The rod photoreceptor-specific neural retina leucine zipper protein Nrl is essential for rod differentiation and plays a critical role in regulating gene expression. In the mouse retina, rods account for 97% of the photoreceptors; however, in the absence of Nrl (Nrl-/-), no rods are present and a concomitant increase in cones is observed. A functional all-cone mouse retina represents a unique opportunity to investigate, at the molecular level, differences between the two photoreceptor subtypes. Using mouse GeneChips (Affymetrix), we have generated expression profiles of the wild-type and Nrl-/- retina at three time-points representing distinct stages of photoreceptor differentiation. Comparative data analysis revealed 161 differentially expressed genes; of which, 78 exhibited significantly lower and 83 higher expression in the Nrl-/- retina. Hierarchical clustering was utilized to predict the function of these genes in a temporal context. The differentially expressed genes primarily encode proteins associated with signal transduction, transcriptional regulation, intracellular transport and other processes, which likely correspond to differences between rods and cones and/or retinal remodeling in the absence of rods. A significant number of these genes may serve as candidates for diseases involving rod or cone dysfunction. Chromatin immunoprecipitation assay showed that in addition to the rod phototransduction genes, Nrl might modulate the promoters of many functionally diverse genes in vivo. Our studies provide molecular insights into differences between rod and cone function, yield interesting candidates for retinal diseases and assist in identifying transcriptional regulatory targets of Nrl.
The Kullback information criterion KIC is a recently developed tool for statistical model selection [I]. KIC serves as an asymptotically unbiased estimator of a variant of the Kullback symmetric divergence, known also as J-divergence. In this paper a bias correction of the Kullback symmetric information criterion is derived for linear models. The conection is of particular use when the sample size is small or when the number of fitted parameters is of moderate to large fraction of the sample sire. For linear regression models, the corrected method called KICc is an exucrly unhiased estimator of a variant of the Kullback symmetric divergence between the true unknown model and the candidate fitted model. Furthermore KICc is found to provide better model order choice than any other asymptotically efficient methods in an application to autoregressive time series models. measures, it functions as a gauge of model disparity, which is arguably more sensitive than either of its individual component. Following the above reasoning, Cavanaugh [ I ] proposed the Kullback information criterion KIC as an asymptotically unbiased estimate of a variant (within a constant) of the J-divergence between the true unknown model and the fitted approximating model. Motivated by the above developments, we propose a bias corrected version of the KIC for linear regression models. The new criterion is shown to outperform classical criteria in a small sample autoregressive modeling. The remainder of this paper is organized as follows. In section 2 we present a short overview of Kullback's directed divergence, AIC, its corrected version AlCc and KIC. In section 3 we introduce the bias corrected version of KIC. Section 4 presents simulation results for autoregressive model selection. We end up by concluding remarks.
The development of therapeutic applications of ultrasound depends notably on the availability of high-performance transducers. New piezocomposite technologies offer performances that have proved to be particularly well adapted for such applications thanks to high power density generation with high efficiency. Moreover this technology enables a wide variety of shapes and the design of array transducers for electronic focusing, scanning and steering of the beam. This article details these advantages as well as other interests such as a large bandwidth or the MRI compatibility allowing the imaging / therapy association. Furthermore, the feasibility of highly focused transducers and complex array structures will be illustrated through various examples.
This paper deals with the problem of adaptive reconstruction and identification of AR processes with randomly missing observations. A new real time algorithm is proposed. It uses combined pseudo-linear RLS algorithm and Kalman filter. It offers an unbiased estimation of the AR parameters and an optimal reconstruction error in the least mean square sense. In addition, thanks to the pseudo-linear RLS identification, this algorithm can be used for the identification of non stationary AR signals. Moreover, simplifications of the algorithm reduces the calculation time, thus this algorithm can be used in real time applications.
This paper introduces a statistical methodology for the identification of differentially expressed genes in DNA microarray experiments based on multiple criteria. These criteria are false discovery rate (FDR), variance-normalized differential expression levels (paired t statistics), and minimum acceptable difference (MAD). The methodology also provides a set of simultaneous FDR confidence intervals on the true expression differences. The analysis can be implemented as a two-stage algorithm in which there is an initial screen that controls only FDR, which is then followed by a second screen which controls both FDR and MAD. It can also be implemented by computing and thresholding the set of FDR P values for each gene that satisfies the MAD criterion. We illustrate the procedure to identify differentially expressed genes from a wild type versus knockout comparison of microarray data
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