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Multi-view spectral clustering can effectively reveal the intrinsic clustering structure among data by performing clustering on the learned optimal embedding across views. Though demonstrating promising performance in various applications, most of existing methods usually linearly combine a group of pre-specified first-order Laplacian matrices to construct the optimal Laplacian matrix, which may result in limited representation capability and insufficient information exploitation. Also, storing and implementing complex operations on the $n\times n$ n × n Laplacian matrices incur intensive storage and computation complexity. To address these issues, this paper first proposes a multi-view spectral clustering algorithm that learns a high-order optimal neighborhood Laplacian matrix, and then extends it to the late fusion version for accurate and efficient multi-view clustering. Specifically, our proposed algorithm generates the optimal Laplacian matrix by searching the neighborhood of the linear combination of both the first-order and high-order base Laplacian matrices simultaneously. By this way, the representative capacity of the learned optimal Laplacian matrix is enhanced, which is helpful to better utilize the hidden high-order connection information among data, leading to improved clustering performance. We design an efficient algorithm with proved convergence to solve the resultant optimization problem. Extensive experimental results on nine datasets demonstrate the superiority of our algorithm against state-of-the-art methods, which verifies the effectiveness and advantages of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Multiple kernel clustering (MKC) algorithm aims to group data into different categories by optimally integrating information from a group of pre-specified kernels. Though demonstrating superiorities in various applications, we observe that existing MKC algorithms usually do not sufficiently consider the local density around individual data samples and excessively limit the representation capacity of the learned optimal kernel, leading to unsatisfying performance. In this paper, we propose an algorithm, called optimal neighborhood MKC with adaptive local kernels (ON-ALK), to address the two issues. In specific, we construct adaptive local kernels to sufficiently consider the local density around individual data samples, where different numbers of neighbors are discriminatingly selected on each sample. Further, the proposed ON-ALK algorithm boosts the representation of the learned optimal kernel via relaxing it into the neighborhood area of weighted combination of the pre-specified kernels. To solve the resultant optimization problem, a three-step iterative algorithm is designed and theoretically proven to be convergent. After that, we also study the generalization bound of the proposed algorithm. Extensive experiments have been conducted to evaluate the clustering performance. As indicated, the algorithm significantly outperforms state-of-the-art methods in recent literatures on six challenging benchmark datasets, verifying its advantages and effectiveness. [ABSTRACT FROM AUTHOR]

Aim: By activating prostacyclin receptors (IP receptors), prostacyclin (PGI2) exerts cardiovascular protective effects such as vasodilation and inhibition of vascular smooth muscle cell (VSMC) proliferation. However, IP receptors are dysfunctional under pathological conditions, and PGI2 produces detrimental effects that are opposite to its physiological protective effects via thromboxane‐prostanoid (TP) receptors. This attempted to investigate whether or not IP receptor dysfunction facilitates the shift of PGI2 action. Methods: The effects of PGI2 and its stable analog iloprost on VSMC phenotypic transformation and proliferation were examined in A10 cells silencing IP receptors, in human aortic VSMCs (HAVSMCs) knocked down IP receptor by CRISPR‐Cas9, or in HAVSMCs transfected with a dysfunctional mutation of IP receptor IPR212C. Results: PGI2/iloprost treatment stimulated cell proliferation, upregulated synthetic proteins and downregulated contractile proteins, suggesting that PGI2/iloprost promotes VSMC phenotypic transformation in IP‐deficient cells. The effect of PGI2/iloprost was prevented by TP antagonist S18886 or TP knockdown, indicating that the VSMC detrimental effect of PGI2 is dependent on TP receptor. RNA sequencing and Western blotting results showed that RhoA/ROCKs, MEK1/2 and JNK signalling cascades were involved. Moreover, IP deficiency increased the distribution of TP receptors at the cell membrane. Conclusion: PGI2 induces VSMC phenotypic transformation when IP receptors are impaired. This is attributed to the activation of TP receptor and its downstream signaling cascades, and to the increased membrane distribution of TP receptors. The VSMC detrimental effect of PGI2 medicated by IP dysfunction and TP activation might probably exacerbate vascular remodelling, accelerating cardiovascular diseases. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm, termed random Fourier extreme learning machine with ℓ 2 , 1 -norm regularization, to improve the robustness and compactness of the widely used extreme learning machine. In specific, we firstly introduce the random Fourier mappings as activation functions to approximate the Gaussian kernel, with the aim to improve the extendibility of the powerful kernel ELM algorithms. We then adopt the ℓ 2 , 1 -norm to eliminate the potential irrelevant neurons, resulting in a more compact and discriminative hidden layer. After that, we propose an efficient algorithm with proved convergence to solve the resultant optimization problem. Extensive experiments have been conducted on 30 benchmark data sets to compare the proposed algorithm with six popular extreme learning algorithms. As observed, our algorithm outperforms the enumerated hidden layer reinforcement algorithms. In addition, it significantly improves the computational efficiency of Gaussian kernel extreme learning machine with comparable classification and regression performance in large scale learning scenarios. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning. Most methods fail to exploit adequate graph information when labeled data is limited, leading to the problem of oversmoothing. To overcome this issue, we propose the Graph Alignment Neural Network (GANN), a simple and effective graph neural architecture. A unique learning algorithm with three alignment rules is proposed to thoroughly explore hidden information for insufficient labels. Firstly, to better investigate attribute specifics, we suggest the feature alignment rule to align the inner product of both the attribute and embedding matrices. Secondly, to properly utilize the higher-order neighbor information, we propose the cluster center alignment rule, which involves aligning the inner product of the cluster center matrix with the unit matrix. Finally, to get reliable prediction results with few labels, we establish the minimum entropy alignment rule by lining up the prediction probability matrix with its sharpened result. Extensive studies on graph benchmark datasets demonstrate that GANN can achieve considerable benefits in semi-supervised node classification and outperform state-of-the-art competitors. • Efficient mining of features and higher-order neighbor info to reduce oversmoothing. • Three rules improve node representations from features, structure, and predictions. • Comparative experiments use eight datasets, fourteen models, and three label rates. • The experiments provide strong evidence of high-quality node representations. [ABSTRACT FROM AUTHOR]

