Search

Abstract Neuropsychiatric symptoms (including anxiety, depression, apathy, impulse–compulsive behaviors and hallucinations) are among the most common non-motor features of Parkinson’s disease. Whether these symptoms should be considered as a direct consequence of the pathophysiologic mechanisms of Parkinson’s disease is controversial. Morphometric similarity network analysis and epicenter mapping approach were performed on T1-weighted images of 505 patients with Parkinson’s disease and 167 age- and sex-matched healthy participants from Parkinson’s Progression Markers Initiative database to reveal the commonalities and specificities of distinct neuropsychiatric symptoms. Abnormal cortical co-alteration pattern in patients with neuropsychiatric symptoms was in somatomotor, vision and frontoparietal regions, with epicenters in somatomotor regions. Apathy, impulse–compulsive behaviors and hallucinations shares structural abnormalities in somatomotor and vision regions, with epicenters in somatomotor regions. In contrast, the cortical abnormalities and epicenters of anxiety and depression were prominent in the default mode network regions. By embedding each symptom within their co-alteration space, we observed a cluster composed of apathy, impulse–compulsive behaviors and hallucinations, while anxiety and depression remained separate. Our findings indicate different structural mechanisms underlie the occurrence and progression of different neuropsychiatric symptoms. Based upon these results, we propose that apathy, impulse-compulsive behaviors and hallucinations are directly related to damage of motor circuit, while anxiety and depression may be the combination effects of primary pathophysiology of Parkinson’s disease and psychosocial causes.

Abstract Genetic generalized epilepsies (GGE) exhibit widespread morphometric alterations in the subcortical structures. Subcortical structures are essential for understanding GGE pathophysiology, but their fine-grained morphological diversity has yet to be comprehensively investigated. Furthermore, the relationships between macroscale morphological disturbances and microscale molecular chemoarchitectures are unclear. High-resolution structural images were acquired from patients with GGE (n = 97) and sex- and age-matched healthy controls (HCs, n = 184). Individual measurements of surface shape features (thickness and surface area) of seven bilateral subcortical structures were quantified. The patients and HCs were then compared vertex-wise, and shape anomalies were co-located with brain neurotransmitter profiles. We found widespread morphological alterations in GGE and prominent disruptions in the thalamus, putamen, and hippocampus. Shape area dilations were observed in the bilateral ventral, medial, and right dorsal thalamus, as well as the bilateral lateral putamen. We found that the shape area deviation pattern was spatially correlated with the norepinephrine transporter and nicotinic acetylcholine (Ach) receptor (α 4 β 2 ) profiles, but a distinct association was seen in the muscarinic Ach receptor (M 1 ). The findings provided a comprehensive picture of subcortical morphological disruptions in GGE, and further characterized the associated molecular mechanisms. This information may increase our understanding of the pathophysiology of GGE.

With the increasing demand for the resilient capacity of transformers in high-proportion renewable energy grids, the overload capacity of transformers and the thermal conductivity of winding insulation materials have been put forward as important requirements. In response to these requirements, a novel ceramic high temperature tolerance insulation winding is proposed in this paper and is applied in a ceramic insulated winding transformer. A combination of simulation and experimentation is used to analyze the thermal-flow characteristics of this transformer. The temperature and flow field distribution characteristics of transformers with both winding insulation methods (ceramic and Nomex paper) are compared. The internal mechanism of reflux near the iron core and high voltage winding under forced air cooling is clarified. A superior outside cooling condition is obtained. A prototype ceramic insulated winding transformer is developed and tested to verify the accuracy of the simulation model. The results show that the hotspot temperature of the ceramic insulated winding transformer under natural cooling is 10.7% lower than that of the Nomex paper insulated winding transformer. With forced air cooling of 2, 3, 4, 5, and 6 m/s applied at the bottom, the hotspot temperature of the ceramic insulated winding transformer is 11.4%, 10.5%, 9.7%, 14.0%, and 17.8% lower than that of the Nomex paper insulation winding transformer, respectively. Under forced air cooling, a reflux phenomenon is observed in both transformers in the vicinity of the iron core and high voltage winding due to the formation of adverse pressure gradient region and wall viscous force blocking. The strength of the reflux is affected by the magnitude of the forced air cooling flow velocity and the construction of the transformer. The optimal range of the external cooling flow velocity for the ceramic insulated winding transformer is 3–4 m/s. The errors between the simulated temperature and the measured temperature at the nine monitoring points of the prototype are all less than 5%, which verifies the effectiveness of the simulation model. The experimental and theoretical basis for the design and application of resilient capacity dry type transformers is provided by this research.

Abstract Pharmacologic targeting of chromatin-associated protein complexes has shown significant responses in KMT2A-rearranged (KMT2A-r) acute myeloid leukemia (AML) but resistance frequently develops to single agents. This points to a need for therapeutic combinations that target multiple mechanisms. To enhance our understanding of functional dependencies in KMT2A-r AML, we have used a proteomic approach to identify the catalytic immunoproteasome subunit PSMB8 as a specific vulnerability. Genetic and pharmacologic inactivation of PSMB8 results in impaired proliferation of murine and human leukemic cells while normal hematopoietic cells remain unaffected. Disruption of immunoproteasome function drives an increase in transcription factor BASP1 which in turn represses KMT2A-fusion protein target genes. Pharmacologic targeting of PSMB8 improves efficacy of Menin-inhibitors, synergistically reduces leukemia in human xenografts and shows preserved activity against Menin-inhibitor resistance mutations. This identifies and validates a cell-intrinsic mechanism whereby selective disruption of proteostasis results in altered transcription factor abundance and repression of oncogene-specific transcriptional networks. These data demonstrate that the immunoproteasome is a relevant therapeutic target in AML and that targeting the immunoproteasome in combination with Menin-inhibition could be a novel approach for treatment of KMT2A-r AML.

