We propose a new Blind Source Separation technique for whole-brain activity estimation that best profits from FMRI's intrinsic spatial sparsity. The Local Sparse Component Analysis (LSCA) combines wavelet analysis...
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Background: The number and size of tree topologies that are being compared by phylogenetic systematists is increasing due to technological advancements in high-throughput DNA sequencing. However, we still lack tools t...
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To overcome the limitations of independent component analysis (ICA), today’s most popular analysis tool for investigating whole-brain spatial activation in resting state functional magnetic resonance imaging (fMRI), ...
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We propose a new Blind Source Separation technique for whole-brain activity estimation that best profits from FMRI's intrinsic spatial sparsity. The Local Sparse Component Analysis (LSCA) combines wavelet analysis...
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We propose a new Blind Source Separation technique for whole-brain activity estimation that best profits from FMRI's intrinsic spatial sparsity. The Local Sparse Component Analysis (LSCA) combines wavelet analysis, group-separable regularizers, contiguity-constrained clusterization and principal components analysis (PCA) into a unique spatial sparse representation of FMRI images towards efficient dimensionality reduction without sacrificing physiological characteristics by avoiding artificial stochastic model constraints. The LSCA outperforms classical PCA source reconstruction for artificial data sets over many noise levels. A real FMRI data illustration reveals resting-state activities in regions hard to observe, such as thalamus and basal ganglia, because of their small spatial scale.
Resting-state Functional Magnetic Resonance Imaging (FMRI) analysis has consistently shown the presence of specific spatial activation patterns. Independent component analysis (ICA) has been the analysis algorithm of ...
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The socioeconomic transformations in the last decades and its consequent changes to societies lifestyle have increased the incidence of chronic diseases. Genomic medicine has suggested that the exposure to risk factor...
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The socioeconomic transformations in the last decades and its consequent changes to societies lifestyle have increased the incidence of chronic diseases. Genomic medicine has suggested that the exposure to risk factors since conception may influence gene expression and consequently induce the development of chronic diseases in adulthood . Scientific papers bringing up these discoveries indicate that epigenetics must be exploited in order to prevent diseases of high prevalence, such as cardiovascular diseases, diabetes and obesity. According to Butte, the effective transformation of results from biomedical research into knowledge that actually improves public health has been considered an important domain of informatics and has been called Translational bioinformatics. This paper proposes the ChronDiseaSSys surveillance system aiming to alert health professionals about human development problems. This system automatically discovers scientific papers relating chronic diseases to risk factors after analyzing patient's clinical records. As a result, the healthcare professional will be able to create a routine setting up the best growing conditions. Since chronic diseases are a serious health problem world wide and lead the causes of mortality with 60% of all deaths, the results suggest that bioinformatics researches are able to directly benefit public health.
Independent Component Analysis (ICA) algorithms are potentially powerful ways of localizing sources of cerebral activity in resting state functional Magnetic Resonance Imaging (fMRI). But the assumptions underling the...
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Independent Component Analysis (ICA) algorithms are potentially powerful ways of localizing sources of cerebral activity in resting state functional Magnetic Resonance Imaging (fMRI). But the assumptions underling the nature of identified sources limits this tool. By creating local one-dimensional approximations, Local Sparse Component Analysis (LSCA) can separate contiguous sources on the basis of their sparse representation into smoothness spaces via the 3D wavelet transformation. In this paper we systematically compare Probabilistic ICA (PICA) and LSCA for analyzing resting state fMRI across healthy participants. We show that the PICA sources usually representing biologically plausible components can in fact be decomposed into several LSCA sources that are not necessarily independent from each other. In addition, we show that LSCA identifies sources that approximate much better the local variations of the blood oxygenation level-dependent (BOLD) signal than PICA sources.
Granular cell tumor (GCT) is a rare neoplasm that can occur in any part of the body, but mostly they are located intraorally. Its histogenetic origin remains controversial, but it probably arises from Schwann cells an...
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Granular cell tumor (GCT) is a rare neoplasm that can occur in any part of the body, but mostly they are located intraorally. Its histogenetic origin remains controversial, but it probably arises from Schwann cells and is generally benign. The tumor is typically asymptomatic and appears as a nodule, with a relatively high predilection for the tongue. This article reports a case of a 72-year-old woman treated at the Center of Oral Diagnosis of the Fundação Hermínio Ometto Dental School. The patient presented with an asymptomatic nodule in the dorsal surface of the tongue for approximately 4 months. The patient was submitted to an excisional biopsy and histopatological examination revealed polyhedral cells with granular aspect. The immunohistochemical staining for S-100 presented strong reactivity, confirming the diagnosis of GCT. Finally, we made a concise discussion about the pathogenesis and fundamental clinico-pathological aspects of GCT making the differential diagnosis.
Many studies have attempted to investigate the genetic susceptibility of Attention-Deficit/Hyperactivity Disorder (ADHD), but without much success. The present study aimed to analyze both single-nucleotide and copy-nu...
Many studies have attempted to investigate the genetic susceptibility of Attention-Deficit/Hyperactivity Disorder (ADHD), but without much success. The present study aimed to analyze both single-nucleotide and copy-number variants contributing to the genetic architecture of ADHD. We generated exome data from 30 Brazilian trios with sporadic ADHD. We also analyzed a Brazilian sample of 503 children/adolescent controls from a High Risk Cohort Study for the Development of Childhood Psychiatric Disorders, and also previously published results of five CNV studies and one GWAS meta-analysis of ADHD involving children/adolescents. The results from the Brazilian trios showed that cases with de novo SNVs tend not to have de novo CNVs and vice-versa. Although the sample size is small, we could also see that various comorbidities are more frequent in cases with only inherited variants. Moreover, using only genes expressed in brain, we constructed two "in silico" protein-protein interaction networks, one with genes from any analysis, and other with genes with hits in two analyses. Topological and functional analyses of genes in this network uncovered genes related to synapse, cell adhesion, glutamatergic and serotoninergic pathways, both confirming findings of previous studies and capturing new genes and genetic variants in these pathways.
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