[Retracted] Mammalian STE20­like kinase 1 regulates pancreatic cancer cell survival and migration through Mfn2­mediated mitophagy.

Following the publication of this paper, it was drawn to the Editors' attention by a concerned reader that certain of the JC­1 staining images in Fig. 2C were strikingly similar to data appearing in different form in other articles written by different authors at different research institutes that had either already been published elsewhere prior to the submission of this paper to Molecular Medicine Reports, or were under consideration for publication at around the same time (a small number of which have been retracted). In addition, the Snail western blot data in Fig. 3E bore a close similarity to certain of the Mfn2 data shown in Fig. 4A. In view of the fact that certain of the contentious data had already apparently been published previously, and owing to a lack of confidence in the presentation of certain of the data in this paper, the Editor of Molecular Medicine Reports has decided that this paper should be retracted from the Journal. The authors were asked for an explanation to account for these concerns, but the Editorial Office did not receive a reply. The Editor apologizes to the readership for any inconvenience caused. [Molecular Medicine Reports 22: 398­404, 2020; DOI: 10.3892/mmr.2020.11098].

Serum extracellular vesicle-derived miR-21-5p and miR-26a-5p as non-invasive diagnostic potential biomarkers for gastric cancer: A preliminary study.

PURPOSE: Gastric cancer is the most common malignancy worldwide and is the third leading cause of cancer-related deaths, urgently requiring an early and non-invasive diagnosis. Circulating extracellular vesicles may emerge as promising biomarkers for the rapid diagnosis in a non-invasive manner. METHODS: Using high-throughput small RNA sequencing, we profiled the small RNA population of serum-derived extracellular vesicles from healthy controls and gastric cancer patients. Differentially expressed microRNAs (miRNAs) were randomly selected and validated by reverse transcription-quantitative real-time polymerase chain reaction. Receiver operating characteristic curves were employed to assess the predictive value of miRNAs for gastric cancer. RESULTS: In this study, 193 differentially expressed miRNAs were identified, of which 152 were upregulated and 41 were significantly downregulated. Among the differently expressed miRNA, the expression levels of miR-21-5p, miR-26a-5p, and miR-27a-3p were significantly elevated in serum-derived extracellular vesicles of gastric cancer patients. The miR-21-5p and miR-27a-3p were closely correlated with the tumor size. Moreover, the expression levels of serum miR-21-5p and miR-26a-5p were significantly decreased in gastric cancer patients after surgery. CONCLUSIONS: The present study discovered the potential of serum miR-21-5p and miR-26a-5p as promising candidates for the diagnostic and prognostic markers of gastric cancer.

Multi-modal long document classification based on Hierarchical Prompt and Multi-modal Transformer.

In the realm of long document classification (LDC), previous research has predominantly focused on modeling unimodal texts, overlooking the potential of multi-modal documents incorporating images. To address this gap, we introduce an innovative approach for multi-modal long document classification based on the Hierarchical Prompt and Multi-modal Transformer (HPMT). The proposed HPMT method facilitates multi-modal interactions at both the section and sentence levels, enabling a comprehensive capture of hierarchical structural features and complex multi-modal associations of long documents. Specifically, a Multi-scale Multi-modal Transformer (MsMMT) is tailored to capture the multi-granularity correlations between sentences and images. This is achieved through the incorporation of multi-scale convolutional kernels on sentence features, enhancing the model's ability to discern intricate patterns. Furthermore, to facilitate cross-level information interaction and promote learning of specific features at different levels, we introduce a Hierarchical Prompt (HierPrompt) block. This block incorporates section-level prompts and sentence-level prompts, both derived from a global prompt via distinct projection networks. Extensive experiments are conducted on four challenging multi-modal long document datasets. The results conclusively demonstrate the superiority of our proposed method, showcasing its performance advantages over existing techniques.

Bridging the Cross-Modality Semantic Gap in Visual Question Answering.

