Applied sensor fault detection, identification and data reconstruction based on PCA and SOMNN for industrial systems

The paper presents two readily implementable approaches for Sensor Fault Detection, Identification (SFD/I) and faulted sensor data reconstruction in complex systems, in real-time. Specifically, Principal Component Analysis (PCA) and Self-Organizing Map Neural Networks (SOMNNs) are demonstrated for use on industrial turbine systems. In the first approach, Squared Prediction Error (SPE) based on the PCA residual space is used for SFD. SPE contribution plot is employed for SFI. A missing value approach from an extension of PCA is applied for faulted sensor data reconstruction. In the second approach, SFD is performed by SOMNN based Estimation Error (EE), and SFI is achieved by EE contribution plot. Data reconstruction is based on an extension of the SOMNN algorithm. The results are compared in each examining stage. The validation of both approaches is demonstrated through experimental data during the commissioning of an industrial 15MW turbine

Effects of steroid hormones on lipid metabolism in sexual dimorphism: A Mendelian randomization study

BackgroundAlthough the role of steroid hormones in lipid levels has been partly discussed in the context of separate sexes, the causal relationship between steroid hormones and lipid metabolism according to sex has not been elucidated because of the limitations of observational studies. We assessed the relationship between steroid hormones and lipid metabolism in separate sexes using a two-sample Mendelian randomization (MR) study.MethodsInstrumental variables for dehydroepiandrosterone sulfate (DHEAS), progesterone, estradiol, and androstenedione were selected. MR analysis was performed using inverse-variance weighted, MR-Egger, weighted median, and MR pleiotropy residual sum and outlier tests. Cochran’s Q test, the MR-Egger intercept test, and leave-one-out analysis were used for sensitivity analyses.ResultsThe results showed that the three steroid hormones affected lipid metabolism and exhibited sex differences. In males, DHEAS was negatively correlated with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein B (P = 0.007; P = 0.006; P = 0.041, respectively), and progesterone was negatively correlated with TC and LDL-C (P = 0.019; P = 0.038, respectively). In females, DHEAS was negatively correlated with TC (P = 0.026) and androstenedione was negatively correlated with triglycerides and apolipoprotein A (P = 0.022; P = 0.009, respectively). No statistically significant association was observed between the estradiol levels and lipid metabolism in male or female participants.ConclusionsOur findings identified sex-specific causal networks between steroid hormones and lipid metabolism. Steroid hormones, including DHEAS, progesterone, and androstenedione, exhibited beneficial effects on lipid metabolism in both sexes; however, the specific lipid profiles affected by steroid hormones differed between the sexes

The expression patterns and correlations of claudin-6, methy-CpG binding protein 2, DNA methyltransferase 1, histone deacetylase 1, acetyl-histone H3 and acetyl-histone H4 and their clinicopathological significance in breast invasive ductal carcinomas

<p>Abstract</p> <p>Background</p> <p>Claudin-6 is a candidate tumor suppressor gene in breast cancer, and has been shown to be regulated by DNA methylation and histone modification in breast cancer lines. However, the expression of claudin-6 in breast invasive ductal carcinomas and correlation with clinical behavior or expression of other markers is unclear. We considered that the expression pattern of claudin-6 might be related to the expression of DNA methylation associated proteins (methyl-CpG binding protein 2 (MeCP2) and DNA methyltransferase 1 (DNMT1)) and histone modification associated proteins (histone deacetylase 1 (HDAC1), acetyl-histone H3 (H3Ac) and acetyl- histone H4 (H4Ac)).</p> <p>Methods</p> <p>We have investigated the expression of claudin-6, MeCP2, HDAC1, H3Ac and H4Ac in 100 breast invasive ductal carcinoma tissues and 22 mammary gland fibroadenoma tissues using immunohistochemistry.</p> <p>Results</p> <p>Claudin-6 protein expression was reduced in breast invasive ductal carcinomas (<it>P </it>< 0.001). In contrast, expression of MeCP2 (<it>P </it>< 0.001), DNMT1 (<it>P </it>= 0.001), HDAC1 (<it>P </it>< 0.001) and H3Ac (<it>P </it>= 0.004) expressions was increased. Claudin-6 expression was inversely correlated with lymph node metastasis (<it>P </it>= 0.021). Increased expression of HDAC1 was correlated with histological grade (<it>P </it>< 0.001), age (<it>P </it>= 0.004), clinical stage (<it>P </it>= 0.007) and lymph node metastasis (<it>P </it>= 0.001). H3Ac expression was associated with tumor size (<it>P </it>= 0.044) and clinical stage of cancers (<it>P </it>= 0.034). MeCP2, DNMT1 and H4Ac expression levels did not correlate with any of the tested clinicopathological parameters (<it>P </it>> 0.05). We identified a positive correlation between MeCP2 protein expression and H3Ac and H4Ac protein expression.</p> <p>Conclusions</p> <p>Our results show that claudin-6 protein is significantly down-regulated in breast invasive ductal carcinomas and is an important correlate with lymphatic metastasis, but claudin-6 down-regulation was not correlated with upregulation of the methylation associated proteins (MeCP2, DNMT1) or histone modification associated proteins (HDAC1, H3Ac, H4Ac). Interestingly, the expression of MeCP2 was positively correlated with the expression of H3Ac and H3Ac protein expression was positively correlated with the expression of H4Ac in breast invasive ductal carcinoma</p> <p>Virtual slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/4549669866581452</url></p

