivity of transition metal atoms at GB dislocation cores in Mo1-xWxS2 monolayers. Different from classical elastic field-driven dislocation segregation in bulk crystals, the chemical selectivity in the 2D crystals originates prominently from variation of atomic coordination numbers at dislocation cores. This observation provides atomic insights into the topological effect on the chemistry of crystal defects in 2D materials.

First-principles study of enhanced magnetic anisotropies in transition-metal atoms doped WS2 monolayer.

Considerable progress in contemporary spintronics has been made in recent years for developing nanoscale data memory and quantum information processing. It is, however, still a great challenge to achieve the ultimate limit of storage bit. 2D materials, fortunately, provide an alternative solution for designing materials with the expected miniaturizing scale, chemical stability as well as giant magnetic anisotropy energy. By performing first-principles calculations, we have examined two possible doping sites on a WS2 monolayer using three kinds of transition metal (TM) atoms (Mn, Fe and Co). It is found that the TM atoms prefer to stay on the W atom site. Additionally, differently from the case of Mn, doping Co and Fe atoms on the W vacancy can achieve perpendicular magnetic anisotropy with a much larger magnitude, which provides a bright prospect for generating atomic-scale magnets of storage devices.

Correlation between Chemical Dopants and Topological Defects in Catalytically Active Nanoporous Graphene.

The interplay between chemical dopants and topological defects plays a crucial role in electrocatalysis of doped graphene. By systematically tuning the curvatures, thereby the density of topological defects, of 3D nanoporous graphene, the intrinsic correlation of topological defects with chemical doping contents and dopant configurations is revealed, shining lights into the structural and chemical origins of HER activities of graphene.