The convolutional neural network (CNN) has been widely applied in medical image segmentation due to its outstanding nonlinear expression ability. However, applications of CNN are often limited by the receptive field, preventing it from modeling global dependencies. The recently proposed transformer architecture, which uses a self-attention mechanism to model global context relationships, has achieved promising results. Swin-Unet is a Unet-like simple transformer semantic segmentation network that combines the dominant feature of both the transformer and Unet. Even so, Swin-Unet has some limitations, such as only learning single-scale contextual features, and it lacks inductive bias and effective multi-scale feature selection for processing local information. To solve these problems, the Residual Local induction bias-Unet (RL-Unet) algorithm is proposed in this paper. First, the algorithm introduces a local induction bias module into the RLSwin-Transformer module and changes the multi-layer perceptron (MLP) into a residual multi-layer perceptron (Res-MLP) module to model local and remote dependencies more effectively and reduce feature loss. Second, a new densely connected double up-sampling module is designed, which can further integrate multi-scale features and improve the segmentation accuracy of the target region. Third, a novel loss function is proposed that can significantly enhance the performance of multiple scales segmentation and the segmentation results for small targets. Finally, experiments were conducted using four datasets: Synapse, BraTS2021, ACDC, and BUSI. The results show that the performance of RL-Unet is better than that of Unet, Swin-Unet, R2U-Net, Attention-Unet, and other algorithms. Compared with them, RL-Unet produces significantly a lower Hausdorff Distance at 95% threshold (HD95) and comparable Dice Similarity Coefficient (DSC) results. Additionally, it exhibits higher accuracy in segmenting small targets.

Mercury (Hg) is a serious metal environmental pollutant. HgCl2 exposure causes pyroptosis. When macrophages are severely stimulated, they often undergo M1 polarization and release inflammatory factors. However, the mechanisms by which mercuric chloride exposure induces macrophage apoptosis, M1 polarization, and inflammatory factors remain unclear. HD11 cells were exposed to different concentrations of Hg chloride (180, 210 and 240 nM HgCl2). The results showed that mercury chloride exposure up-regulated ROS, C-Nrf2 and its downstream factors (NQO1 and HO-1), and down-regulated N-Nrf2. In addition, the expressions of focal death-related indicators (Caspase-1, NLRP3, GSDMD, etc.), M1 polarization marker CD86 and inflammatory factors (TNF-α, IL-1β) increased, and the above changes were related to mercury. Oxidative stress inhibitor (NAC) can block ROS/ NrF2-mediated oxidative stress, inhibit mercury-induced pyroptosis and M1 polarization, and effectively reduce the release of inflammatory factors. The addition of Vx-765 to inhibit pyroptosis can effectively alleviate M1 polarization of HD11 cells and reduce the expression of inflammatory factors. HgCl2 mediates pyroptosis of HD11 cells by regulating ROS/Nrf2/NLRP3, promoting M1 polarization and the release of inflammatory factors.

Abstract Functional connectivity hierarchy is an important principle in the process of brain functional organization and an important feature reflecting brain development. However, atypical brain network hierarchy organization in Rolandic epilepsy have not been systematically investigated. We examined connectivity alteration with age and its relation to epileptic incidence, cognition, or underlying genetic factors in 162 cases of Rolandic epilepsy and 117 typically developing children, by measuring fMRI multi-axis functional connectivity gradients. Rolandic epilepsy is characterized by contracting and slowing expansion of the functional connectivity gradients, highlighting the atypical age-related change of the connectivity hierarchy in segregation properties. The gradient alterations are relevant to seizure incidence, cognition, and connectivity deficit, and development-associated genetic basis. Collectively, our approach provides converging evidence for atypical connectivity hierarchy as a system-level substrate of Rolandic epilepsy, suggesting this is a disorder of information processing across multiple functional domains, and established a framework for large-scale brain hierarchical research.

A highly intelligent system often draws lessons from the unique abilities of humans. Current humanlike models, however, mainly focus on biological behavior, and the brain functions of humans are often overlooked. By drawing inspiration from brain science, this article shows how aspects of brain processing such as sensing, preprocessing, cognition, obstacle learning, behavior, strategy learning, pre-action, and action can be melded together in a coherent manner with cognitive control architecture. This work is based on the notion that the anti-collision response is activated in sequence, which starts from obstacle sensing to action. In the process of collision avoidance, cognition and learning modules continuously control the UAV’s repertoire. Furthermore, simulated and experimental results show that the proposed architecture is effective and feasible.