AF17 Competes With AF9 for Binding to DOT1A to up-Regulate Transcription of Epithelial NA\u3csup\u3e+\u3c/sup\u3e Channel α

We previously reported that Dot1a*AF9 complex represses transcription of the epithelial Na+ channel subunit α (α-ENaC) gene in mouse inner medullary collecting duct mIMCD3 cells and mouse kidney. Aldosterone relieves this repression by down-regulating the complex through various mechanisms. Whether these mechanisms are sufficient and conserved in human cells or can be applied to other aldosterone-regulated genes remains largely unknown. Here we demonstrate that human embryonic kidney 293T cells express the three ENaC subunits and all of the ENaC transcriptional regulators examined. These cells respond to aldosterone and display benzamil-sensitive Na+ currents, as measured by whole-cell patch clamping. We also show that AF17 and AF9 competitively bind to the same domain of Dot1a in multiple assays and have antagonistic effects on expression of an α-ENaC promoter-luciferase construct. Overexpression of Dot1a or AF9 decreased mRNA expression of the ENaC subunits and their transcriptional regulators and reduced benzamil-sensitive Na+ currents. AF17 over-expression caused the opposite effects, accompanied by redirection of Dot1a from the nucleus to the cytoplasm and reduction in histone H3 K79 methylation. The nuclear export inhibitor leptomycin B blocked the effect of AF17 overexpression on H3 K79 hypomethylation. RNAi-mediated knockdown of AF17 yielded nuclear enrichment of Dot1a and histone H3 K79 hypermethylation. As with AF9, AF17 displays nuclear and cytoplasmic co-localization with Sgk1. Therefore, AF17 competes with AF9 to bind Dot1a, decreases Dot1a nuclear expression by possibly facilitating its nuclear export, and relieves Dot1a*AF9-mediated repression of α-ENaC and other target genes

Identification and Function Prediction of Novel MicroRNAs in Laoshan Dairy Goats

MicroRNAs are a class of endogenous small RNAs that play important roles in post-transcriptional gene regulation by directing degradation of mRNAs or facilitating repression of target gene translation. In this study, three small RNA cDNA libraries from the mammary gland tissues of Laoshan dairy goats (Capra hircus) were constructed and sequenced, individually. Through Solexa high-throughput sequencing and bioinformatics analysis, we obtained 50 presumptive novel miRNAs candidates, and 55,448 putative target genes were predicted. GO annotations and KEGG pathway analyses showed the majority of target genes were involved in various biological processes and metabolic pathways. Our results discovered more information about the regulation network between miRNAs and mRNAs and paved a foundation for the molecular genetics of mammary gland development in goats

Enhancing Localization of Mobile Robots in Distributed Sensor Environments for Reliable Proximity Service Applications

Mobile robots can effectively coordinate information among sensor nodes in a distributed physical proximity. Accurately locating the mobile robots in such a distributed scenario is an essential requirement, such that the mobile robots can be instructed to coordinate with the appropriate sensor nodes. Packet loss is one of the prevailing issues on such wireless sensor network-based mobile robot localization applications. The packet loss might result from node failure, data transmission delay, and communication channel instability, which could significantly affect the transmission quality of the wireless signals. Such issues affect the localization accuracy of the mobile robot applications to an overwhelming margin, causing localization failures. To this end, this paper proposes an improved Unscented Kalman Filter-based localization algorithm to reduce the impacts of packet loss in the localization process. Rather than ignoring the missing measurements caused by packet loss, the proposed algorithm exploits the calculated measurement errors to estimate and compensate for the missing measurements. Some simulation experiments are conducted by subjecting the proposed algorithm with various packet loss rates, to evaluate its localization accuracy. The simulations demonstrate that the average localization error of the robot is 0.39 m when the packet loss rate is less than 90%, and the average running time of each iteration is 0.295 ms. The achieved results show that the proposed algorithm exhibits significant tolerance to packet loss while locating mobile robots in real-time, to achieve reliable localization accuracy and outperforms the existing UKF algorithm