The objective of visual question answering (VQA) is to adequately comprehend a question and identify relevant contents in an image that can provide an answer. Existing approaches in VQA often combine visual and question features directly to create a unified cross-modality representation for answer inference. However, this kind of approach fails to bridge the semantic gap between visual and text modalities, resulting in a lack of alignment in cross-modality semantics and the inability to match key visual content accurately. In this article, we propose a model called the caption bridge-based cross-modality alignment and contrastive learning model (CBAC) to address the issue. The CBAC model aims to reduce the semantic gap between different modalities. It consists of a caption-based cross-modality alignment module and a visual-caption (V-C) contrastive learning module. By utilizing an auxiliary caption that shares the same modality as the question and has closer semantic associations with the visual, we are able to effectively reduce the semantic gap by separately matching the caption with both the question and the visual to generate pre-alignment features for each, which are then used in the subsequent fusion process. We also leverage the fact that V-C pairs exhibit stronger semantic connections compared to question-visual (Q-V) pairs to employ a contrastive learning mechanism on visual and caption pairs to further enhance the semantic alignment capabilities of single-modality encoders. Extensive experiments conducted on three benchmark datasets demonstrate that the proposed model outperforms previous state-of-the-art VQA models. Additionally, ablation experiments confirm the effectiveness of each module in our model. Furthermore, we conduct a qualitative analysis by visualizing the attention matrices to assess the reasoning reliability of the proposed model.

Redundancy Is Not What You Need: An Embedding Fusion Graph Auto-Encoder for Self-Supervised Graph Representation Learning.

Attribute graphs are a crucial data structure for graph communities. However, the presence of redundancy and noise in the attribute graph can impair the aggregation effect of integrating two different heterogeneous distributions of attribute and structural features, resulting in inconsistent and distorted data that ultimately compromises the accuracy and reliability of attribute graph learning. For instance, redundant or irrelevant attributes can result in overfitting, while noisy attributes can lead to underfitting. Similarly, redundant or noisy structural features can affect the accuracy of graph representations, making it challenging to distinguish between different nodes or communities. To address these issues, we propose the embedded fusion graph auto-encoder framework for self-supervised learning (SSL), which leverages multitask learning to fuse node features across different tasks to reduce redundancy. The embedding fusion graph auto-encoder (EFGAE) framework comprises two phases: pretraining (PT) and downstream task learning (DTL). During the PT phase, EFGAE uses a graph auto-encoder (GAE) based on adversarial contrastive learning to learn structural and attribute embeddings separately and then fuses these embeddings to obtain a representation of the entire graph. During the DTL phase, we introduce an adaptive graph convolutional network (AGCN), which is applied to graph neural network (GNN) classifiers to enhance recognition for downstream tasks. The experimental results demonstrate that our approach outperforms state-of-the-art (SOTA) techniques in terms of accuracy, generalization ability, and robustness.

Prognosis and clinical significance of Piezo2 in tumor: a meta-analysis and database validation.

OBJECTIVE: The objective of this study is to assess the correlation between Piezo2 and tumors through a comprehensive meta-analysis and database validation. METHODS: Case-control studies investigating the association between Piezo2 and tumors were obtained from various databases, including China National Knowledge Infrastructure (CNKI), SinoMed, Embase, Web of Science, The Cochrane Library, and PubMed. The search was performed from the inception of each database up until May 2023. Two researchers independently screened the literature, extracted data, and assessed the quality of the included studies. Metaanalysis of the included literature was conducted using Stata 12.0 software. Additionally, the Gene Expression Profiling Interactive Analysis (GEPIA) database predicted a correlation between Piezo2 expression and prognostic value in tumor patients. RESULTS: A total of three studies, involving a combined sample size of 392 participants, were included in the meta-analysis. The findings revealed that the expression level of Piezo2 in tumor patients was not significantly associated with age, gender, or tumor size. However, it was found to be positively correlated with lymphatic invasion (OR = 7.89, 95%CI: 3.96-15.73) and negatively correlated with invasion depth (OR = 0.17, 95%CI: 0.06-0.47), TNM stage (OR = 0.48, 95%CI: 0.27-0.87), and histological grade (OR = 0.40, 95%CI: 0.21-0.77). Confirming these findings, the GEPIA database indicated that high expression of Piezo2 was associated with poor prognosis of disease-free survival in patients with colon adenocarcinoma (HR = 1.6, P = 0.049) and gastric cancer (HR = 1.6, P = 0.017). CONCLUSION: Piezo2 may be associated with poor prognosis and clinicopathological parameters in tumor patients.

Construction of a Cuproptosis-Related Gene Signature for Predicting Prognosis in Gastric Cancer.