In this study, a medium- and long-term power load prediction method is proposed based on the two-layer categorical boosting (CatBoost) algorithm with multi-dimensional feature considerations. Simultaneously, the influences of economic fluctuation, power generation disruption, and meteorological data on power load are considered, whereby the dimension of power-load forecasting data characteristics is broadened. A randomised search cross-validation (CV) regression model is also applied to model parameter optimisation. Real data from a province in northeast China were used for the training and test sets. Compared with nine advanced load prediction models, including eXtreme gradient boosting and adaptive boosting, the coefficient of determination (R2) of the proposed method was 0.925, mean average percentage error (MAPE) was 0.0158, and root-mean-square error (RMSE) was 274.2036. In this study, a popular, viable artificial intelligence technology, two-layer CatBoost, was explored, and multi-dimensional external variables of power generation were added for the first time for load prediction. Finally, a higher accuracy load forecasting tree model is presented. The method has good potential for use in medium- and long-term power-load forecasting applications.

The construction of heterojunction structure is an effective strategy for solving photogenerated electron-hole recombination. In this work, a kind of 1D/2D S-scheme BiPO4/BiOBr heterojunction film is designed and constructed by in-situ electrochemical-ion-exchange method on Bi plate. The S-scheme transfer path of photogenerated electrons of BiPO4/BiOBr film is collaboratively confirmed through DFT calculation and in-situ X-ray photoelectron spectra. As-prepared S-scheme BiPO4/BiOBr heterojunction film has the higher CO2 photoreduction activity (296.5 μmol·g−1) to CO, 1.9 times than pure BiOBr film, which should be mainly attributed to improved light utilization efficiency and effective separation/migration of photogenerated electron-hole pairs.

BackgroundThe overall evidence base of anti-inflammatory therapies in patients with type 2 diabetes mellitus (T2DM) has not been systematically evaluated. The purpose of this study was to assess the effects of anti-inflammatory therapies on glycemic control in patients with T2DM.MethodsPubMed, Embase, Web of Science, and Cochrane Library were searched up to 21 September 2022 for randomized controlled trials (RCTs) with anti-inflammatory therapies targeting the proinflammatory cytokines, cytokine receptors, and inflammation-associated nuclear transcription factors in the pathogenic processes of diabetes, such as interleukin-1β (IL-1β), interleukin-1β receptor (IL-1βR), tumor necrosis factor-α (TNF-α), and nuclear factor-κB (NF-κB). We synthesized data using mean difference (MD) and 95% confidence interval (CI). Heterogeneity between studies was assessed by I2 tests. Sensitivity and subgroup analyses were also conducted.ResultsWe included 16 RCTs comprising 3729 subjects in the meta-analyses. Anti-inflammatory therapies can significantly reduce the level of fasting plasma glucose (FPG) (MD = - 10.04; 95% CI: -17.69, - 2.40; P = 0.01), glycated haemoglobin (HbA1c) (MD = - 0.37; 95% CI: - 0.51, - 0.23; P < 0.00001), and C-reactive protein (CRP) (MD = - 1.05; 95% CI: - 1.50, - 0.60; P < 0.00001) compared with control, and therapies targeting IL-1β in combination with TNF-α have better effects on T2DM than targeting IL-1β or TNF-α alone. Subgroup analyses suggested that patients with short duration of T2DM may benefit more from anti-inflammatory therapies.ConclusionOur meta-analyses indicate that anti-inflammatory therapies targeting the pathogenic processes of diabetes can significantly reduce the level of FPG, HbA1c, and CRP in patients with T2DM.

Polystyrene microplastics (PS-MPs) affect the immune defense function on carp (Cyprinus carpio). The PS-MPs model of carp was established by feeding with PS-MPs particle size of 8 µm and concentration of 1000 ng/L water. Hepatopancreas function test revealed the activities of AKP, ALT, AST and LDH abnormal increase. PS-MPs induced tissue damage and lead to abnormal hepatopancreas function. The PS-MPs also induced a oxidative stress with the antioxidant enzymes SOD, CAT, GSH-PX, and T-AOC activities decreasing and reactive oxygen species (ROS) excessive accumulation. PS-MPs activated the Toll like receptor-2 (TLR2) signaling pathway. The mRNA and protein expressions of TLR2, Myeloid differentiation primary response 88 (MyD88), tumor necrosis factor receptor-associated factor 6 (TRAF6), NF-κB p65, Tumor necrosis factor (TNF-α), Interleukin-1β (IL-1β), Inducible Nitric Oxide Synthase (iNOS), and cycooxygenase 2(COX2) was revealed increased in both hepatopancreas and hepatocytes with the qPCR and Western blotting analysis mode. ELISA showed the expressions of TNF-α, IL-1β, iNOS, and COX2 inflammatory molecule were increased in both hepatopancreas and hepatocytes. The results showed that PS-MPs caused a serious injure in the hepatopancreas and brought serious effects on the inflammatory response of carp. The present study displayed the harm caused by PS-MPs in freshwater fish, and provided some suggestions and references for toxicological studies of microplastics in freshwater environment.

The purpose of knowledge representation learning is to learn the vector representation of research objects projected by a matrix in low-dimensional vector space and explore the relationship between embedded objects in low-dimensional space. However, most methods only consider the triple structure in the knowledge graph and ignore the additional information related to the triple, especially the text description information. In this paper, we propose a knowledge graph representation model with a symmetric architecture called Joint Knowledge Representation Learning of Text Description and Knowledge Graph (JKRL), which models the entity description and relationship description of the triple structure for joint representation learning of knowledge and balances the contribution of the triple structure and text description in the process of vector learning. First, we adopt the TransE model to learn the structural vector representations of entities and relations, and then use a CNN model to encode the entity description to obtain the text representation of the entity. To semantically encode the relation descriptions, we designed an Attention-Bi-LSTM text encoder, which introduces an attention mechanism into the Bi-LSTM model to calculate the semantic relevance between each word in the sentence and different relations. In addition, we also introduce position features into word features in order to better encode word order information. Finally, we define a joint evaluation function to learn the joint representation of structural and textual representations. The experiments show that compared with the baseline methods, our model achieves the best performance on both Mean Rank and Hits@10 metrics. The accuracy of the triple classification task on the FB15K dataset reached 93.2%.

