PathMLP: Smooth Path Towards High-order Homophily

Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be connected to nodes with the same label, challenging the homophily assumption of classical graph neural networks (GNNs) and impeding their performance. Intriguingly, we observe that certain high-order information on heterophilous data exhibits high homophily, which motivates us to involve high-order information in node representation learning. However, common practices in GNNs to acquire high-order information mainly through increasing model depth and altering message-passing mechanisms, which, albeit effective to a certain extent, suffer from three shortcomings: 1) over-smoothing due to excessive model depth and propagation times; 2) high-order information is not fully utilized; 3) low computational efficiency. In this regard, we design a similarity-based path sampling strategy to capture smooth paths containing high-order homophily. Then we propose a lightweight model based on multi-layer perceptrons (MLP), named PathMLP, which can encode messages carried by paths via simple transformation and concatenation operations, and effectively learn node representations in heterophilous graphs through adaptive path aggregation. Extensive experiments demonstrate that our method outperforms baselines on 16 out of 20 datasets, underlining its effectiveness and superiority in alleviating the heterophily problem. In addition, our method is immune to over-smoothing and has high computational efficiency

Heightened expression of MICA enhances the cytotoxicity of NK cells or CD8+T cells to human corneal epithelium in vitro

BACKGROUND: Major-histocompatibility-complex class I-related chain A (MICA) antigens are the ligands of NKG2D, which is an activating or coactivating receptor expressed on human NK cells and CD8(+)T cells. We sought to determine whether MICA expression in human corneal epithelium (HCE) could affect the cytotoxicity mediated by NK cells or CD8(+)T cells. METHODS: Cell cultures of HCE were harvested from human donor eyes. Flow cytometric analysis and ELISA was performed to determine the levels of MICA expression on HCE. Then, HCE was transfected with a lentivirus vector expressing MICA and GFP. Flow cytometric analysis, RT-PCR, western blot and ELISA were performed to check the levels of MICA expression. For cytotoxicity testing, allogeneic NK cells and CD8(+)T cells were isolated from peripheral blood mononuclear cells of healthy volunteers by magnetic cell sorting. The cytolytic activity of NK cells and CD8(+)T cells was assessed against MICA-transfected HCE (NK cells: E:T ratio = 3:1; CD8(+)T cells: E:T ratio = 10:1) using the nonradioactive cytotoxicity detection kit lactate deshydrogenase. RESULTS: Surface expression of MICA on corneal epithelium was identified at a low level. A cell line of stable human MICA-transfected corneal epithelium was successfully established. Heightened expression of MICA on HCE was found to promote the cytotoxicity mediated by NK cells or CD8(+)T cells, which could be blocked by an anti-MICA antibody. CONCLUSION: MICA molecules may contribute to cytotoxic responses mediated by activated immune effector cells in corneal epithelium immunity

Reconfiguration of static and dynamic thalamo-cortical network functional connectivity of epileptic children with generalized tonic-clonic seizures

ObjectiveA number of studies in adults and children with generalized tonic-clonic seizure (GTCS) have reported the alterations in morphometry, functional activity, and functional connectivity (FC) in the thalamus. However, the neural mechanisms underlying the alterations in the thalamus of patients with GTCS are not well understood, particularly in children. The aim of the current study was to explore the temporal properties of functional pathways connecting thalamus in children with GTCS.MethodsHere, we recruited 24 children with GTCS and 36 age-matched healthy controls. Static and dynamic FC approaches were used to evaluate alterations in the temporal variability of thalamo-cortical networks in children with GTCS. The dynamic effective connectivity (dEC) method was also used to evaluate the directions of the fluctuations in effective connectivity. In addition, the relationships between the dynamic properties and clinical features were assessed.ResultsThe static FC analysis presented significantly decreased connectivity patterns between the bilateral thalamus and between the thalamus and right inferior temporal gyrus. The dynamic connectivity analysis found decreased FC variability in the thalamo-cortical network of children with epilepsy. Dynamic EC analyses identified increased connectivity variability from the frontal gyrus to the bilateral thalamus, and decreased connectivity variability from the right thalamus to the left thalamus and from the right thalamus to the right superior parietal lobe. In addition, correlation analysis revealed that both static FC and connectivity temporal variability in the thalamo-cortical network related to the clinical features (epilepsy duration and epilepsy onset time).SignificanceOur findings of both increased and decreased connectivity variability in the thalamo-cortical network imply a dynamic restructuring of the functional pathways connecting the thalamus in children with GTCS. These alterations in static and temporal dynamic pathways connecting the bilateral thalamus may extend our understanding of the neural mechanisms underlying the GTCS in children

IFN-γ Regulates the Expression of MICA in Human Corneal Epithelium Through miRNA4448 and NFκB

