Towards Self-Interpretable Graph-Level Anomaly Detection

Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable dissimilarity compared to the majority in a collection. However, current works primarily focus on evaluating graph-level abnormality while failing to provide meaningful explanations for the predictions, which largely limits their reliability and application scope. In this paper, we investigate a new challenging problem, explainable GLAD, where the learning objective is to predict the abnormality of each graph sample with corresponding explanations, i.e., the vital subgraph that leads to the predictions. To address this challenging problem, we propose a Self-Interpretable Graph aNomaly dETection model (SIGNET for short) that detects anomalous graphs as well as generates informative explanations simultaneously. Specifically, we first introduce the multi-view subgraph information bottleneck (MSIB) framework, serving as the design basis of our self-interpretable GLAD approach. This way SIGNET is able to not only measure the abnormality of each graph based on cross-view mutual information but also provide informative graph rationales by extracting bottleneck subgraphs from the input graph and its dual hypergraph in a self-supervised way. Extensive experiments on 16 datasets demonstrate the anomaly detection capability and self-interpretability of SIGNET.Comment: 23 pages; accepted to NeurIPS 202

Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting

Due to the nonstationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasting models greatly suffer from distribution drift and degrade the forecasting performance over time. Existing methods address distribution drift via adapting to the latest arrived data or self-correcting per the meta knowledge derived from future data. Despite their great success in MTS forecasting, these methods hardly capture the intrinsic distribution changes, especially from a distributional perspective. Accordingly, we propose a novel framework temporal conditional variational autoencoder (TCVAE) to model the dynamic distributional dependencies over time between historical observations and future data in MTSs and infer the dependencies as a temporal conditional distribution to leverage latent variables. Specifically, a novel temporal Hawkes attention mechanism represents temporal factors subsequently fed into feed-forward networks to estimate the prior Gaussian distribution of latent variables. The representation of temporal factors further dynamically adjusts the structures of Transformer-based encoder and decoder to distribution changes by leveraging a gated attention mechanism. Moreover, we introduce conditional continuous normalization flow to transform the prior Gaussian to a complex and form-free distribution to facilitate flexible inference of the temporal conditional distribution. Extensive experiments conducted on six real-world MTS datasets demonstrate the TCVAE's superior robustness and effectiveness over the state-of-the-art MTS forecasting baselines. We further illustrate the TCVAE applicability through multifaceted case studies and visualization in real-world scenarios.Comment: 13 pages, 6 figures, submitted to IEEE Transactions on Neural Networks and Learning Systems (TNNLS

Test Verification and Application of a Longitudinal Temperature Force Testing Method for Long Seamless Rails Using FBG Strain Sensor

In order to evaluate the health status of continuous welded rail accurately, a deduction on the FBG sensing principle has been made with regard to the temperature variation of test specimens under different constraint conditions. A long seamless rail testing solution and its on-site application are designed based on this deduction. According to the verification experiments of sensing principle inside, the effect of the reference temperature on the FBG temperature and strain sensitivity coefficient within −30°C~30°C is not higher than 0.05%; the maximum relative error of single point between the tested and theoretical results of test specimen under constrained condition is 3.2%; and the maximum relative error of slopes of fitted straight lines based on the tested and theoretical results within the entire test temperature range is 2.3%, verifying the deduced FBG sensing principle with regard to the test specimen under constrained condition. The maximum error of the longitudinal temperature force between the on-site tested results and calculated results in long seamless rails is only 6.1 kN, the corresponding rail temperature variation is 0.3°C, and the accumulated error is controllable within 5%

Ligand and structure-based approaches for the exploration of structure–activity relationships of fusidic acid derivatives as antibacterial agents

