Characterization of system status signals for multivariate time series discretization based on frequency and amplitude variation

Many fault detection methods have been proposed for monitoring the health of various industrial systems. Characterizing the monitored signals is a prerequisite for selecting an appropriate detection method. However, fault detection methods tend to be decided with user???s subjective knowledge or their familiarity with the method, rather than following a predefined selection rule. This study investigates the performance sensitivity of two detection methods, with respect to status signal characteristics of given systems: abrupt variance, characteristic indicator, discernable frequency, and discernable index. Relation between key characteristics indicators from four different real-world systems and the performance of two fault detection methods using pattern recognition are evaluated

FAULT DETECTION AND PREDICTION IN ELECTROMECHANICAL SYSTEMS VIA THE DISCRETIZED STATE VECTOR-BASED PATTERN ANALYSIS OF MULTI-SENSOR SIGNALS

Department of System Design and Control EngineeringIn recent decades, operation and maintenance strategies for industrial applications have evolved from corrective maintenance and preventive maintenance, to condition-based monitoring and eventually predictive maintenance. High performance sensors and data logging technologies have enabled us to monitor the operational states of systems and predict fault occurrences. Several time series analysis methods have been proposed in the literature to classify system states via multi-sensor signals. Since the time series of sensor signals is often characterized as very-short, intermittent, transient, highly nonlinear, and non-stationary random signals, they make time series analyses more complex. Therefore, time series discretization has been popularly applied to extract meaningful features from original complex signals. There are several important issues to be addressed in discretization for fault detection and prediction: (i) What is the fault pattern that represents a system???s faulty states, (ii) How can we effectively search for fault patterns, (iii) What is a symptom pattern to predict fault occurrences, and (iv) What is a systematic procedure for online fault detection and prediction. In this regard, this study proposes a fault detection and prediction framework that consists of (i) definition of system???s operational states, (ii) definitions of fault and symptom patterns, (iii) multivariate discretization, (iv) severity and criticality analyses, and (v) online detection and prediction procedures. Given the time markers of fault occurrences, we can divide a system???s operational states into fault and no-fault states. We postulate that a symptom state precedes the occurrence of a fault within a certain time period and hence a no-fault state consists of normal and symptom states. Fault patterns are therefore found only in fault states, whereas symptom patterns are either only found in the system???s symptom states (being absent in the normal states) or not found in the given time series, but similar to fault patterns. To determine the length of a symptom state, we present a symptom pattern-based iterative search method. In order to identify the distinctive behaviors of multi-sensor signals, we propose a multivariate discretization approach that consists mainly of label definition, label specification, and event codification. Discretization parameters are delicately controlled by considering the key characteristics of multi-sensor signals. We discuss how to measure the severity degrees of fault and symptom patterns, and how to assess the criticalities of fault states. We apply the fault and symptom pattern extraction and severity assessment methods to online fault detection and prediction. Finally, we demonstrate the performance of the proposed framework through the following six case studies: abnormal cylinder temperature in a marine diesel engine, automotive gasoline engine knockings, laser weld defects, buzz, squeak, and rattle (BSR) noises from a car door trim (using a typical acoustic sensor array and using acoustic emission sensors respectively), and visual stimuli cognition tests by the P300 experiment.ope

Self-care use patterns in the UK, US, Australia, and Japan: a multinational web-based survey

AbstractBackgroundThe trend toward patient- or consumer-centered healthcare has been accelerated by advances in technology, consumer empowerment, and a shift from infectious to chronic diseases. The purpose of this study was to examine the growing self-care market by analyzing self-care patterns.MethodsWe conducted a cross-sectional, web-based survey involving adults from nine major cities in the UK, the USA, Australia, and Japan. This study examined the extent and frequency of self-care, self-care expenditure, sources of self-care information, and reasons for self-care in each country.ResultsThe results showed that the prevalence of self-care was highest in Japan (54.9%), followed by the UK (43.1%), the USA (42.5%), and Australia (40.4%). The primary reason for practicing self-care was “to manage my healthcare myself” (cited by 45.7%, 59.5%, 49.2%, and 4.1% of participants in Australia, Japan, the UK, and the USA, respectively). Significant linear associations were observed between age and the prevalence of self-care in all countries (p<0.05), indicating that self-care prevalence decreased with age in the UK, the USA, and Australia, and increased with age in Japan. The frequency with which self-care was practiced was positively correlated with age in the USA (p<0.05), Australia (p<0.01), and Japan (p<0.05). In addition to acquaintances, internet search engines and information obtained from pharmacies were considered reliable and widely used sources of self-care information.ConclusionWhen developing self-care products or services, healthcare providers and policymakers should consider self-care patterns

Effect of rutin from tartary buckwheat sprout on serum glucose-lowering in animal model of type 2 diabetes

This study investigates the anti-diabetic effects of rutin from tartary buckwheat sprout in type 2 diabetes mouse model. The rutin content in tartary buckwheat sprout (TBS) is five times higher than that found in common buckwheat sprout (CBS) as evident from high-performance liquid chromatography analysis. Administration of either rutin or TBS ethanolic extract to diabetes mice decreased the serum glucose level significantly. Rutin down-regulated the expression levels of protein-tyrosine phosphatase 1B; negative regulator of insulin pathway, both transcriptionally and translationally in myocyte C2C12 in a dose-dependent manner. In conclusion, rutin can play a critical role in down-regulation of serum glucose level in type 2 diabetes

A prion-like domain in ELF3 functions as a thermosensor in Arabidopsis.