PurposeMajor histocompatibility complex class I-related chain A (MICA), a non-classical major histocompatibility complex molecule, can stimulate or co-stimulate CD8+ T cells or natural killer (nk) cells, thus affecting cornea allograft survival. This study investigated IFN-γ regulation of MICA expression levels in human corneal epithelium by miRNA4448.MethodsMICA expression levels in human corneal epithelial cells (HCECs) stimulated with IFN-γ were detected by qRT-PCR and an enzyme-linked immunosorbent assay, and differential miRNA expression levels were measured. qRT-PCR, Western blotting, and immunofluorescence staining revealed nuclear factor kappa B (NFκB)/P65 expression in IFN-γ-treated and miRNA4448-overexpressed HCECs. A luciferase reporter assay was used to predict the interaction between NFκB and MICA. Additionally, HCECs were transfected with MICA plasmid or treated with IFN-γ and NKG2D-mAb and cocultured with NK cells and CD8+ T cells. Cell apoptosis was measured using Annexin V/PI staining. qRT-PCR detected the expression of anti-apoptosis factor Survivin and apoptosis factor Caspase 3 in MICA-transfected and IFN-γ-treated HCECs after co-culturing with NK cells and CD8+ T cells.ResultsIFN-γ (500 ng/ml, 24 h) upregulated MICA expression in HCECs in vitro. Among six differentially expressed microRNAs, miRNA4448 levels decreased the most after IFN-γ treatment. The overexpression of miRNA4448 decreased MICA expression. miRNA4448 downregulated NFκB/P65 expression in IFN-γ-induced HCEC, and it was determined that NFκB/P65 directly targeted MICA by binding to the promotor region. A coculture with NK cells and CD8+ T cells demonstrated that MICA overexpression enhanced HCEC apoptosis, which could be inhibited by NKG2D-mAb. Simultaneously, Survivin mRNA expression decreased and Caspase3 mRNA expression increased upon the interaction between MICA and NK (CD8+ T) cells in HCECs.ConclusionIFN-γ enhances the expression of MICA in HCECs by modulating miRNA4448 and NFκB/P65 levels, thereby contributing to HCEC apoptosis induced by NK and CD8+ T cells. This discovery may lead to new insights into the pathogenesis of corneal allograft rejection

Output Feedback Adaptive Dynamic Surface Control of Permanent Magnet Synchronous Motor with Uncertain Time Delays via RBFNN

This paper focuses on an adaptive dynamic surface control based on the Radial Basis Function Neural Network for a fourth-order permanent magnet synchronous motor system wherein the unknown parameters, disturbances, chaos, and uncertain time delays are presented. Neural Network systems are used to approximate the nonlinearities and an adaptive law is employed to estimate accurate parameters. Then, a simple and effective controller has been obtained by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed control has been illustrated through simulation results

Different carrier recombination processes in CsPbBr3 quantum dots and microcrystals

Good understanding of carrier generation and recombination is crucial for applications in LEDs, lasers, single photon sources and so on. In this work, we studied and compared charge generation and recombination processes of CsPbBr3 QDs and microcrystals (MCs) in order to obtain clearer pictures for their photoluminescence. Power dependent photoluminescence experiments show that excitation species and recombination process are quite different in CsPbBr3 QDs and MCs, where the MCs contain non-radiative dark states resulting a superlinear power dependence of the PL intensity at low excitation power while the QDs materials show linear dependence. At high excitation power, trions are generated in the QDs samples.Accepted versio

A Dynamic Optimization-Based Ensemble Learning Method for Traditional Chinese Medicine Named Entity Recognition

The importance of named entity identification in traditional Chinese medicine (TCM) as the basis for supporting downstream tasks is receiving increasing attention. Deep learning-based methods have been widely used for related tasks. However, most current methods do not deal well with two common TCM entity recognition problems: an unbalanced number of entities and sparse entities. To solve these problems, we propose an ensemble learning method based on dynamic optimization. In this study, we first use bidirectional encoder representations from transformers (BERT) to extract word vectors and then further extract features based on BERT-bidirectional long short-term memory (BiLSTM). Then, we dynamically adjust the entity class and fusion weights of ensemble learning according to the entity distribution and sparsity of each batch. Finally, the prediction results are output through the conditional random field (CRF) layer. This approach allows the model to dynamically focus on difficult samples and to improve the update weights of the most beneficial learning tasks. In addition, we introduce a reduction factor to reduce the magnitude of the parameter updates when the entities are sparse. This prevents the model from being unduly disrupted by nonentity information. Therefore, our model can effectively reduce the negative impact of unbalanced numbers of entities and sparse entities. The experimental results show that our model achieves the best results on a publicly available TCM entity recognition dataset and has a faster convergence rate than the baseline model. Compared to the baseline model BERT-BiLSTM-CRF, our method improves the F1-score by 0.56, further demonstrating its effectiveness

A Multimodal Sentiment Analysis Approach Based on a Joint Chained Interactive Attention Mechanism

The objective of multimodal sentiment analysis is to extract and integrate feature information from text, image, and audio data accurately, in order to identify the emotional state of the speaker. While multimodal fusion schemes have made some progress in this research field, previous studies still lack adequate approaches for handling inter-modal information consistency and the fusion of different categorical features within a single modality. This study aims to effectively extract sentiment coherence information among video, audio, and text and consequently proposes a multimodal sentiment analysis method named joint chain interactive attention (VAE-JCIA, Video Audio Essay–Joint Chain Interactive Attention). In this approach, a 3D CNN is employed for extracting facial features from video, a Conformer is employed for extracting audio features, and a Funnel-Transformer is employed for extracting text features. Furthermore, the joint attention mechanism is utilized to identify key regions where sentiment information remains consistent across video, audio, and text. This process acquires reinforcing features that encapsulate information regarding consistency among the other two modalities. Inter-modal feature interactions are addressed through chained interactive attention, and multimodal feature fusion is employed to efficiently perform emotion classification. The method is experimentally validated on the CMU-MOSEI dataset and the IEMOCAP dataset. The experimental results demonstrate that the proposed method significantly enhances the performance of the multimodal sentiment analysis model