MTGFlow: Unsupervised Multivariate Time Series Anomaly Detection via Dynamic Graph and Entity-aware Normalizing Flow

Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required. However, preparing such a dataset is very laborious since each single data instance should be fully guaranteed to be normal. It is, therefore, desired to explore multivariate time series anomaly detection methods based on the dataset without any label knowledge. In this paper, we propose MTGFlow, an unsupervised anomaly detection approach for Multivariate Time series anomaly detection via dynamic Graph and entity-aware normalizing Flow, leaning only on a widely accepted hypothesis that abnormal instances exhibit sparse densities than the normal. However, the complex interdependencies among entities and the diverse inherent characteristics of each entity pose significant challenges on the density estimation, let alone to detect anomalies based on the estimated possibility distribution. To tackle these problems, we propose to learn the mutual and dynamic relations among entities via a graph structure learning model, which helps to model accurate distribution of multivariate time series. Moreover, taking account of distinct characteristics of the individual entities, an entity-aware normalizing flow is developed to describe each entity into a parameterized normal distribution, thereby producing fine-grained density estimation. Incorporating these two strategies, MTGFlowachieves superior anomaly detection performance. Experiments on the real-world datasets are conducted, demonstrating that MTGFlow outperforms the state-of-the-art (SOTA) by 5.0% and 1.6% AUROC for SWaT and WADI datasets respectively. Also, through the anomaly scores contributed by individual entities, MTGFlow can provide explanation information for the detection results

An H5N1 M2e-based multiple antigenic peptide vaccine confers heterosubtypic protection from lethal infection with pandemic 2009 H1N1 virus

Background. A 2009 global influenza pandemic caused by a novel swine-origin H1N1 influenza A virus has posted an increasing threat of a potential pandemic by the highly pathogenic avian influenza (HPAI) H5N1 virus, driving us to develop an influenza vaccine which confers cross-protection against both H5N1 and H1N1 viruses. Previously, we have shown that a tetra-branched multiple antigenic peptide (MAP) vaccine based on the extracellular domain of M2 protein (M2e) from H5N1 virus (H5N1-M2e-MAP) induced strong immune responses and cross-protection against different clades of HPAI H5N1 viruses. In this report, we investigated whether such M2e-MAP presenting the H5N1-M2e consensus sequence can afford heterosubtypic protection from lethal challenge with the pandemic 2009 H1N1 virus. Results. Our results demonstrated that H5N1-M2e-MAP plus Freund's or aluminum adjuvant induced strong cross-reactive IgG antibody responses against M2e of the pandemic H1N1 virus which contains one amino acid variation with M2e of H5N1 at position 13. These cross-reactive antibodies may maintain for 6 months and bounced back quickly to the previous high level after the 2nd boost administered 2 weeks before virus challenge. H5N1-M2e-MAP could afford heterosubtypic protection against lethal challenge with pandemic H1N1 virus, showing significant decrease of viral replications and obvious alleviation of histopathological damages in the challenged mouse lungs. 100% and 80% of the H5N1-M2e-MAP-vaccinated mice with Freund's and aluminum adjuvant, respectively, survived the lethal challenge with pandemic H1N1 virus. Conclusions. Our results suggest that H5N1-M2e-MAP has a great potential to prevent the threat from re-emergence of pandemic H1N1 influenza and possible novel influenza pandemic due to the reassortment of HPAI H5N1 virus with the 2009 swine-origin H1N1 influenza virus. © 2010 Zhao et al; licensee BioMed Central Ltd.published_or_final_versio

An M2e-based multiple antigenic peptide vaccine protects mice from lethal challenge with divergent H5N1 influenza viruses

<p>Abstract</p> <p>Background</p> <p>A growing concern has raised regarding the pandemic potential of the highly pathogenic avian influenza (HPAI) H5N1 viruses. Consequently, there is an urgent need to develop an effective and safe vaccine against the divergent H5N1 influenza viruses. In the present study, we designed a tetra-branched multiple antigenic peptide (MAP)-based vaccine, designated M2e-MAP, which contains the sequence overlapping the highly conserved extracellular domain of matrix protein 2 (M2e) of a HPAI H5N1 virus, and investigated its immune responses and cross-protection against different clades of H5N1 viruses.</p> <p>Results</p> <p>Our results showed that M2e-MAP vaccine induced strong M2e-specific IgG antibody responses following 3-dose immunization of mice with M2e-MAP in the presence of Freunds' or aluminium (alum) adjuvant. M2e-MAP vaccination limited viral replication and attenuated histopathological damage in the challenged mouse lungs. The M2e-MAP-based vaccine protected immunized mice against both clade1: VN/1194 and clade2.3.4: SZ/406H H5N1 virus challenge, being able to counteract weight lost and elevate survival rate following lethal challenge of H5N1 viruses.</p> <p>Conclusions</p> <p>These results suggest that M2e-MAP presenting M2e of H5N1 virus has a great potential to be developed into an effective subunit vaccine for the prevention of infection by a broad spectrum of HPAI H5N1 viruses.</p