Multi-view clustering has attracted growing attention owing to its capabilities of aggregating information from various sources and its promising horizons in public affairs. Up till now, many advanced approaches have been proposed in recent literature. However, there are several ongoing difficulties to be tackled. One common dilemma occurs while attempting to align the features of different views. Moreover, due to the fact that many existing multi-view clustering algorithms stem from spectral clustering, this results to cubic time complexity w.r.t. the number of dataset. However, we propose Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent(MVSC-HFD) to tackle the discrepancy among views through hierarchical feature descent and project to a common subspace(STAGE 1), which reveals dependency of different views. We further reduce the computational complexity to linear time cost through a unified sampling strategy in the common subspace(STAGE 2), followed by anchor-based subspace clustering to learn the bipartite graph collectively(STAGE 3). Extensive experimental results on public benchmark datasets demonstrate that our proposed model consistently outperforms the state-of-the-art techniques. Our code is publicly available at https://github.com/QiyuanOu/MVSC-HFD/tree/main. • MVSC-HFD has high adaptability to various dimensions of different views. • Tackle the discrepancy among different views. • Reduce multiple views of different dimensions hierarchically to a common subspace. • Uniformly learn anchors in common subspace and construct bipartite graphs. • Low spatial and time consumption which are linear to data size. [ABSTRACT FROM AUTHOR]