This study aimed to develop and validate a cuproptosis-related gene signature for the prognosis of gastric cancer. The data in TCGA GC TPM format from UCSC were extracted for analysis, and GC samples were randomly divided into training and validation groups. Pearson correlation analysis was used to obtain cuproptosis-related genes co-expressed with 19 Cuproptosis genes. Univariate Cox and Lasso regression analyses were used to obtain cuproptosis-related prognostic genes. Multivariate Cox regression analysis was used to construct the final prognostic risk model. The risk score curve, Kaplan-Meier survival curves, and ROC curve were used to evaluate the predictive ability of Cox risk model. Finally, the functional annotation of the risk model was obtained through enrichment analysis. Then, a six-gene signature was identified in the training cohort and verified among all cohorts using Cox regression analyses and Kaplan-Meier plots, demonstrating its independent prognostic significance for gastric cancer. In addition, ROC analysis confirmed the significant predictive potential of this signature for the prognosis of gastric cancer. Functional enrichment analysis was mainly related to cell-matrix function. Therefore, a new cuproptosis-related six-gene signature (ACLY, FGD6, SERPINE1, SPATA13, RANGAP1, and ADGRE5) was constructed for the prognosis of gastric cancer, allowing for tailored prediction of outcome and the formulation of novel therapeutics for gastric cancer patients.

Global and Local Interactive Perception Network for Referring Image Segmentation.

The effective modal fusion and perception between the language and the image are necessary for inferring the reference instance in the referring image segmentation (RIS) task. In this article, we propose a novel RIS network, the global and local interactive perception network (GLIPN), to enhance the quality of modal fusion between the language and the image from the local and global perspectives. The core of GLIPN is the global and local interactive perception (GLIP) scheme. Specifically, the GLIP scheme contains the local perception module (LPM) and the global perception module (GPM). The LPM is designed to enhance the local modal fusion by the correspondence between word and image local semantics. The GPM is designed to inject the global structured semantics of images into the modal fusion process, which can better guide the word embedding to perceive the whole image's global structure. Combined with the local-global context semantics fusion, extensive experiments on several benchmark datasets demonstrate the advantage of the proposed GLIPN over most state-of-the-art approaches.

Graph structure learning layer and its graph convolution clustering application.

To learn the embedding representation of graph structure data corrupted by noise and outliers, existing graph structure learning networks usually follow the two-step paradigm, i.e., constructing a "good" graph structure and achieving the message passing for signals supported on the learned graph. However, the data corrupted by noise may make the learned graph structure unreliable. In this paper, we propose an adaptive graph convolutional clustering network that alternatively adjusts the graph structure and node representation layer-by-layer with back-propagation. Specifically, we design a Graph Structure Learning layer before each Graph Convolutional layer to learn the sparse graph structure from the node representations, where the graph structure is implicitly determined by the solution to the optimal self-expression problem. This is one of the first works that uses an optimization process as a Graph Network layer, which is obviously different from the function operation in traditional deep learning layers. An efficient iterative optimization algorithm is given to solve the optimal self-expression problem in the Graph Structure Learning layer. Experimental results show that the proposed method can effectively defend the negative effects of inaccurate graph structures. The code is available at https://github.com/HeXiax/SSGNN.

Prognostic significance of N6-methyladenosine-modified related chemotransferase METTL3 in gastric carcinoma: Evidence from meta-analysis.

