147 research outputs found
Noncoding RNA in the Regulation of Acute Aortic Dissection: From Profile to Mechanism
No full textAortic dissection is a life-threatening condition caused by a tear in the intimal layer of the aorta or bleeding within the aortic wall, resulting in the separation of the layers of the aortic wall. As Nienaber reported, aortic dissection is most common in people 65–75 years old and has an incidence of 35 cases per 100,000 people per year in this population. Many pathogenic factors are involved in aortic dissection, including hypertension, dyslipidemia, and abnormality of the aortic intima caused by genetic variation. However, with the development of gene sequencing and transgenic technology, genetic methods are being used for the diagnosis and treatment of diseases, including acute aortic dissection. Genetic research on acute aortic dissection began around 2006. Recently, research on acute aortic dissection has mainly focused on microRNA (miRNA). Studies have found that miRNA plays a critical regulatory role in the occurrence and development of acute aortic dissection. By regulating miRNA expression, acute aortic dissection can be prevented and treated
Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer
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Analysis of the DNN-kWTA Network Model with Drifts in the Offset Voltages of Threshold Logic Units
No full textDirect regression modelling of high-order moments in big data
No full textBig data problems present great challenges to statistical analyses, especially from the computational side. In this paper, we consider regression estimation of high-order moments in big data problems based on the U-statistic-based Functional Regression Model (U-FRM) model. The U-FRM model is a nonparametric method that allows direct estimation of higher-order moments without imposing parametric assumptions on the high order-moments. Despite this modeling advantage, its estimation relies on a U-statistics based estimating equation whose computational complexity is generally too high for big data. In this paper, we propose using the "divide-and-conquer" strategy to construct a computationally more succinct surrogate estimating equation. Through both theoretical proof and simulations, we show that our method significantly reduces the computational time and meanwhile enjoys the same asymptotic behavior as the original estimation method. We then apply our method to a genomic problem to illustrate its performance on real data.SCI(E)[email protected]; [email protected]
Realization of Fault Tolerance for Spiking Neural Networks with Particle Swarm Optimization
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Model data from large ensemble model experiments with respect to Storm surge
No full textThis is the model data from the submitted manuscript entitled "Storm surge risk under strengthening and accelerating trends of landfalling tropical cyclones".
The major data in this manuscript includes the following four parts:
(1) the track, maximum wind speed (MWS), forward speed (FS), and maximum wind radius (MWR) of tropical cyclones were from the Joint Typhoon Warning Center (JTWC) of the U.S. Naval Pacific Meteorology Oceanography Center in Hawaii.
(2) Four typical constructed windspeeds during Tropical Storm Nida, Typhoon Vicente, Severe Typhoon Hato, and Supertyphoon Mangkhut are from the improved Emanuel [2004] model [Emanuel and Rotunno, 2011].
(3) two long-term observation stations in the PRE and Daya Bay are from the National Marine Data Center (http://mds.nmdis.org.cn/).
(4) 8 statistics of large-ensemble data were simulated by finite-volume community ocean model (FVCOM, Chen et al., [2003])
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