The transcriptional interplay of human RNA polymerase I (RNA Pol I), RNA Pol II, and RNA Pol III remains largely uncharacterized due to limited integrative genomic analyses for all three enzymes. To address this gap, we applied a uniform framework to quantify global RNA Pol I, RNA Pol II, and RNA Pol III occupancies and identify both canonical and noncanonical patterns of gene localization. Most notably, our survey captures unexpected RNA Pol III recruitment at promoters of specific protein-coding genes. We show that such RNA Pol III-occupied promoters are enriched for small nascent RNAs terminating in a run of 4 Ts-a hallmark of RNA Pol III termination indicative of constrained RNA Pol III transcription. Taken further, RNA Pol III disruption generally reduces the expression of RNA Pol III-occupied protein-coding genes, suggesting RNA Pol III recruitment and transcription enhance RNA Pol II activity. These findings resemble analogous patterns of RNA Pol II activity at RNA Pol III-transcribed genes, altogether uncovering a reciprocal form of crosstalk between RNA Pol II and RNA Pol III., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Recurrent pregnancy loss (RPL) affects women's reproductive health seriously, with immune dysfunction playing a key role in its cause, yet the exact mechanisms remain elusive. We aim to investigate potential mechanisms and identify biomarkers linked to RPL., Methods: Immune cytokine testing and metabolomic profiling were conducted on the serum of 34 RPL patients and 30 healthy individuals. The metabolic pathways of the differential metabolites were analyzed, and specific metabolites were validated through targeted profiling. Potential biomarkers were identified, and the relationships between immune cytokines and differential metabolites were explored., Results: In the RPL group, serum interleukin-6 and interleukin-10 levels were significantly higher, while interleukin-2 and interferon-γ were significantly lower. A total of 296 differential metabolites were detected by untargeted metabolomic profiling between the RPL and control groups, with most linked to amino acid metabolism. Targeted metabolomic profiling of amino acid metabolism revealed upregulation of indole-3-acetic acid, tyrosine, glycine, isoleucine, tryptophan, lysine, aspartic acid, arginine, leucine, threonine, glutamic acid, cystine, and phenylpyruvic acid (PPA) in the RPL group. Moreover, PPA and 5-hydroxy-L-tryptophan showed great potential in predicting RPL in a diagnostic model. Cystine and tyrosine were associated with immune cytokines in correlation analysis., Conclusion: The study highlights the role of amino acid metabolism in RPL pathogenesis, suggesting that PPA and 5-HTP may be potential predictive indicators, while cystine and tyrosine may potentially regulate immune responses related to RPL. Further investigation into the molecular mechanisms underlying these findings could potentially result in the creation of novel diagnostic and therapeutic approaches for RPL., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ye, Ma, Guo, Guo, Li, Zhou, Hu, Hong, Xie and Wang.)

Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging task. However, we observe that the existing methods suffer from the representation collapse problem and tend to encode samples with different classes into the same latent embedding. Consequently, the discriminative capability of nodes is limited, resulting in suboptimal clustering performance. To address this problem, we propose a novel deep graph clustering algorithm termed improved dual correlation reduction network (IDCRN) through improving the discriminative capability of samples. Specifically, by approximating the cross-view feature correlation matrix to an identity matrix, we reduce the redundancy between different dimensions of features, thus improving the discriminative capability of the latent space explicitly. Meanwhile, the cross-view sample correlation matrix is forced to approximate the designed clustering-refined adjacency matrix to guide the learned latent representation to recover the affinity matrix even across views, thus enhancing the discriminative capability of features implicitly. Moreover, we avoid the collapsed representation caused by the oversmoothing issue in graph convolutional networks (GCNs) through an introduced propagation regularization term, enabling IDCRN to capture the long-range information with the shallow network structure. Extensive experimental results on six benchmarks have demonstrated the effectiveness and efficiency of IDCRN compared with the existing state-of-the-art deep graph clustering algorithms. The code of IDCRN is released at https://github.com/yueliu1999/IDCRN. Besides, we share a collection of deep graph clustering, including papers, codes, and datasets at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering.

RNA polymerase III (Pol III) activity in cancer is linked to the production of small noncoding (nc)RNAs that are otherwise silent in most tissues. snaR-A (small NF90-associated RNA isoform A) - a hominid-specific ncRNA shown to enhance cell proliferation, migration, and invasion - is a cancer-emergent Pol III product that remains largely uncharacterized despite promoting growth phenotypes. Here, we applied a combination of genomic and biochemical approaches to study the biogenesis and subsequent protein interactions of snaR-A and to better understand its role as a putative driver of cancer progression. By profiling the chromatin landscapes across a multitude of primary tumor types, we show that predicted snaR-A upregulation is broadly linked with unfavorable outcomes among cancer patients. At the molecular level, we unexpectedly discover widespread interactions between snaR-A and mRNA splicing factors, including SF3B2 - a core component of the U2 small nuclear ribonucleoprotein (snRNP). We find that SF3B2 levels are sensitive to high snaR-A abundance and that depletion of snaR-A alone is sufficient to decrease intron retention levels across subpopulations of mRNA enriched for U2 snRNP occupancy. snaR-A sensitive genes are characterized by high GC content, close spatial proximity to nuclear bodies concentrated in pre-mRNA splicing factors, and functional enrichment for proteins involved in deacetylation and autophagy. We highlight examples of splicing misregulation and increased protein levels following snaR-A depletion for a wide-ranging set of factors, suggesting snaR-A-driven splicing defects may have far-reaching effects that re-shape the cellular proteome. These findings clarify the molecular activities and consequences of snaR-A in cancer, and altogether establish a novel mechanism through which Pol III overactivity may promote tumorigenesis., Competing Interests: Competing interest statement The authors declare no competing interests

