Deep Semi-supervised Anomaly Detection with Metapath-based Context Knowledge

Graph anomaly detection has attracted considerable attention in recent years. This paper introduces a novel approach that leverages metapath-based semi-supervised learning, addressing the limitations of previous methods. We present a new framework, Metapath-based Semi-supervised Anomaly Detection (MSAD), incorporating GCN layers in both the encoder and decoder to efficiently propagate context information between abnormal and normal nodes. The design of metapath-based context information and a specifically crafted anomaly community enhance the process of learning differences in structures and attributes, both globally and locally. Through a comprehensive set of experiments conducted on seven real-world networks, this paper demonstrates the superiority of the MSAD method compared to state-of-the-art techniques. The promising results of this study pave the way for future investigations, focusing on the optimization and analysis of metapath patterns to further enhance the effectiveness of anomaly detection on attributed networks

Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges

Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems. Graph anomalies are patterns in a graph that do not conform to normal patterns expected of the attributes and/or structures of the graph. In recent years, graph neural networks (GNNs) have been studied extensively and have successfully performed difficult machine learning tasks in node classification, link prediction, and graph classification thanks to the highly expressive capability via message passing in effectively learning graph representations. To solve the graph anomaly detection problem, GNN-based methods leverage information about the graph attributes (or features) and/or structures to learn to score anomalies appropriately. In this survey, we review the recent advances made in detecting graph anomalies using GNN models. Specifically, we summarize GNN-based methods according to the graph type (i.e., static and dynamic), the anomaly type (i.e., node, edge, subgraph, and whole graph), and the network architecture (e.g., graph autoencoder, graph convolutional network). To the best of our knowledge, this survey is the first comprehensive review of graph anomaly detection methods based on GNNs.Comment: 9 pages, 2 figures, 1 tables; to appear in the IEEE Access (Please cite our journal version.

SSumM: Sparse Summarization of Massive Graphs

Given a graph G and the desired size k in bits, how can we summarize G within k bits, while minimizing the information loss? Large-scale graphs have become omnipresent, posing considerable computational challenges. Analyzing such large graphs can be fast and easy if they are compressed sufficiently to fit in main memory or even cache. Graph summarization, which yields a coarse-grained summary graph with merged nodes, stands out with several advantages among graph compression techniques. Thus, a number of algorithms have been developed for obtaining a concise summary graph with little information loss or equivalently small reconstruction error. However, the existing methods focus solely on reducing the number of nodes, and they often yield dense summary graphs, failing to achieve better compression rates. Moreover, due to their limited scalability, they can be applied only to moderate-size graphs. In this work, we propose SSumM, a scalable and effective graph-summarization algorithm that yields a sparse summary graph. SSumM not only merges nodes together but also sparsifies the summary graph, and the two strategies are carefully balanced based on the minimum description length principle. Compared with state-of-the-art competitors, SSumM is (a) Concise: yields up to 11.2X smaller summary graphs with similar reconstruction error, (b) Accurate: achieves up to 4.2X smaller reconstruction error with similarly concise outputs, and (c) Scalable: summarizes 26X larger graphs while exhibiting linear scalability. We validate these advantages through extensive experiments on 10 real-world graphs.Comment: to be published in the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '20

Inverted Takotsubo-Like Left Ventricular Dysfunction with Pulmonary Oedema Developed after Caesarean Delivery Complicated by Massive Haemorrhage in a Severe Preeclamptic Parturient with a Prolonged Painful Labour

Inverted takotsubo cardiomyopathy (TTC), a variant of stress-induced cardiomyopathy, features transient myocardial dysfunction characterized by a hyperdynamic left ventricular apex and akinesia of the base. Herein, we describe a 38-year-old primigravida with severe preeclampsia who had active labour for 4 h followed by an emergency caesarean delivery. She developed postpartum haemorrhage due to uterine atony complicated by pulmonary oedema, which was managed with large-volume infusion and hysterectomy. Her haemodynamic instability was associated with cardiac biomarkers indicative of diffuse myocardial injury and echocardiographic findings of an “inverted” TTC. The patient was almost fully recovered one month later. Our case shows that a reversible inverted TTC may result from a prolonged painful labour. TTC should be listed in the differential diagnosis of the patient presenting with pulmonary oedema of unknown origin, especially in patients with severe preeclampsia

ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing for Open Compound Domain Adaptation in Semantic Segmentation

Open compound domain adaptation (OCDA) considers the target domain as the compound of multiple unknown homogeneous subdomains. The goal of OCDA is to minimize the domain gap between the labeled source domain and the unlabeled compound target domain, which benefits the model generalization to the unseen domains. Current OCDA for semantic segmentation methods adopt manual domain separation and employ a single model to simultaneously adapt to all the target subdomains. However, adapting to a target subdomain might hinder the model from adapting to other dissimilar target subdomains, which leads to limited performance. In this work, we introduce a multi-teacher framework with bidirectional photometric mixing to separately adapt to every target subdomain. First, we present an automatic domain separation to find the optimal number of subdomains. On this basis, we propose a multi-teacher framework in which each teacher model uses bidirectional photometric mixing to adapt to one target subdomain. Furthermore, we conduct an adaptive distillation to learn a student model and apply consistency regularization to improve the student generalization. Experimental results on benchmark datasets show the efficacy of the proposed approach for both the compound domain and the open domains against existing state-of-the-art approaches.Comment: Accepted to ECCV 202

Contribution of actin filaments and microtubules to cell elongation and alignment depends on the grating depth of microgratings

