Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms

Dynamic Python-Based Method Provides Quantitative Analysis of Intercellular Junction Organization During S. pneumoniae Infection of the Respiratory Epithelium

Many respiratory pathogens compromise epithelial barrier function during lung infection by disrupting intercellular junctions, such as adherens junctions and tight junctions, that maintain intercellular integrity. This includes Streptococcus pneumoniae, a leading cause of pneumonia, which can successfully breach the epithelial barrier and cause severe infections such as septicemia and meningitis. Fluorescence microscopy analysis on intercellular junction protein manipulation by respiratory pathogens has yielded major advances in our understanding of their pathogenesis. Unfortunately, a lack of automated image analysis tools that can tolerate variability in sample-sample staining has limited the accuracy in evaluating intercellular junction organization quantitatively. We have created an open source, automated Python computer script called “Intercellular Junction Organization Quantification” or IJOQ that can handle a high degree of sample-sample staining variability and robustly measure intercellular junction integrity. In silico validation of IJOQ was successful in analyzing computer generated images containing varying degrees of simulated intercellular junction disruption. Accurate IJOQ analysis was further confirmed using images generated from in vitro and in vivo bacterial infection models. When compared in parallel to a previously published, semi-automated script used to measure intercellular junction organization, IJOQ demonstrated superior analysis for all in vitro and in vivo experiments described herein. These data indicate that IJOQ is an unbiased, easy-to-use tool for fluorescence microscopy analysis and will serve as a valuable, automated resource to rapidly quantify intercellular junction disruption under diverse experimental conditions

Independent causal effect of migraines on Alzheimer’s disease risk: a multivariate Mendelian randomization study

BackgroundThe observational studies investigated the impact of migraine on Alzheimer’s Disease (AD). However, these findings were limited by confounding factors and reverse causation, leading to contradictory results.MethodsWe utilized Univariable Mendelian Randomization (UVMR) to explore the link between migraine (13,971 cases/470,627 controls) and AD risk (Bellenguez et al., 39,106 cases/46,828 controls; FinnGen, 111,471 cases/111,471 controls). Meta-analysis was performed for comprehensive synthesis. Employing Multivariable Mendelian Randomization (MVMR), we created models incorporating migraine and 35 potential AD risk factors, examining migraine’s independent impact on AD onset risk under considering these factors.ResultsThe meta-analysis of inverse variance weighted MR results, combining data from Bellenguez et al. (odds ratio (OR) [95% confidence interval (CI)]: 1.5717 [1.1868–2.0814], p = 0.0016) and FinnGen (OR [95% CI]: 1.2904 [0.5419–3.0730], p = 0.5646), provided evidence for a causal relationship between genetically predicted migraine and the heightened risk of AD occurrence (OR [95% CI]: 1.54 [1.18, 2.00], p < 0.01). After adjusting for Diastolic blood pressure (OR [95% CI]: 1.4120 [0.8487–2.3493], p = 0.1840) and Tumor necrosis factor alpha (OR [95% CI]: 1.2411 [0.8352–1.8443], p = 0.2852), no discernible association was detected between migraine and the risk of AD.ConclusionThis study offers compelling evidence indicating a significant correlation between genetically predicted migraine and an elevated risk of AD

Long Noncoding RNA Can Be a Probable Mechanism and a Novel Target for Diagnosis and Therapy in Fragile X Syndrome

Fragile X syndrome (FXS) is the most common congenital hereditary disease of low intelligence after Down syndrome. Its main pathogenic gene is fragile X mental retardation 1 (FMR1) gene associated with intellectual disability, autism, and fragile X-related primary ovarian insufficiency (FXPOI) and fragile X-associated tremor/ataxia syndrome (FXTAS). FMR1 gene transcription leads to the absence of fragile X mental retardation protein (FMRP). How to relieve or cure disorders associated with FXS has also become a clinically disturbing problem. Previous studies have recently shown that long noncoding RNAs (lncRNAs) contribute to the pathogenesis. And it has been identified that several lncRNAs including FMR4, FMR5, and FMR6 contribute to developing FXPOI/FXTAS, originating from the FMR1 gene locus. FMR4 is a product of RNA polymerase II and can regulate the expression of relevant genes during differentiation of human neural precursor cells. FMR5 is a sense-oriented transcript while FMR6 is an antisense lncRNA produced by the 3′ UTR of FMR1. FMR6 is likely to contribute to developing FXPOI, and it overlaps exons 15–17 of FMR1 as well as two microRNA binding sites. Additionally, BC1 can bind FMRP to form an inhibitory complex and lncRNA TUG1 also can control axonal development by directly interacting with FMRP through modulating SnoN–Ccd1 pathway. Therefore, these lncRNAs provide pharmaceutical targets and novel biomarkers. This review will: (1) describe the clinical manifestations and traditional pathogenesis of FXS and FXTAS/FXPOI; (2) summarize what is known about the role of lncRNAs in the pathogenesis of FXS and FXTAS/FXPOI; and (3) provide an outlook of potential effects and future directions of lncRNAs in FXS and FXTAS/FXPOI researches

Framework to Create Cloud-Free Remote Sensing Data Using Passenger Aircraft as the Platform

Cloud removal in optical remote sensing imagery is essential for many Earth observation applications.Due to the inherent imaging geometry features in satellite remote sensing, it is impossible to observe the ground under the clouds directly; therefore, cloud removal algorithms are always not perfect owing to the loss of ground truth. Passenger aircraft have the advantages of short visitation frequency and low cost. Additionally, because passenger aircraft fly at lower altitudes compared to satellites, they can observe the ground under the clouds at an oblique viewing angle. In this study, we examine the possibility of creating cloud-free remote sensing data by stacking multi-angle images captured by passenger aircraft. To accomplish this, a processing framework is proposed, which includes four main steps: 1) multi-angle image acquisition from passenger aircraft, 2) cloud detection based on deep learning semantic segmentation models, 3) cloud removal by image stacking, and 4) image quality enhancement via haze removal. This method is intended to remove cloud contamination without the requirements of reference images and pre-determination of cloud types. The proposed method was tested in multiple case studies, wherein the resultant cloud- and haze-free orthophotos were visualized and quantitatively analyzed in various land cover type scenes. The results of the case studies demonstrated that the proposed method could generate high quality, cloud-free orthophotos. Therefore, we conclude that this framework has great potential for creating cloud-free remote sensing images when the cloud removal of satellite imagery is difficult or inaccurate

Ergosterol Alleviates Kidney Injury in Streptozotocin-Induced Diabetic Mice

permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Ergosterol (ERG) has been widely used in the development of novel drugs due to its unique physiological function. However, little is known about the protective effects of ERG on diabetes. Hence, the current study was designed to evaluate the positive role of ergosterol on streptozotocin- (STZ-) induced diabetes in mice. Oral glucose tolerance test (OGTT) was carried out to assess blood glucose level. Biochemical parameters such as uric acid, creatinine, serum insulin, triglycerides (TG), and total cholesterol (TC) were alsomeasured. Pathological condition of kidney was examined by hematoxylin-eosin (H&E) staining.The expressions of PI3K, p-PI3K, Akt, p-Akt, NF