Introduction: Fusidic acid (FA) has been widely applied in the clinical prevention and treatment of bacterial infections. Nonetheless, its clinical application has been limited due to its narrow antimicrobial spectrum and some side effects.Purpose: Therefore, it is necessary to explore the structure–activity relationships of FA derivatives as antibacterial agents to develop novel ones possessing a broad antimicrobial spectrum.Methods and result: First, a pharmacophore model was established on the nineteen FA derivatives with remarkable antibacterial activities reported in previous studies. The common structural characteristics of the pharmacophore emerging from the FA derivatives were determined as those of six hydrophobic centers, two atom centers of the hydrogen bond acceptor, and a negative electron center around the C-21 field. Then, seven FA derivatives have been designed according to the reported structure–activity relationships and the pharmacophore characteristics. The designed FA derivatives were mapped on the pharmacophore model, and the Qfit values of all FA derivatives were over 50 and FA-8 possessed the highest value of 82.66. The molecular docking studies of the partial target compounds were conducted with the elongation factor G (EF-G) of S. aureus. Furthermore, the designed FA derivatives have been prepared and their antibacterial activities were evaluated by the inhibition zone test and the minimum inhibitory concentration (MIC) test. The derivative FA-7 with a chlorine group as the substituent group at C-25 of FA displayed the best antibacterial property with an MIC of 3.125 µM. Subsequently, 3D-QSAR was carried on all the derivatives by using the CoMSIA mode of SYBYL-X 2.0.Conclusion: Hence, a computer-aided drug design model was developed for FA, which can be further used to optimize FA derivatives as highly potent antibacterial agents

Synthesis and Biological Evaluation of Novel Fusidic Acid Derivatives as Two-in-One Agent with Potent Antibacterial and Anti-Inflammatory Activity

Fusidic acid (FA), a narrow-spectrum antibiotics, is highly sensitive to various Gram-positive cocci associated with skin infections. It has outstanding antibacterial effects against certain Gram-positive bacteria whilst no cross-resistance with other antibiotics. Two series of FA derivatives were synthesized and their antibacterial activities were tested. A new aromatic side-chain analog, FA-15 exhibited good antibacterial activity with MIC values in the range of 0.781-1.563 µM against three strains of Staphylococcus spp. Furthermore, through the assessment by the kinetic assay, similar characteristics of bacteriostasis by FA and its aromatic derivatives were observed. In addition, anti-inflammatory activities of FA and its aromatic derivatives were evaluated by using a 12-O-tetradecanoylphorbol-13-acetate (TPA) induced mouse ear edema model. The results also indicated that FA and its aromatic derivatives effectively reduced TPA-induced ear edema in a dose-dependent manner. Following, multiform computerized simulation, including homology modeling, molecular docking, molecular dynamic simulation and QSAR was conducted to clarify the mechanism and regularity of activities. Overall, the present work gave vital clues about structural modifications and has profound significance in deeply scouting for bioactive potentials of FA and its derivatives

Dynamic Analysis of the Coupling Coordination Relationship between Urbanization and Water Resource Security and Its Obstacle Factor

Water resource security is an important condition for socio-economic development. Recently, the process of urbanization brings increasing pressures on water resources. Thus, a good understanding of harmonious development of urbanization and water resource security (WRS) systems is necessary. This paper examined the coordination state between urbanization and WRS and its obstacle factors in Beijing city, utilizing the improved coupling coordination degree (ICCD) model, obstacle degree model, and indicator data from 2008 to 2017. Results indicated that: (1) The coupling coordination degree between WRS and urbanization displayed an overall upward tendency during the 2008–2017 period; the coupling coordination state has changed from an imbalanced state into a good coordination state, experiencing from a high-speed development stage (2008–2010), through a steady growth stage (2010–2014), towards a low-speed growth (2014–2017). (2) In urbanization system, both the social and spatial urbanizations have the greatest obstruction to the development of urbanization-WRS system. The subsystems of pressure and state are the domain obstacle subsystems in WRS system. These results can provide important support for urban planning and water resource protection in the future, and hold great significance for urban sustainable development