BACKGROUND: N6-methyladenosine (m6A) methylation is known as the research hotspot for tumor epimodification, and its associated methyltransferase-like3 (METTL3) is significantly differentially expressed in gastric carcinoma, but its clinical value has not been summarized. This meta-analysis aimed to evaluate the prognostic significance of METTL3 in gastric carcinoma. MATERIAL AND METHODS: Databases, including PubMed, EMBASE (Ovid platform), Science Direct, Scopus, MEDLINE, Google Scholar, Web of Science, and Cochrane Library, were used to identify relevant eligible studies. The endpoints included overall survival, progression-free survival, recurrence-free survival, post-progression survival, and disease-free survival. Hazard ratios (HR) with 95% confidence intervals (CI) were used to correlate METTL3 expression with prognosis. Subgroup and sensitivity analyses were performed. RESULTS: Seven eligible studies involving 3034 gastric carcinoma patients were recruited for this meta-analysis. The analysis showed that high METTL3 expression was associated with significantly poorer overall survival (HR = 2.37, 95% CI 1.66-3.39, P < 0.01) and unfavorable disease-free survival (HR = 2.58, 95% CI 1.97-3.38, P < 0.01), as did unfavorable progression-free survival (HR = 1.48, 95% CI 1.19-1.84, P < 0.01)/recurrence-free survival (HR = 2.62, 95% CI 1.93-5.62, P < 0.01)/post-progression survival (HR = 1.53, 95% CI 1.22-1.91, P < 0.01). Subgroup analysis found that high METTL3 expression was associated with worse overall survival in patients with Chinese (HR = 2.21, 95% CI 1.48-3.29, P < 0.01), in studies with sample source from formalin-fixed, paraffin-embedded tissues (HR = 2.66, 95% CI 1.79-3.94, P < 0.01), and the reported directly from articles group (HR = 2.42, 95% CI 1.66-3.53, P < 0.01). The subgroup analysis that was performed based on sample size, detected method, and follow-up showed the same results. CONCLUSIONS: High expression of METTL3 predicts poor prognosis in gastric carcinoma, indicating promise for METTL3 as a prognostic biomarker.Systematic review registration: https://www.crd.york.ac.uk/prospero, ID = CRD42023408519.

QDN: A Quadruplet Distributor Network for Temporal Knowledge Graph Completion.

Temporal knowledge graph completion (TKGC) is an extension of the traditional static knowledge graph completion (SKGC) by introducing the timestamp. The existing TKGC methods generally translate the original quadruplet to the form of the triplet by integrating the timestamp into the entity/relation, and then use SKGC methods to infer the missing item. However, such an integrating operation largely limits the expressive ability of temporal information and ignores the semantic loss problem due to the fact that entities, relations, and timestamps are located in different spaces. In this article, we propose a novel TKGC method called the quadruplet distributor network (QDN), which independently models the embeddings of entities, relations, and timestamps in their specific spaces to fully capture the semantics and builds the QD to facilitate the information aggregation and distribution among them. Furthermore, the interaction among entities, relations, and timestamps is integrated using a novel quadruplet-specific decoder, which stretches the third-order tensor to the fourth-order to satisfy the TKGC criterion. Equally important, we design a novel temporal regularization that imposes a smoothness constraint on temporal embeddings. Experimental results show that the proposed method outperforms the existing state-of-the-art TKGC methods. The source codes of this article are available at https://github.com/QDN for Temporal Knowledge Graph Completion.git.

Domain Adaptation as Optimal Transport on Grassmann Manifolds.

Domain adaptation in the Euclidean space is a challenging task on which researchers recently have made great progress. However, in practice, there are rich data representations that are not Euclidean. For example, many high-dimensional data in computer vision are in general modeled by a low-dimensional manifold. This prompts the demand of exploring domain adaptation between non-Euclidean manifold spaces. This article is concerned with domain adaption over the classic Grassmann manifolds. An optimal transport-based domain adaptation model on Grassmann manifolds has been proposed. The model implements the adaption between datasets by minimizing the Wasserstein distances between the projected source data and the target data on Grassmann manifolds. Four regularization terms are introduced to keep task-related consistency in the adaptation process. Furthermore, to reduce the computational cost, a simplified model preserving the necessary adaption property and its efficient algorithm is proposed and tested. The experiments on several publicly available datasets prove the proposed model outperforms several relevant baseline domain adaptation methods.

Metro Passenger-Flow Representation via Dynamic Mode Decomposition and Its Application.

Passenger-flow anomaly detection and prediction are essential tasks for intelligent operation of the metro system. Accurate passenger-flow representation is the foundation of them. However, spatiotemporal dependencies, complex dynamic changes, and anomalies of passenger-flow data bring great challenges to data representation. Taking advantage of the time-varying characteristics of data, we propose a novel passenger-flow representation model based on low-rank dynamic mode decomposition (DMD), which also integrates the global low-rank nature and sparsity to explore the spatiotemporal consistency of data and depict abrupt data, respectively. The model can detect anomalies and predict short-term passenger flow conveniently and flexibly. For anomaly detection, we further introduce a strong temporal Toeplitz regularization to characterize the temporal periodic change of data, so as to more accurately detect anomalies. We conduct experiments with smart card transaction data from the Beijing metro system to assess the performance of the model in two use cases. In terms of anomaly detection, the experimental results demonstrate that our method can detect anomalies efficiently, especially for time sequence anomalies. As for short-term prediction, our model is superior to other methods in most cases.