The accuracy of dam deformation prediction is a key issue that needs to be addressed due to the many factors that influence dam deformation. In this paper, a dam deformation prediction model based on IEALL (IGWO-EEMD-ARIMA-LSTM-LSTM) is proposed for a single-point scenario. The IEALL model is based on the idea of a combination model. Firstly, EEMD is used to decompose the dam deformation data, and then the ARIMA and LSTM models are selected for prediction. To address the problem of low prediction accuracy caused by simple linear addition of prediction results from different models in traditional combination models, the LSTM model is used to learn the combination relationship of different model prediction results. The problem of neural network parameters falling into local optima due to random initialization is addressed by using the improved gray wolf optimization (IGWO) to optimize multiple parameters in the IEALL combination model to obtain the optimal parameters. For the multi-point scenario of dam deformation, based on the IEALL model, a dam deformation prediction model based on spatio-temporal correlation and IEALL (STAGCN-IEALL) is proposed. This model introduces graph convolutional neural networks (GCN) to extract spatial features from multi-point sequences, increasing the model’s ability to express spatial dimensions. To address the dynamic correlation between different points in the deformation sequence at any time and the dynamic dependence on different points at any given time, spatio-temporal attention mechanisms are introduced to capture dynamic correlation from both spatial and temporal dimensions. Experimental results showed that compared to ST-GCN, IEALL reduced the RMSE, MAE, and MAPE by 16.06%, 14.72%, and 21.19%. Therefore, the proposed model effectively reduces the prediction error and can more accurately predict the trend of dam deformation changes.

Path planning is an important part of the unmanned aerial vehicle (UAV) to realize its autonomous capabilities. Aiming at the shortcomings of the traditional ant colony algorithm-based trajectory planning method, which has slow convergence speed and easy to fall into the local optimum, a path planning method based on the improved ant colony algorithm is proposed. First, a dynamic adjusting factor is added into the heuristic function to improve the directivity of path selection and search speed. Then, the state transition strategy is improved to solve the problem of slow convergence in the initial stage and easy to fall into local optimum in the later stage. Finally, the path inflection point is smoothly optimized through the cubic B-spline curve. Simulation results show that the improved ant colony algorithm can quickly converge to the optimal path and well adapt to the flight requirements of multirotor UAV.

Unmanned aerial vehicles (UAVs) have played an important role in recent high-tech local wars. Seizing air control rights with UAVs will undoubtedly be a popular topic in future military development. Autonomous air combat is complex, antagonistic and mutable, and consequently, the decision-making that depends on unmanned systems is extremely challenging with very little research having been conducted on it. An intelligent air combat learning system inspired by the learning mechanisms of the brain is proposed in this paper. In accordance with research on learning, knowledge and memory, we constructed a cognitive mechanism model of the brain. Based on this model and the inferential abilities of humans, a long short-term hierarchical multi-line learning system is established. Then, the bio-inspired architecture and the basic learning principle of the system are clarified. Taking advantage of the conclusions of studies on information theory, the relationship between the knowledge updating cycle and the system learning performance is analysed. The updating cycle length adjustment problem is transformed into an optimization problem optimization problem, and system performance improvement is guaranteed. Experiments show that the system designed in this paper can acquire confrontation abilities through self-learning without prior rules; the parallel universe mechanism can significantly improve the system's learning speed when the number of parallels is within 40, and the performance of the system improves gradually and continuously. The system can master actions similar to classical tactical manoeuvres such as the high yo-yo and the barrel-roll-attack without prior knowledge. Compared with the Bayesian inference and moving horizon optimization (BI&MHO) method, the learning system proposed in this paper is more flexible in situation assessment and in the prediction of opponents' actions. Although it cannot be deployed quickly, it has a continuous learning ability.

The intestine has many types of cells that are present mostly in the epithelium and lamina propria. The importance of the intestinal cells for the mammalian mucosal immune system is well-established. However, there is no in-depth information about many of the intestinal cells in teleosts. In our previous study, we reported that adherent intestinal cells (AIC) predominantly express macrophage-related genes. To gather further evidence that AIC include macrophage-like cells, we compared their phagocytic activity and morphology with those of adherent head kidney cells (AKC), previously characterized as macrophage-like cells. We also compared equally abundant as well as differentially expressed mRNAs and miRNAs between AIC and AKC. AIC had lower phagocytic activity and were larger and more circular than macrophage-like AKC. RNA-Seq data revealed that there were 18309 mRNAs, with 59 miRNAs that were equally abundant between AIC and AKC. Integrative analysis of the mRNA and miRNA transcriptomes revealed macrophage heterogeneity in both AIC and AKC. In addition, analysis of AIC and AKC transcriptomes revealed functional characteristics of mucosal and systemic macrophages. Five pairs with significant negative correlations between miRNA and mRNAs were linked to macrophages and epithelial cells and their interaction could be pointing to macrophage activation and differentiation. The potential macrophage markers suggested in this study should be investigated under different immune conditions to understand the exact macrophage phenotypes.

