Lidar is an active remote sensing technology that has many advantages, but the echo lidar signal extremely susceptible to noise and complex atmospheric environment, which affects effective detection range retrieval accuracy. In this paper, a wavelet transform (WT) locally weighted scatterplot smoothing (LOWESS) based on ensemble empirical mode decomposition (EEMD) for Rayleigh denoising was pro...

When using cDNA microarrays, normalization to correct labeling bias is a common preliminary step before further data analysis is applied, its objective being to reduce the variation between arrays. To date, assessment of the effectiveness of normalization has mainly been confined to the ability to detect differentially expressed genes. Since a major use of microarrays is the expression-based ph...

Cerebral metabolism is critically dependent on the regulation of cerebral blood flow (CBF), so it would be expected that vascular mechanisms that play a critical role in CBF regulation would be tightly conserved across individuals. However, the relationships between blood pressure (BP) and cerebral blood velocity fluctuations exhibit inter-individual variations consistent with heterogeneity in ...

Project Abstract : This thesis aims to develop better tools for microarray data analysis, thus reducing the false discovery rate. Microarray experiments have many sources of systematic variation which affect the measured gene expression levels. Normalization is the name of the process which is used to describe the process of removing such variation. Out of all the existing normalization process...

In this paper a robust data-driven procedure for decomposing seasonal time series based on a generalized Berlin Method (BV, Berliner Verfahren) as proposed by Heiler and Michels (1994) is discussed. The basic robust algorithm used here is an adaptation of the LOWESS (LOcally Weighted Scatterplot Smoothing) procedure (Cleveland, 1979). For selecting the optimal bandwidth the simple double smooth...

This paper investigates subset normalization to adjust for location biases (e.g., splotches) combined with global normalization for intensity biases (e.g., saturation). A data set from a toxicogenomic experiment using the same control and the same treated sample hybridized to six different microarrays is used to contrast the different normalization methods. Simple t-tests were used to compare t...

Acquisition of microarray data is prone to systematic errors. A correction, called normalisation, must be applied to the data before further analysis is performed. With many normalisation techniques published and in use, the best way of executing this correction remains an open question. In this study, a variety of single-slide normalisation techniques, and different parameter settings for thes...

Background and aims There seems to be a U-shaped relation between hemoglobin level ischemic stroke severity. We aimed explore possible causes of this relation. Methods All patients with 2006 2016 admitted within 3 hours onset were included. Hemoglobin NIHSS score obtained on admission. Modified Rankin Scale (mRS) was day 7. Locally weighted scatterplot smoothing (lowess smoother) curves display...

Some normalization methods assume that the two channels of intensities are related by a constant so that Microarray experiments allow RNA level measurements for many the center of the distribution of the log of the ratio is genes in multiple samples. However, mining the biological information shifted to zero. Chen et al. (1997) proposed an iterative from the large sets of data generated by micr...

The overexpression of recombinant proteins in microorganisms may lead to a metabolic depression or collapse of the cell factory. In order to understand this process and to optimize the cellular productivity the stress response was investigated. The expression of the recombinant human superoxide dismutase (SOD) was induced under steady state conditions and the expression of all 4289 protein codi...