Temperature controls plant growth and development, and climate change has already altered the phenology of wild plants and crops1. However, the mechanisms by which plants sense temperature are not well understood. The evening complex is a major signalling hub and a core component of the plant circadian clock2,3. The evening complex acts as a temperature-responsive transcriptional repressor, providing rhythmicity and temperature responsiveness to growth through unknown mechanisms2,4-6. The evening complex consists of EARLY FLOWERING 3 (ELF3)4,7, a large scaffold protein and key component of temperature sensing; ELF4, a small α-helical protein; and LUX ARRYTHMO (LUX), a DNA-binding protein required to recruit the evening complex to transcriptional targets. ELF3 contains a polyglutamine (polyQ) repeat8-10, embedded within a predicted prion domain (PrD). Here we find that the length of the polyQ repeat correlates with thermal responsiveness. We show that ELF3 proteins in plants from hotter climates, with no detectable PrD, are active at high temperatures, and lack thermal responsiveness. The temperature sensitivity of ELF3 is also modulated by the levels of ELF4, indicating that ELF4 can stabilize the function of ELF3. In both Arabidopsis and a heterologous system, ELF3 fused with green fluorescent protein forms speckles within minutes in response to higher temperatures, in a PrD-dependent manner. A purified fragment encompassing the ELF3 PrD reversibly forms liquid droplets in response to increasing temperatures in vitro, indicating that these properties reflect a direct biophysical response conferred by the PrD. The ability of temperature to rapidly shift ELF3 between active and inactive states via phase transition represents a previously unknown thermosensory mechanism

Exploring aryl hydrocarbon receptor expression and distribution in the tumor microenvironment, with a focus on immune cells, in various solid cancer types

IntroductionAryl hydrocarbon receptor (AhR) is a transcription factor that performs various functions upon ligand activation. Several studies have explored the role of AhR expression in tumor progression and immune surveillance. Nevertheless, investigations on the distribution of AhR expression, specifically in cancer or immune cells in the tumor microenvironment (TME), remain limited. Examining the AhR expression and distribution in the TME is crucial for gaining insights into the mechanism of action of AhR-targeting anticancer agents and their potential as biomarkers.MethodsHere, we used multiplexed immunohistochemistry (mIHC) and image cytometry to investigate the AhR expression and distribution in 513 patient samples, of which 292 are patients with one of five solid cancer types. Additionally, we analyzed the nuclear and cytosolic distribution of AhR expression.ResultsOur findings reveal that AhR expression was primarily localized in cancer cells, followed by stromal T cells and macrophages. Furthermore, we observed a positive correlation between the nuclear and cytosolic expression of AhR, indicating that the expression of AhR as a biomarker is independent of its localization. Interestingly, the expression patterns of AhR were categorized into three clusters based on the cancer type, with high AhR expression levels being found in regulatory T cells (Tregs) in non-small cell lung cancer (NSCLC).DiscussionThese findings are anticipated to serve as pivotal evidence for the design of clinical trials and the analysis of the anticancer mechanisms of AhR-targeting therapies

YH29407 with anti-PD-1 ameliorates anti-tumor effects via increased T cell functionality and antigen presenting machinery in the tumor microenvironment

Among cancer cells, indoleamine 2, 3-dioxygenase1 (IDO1) activity has been implicated in improving the proliferation and growth of cancer cells and suppressing immune cell activity. IDO1 is also responsible for the catabolism of tryptophan to kynurenine. Depletion of tryptophan and an increase in kynurenine exert important immunosuppressive functions by activating regulatory T cells and suppressing CD8+ T and natural killer (NK) cells. In this study, we compared the anti-tumor effects of YH29407, the best-in-class IDO1 inhibitor with improved pharmacodynamics and pharmacokinetics, with first and second-generation IDO1 inhibitors (epacadostat and BMS-986205, respectively). YH29407 treatment alone and anti-PD-1 (aPD-1) combination treatment induced significant tumor suppression compared with competing drugs. In particular, combination treatment showed the best anti-tumor effects, with most tumors reduced and complete responses. Our observations suggest that improved anti-tumor effects were caused by an increase in T cell infiltration and activity after YH29407 treatment. Notably, an immune depletion assay confirmed that YH29407 is closely related to CD8+ T cells. RNA-seq results showed that treatment with YH29407 increased the expression of genes involved in T cell function and antigen presentation in tumors expressing ZAP70, LCK, NFATC2, B2M, and MYD88 genes. Our results suggest that an IDO1 inhibitor, YH29407, has enhanced PK/PD compared to previous IDO1 inhibitors by causing a change in the population of CD8+ T cells including infiltrating T cells into the tumor. Ultimately, YH29407 overcame the limitations of the competing drugs and displayed potential as an immunotherapy strategy in combination with aPD-1

Multivariate discretization via dynamic time segment for fault pattern extraction

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Abrupt variance and discernibility analyses of multi-sensor signals for fault pattern extraction

High performance sensors and data logging technologies have enabled us to monitor the operational states of systems and predict fault occurrences. Because a large number of sensors are monitored, it is important to identify significant sensor signals for detecting faults. As signal behaviors become increasingly scattered and complex, it is difficult to investigate the gradient relationship with the operational states of a system; therefore, we analyze the abrupt variance and the discernibility index of multi-sensor signals by extending the conventional statistical variance and the Fisher criterion. Based on the two novel characteristics of sensor signals, we select the most significant sensors to detect abnormal cylinder temperature and engine knocking. Thus, the proposed analyses lead to improved detection results when the multi-sensor signals existed in multiple overlapping regions regardless of the operational states