Preliminary studies have shown the feasibility of deep learning (DL)-based super-resolution (SR) technique for reconstructing thick-slice/gap diagnostic MR images into high-resolution isotropic data, which would be of great significance for brain research field if the vast amount of diagnostic MRI data could be successively put into brain morphometric study. However, less evidence has addressed the practicability of the strategy, because lack of a large-sample available real data for constructing DL model. In this work, we employed a large cohort (n = 2052) of peculiar data with both low through-plane resolution diagnostic and high-resolution isotropic brain MR images from identical subjects. By leveraging a series of SR approaches, including a proposed novel DL algorithm of Structure Constrained Super Resolution Network (SCSRN), the diagnostic images were transformed to high-resolution isotropic data to meet the criteria of brain research in voxel-based and surface-based morphometric analyses. We comprehensively assessed image quality and the practicability of the reconstructed data in a variety of morphometric analysis scenarios. We further compared the performance of SR approaches to the ground truth high-resolution isotropic data. The results showed (i) DL-based SR algorithms generally improve the quality of diagnostic images and render morphometric analysis more accurate, especially, with the most superior performance of the novel approach of SCSRN. (ii) Accuracies vary across brain structures and methods, and (iii) performance increases were higher for voxel than for surface based approaches. This study supports that DL-based image super-resolution potentially recycle huge amount of routine diagnostic brain MRI deposited in sleeping state, and turning them into useful data for neurometric research.

In this study, the dynamic mechanical properties of steel fiber reinforced concrete under the influence of freeze–thaw cycles were studied. The studied parameters include steel fiber content (0%, 1% and 2%), confining pressures (0, 5 and 10 MPa) and strain rates (10−5/s, 10−4/s, 10−3/s and 10−2/s). Performance was also evaluated, including triaxial compressive strength, peak strain, the relationship between stress and strain, failure mode and microstructure. The results show that with the increase in F–T cycles, the compressive strength and energy absorption capacity of concrete gradually decrease. The mechanical properties of concrete increased with the addition of steel fibers during F–T cycles, and the optimum amount of steel fiber to enhance resistance to F–T cycles is 1% within the evaluation range. In this study, the effects of strain rate and confining pressure on the strength and failure mode of concrete after fiber addition are studied. Both the dynamic increase factor and the concrete strength increase linearly with the increase of strain rate, the dynamic increase factor is characterized by an increase in intensity caused by strain rate. When there is no confining, the crack direction of the concrete specimen is parallel to the stress loading direction, and when there is confining, it is manifested as oblique shear failure. The results of scanning electron microscopy analysis of the microstructure demonstrate the performance results at the macroscopic level (compressive strength and peak strain).

Genetic generalized epilepsy is a network disorder typically involving distributed areas identified by classical neuroanatomy. However, the finer topological relationships in terms of continuous spatial arrangement between these systems are still ambiguous. Connectome gradients provide the topological representations of human macroscale hierarchy in an abstract low-dimensional space by embedding the functional connectome into a set of axes. Leveraging connectome gradients, we systematically scrutinized abnormalities of functional connectome gradient in patients with genetic generalized epilepsy with tonic-clonic seizure (GGE-GTCS, n = 78) compared to healthy controls (HC, n = 85), and further examined the reproducibility across multiple processing configurations and in an independent validation sample (patients with GGE-GTCS, n = 28; HC, n = 31). Our findings demonstrated an extended principal gradient at different spatial scales, network-level and vertex-level, in patients with GGE-GTCS. We found consistent results across processing parameters and in validation sample. The extended principal gradient revealed the excessive functional segregation between unimodal and transmodal systems associated with duration of epilepsy and age at seizure onset in patients. Furthermore, the connectivity profile of regions with abnormal principal gradients verified the disrupted functional hierarchy revealed by gradients. Together, our findings provided a novel view of functional system hierarchy alterations, which facilitated a continuous spatial arrangement of macroscale networks, to increase our understanding of the functional connectome hierarchy in generalized epilepsy.

Brain structural covariance network (SCN) can delineate the brain synchronized alterations in a long-range time period. It has been used in the research of cognition or neuropsychiatric disorders. Recently, causal analysis of structural covariance network (CaSCN), winner-take-all and cortex–subcortex covariance network (WTA-CSSCN), and modulation analysis of structural covariance network (MOD-SCN) have expended the technology breadth of SCN. However, the lack of user-friendly software limited the further application of SCN for the research. In this work, we developed the graphical user interface (GUI) toolkit of brain structural covariance connectivity based on MATLAB platform. The software contained the analysis of SCN, CaSCN, MOD-SCN, and WTA-CSSCN. Also, the group comparison and result-showing modules were included in the software. Furthermore, a simple showing of demo dataset was presented in the work. We hope that the toolkit could help the researchers, especially clinical researchers, to do the brain covariance connectivity analysis in further work more easily.

Humans have a fundamental ability that is fulfilling visual perception with complex brain functions, while intelligent sensors onboard UAVs do not have. The difficulties mainly lie in the inexhaustive study of brain science and the homologous mathematical description. In this paper, a visual cognition invariance mechanism has been proposed, which first review studies in brain research, moving from theoretical to practical. These terms are then reconsidered from brain pathways of visual perception. Next, a conceptual model of visual cognition invariance mechanism is proposed and the mathematical deduction is fulfilled. Furthermore, an experimental unmanned aerial vehicle is built to practically implement the proposed algorithm. The simulation results and experimental practices have validated the effectiveness and celerity of our method. Finally, a general discussion and proposals for addressing future issues are given.