Hierarchical Graph Convolutional Networks for Structured Long Document Classification.

Long document classification (LDC) has been a focused interest in natural language processing (NLP) recently with the exponential increase of publications. Based on the pretrained language models, many LDC methods have been proposed and achieved considerable progression. However, most of the existing methods model long documents as sequences of text while omitting the document structure, thus limiting the capability of effectively representing long texts carrying structure information. To mitigate such limitation, we propose a novel hierarchical graph convolutional network (HGCN) for structured LDC in this article, in which a section graph network is proposed to model the macrostructure of a document and a word graph network with a decoupled graph convolutional block is designed to extract the fine-grained features of a document. In addition, an interaction strategy is proposed to integrate these two networks as a whole by propagating features between them. To verify the effectiveness of the proposed model, four structured long document datasets are constructed, and the extensive experiments conducted on these datasets and another unstructured dataset show that the proposed method outperforms the state-of-the-art related classification methods.

Logarithmic Schatten- p Norm Minimization for Tensorial Multi-View Subspace Clustering.

The low-rank tensor could characterize inner structure and explore high-order correlation among multi-view representations, which has been widely used in multi-view clustering. Existing approaches adopt the tensor nuclear norm (TNN) as a convex approximation of non-convex tensor rank function. However, TNN treats the different singular values equally and over-penalizes the main rank components, leading to sub-optimal tensor representation. In this paper, we devise a better surrogate of tensor rank, namely the tensor logarithmic Schatten- p norm ([Formula: see text]N), which fully considers the physical difference between singular values by the non-convex and non-linear penalty function. Further, a tensor logarithmic Schatten- p norm minimization ([Formula: see text]NM)-based multi-view subspace clustering ([Formula: see text]NM-MSC) model is proposed. Specially, the proposed [Formula: see text]NM can not only protect the larger singular values encoded with useful structural information, but also remove the smaller ones encoded with redundant information. Thus, the learned tensor representation with compact low-rank structure will well explore the complementary information and accurately characterize the high-order correlation among multi-views. The alternating direction method of multipliers (ADMM) is used to solve the non-convex multi-block [Formula: see text]NM-MSC model where the challenging [Formula: see text]NM problem is carefully handled. Importantly, the algorithm convergence analysis is mathematically established by showing that the sequence generated by the algorithm is of Cauchy and converges to a Karush-Kuhn-Tucker (KKT) point. Experimental results on nine benchmark databases reveal the superiority of the [Formula: see text]NM-MSC model.

Multi-Attribute Subspace Clustering via Auto-Weighted Tensor Nuclear Norm Minimization.

Self-expressiveness based subspace clustering methods have received wide attention for unsupervised learning tasks. However, most existing subspace clustering methods consider data features as a whole and then focus only on one single self-representation. These approaches ignore the intrinsic multi-attribute information embedded in the original data feature and result in one-attribute self-representation. This paper proposes a novel multi-attribute subspace clustering (MASC) model that understands data from multiple attributes. MASC simultaneously learns multiple subspace representations corresponding to each specific attribute by exploiting the intrinsic multi-attribute features drawn from original data. In order to better capture the high-order correlation among multi-attribute representations, we represent them as a tensor in low-rank structure and propose the auto-weighted tensor nuclear norm (AWTNN) as a superior low-rank tensor approximation. Especially, the non-convex AWTNN fully considers the difference between singular values through the implicit and adaptive weights splitting during the AWTNN optimization procedure. We further develop an efficient algorithm to optimize the non-convex and multi-block MASC model and establish the convergence guarantees. A more comprehensive subspace representation can be obtained via aggregating these multi-attribute representations, which can be used to construct a clustering-friendly affinity matrix. Extensive experiments on eight real-world databases reveal that the proposed MASC exhibits superior performance over other subspace clustering methods.

Parallelly Adaptive Graph Convolutional Clustering Model.

