<div>Abstract<p>Pancreatic cancer is among the most well-characterized cancer types, yet a large proportion of the heritability of pancreatic cancer risk remains unclear. Here, we performed a large transcriptome-wide association study to systematically investigate associations between genetically predicted gene expression in normal pancreas tissue and pancreatic cancer risk. Using data from 305 subjects of mostly European descent in the Genotype-Tissue Expression Project, we built comprehensive genetic models to predict normal pancreas tissue gene expression, modifying the UTMOST (unified test for molecular signatures). These prediction models were applied to the genetic data of 8,275 pancreatic cancer cases and 6,723 controls of European ancestry. Thirteen genes showed an association of genetically predicted expression with pancreatic cancer risk at an FDR ≤ 0.05, including seven previously reported genes (<i>INHBA, SMC2, ABO, PDX1, RCCD1, CFDP1</i>, and <i>PGAP3</i>) and six novel genes not yet reported for pancreatic cancer risk [6q27: <i>SFT2D1</i> OR (95% confidence interval (CI), 1.54 (1.25–1.89); 13q12.13: <i>MTMR6</i> OR (95% CI), 0.78 (0.70–0.88); 14q24.3: <i>ACOT2</i> OR (95% CI), 1.35 (1.17–1.56); 17q12: <i>STARD3</i> OR (95% CI), 6.49 (2.96–14.27); 17q21.1: <i>GSDMB</i> OR (95% CI), 1.94 (1.45–2.58); and 20p13: <i>ADAM33</i> OR (95% CI): 1.41 (1.20–1.66)]. The associations for 10 of these genes (<i>SFT2D1, MTMR6, ACOT2, STARD3, GSDMB, ADAM33, SMC2, RCCD1, CFDP1</i>, and <i>PGAP3</i>) remained statistically significant even after adjusting for risk SNPs identified in previous genome-wide association study. Collectively, this analysis identified novel candidate susceptibility genes for pancreatic cancer that warrant further investigation.</p>Significance:<p>A transcriptome-wide association analysis identified seven previously reported and six novel candidate susceptibility genes for pancreatic cancer risk.</p></div>
<p>Association results after the adjustment for nearby GWAS index SNPs for genes with predicted gene expression levels associated with ovarian cancer risk at P < 2.21E-6.</p>
Abstract: Based on the cement hydration kinetics model proposed by R.Berliner, taking into account the factors such as each chemical phase of minerals, curing temperature, water-cement ratio, the final hydration degree and fineness of cement, a theoretical hydration kinetics equations was established in this paper. It can be used to predict the hydration rate and the change of hydration degree.
ABSTRACT Batteries are prevalent energy storage devices, and their failures can cause huge losses such as the shutdown of entire systems. Therefore, the prognostic health management of batteries to increase their availability is highly desirable. This work focuses on improving the serviceability of batteries for wireless sensor networks (WSNs) deployed in remote and hard‐to‐reach places. We propose an active management strategy such that the batteries in a network will attain similar end‐of‐life times, in addition to lifetime extension. The fundamental idea is to adaptively adjust the node quality‐of‐service (QoS) to actively manage their degradation processes, while ensuring a minimum level of network QoS. The framework first executes a prognostic algorithm that can predict the remaining useful life (RUL) of a battery, given its assigned node‐level QoS. A Bayesian optimization framework with an augmented Lagrangian method has been adopted to efficiently solve the developed black‐box constrained optimization problem. A Matlab Simulink model based on a truss bridge structure health monitoring network is built considering the battery aging and temperature effects. Compared with the benchmark models, the proposed strategy demonstrates a more extended network lifespan and uniform working time ratio.
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