Rs-fMRI can provide rich information about functional processes in the brain with a large array of imaging parameters and is also suitable for investigating the biological processes in cerebral gliomas. We aimed to propose an imaging analysis method of RP-Rs-fMRIomics by adopting omics analysis on rs-fMRI with exhaustive regional parameters and subsequently estimating its feasibility on the prediction diagnosis of gliomas. In this retrospective study, preoperative rs-fMRI data were acquired from patients confirmed with diffuse gliomas (n = 176). A total of 420 features were extracted through measuring 14 regional parameters of rs-fMRI as much as available currently in 10 specific narrow frequency bins and three parts of gliomas. With a randomly split training and testing dataset (ratio 7:3), four classifiers were implemented to construct and optimize RP-Rs-fMRIomics models for predicting glioma grade, IDH status and Karnofsky Performance Status scores. The RP-Rs-fMRIomics models (AUROC 0.988, 0.905, 0.801) were superior to the corresponding traditional single rs-fMRI index (AUROC 0.803, 0.731, 0.632) in predicting glioma grade, IDH and survival. The RP-Rs-fMRIomics analysis, featuring high interpretability, was competitive for prediction of glioma grading, IDH genotype and prognosis. The method expanded the clinical application of rs-fMRI and also contributed a new imaging analysis for brain tumor research.

Our knowledge of the intestinal immune system of fish is rather limited compared to mammals. Very little is known about the immune cells including the phagocytic cells in fish intestine. Hence, employing imaging flow cytometry and RNA sequencing, we studied adherent cells isolated from healthy Atlantic salmon. Phagocytic activity and selected gene expression of adherent cells from the distal intestine (adherent intestinal cells, or AIC) were compared with those from head kidney (adherent kidney cells, or AKC). Phagocytic activity of the two cell types was assessed based on the uptake of Escherichia coli BioParticlesTM. AIC showed phagocytic ability but the phagocytes were of different morphology compared to AKC. Transcriptomic analysis revealed that AIC expressed genes associated with macrophages, T cells, and endothelial cells. Heatmap analysis of selected genes indicated that the adherent cells from the two organs had apparently higher expression of macrophage-related genes. We believe that the adherent intestinal cells have phagocytic characteristics and high expression of genes commonly associated with macrophages. We envisage the possibilities for future studies on enriched populations of adherent intestinal cells.

This study aimed to delineate cortico-striato-thalamo-cerebellar network profiles based on static and dynamic connectivity analysis in genetic generalized and focal epilepsies with generalized tonic-clonic seizures, and to evaluate its potential for distinguishing these two epilepsy syndromes. A total of 342 individuals participated in the study (114 patients with genetic generalized epilepsy with generalized tonic-clonic seizures (GE-GTCS), and 114 age- and sex-matched patients with focal epilepsy with focal to bilateral tonic-clonic seizure (FE-FBTS), 114 healthy controls). Resting-state fMRI data were examined through static and dynamic functional connectivity (dFC) analyses, constructing cortico-striato-thalamo-cerebellar networks. Network patterns were compared between groups, and were correlated to epilepsy duration. A pattern-learning algorithm was applied to network features for classifying both epilepsy syndromes. FE-FBTS and GE-GTCS both presented with altered functional connectivity in subregions of the motor/premotor and somatosensory networks. Among these two groups, the connectivity within the cerebellum increased in the static, while the dFC variability decreased; conversely, the connectivity of the thalamus decreased in FE-FBTS and increased in GE-GTCS in the static state. Connectivity differences between patient groups were mainly located in the thalamus and cerebellum, and correlated with epilepsy duration. Support vector machine (SVM) classification had accuracies of 66.67%, 68.42%, and 77.19% when using static, dynamic, and combined approaches to categorize GE-GTCS and FE-GTCS. Network features with high discriminative ability predominated in the thalamic and cerebellar connectivities. The network embedding of the thalamus and cerebellum likely plays an important differential role in GE-GTCS and FE-FBTS, and could serve as an imaging biomarker for differential diagnosis.

Background: The rs405509 polymorphism ofthe apolipoprotein E (APOE) promoter is related to Alzheimer’sdisease (AD). The T/T allele of rs405509 is known to decrease the transcription of the APOE gene and lead to impairments in specific brain structural networks with aging; thus, it is an important risk factor for AD. However, it remains unknown whether rs405509 affects brain functional connectivity (FC) in aging.Methods: We investigated the effect of the rs405509 genotype (T/T vs. G-allele) on age-related brain FC using functional magnetic resonance imaging. Forty-five elderly TT carriers and 45 elderly G-allele carriers were scanned during a working memory (WM) task.Results: We found that TT carriers showed an accelerated age-related increase in functional activation in the left postcentral gyrus compared with G-allele carriers. Furthermore, the FC between the left postcentral gyrus and some key regions during WM performance, including the right caudal and superior frontal sulcus (SFS), was differentially modulated by age across rs405509 genotype groups.Conclusions: These results demonstrate that the rs405509 T/T allele of APOE causes an age-related brain functional decline in nondemented elderly people, which may be beneficial for understanding the neural mechanisms of rs405509-related cognitive aging and AD pathogenesis.

