Semi-supervised Time Series Anomaly Detection Model Based on LSTM Autoencoder

Nowadays, time series data is more and more likely to appear in various real-world systems, such as power plants, medical care, etc. In these systems, time series anomaly detection is necessary, which involves predictive maintenance, intrusion detection, anti-fraud, cloud platform monitoring and management, etc. Generally, the anomaly detection of time series is regarded as an unsupervised learning problem. However, in a real scenario, in addition to a large set of unlabeled data, there is usually a small set of available labeled data, such as normal or abnormal data sets labeled by experts. Only a few methods use labeled data, and the existing semi-supervised algorithms are not yet suitable for the field of time series anomaly detection. In this work, we propose a semi-supervised time series anomaly detection model based on LSTM autoencoder. We improve the loss function of the LSTM autoencoder so that it can be affected by unlabeled data and labeled data at the same time, and learn the distribution of unlabeled data and labeled data at the same time by minimizing the loss function. In a large number of experiments on the Yahoo! Webscope S5 and NAB data sets, we compared the performance of the unsupervised model and the semi-supervised model of the same network framework to prove that the performance of the semi-supervised model is improved compared to the unsupervised model

Hard Disk Failure Prediction via Transfer Learning

Due to the large-scale growth of data, the storage scale of data centers is getting larger and larger. Hard disk is the main storage medium, once a failure occurs, it will bring huge losses to users and enterprises. In order to improve the reliability of storage systems, many machine learning methods have been widely employed to predict hard disk failure in the past few decades. However, due to the large number of different models of hard disks in the heterogeneous disk system, traditional machine learning methods cannot build a general model. Inspired by a DANN based unsupervised domain adaptation approach for image classification, in this paper, we propose the DFPTL (Disk Failure Prediction via Transfer Learning) approach, which introduce the DANN approach to predict failure in heterogeneous disk systems by reducing the distribution differences between different models of disk datasets. This approach only needs unlabeled data (the target domain) of a specific disk model and the labeled data (the source domain) collected from a different disk model from the same manufacturer. Experimental results on real-world datasets demonstrate that DFPTL can achieve adaptation effect in the presence of domain shifts and outperform traditional machine learning algorithms

Analysis of Immunoglobulin Transcripts in the Ostrich Struthio camelus, a Primitive Avian Species

Previous studies on the immunoglobulin (Ig) genes in avian species are limited (mainly to galliformes and anseriformes) but have revealed several interesting features, including the absence of the IgD and Igκ encoding genes, inversion of the IgA encoding gene and the use of gene conversion as the primary mechanism to generate an antibody repertoire. To better understand the Ig genes and their evolutionary development in birds, we analyzed the Ig genes in the ostrich (Struthio camelus), which is one of the most primitive birds. Similar to the chicken and duck, the ostrich expressed only three IgH chain isotypes (IgM, IgA and IgY) and λ light chains. The IgM and IgY constant domains are similar to their counterparts described in other vertebrates. Although conventional IgM, IgA and IgY cDNAs were identified in the ostrich, we also detected a transcript encoding a short membrane-bound form of IgA (lacking the last two CH exons) that was undetectable at the protein level. No IgD or κ encoding genes were identified. The presence of a single leader peptide in the expressed heavy chain and light chain V regions indicates that gene conversion also plays a major role in the generation of antibody diversity in the ostrich. Because the ostrich is one of the most primitive living aves, this study suggests that the distinct features of the bird Ig genes appeared very early during the divergence of the avian species and are thus shared by most, if not all, avian species

Genetic Removal of the CH1 Exon Enables the Production of Heavy Chain-Only IgG in Mice

Nano-antibodies possess great potential in many applications. However, they are naturally derived from heavy chain-only antibodies (HcAbs), which lack light chains and the CH1 domain, and are only found in camelids and sharks. In this study, we investigated whether the precise genetic removal of the CH1 exon of the γ1 gene enabled the production of a functional heavy chain-only IgG1 in mice. IgG1 heavy chain dimers lacking associated light chains were detected in the sera of the genetically modified mice. However, the genetic modification led to decreased expression of IgG1 but increased expression of other IgG subclasses. The genetically modified mice showed a weaker immune response to specific antigens compared with wild type mice. Using a phage-display approach, antigen-specific, single domain VH antibodies could be screened from the mice but exhibited much weaker antigen binding affinity than the conventional monoclonal antibodies. Although the strategy was only partially successful, this study confirms the feasibility of producing desirable nano-bodies with appropriate genetic modifications in mice

Lack of association of the CIITA -168A→G promoter SNP with myasthenia gravis and its role in autoimmunity

<p>Abstract</p> <p>Background</p> <p>The major histocompatibility complex class II transactivator (CIITA) regulates MHC class II gene expression. A promoter SNP -168A→G (rs3087456) has previously been shown to be associated with susceptibility to several immune mediated disorders, including rheumatoid arthritis (RA), multiple sclerosis (MS) and myocardial infarction (MI). Myasthenia gravis (MG) is an autoimmune disorder which has previously been shown to be associated with polymorphisms of several autoimmune predisposing genes, including <it>IL-1</it>, <it>PTPN22</it>, <it>TNF-α </it>and the <it>MHC</it>. In order to determine if allelic variants of rs3087456 increase predisposition to MG, we analyzed this SNP in our Swedish cohort of 446 MG patients and 1866 controls.</p> <p>Results</p> <p>No significant association of the SNP with MG was detected, neither in the patient group as a whole, nor in any clinical subgroup. The vast majority of previous replication studies have also not found an association of the SNP with autoimmune disorders.</p> <p>Conclusions</p> <p>We thus conclude that previous findings with regard to the role of the <it>CIITA </it>-168A→G SNP in autoimmunity may have to be reconsidered.</p