Benefiting from exploiting the data topological structure, graph convolutional network (GCN) has made considerable improvements in processing clustering tasks. The performance of GCN significantly relies on the quality of the pretrained graph, while the graph structures are often corrupted by noise or outliers. To overcome this problem, we replace the pre-trained and fixed graph in GCN by the adaptive graph learned from the data. In this article, we propose a novel end-to-end parallelly adaptive graph convolutional clustering (AGCC) model with two pathway networks. In the first pathway, an adaptive graph convolutional (AGC) module alternatively updates the graph structure and the data representation layer by layer. The updated graph can better reflect the data relationship than the fixed graph. In the second pathway, the auto-encoder (AE) module aims to extract the latent data features. To effectively connect the AGC and AE modules, we creatively propose an attention-mechanism-based fusion (AMF) module to weight and fuse the data representations of the two modules, and transfer them to the AGC module. This simultaneously avoids the over-smoothing problem of GCN. Experimental results on six public datasets show that the effectiveness of the proposed AGCC compared with multiple state-of-the-art deep clustering methods. The code is available at https://github.com/HeXiax/AGCC.

Tracking the sliding of grain boundaries at the atomic scale.

Grain boundaries (GBs) play an important role in the mechanical behavior of polycrystalline materials. Despite decades of investigation, the atomic-scale dynamic processes of GB deformation remain elusive, particularly for the GBs in polycrystals, which are commonly of the asymmetric and general type. We conducted an in situ atomic-resolution study to reveal how sliding-dominant deformation is accomplished at general tilt GBs in platinum bicrystals. We observed either direct atomic-scale sliding along the GB or sliding with atom transfer across the boundary plane. The latter sliding process was mediated by movements of disconnections that enabled the transport of GB atoms, leading to a previously unrecognized mode of coupled GB sliding and atomic plane transfer. These results enable an atomic-scale understanding of how general GBs slide in polycrystalline materials.

Probabilistic Linear Discriminant Analysis Based on L1-Norm and Its Bayesian Variational Inference.

Probabilistic linear discriminant analysis (PLDA) is a very effective feature extraction approach and has obtained extensive and successful applications in supervised learning tasks. It employs the squared L2 -norm to measure the model errors, which assumes a Gaussian noise distribution implicitly. However, the noise in real-life applications may not follow a Gaussian distribution. Particularly, the squared L2 -norm could extremely exaggerate data outliers. To address this issue, this article proposes a robust PLDA model under the assumption of a Laplacian noise distribution, called L1-PLDA. The learning process employs the approach by expressing the Laplacian density function as a superposition of an infinite number of Gaussian distributions via introducing a new latent variable and then adopts the variational expectation-maximization (EM) algorithm to learn parameters. The most significant advantage of the new model is that the introduced latent variable can be used to detect data outliers. The experiments on several public databases show the superiority of the proposed L1-PLDA model in terms of classification and outlier detection.

A Decoder-Free Variational Deep Embedding for Unsupervised Clustering.

In deep clustering frameworks, autoencoder (AE)- or variational AE-based clustering approaches are the most popular and competitive ones that encourage the model to obtain suitable representations and avoid the tendency for degenerate solutions simultaneously. However, for the clustering task, the decoder for reconstructing the original input is usually useless when the model is finished training. The encoder-decoder architecture limits the depth of the encoder so that the learning capacity is reduced severely. In this article, we propose a decoder-free variational deep embedding for unsupervised clustering (DFVC). It is well known that minimizing reconstruction error amounts to maximizing a lower bound on the mutual information (MI) between the input and its representation. That provides a theoretical guarantee for us to discard the bloated decoder. Inspired by contrastive self-supervised learning, we can directly calculate or estimate the MI of the continuous variables. Specifically, we investigate unsupervised representation learning by simultaneously considering the MI estimation of continuous representations and the MI computation of categorical representations. By introducing the data augmentation technique, we incorporate the original input, the augmented input, and their high-level representations into the MI estimation framework to learn more discriminative representations. Instead of matching to a simple standard normal distribution adversarially, we use end-to-end learning to constrain the latent space to be cluster-friendly by applying the Gaussian mixture distribution as the prior. Extensive experiments on challenging data sets show that our model achieves higher performance over a wide range of state-of-the-art clustering approaches.