Fungi, particularly yeasts, are known essential components of the host microbiota but their functional relevance in development of immunity and physiological processes of fish remains to be elucidated. In this study, we used a transcriptomic approach and a germ-free (GF) fish model to determine the response of newly hatched zebrafish larvae after 24 h exposure to Pseudozyma sp. when compared to conventionally-raised (CR) larvae. We observed 59 differentially expressed genes in Pseudozyma-exposed GF zebrafish larvae compared to their naïve control siblings. Surprisingly, in CR larvae, there was not a clear transcriptome difference between Pseudozyma-exposed and control larvae. Differentially expressed genes in GF larvae were involved in host metabolic pathways, mainly peroxisome proliferator-activated receptors, steroid hormone biosynthesis, drug metabolism and bile acid biosynthesis. We also observed a significant change in the transcript levels of immune-related genes, namely complement component 3a, galectin 2b, ubiquitin specific peptidase 21, and aquaporins. Nevertheless, we did not observe any significant response at the cellular level, since there were no differences between neutrophil migration or proliferation between control and yeast-exposed GF larvae. Our findings reveal that exposure to Pseudozyma sp. may affect metabolic pathways and immune-related processes in germ-free zebrafish, suggesting that commensal yeast likely play a significant part in the early development of fish larvae.

Objectives: To investigate the performance of substate classification of children with benign epilepsy with centrotemporal spikes (BECTS) by granger causality density (GCD) based support vector machine (SVM) model.Methods: Forty-two children with BECTS (21 females, 21 males; mean age, 8.6 ± 1.96 years) were classified into interictal epileptic discharges (IEDs; 11 females, 10 males) and non-IEDs (10 females, 11 males) substates depending on presence of central-temporal spikes or not. GCD was calculated on four metrics, including inflow, outflow, total-flow (inflow + outflow) and int-flow (inflow – outflow) connectivity. SVM classifier was applied to discriminate the two substates.Results: The Rolandic area, caudate, dorsal attention network, visual cortex, language networks, and cerebellum had discriminative effect on distinguishing the two substates. Relative to each of the four GCD metrics, using combined metrics could reach up the classification performance (best value; AUC, 0.928; accuracy rate, 90.83%; sensitivity, 90%; specificity, 95%), especially for the combinations with more than three GCD metrics. Specially, combined the inflow, outflow and int-flow metric received the best classification performance with the highest AUC value, classification accuracy and specificity. Furthermore, the GCD-SVM model received good and stable classification performance across 14 dimension reduced data sets.Conclusions: The GCD-SVM model could be used for BECTS substate classification, which might have the potential to provide a promising model for IEDs detection. This may help assist clinicians for administer drugs and prognosis evaluation.

Background: Repetitive transcranial magnetic stimulation (rTMS) can modulate cortical excitability, and may be beneficial for motor recovery after stroke. However, the neuroplasticity effects of rTMS have not been thoroughly investigated in the early stage after stroke. Objective: To comprehensively assess the effects of high- and low-frequency repetitive transcranial magnetic stimulations on motor recovery in early stroke patients, using a randomized controlled trial based on clinical, neurophysiological and functional imaging assessments. Methods: Sixty hospitalized, first-ever ischemic stroke patients (within 2 weeks after stroke) with motor deficits were randomly allocated to receive, in addition to standard physical therapy, five consecutive sessions of either: (1) High-frequency (HF) rTMS at 10 Hz over the ipsilesional primary motor cortex (M1); (2) Low-frequency (LF) rTMS at 1 Hz over the contralesional M1; (3) sham rTMS. The primary outcome measure was a motor impairment score (Upper Extremity Fugl-Meyer) evaluated at baseline, after rTMS intervention, and at 3-month follow-up. Cortical excitability and functional magnetic resonance imaging (fMRI) data were obtained within 24 h before and after rTMS intervention. Analyses of variance were conducted to compare the recovery effects among the three rTMS groups, assessed using clinical, neurophysiological and fMRI tests. Results: Motor improvement was significantly larger in the two rTMS groups than in the control group. The HF-rTMS group showed significantly increased cortical excitability and motor-evoked fMRI activation in ipsilesional motor areas, whereas the LF-rTMS group had significantly decreased cortical excitability and motor-evoked fMRI activation in contralesional motor areas. Activity in ipsilesional motor cortex significantly correlated with motor function, after intervention as well as at 3-month follow-up. Conclusion: HF- and LF-rTMS can both improve motor function by modulating motor cortical activation in the early phase of stroke. Keywords: Transcranial magnetic stimulation, Motor recovery, Stroke, Neuroplasticity, fMRI, Interhemispheric inhibition

Humans have a fundamental ability, that is, to share vision among each other to fulfill common goals, which cooperative UAVs do not have. The difficulties mainly lie in the homologous mathematical description of humans and elusive experimental practice. This paper proposed a parallel multiview splicing on clouds, which first review both theory and practice studies in UAVs. These terms are then reconsidered from humans’ vision sharing. Next, a conceptual model of parallel multiview splicing on clouds is proposed and the mathematical deduction if fulfilled. Furthermore, an experimental cooperative UAVs platform is built to practically implement the algorithms. Both the simulated and practiced results have validated the feasibility of our method. Finally, a general discussion and proposals for addressing future issues are given.