RNA polymerase (Pol) I, II, and III are most commonly described as having distinct roles in synthesizing ribosomal RNA (rRNA), messenger RNA (mRNA), and specific small noncoding (nc)RNAs, respectively. This delineation of transcriptional responsibilities is not definitive, however, as evidenced by instances of Pol II recruitment to genes conventionally transcribed by Pol III, including the co-transcription of RPPH1 - the catalytic RNA component of RNase P. A comprehensive understanding of the interplay between RNA polymerase complexes remains lacking, however, due to limited comparative analyses for all three enzymes. To address this gap, we applied a uniform framework for quantifying global Pol I, II, and III occupancies that integrates currently available human RNA polymerase ChIP-seq datasets. Occupancy maps are combined with a comprehensive multi-class promoter set that includes protein-coding genes, noncoding genes, and repetitive elements. While our genomic survey appropriately identifies recruitment of Pol I, II, and III to canonical target genes, we unexpectedly discover widespread recruitment of the Pol III machinery to promoters of specific protein-coding genes, supported by colocalization patterns observed for several Pol III-specific subunits. We show that Pol III-occupied Pol II promoters are enriched for small, nascent RNA reads terminating in a run of 4 Ts, a unique hallmark of Pol III transcription termination and evidence of active Pol III activity at these sites. Pol III disruption differentially modulates the expression of Pol III-occupied coding genes, which are functionally enriched for ribosomal proteins and genes broadly linked to unfavorable outcomes in cancer. Our map also identifies additional, currently unannotated genomic elements occupied by Pol III with clear signatures of nascent RNA species that are sensitive to disruption of La (SSB) - a Pol III-related RNA chaperone protein. These findings reshape our current understanding of the interplay between Pols II and III and identify potentially novel small ncRNAs with broad implications for gene regulatory paradigms and RNA biology., Competing Interests: Competing interest statement The authors declare no competing interests

Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast to static graphs, temporal graphs are typically organized as node interaction sequences over continuous time rather than an adjacency matrix. Most temporal graph learning methods model current interactions by incorporating historical neighborhood. However, such methods only consider first-order temporal information while disregarding crucial high-order structural information, resulting in suboptimal performance. To address this issue, we propose a self-supervised method called S2T for temporal graph learning, which extracts both temporal and structural information to learn more informative node representations. Notably, the initial node representations combine first-order temporal and high-order structural information differently to calculate two conditional intensities. An alignment loss is then introduced to optimize the node representations, narrowing the gap between the two intensities and making them more informative. Concretely, in addition to modeling temporal information using historical neighbor sequences, we further consider structural knowledge at both local and global levels. At the local level, we generate structural intensity by aggregating features from high-order neighbor sequences. At the global level, a global representation is generated based on all nodes to adjust the structural intensity according to the active statuses on different nodes. Extensive experiments demonstrate that the proposed model S2T achieves at most 10.13% performance improvement compared with the state-of-the-art competitors on several datasets.

With the development of various applications, such as recommendation systems and social network analysis, graph data have been ubiquitous in the real world. However, graphs usually suffer from being absent during data collection due to copyright restrictions or privacy-protecting policies. The graph absence could be roughly grouped into attribute-incomplete and attribute-missing cases. Specifically, attribute-incomplete indicates that a portion of the attribute vectors of all nodes are incomplete, while attribute-missing indicates that all attribute vectors of partial nodes are missing. Although various graph imputation methods have been proposed, none of them is custom-designed for a common situation where both types of graph absence exist simultaneously. To fill this gap, we develop a novel graph imputation network termed revisiting initializing then refining (RITR), where both attribute-incomplete and attribute-missing samples are completed under the guidance of a novel initializing-then-refining imputation criterion. Specifically, to complete attribute-incomplete samples, we first initialize the incomplete attributes using Gaussian noise before network learning, and then introduce a structure-attribute consistency constraint to refine incomplete values by approximating a structure-attribute correlation matrix to a high-order structure matrix. To complete attribute-missing samples, we first adopt structure embeddings of attribute-missing samples as the embedding initialization, and then refine these initial values by adaptively aggregating the reliable information of attribute-incomplete samples according to a dynamic affinity structure. To the best of our knowledge, this newly designed method is the first end-to-end unsupervised framework dedicated to handling hybrid-absent graphs. Extensive experiments on six datasets have verified that our methods consistently outperform the existing state-of-the-art competitors. Our source code is available at https://github.com/WxTu/RITR.