Towards the identification of a genetic basis for Landau-Kleffner syndrome.

OBJECTIVE: To establish the genetic basis of Landau-Kleffner syndrome (LKS) in a cohort of two discordant monozygotic (MZ) twin pairs and 11 isolated cases. METHODS: We used a multifaceted approach to identify genetic risk factors for LKS. Array comparative genomic hybridization (CGH) was performed using the Agilent 180K array. Whole genome methylation profiling was undertaken in the two discordant twin pairs, three isolated LKS cases, and 12 control samples using the Illumina 27K array. Exome sequencing was undertaken in 13 patients with LKS including two sets of discordant MZ twins. Data were analyzed with respect to novel and rare variants, overlapping genes, variants in reported epilepsy genes, and pathway enrichment. RESULTS: A variant (cG1553A) was found in a single patient in the GRIN2A gene, causing an arginine to histidine change at site 518, a predicted glutamate binding site. Following copy number variation (CNV), methylation, and exome sequencing analysis, no single candidate gene was identified to cause LKS in the remaining cohort. However, a number of interesting additional candidate variants were identified including variants in RELN, BSN, EPHB2, and NID2. SIGNIFICANCE: A single mutation was identified in the GRIN2A gene. This study has identified a number of additional candidate genes including RELN, BSN, EPHB2, and NID2. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here.

Application of the Mutual Information Minimization to speaker recognition / identification improvement

In this paper we propose the inversion of nonlinear distortions in order to improve the recognition rates of a speaker recognizer system. We study the effect of saturations on the test signals, trying to take into account real situations where the training material has been recorded in a controlled situation but the testing signals present some mismatch with the input signal level (saturations). The experimental results for speaker recognition shows that a combination of several strategies can improve the recognition rates with saturated test sentences from 80% to 89.39%, while the results with clean speech (without saturation) is 87.76% for one microphone, and for speaker identification can reduce the minimum detection cost function with saturated test sentences from 6.42% to 4.15%, while the results with clean speech (without saturation) is 5.74% for one microphone and 7.02% for the other one.

Blind channel deconvolution of real world signals using source separation techniques

In this paper we present a method for blind deconvolution of linear channels based on source separation techniques, for real word signals. This technique applied to blind deconvolution problems is based in exploiting not the spatial independence between signals but the temporal independence between samples of the signal. Our objective is to minimize the mutual information between samples of the output in order to retrieve the original signal. In order to make use of use this idea the input signal must be a non-Gaussian i.i.d. signal. Because most real world signals do not have this i.i.d. nature, we will need to preprocess the original signal before the transmission into the channel. Likewise we should assure that the transmitted signal has non-Gaussian statistics in order to achieve the correct function of the algorithm. The strategy used for this preprocessing will be presented in this paper. If the receiver has the inverse of the preprocess, the original signal can be reconstructed without the convolutive distortion.

Identification and staining of distinct populations of secretory organelles in astrocytes.

Increasing evidence indicates that astrocytes, the most abundant glial cell type in the brain, respond to an elevation in cytoplasmic calcium concentration ([Ca(2+)]i) by releasing chemical transmitters (also called gliotransmitters) via regulated exocytosis of heterogeneous classes of organelles. By this process, astrocytes exert modulatory influences on neighboring cells and are thought to participate in the control of synaptic circuits and cerebral blood flow. Studying the properties of exocytosis in astrocytes is a challenge, because the cell biological basis of this process is incompletely defined. Astrocytic exocytosis involves multiple populations of secretory vesicles, including synaptic-like microvesicles (SLMVs), dense-core granules (DCGs), and lysosomes. Here we summarize the available information for identifying individual populations of secretory organelles in astrocytes, including DCGs, SLMVs, and lysosomes, and present experimental procedures for specifically staining such populations.