The objective was to study the modulation effects of levetiracetam on the fMRI activation/deactivation patterns associated with centrotemporal spikes (CTS) in Rolandic epilepsy. Forty patients with Rolandic epilepsy, including levetiracetam-medicated patients (n = 20) and drug-naive patients (n = 20), were studied. Single and sequential hemodynamic response functions-based EEG-fMRI analysis was performed to detect dynamic activation/deactivation associated with CTS. Comparisons of spatiotemporal features of activation/deactivation were performed between the two groups. Both the groups (CTS were detected in 12 cases of levetiracetam-medicated group, and 11 cases of drug-naive group) showed CTS-associated activation in the Rolandic cortex, whereas activation strength, time-to-peak delay, and overall activation were diminished in the levetiracetam-medicated group. Moreover, the drug-naive group showed deactivation in the regions engaged in higher cognition networks compared with the levetiracetam-medicated group. Levetiracetam inhibits CTS-associated activation intensity and alters the temporal pattern of this activation in the epileptogenic regions, and it also affects the brain deactivation related to higher cognition networks. The findings sheds a light on the pharmocological mechanism of levetiracetam therapy on Rolandic epilepsy.

Purpose. This study was aimed at evaluating the motor cortical excitability and connectivity underlying the neural mechanism of motor deficit in acute stroke by the combination of functional magnetic resonance imaging (fMRI) and electrophysiological measures. Methods. Twenty-five patients with motor deficit after acute ischemic stroke were involved. General linear model and dynamic causal model analyses were applied to fMRI data for detecting motor-related activation and effective connectivity of the motor cortices. Motor cortical excitability was determined as a resting motor threshold (RMT) of motor evoked potential detected by transcranial magnetic stimulation (TMS). fMRI results were correlated with cortical excitability and upper extremity Fugl-Meyer assessment scores, respectively. Results. Greater fMRI activation likelihood and motor cortical excitability in the ipsilesional primary motor area (M1) region were associated with better motor performance. During hand movements, the inhibitory connectivity from the contralesional to the ipsilesional M1 was correlated with the degree of motor impairment. Furthermore, ipsilesional motor cortex excitability was correlated with an enhancement of promoting connectivity in ipsilesional M1 or a reduction of interhemispheric inhibition in contralesional M1. Conclusions. The study suggested that a dysfunction of the ipsilesional M1 and abnormal interhemispheric interactions might underlie the motor disability in acute ischemic stroke. Modifying the excitability of the motor cortex and correcting the abnormal motor network connectivity associated with the motor deficit might be the therapeutic target in early neurorehabilitation for stroke patients.

With photoplethysmograph (PPG) sensors showing increasing potential in wearable health monitoring, the challenging problem of motion artifact (MA) removal during intensive exercise has become a popular research topic. In this study, a novel method that combines heart rate frequency (HRF) estimation and notch filtering is proposed. The proposed method applies a cascaded adaptive noise cancellation (ANC) based on the least mean squares (LMS)-Newton algorithm for preliminary motion artifacts reduction, and further adopts special heart rate frequency tracking and correction schemes for accurate HRF estimation. Finally, notch filters are employed to restore the PPG signal with estimated HRF based on its quasi-periodicity. On an open source data set that features intensive running exercise, the proposed method achieves a competitive mean average absolute error (AAE) result of 0.92 bpm for HR estimation. The practical experiments are carried out with the PPG evaluation platform developed by ourselves. Under three different intensive motion patterns, a 0.89 bpm average AAE result is achieved with the average correlation coefficient between recovered PPG signal and reference PPG signal reaching 0.86. The experimental results demonstrate the effectiveness of the proposed method for accurate HR estimation and robust MA removal in PPG during intensive exercise.

In tsetse flies, nutrients for intrauterine larval development are synthesized by the modified accessory gland (milk gland) and provided in mother's milk during lactation. Interference with at least two milk proteins has been shown to extend larval development and reduce fecundity. The goal of this study was to perform a comprehensive characterization of tsetse milk proteins using lactation-specific transcriptome/milk proteome analyses and to define functional role(s) for the milk proteins during lactation. Differential analysis of RNA-seq data from lactating and dry (non-lactating) females revealed enrichment of transcripts coding for protein synthesis machinery, lipid metabolism and secretory proteins during lactation. Among the genes induced during lactation were those encoding the previously identified milk proteins (milk gland proteins 1-3, transferrin and acid sphingomyelinase 1) and seven new genes (mgp4-10). The genes encoding mgp2-10 are organized on a 40 kb syntenic block in the tsetse genome, have similar exon-intron arrangements, and share regions of amino acid sequence similarity. Expression of mgp2-10 is female-specific and high during milk secretion. While knockdown of a single mgp failed to reduce fecundity, simultaneous knockdown of multiple variants reduced milk protein levels and lowered fecundity. The genomic localization, gene structure similarities, and functional redundancy of MGP2-10 suggest that they constitute a novel highly divergent protein family. Our data indicates that MGP2-10 function both as the primary amino acid resource for the developing larva and in the maintenance of milk homeostasis, similar to the function of the mammalian casein family of milk proteins. This study underscores the dynamic nature of the lactation cycle and identifies a novel family of lactation-specific proteins, unique to Glossina sp., that are essential to larval development. The specificity of MGP2-10 to tsetse and their critical role during lactation suggests that these proteins may be an excellent target for tsetse-specific population control approaches.