GADY: Unsupervised Anomaly Detection on Dynamic Graphs

Anomaly detection on dynamic graphs refers to detecting entities whose behaviors obviously deviate from the norms observed within graphs and their temporal information. This field has drawn increasing attention due to its application in finance, network security, social networks, and more. However, existing methods face two challenges: dynamic structure constructing challenge - difficulties in capturing graph structure with complex time information and negative sampling challenge - unable to construct excellent negative samples for unsupervised learning. To address these challenges, we propose Unsupervised Generative Anomaly Detection on Dynamic Graphs (GADY). To tackle the first challenge, we propose a continuous dynamic graph model to capture the fine-grained information, which breaks the limit of existing discrete methods. Specifically, we employ a message-passing framework combined with positional features to get edge embeddings, which are decoded to identify anomalies. For the second challenge, we pioneer the use of Generative Adversarial Networks to generate negative interactions. Moreover, we design a loss function to alter the training goal of the generator while ensuring the diversity and quality of generated samples. Extensive experiments demonstrate that our proposed GADY significantly outperforms the previous state-of-the-art method on three real-world datasets. Supplementary experiments further validate the effectiveness of our model design and the necessity of each module

CLE Diffusion: Controllable Light Enhancement Diffusion Model

Low light enhancement has gained increasing importance with the rapid development of visual creation and editing. However, most existing enhancement algorithms are designed to homogeneously increase the brightness of images to a pre-defined extent, limiting the user experience. To address this issue, we propose Controllable Light Enhancement Diffusion Model, dubbed CLE Diffusion, a novel diffusion framework to provide users with rich controllability. Built with a conditional diffusion model, we introduce an illumination embedding to let users control their desired brightness level. Additionally, we incorporate the Segment-Anything Model (SAM) to enable user-friendly region controllability, where users can click on objects to specify the regions they wish to enhance. Extensive experiments demonstrate that CLE Diffusion achieves competitive performance regarding quantitative metrics, qualitative results, and versatile controllability. Project page: \url{https://yuyangyin.github.io/CLEDiffusion/

KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models

Large language models (LLMs) demonstrate remarkable performance on knowledge-intensive tasks, suggesting that real-world knowledge is encoded in their model parameters. However, besides explorations on a few probing tasks in limited knowledge domains, it is not well understood how to evaluate LLMs' knowledge systematically and how well their knowledge abilities generalize, across a spectrum of knowledge domains and progressively complex task formats. To this end, we propose KGQuiz, a knowledge-intensive benchmark to comprehensively investigate the knowledge generalization abilities of LLMs. KGQuiz is a scalable framework constructed from triplet-based knowledge, which covers three knowledge domains and consists of five tasks with increasing complexity: true-or-false, multiple-choice QA, blank filling, factual editing, and open-ended knowledge generation. To gain a better understanding of LLMs' knowledge abilities and their generalization, we evaluate 10 open-source and black-box LLMs on the KGQuiz benchmark across the five knowledge-intensive tasks and knowledge domains. Extensive experiments demonstrate that LLMs achieve impressive performance in straightforward knowledge QA tasks, while settings and contexts requiring more complex reasoning or employing domain-specific facts still present significant challenges. We envision KGQuiz as a testbed to analyze such nuanced variations in performance across domains and task formats, and ultimately to understand, evaluate, and improve LLMs' knowledge abilities across a wide spectrum of knowledge domains and tasks

Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks

Online movie review platforms are providing crowdsourced feedback for the film industry and the general public, while spoiler reviews greatly compromise user experience. Although preliminary research efforts were made to automatically identify spoilers, they merely focus on the review content itself, while robust spoiler detection requires putting the review into the context of facts and knowledge regarding movies, user behavior on film review platforms, and more. In light of these challenges, we first curate a large-scale network-based spoiler detection dataset LCS and a comprehensive and up-to-date movie knowledge base UKM. We then propose MVSD, a novel Multi-View Spoiler Detection framework that takes into account the external knowledge about movies and user activities on movie review platforms. Specifically, MVSD constructs three interconnecting heterogeneous information networks to model diverse data sources and their multi-view attributes, while we design and employ a novel heterogeneous graph neural network architecture for spoiler detection as node-level classification. Extensive experiments demonstrate that MVSD advances the state-of-the-art on two spoiler detection datasets, while the introduction of external knowledge and user interactions help ground robust spoiler detection. Our data and code are available at https://github.com/Arthur-Heng/Spoiler-DetectionComment: EMNLP 202

Transient receptor potential channel 1 deficiency impairs host defense and proinflammatory responses to bacterial infection by regulating protein kinase Cα signaling