Additional file 1: Figure S1. (A) A phase contrast image of TCPS surface. Bar, 100 μm. (B) An imageshowing FN-lines (1 μm line and spacing) obtained by Atomic Force Microscopy (AFM) (Dimension 3100with a Nanoscope III controller, Digital Instruments) using silicon cantilevers (spring constant; 50 Nm-1)(RTESP, Veeco Probes) in contact mode. (C-E) SEM (Scanning electron microscopy) (6010 LV, JEOL)images showing the cross section of three different microgratings; 1 μm gratings with 0.35 um depth (C) and1 μm depth (D) and 2 μm gratings with 2 μm depth (E). Figure S2. (A) Fluorescence image of a RPE-1 cell stably expressing GFP/centrin cell on 1 μm gratings (1 μm deep). Bar, 30 μm. A yellow arrow indicates the direction of cell elongation. (B) Average cell aspect ratio (R) of cells on 1 μm gratings (0.35 or 1 μm deep) and 2 μm gratings with/without CD treatment. n: number of cells. ***P < 0.001. Data were analyzed using one-way ANOVA and a Bonferroni post hoc test. Error bar denotes the standard deviation of the mean. Figure S3. Alignment of actin and vinculin to the different substrates (Flat TCPS surface, FN-lines, and 1 μm gratings (0.35 or 1μm deep)). The alignment angle was measured as an angle difference of actin or vinculin orientation to the long axis of a cell on flat PDMS surface or the long axis of the FN-line or each micrograting. #: the number of cells. Error bar denotes the standard deviation of the mean. Figure S4. Merged image of MTs (Green fluorescence) and pattern (phase contrast) of cells on 1 μm grating (1 μm deep) in the presenceof CD at 1 μM

Identification and Characterization of mRNA and lncRNA Expression Profiles in Age-Related Hearing Loss

Objectives Age-related hearing loss (ARHL), or presbycusis, is caused by disorders of sensory hair cells and auditory neurons. Many studies have suggested that the accumulation of mitochondrial DNA damage, the production of reactive oxygen species, noise, inflammation, and decreased antioxidant function are associated with subsequent cochlear senescence in response to aging stress. Long non-coding RNA (lncRNA) has been reported to play important roles in various diseases. However, the function of lncRNA in ARHL remains unclear. In this study, we analyzed the common expression profiles of messenger RNA (mRNA) and lncRNA through ARHL-related RNA-sequencing datasets. Methods We selected and downloaded three different sets of RNA-sequencing data for ARHL. We performed differential expression analysis to find common mRNA and lncRNA profiles in the cochleae of aged mice compared to young mice. Gene Ontology (GO) analysis was used for functional exploration. Real-time quantitative reverse-transcription polymerase chain reaction (qRT-PCR) was performed to validate mRNAs and lncRNAs. In addition, we performed trans target prediction analysis with differentially expressed mRNAs and lncRNAs to understand the function of these mRNAs and lncRNAs in ARHL. Results We identified 112 common mRNAs and 10 common lncRNAs in the cochleae of aged mice compared to young mice. GO analysis showed that the 112 upregulated mRNAs were enriched in the defense response pathway. When we performed qRT-PCR with 1 mM H2O2-treated House Ear Institute-Organ of Corti 1 (HEI-OC1) cells, the qRT-PCR results were consistent with the RNA-sequencing analysis data. lncRNA-mRNA networks were constructed using the 10 common lncRNAs and 112 common mRNAs in ARHL. Conclusion Our study provides a comprehensive understanding of the common mRNA and lncRNA expression profiles in ARHL. Knowledge of ARHL-associated mRNAs and lncRNAs could be useful for better understanding ARHL and these mRNAs and lncRNAs might be a potential therapeutic target for preventing ARHL

PIV-MyoMonitor: an accessible particle image velocimetry-based software tool for advanced contractility assessment of cardiac organoids

Induced pluripotent stem cell (iPSC)-derived cardiac organoids offer a versatile platform for personalized cardiac toxicity assessment, drug screening, disease modeling, and regenerative therapies. While previous image-based contractility analysis techniques allowed the assessment of contractility of two-dimensional cardiac models, they face limitations, including encountering high noise levels when applied to three-dimensional organoid models and requiring expensive equipment. Additionally, they offer fewer functional parameters compared to commercial software. To address these challenges, we developed an open-source, particle image velocimetry-based software (PIV-MyoMonitor) and demonstrated its capacity for accurate contractility analysis in both two- and three-dimensional cardiac models using standard lab equipment. Comparisons with four other open-source software programs highlighted the capability of PIV-MyoMonitor for more comprehensive quantitative analysis, providing 22 functional parameters and enhanced video outputs. We showcased its applicability in drug screening by characterizing the response of cardiac organoids to a known isotropic drug, isoprenaline. In sum, PIV-MyoMonitor enables reliable contractility assessment across various cardiac models without costly equipment or software. We believe this software will benefit a broader scientific community

Microvasculature remodeling in the mouse lower gut during inflammaging

Inflammaging is defined as low-grade, chronic, systemic inflammation in aging, in the absence of overt infection. Age-associated deterioration of gastrointestinal function could be ascribed to the inflammaging, although evidence is yet to emerge. Here we show that microvessels in aging mouse intestine were progressively deprived of supportive structures, microvessel-associated pericytes and adherens junction protein vascular endothelial (VE)-cadherin, and became leaky. This alteration was ascribed to up-regulation of angiopoetin-2 in microvascular endothelial cells. Up-regulation of the angiopoietin-2 was by TNF-α, originated from M2-like residential CD206 + macrophages, proportion of which increases as animal ages. It was concluded that antigenic burdens encountered in intestine throughout life create the condition of chronic stage of inflammation, which accumulates M2-like macrophages expressing TNF-α. The TNF-α induces vascular leakage to facilitate recruitment of immune cells into intestine under the chronic inflammatory setting. © Author(s) 2017.1