Transient receptor potential channel 1 (TRPC1) is a nonselective cation channel that is required for Ca2+ homeostasis necessary for cellular functions. However, whether TRPC1 is involved in infectious disease remains unknown. Here, we report a novel function for TRPC1 in host defense against Gram-negative bacteria. TRPC1-/- mice exhibited decreased survival, severe lung injury, and systemic bacterial dissemination upon infection. Furthermore, silencing of TRPC1 showed decreased Ca2+ entry, reduced proinflammatory cytokines, and lowered bacterial clearance. Importantly, TRPC1 functioned as an endogenous Ca2+ entry channel critical for proinflammatory cytokine production in both alveolar macrophages and epithelial cells. We further identified that bacterium-mediated activation of TRPC1 was dependent on Toll-like receptor 4 (TLR4), which induced endoplasmic reticulum (ER) store depletion. After activation of phospholipase Cγ (PLC-γ), TRPC1 mediated Ca2+ entry and triggered protein kinase Cα (PKC-α) activity to facilitate nuclear translocation of NF-kB/Jun N-terminal protein kinase (JNK) and augment the proinflammatory response, leading to tissue damage and eventually mortality. These findings reveal that TRPC1 is required for host defense against bacterial infections through the TLR4-TRPC1-PKCγ signaling circuit.Fil: Zhou, Xikun. University Of North Dakota; Estados Unidos. West China Hospital Of Sichuan University; ChinaFil: Ye, Yan. University Of North Dakota; Estados UnidosFil: Sun, Yuyang. University Of North Dakota; Estados UnidosFil: Li, Xuefeng. West China Hospital Of Sichuan University; China. University Of North Dakota; Estados UnidosFil: Wang, Wenxue. University Of North Dakota; Estados UnidosFil: Privratsky, Breanna. University Of North Dakota; Estados UnidosFil: Tan, Shirui. University Of North Dakota; Estados UnidosFil: Zhou, Zongguang. West China Hospital Of Sichuan University; ChinaFil: Huang, Canhua. West China Hospital Of Sichuan University; ChinaFil: Wei, Yu-Quan. West China Hospital Of Sichuan University; ChinaFil: Birnbaumer, Lutz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. National Institute Of Environmental Health Sciences; Estados UnidosFil: Singh, Brij B.. University Of North Dakota; Estados UnidosFil: Wu, Min. University Of North Dakota; Estados Unido

2,4-Dinitro­benzaldehyde hydrazone

The title compound, C7H6N4O4, plays an important role in the synthesis of biologically active compounds. The planar hydrazone group is oriented at a dihedral angle of 8.27 (3)° with respect to the benzene ring. In the crystal structure, inter­molecular N—H⋯O and N—H⋯N hydrogen bonds link the mol­ecules

Intermittent-Hypoxia-Induced Autophagy Activation Through the ER-Stress-Related PERK/eIF2α/ATF4 Pathway is a Protective Response to Pancreatic β-Cell Apoptosis

Background/Aims: Intermittent hypoxia (IH) causes apoptosis in pancreatic β-cells, but the potential mechanisms remain unclear. Endoplasmic reticulum (ER) stress, autophagy, and apoptosis are interlocked in an extensive crosstalk. Thus, this study aimed to investigate the contributions of ER stress and autophagy to IH-induced pancreatic β-cell apoptosis. Methods: We established animal and cell models of IH, and then inhibited autophagy and ER stress by pharmacology and small interfering RNA (siRNA) in INS-1 cells and rats. The levels of biomarkers for autophagy, ER stress, and apoptosis were evaluated by immunoblotting and immunofluorescence. The number of autophagic vacuoles was observed by transmission electron microscopy. Results: IH induced autophagy activation both in vivo and in vitro, as evidenced by increased autophagic vacuole formation and LC3 turnover, and decreased SQSTM1 level. The levels of ER-stress-related proteins, including GRP78, CHOP, caspase 12, phosphorylated (p)-protein kinase RNA-like ER kinase (PERK), p-eIF2α, and activating transcription factor 4 (ATF4) were increased under IH conditions. Inhibition of ER stress with tauroursodeoxycholic acid or 4-phenylbutyrate partially blocked IH-induced autophagy in INS-1 cells. Furthermore, inhibition of PERK with GSK2606414 or siRNA blocked the ERstress-related PERK/eIF2α/ATF4 signaling pathway and inhibited autophagy induced by IH, which indicates that IH-induced autophagy activation is dependent on this signaling pathway. Promoting autophagy with rapamycin alleviated IH-induced apoptosis, whereas inhibition of autophagy with chloroquine or autophagy-related gene (Atg5 and Atg7) siRNA aggravated pancreatic β-cell apoptosis caused by IH. Conclusion: IH induces autophagy activation through the ER-stress-related PERK/eIF2α/ATF4 signaling pathway, which is a protective response to pancreatic β-cell apoptosis caused by IH

Adaptive noise suppression for low-S/N microseismic data based on ambient-noise-assisted multivariate empirical mode decomposition

Microseismic monitoring data may be seriously contaminated by complex and nonstationary interference noises produced by mechanical vibration, which significantly impact the data quality and subsequent data-processing procedure. One challenge in microseismic data processing is separating weak seismic signals from varying noisy data. To address this issue, we proposed an ambient-noise-assisted multivariate empirical mode decomposition (ANA-MEMD) method for adaptively suppressing noise in low signal-to-noise (S/N) microseismic data. In the proposed method, a new multi-channel record is produced by combining the noisy microseismic signal with preceding ambient noises. The multi-channel record is then decomposed using multivariate empirical mode decomposition (MEMD) into multivariate intrinsic mode functions (MIMFs). Then, the MIMFs corresponding to the main ambient noises can be identified by calculating and sorting energy percentage in descending order. Finally, the IMFs associated with strong interference noise, high-frequency and low-frequency noise are filtered out and suppressed by the energy percentage and frequency range. We investigate the feasibility and reliability of the proposed method using both synthetic data and field data. The results demonstrate that the proposed method can mitigate the mode mixing problem and clarify the main noise contributors by adding additional ambient-noise-assisted channels, hence separating the microseismic signal and ambient noise effectively and enhancing the S/Ns of microseismic signals

Clustered Distribution of Natural Product Leads of Drugs in the Chemical Space as Influenced by the Privileged Target-Sites

10.1038/srep09325Scientific Reports