Estimación de la frecuencia fundamental de señales de voz usando transfromada wavelet

En la estimación de la frecuencia fundamental de señales de voz usando transformada Wavelet es común usar el hecho de que ocurren máximos locales a través de las escalas de descomposición en la vecindad del instante de cierre glótico (Glottal Closure Instant-GCI). Dichos métodos se basan en la correlación de las posiciones de los máximos locales para varias escalas de descomposición; pero ello no es tan simple porque existen muchos máximos locales en una señal de voz y, además, las escalas correspondientes a las frecuencias altas son fácilmente afectadas por el ruido. Se propone un método basado en la determinación y correlación de las distancias para cada escala de descomposición, el cual funciona ante perturbaciones de ruido blanco gausiano. Su desempeño se compara respecto a la base de datos Keele Pitch Database con el método SIFT(Simplified Inverse Filtering Tracking) el cual es un método de estimación de la frecuencia fundamental comúnmente usado en sistemas comerciales

Metabolic phenotyping of opioid and psychostimulant addiction: A novel approach for biomarker discovery and biochemical understanding of the disorder.

Despite the progress in characterising the pharmacological profile of drugs of abuse, their precise biochemical impact remains unclear. The metabolome reflects the multifaceted biochemical processes occurring within a biological system. This includes those encoded in the genome but also those arising from environmental/exogenous exposures and interactions between the two. Using metabolomics, the biochemical derangements associated with substance abuse can be determined as the individual transitions from recreational drug to chronic use (dependence). By understanding the biomolecular perturbations along this time course and how they vary across individuals, metabolomics can elucidate biochemical mechanisms of the addiction cycle (dependence/withdrawal/relapse) and predict prognosis (recovery/relapse). In this review, we summarise human and animal metabolomic studies in the field of opioid and psychostimulant addiction. We highlight the importance of metabolomics as a powerful approach for biomarker discovery and its potential to guide personalised pharmacotherapeutic strategies for addiction targeted towards the individual's metabolome

The Helicobacter pylori Genome Project : insights into H. pylori population structure from analysis of a worldwide collection of complete genomes

Helicobacter pylori, a dominant member of the gastric microbiota, shares co-evolutionary history with humans. This has led to the development of genetically distinct H. pylori subpopulations associated with the geographic origin of the host and with differential gastric disease risk. Here, we provide insights into H. pylori population structure as a part of the Helicobacter pylori Genome Project (HpGP), a multi-disciplinary initiative aimed at elucidating H. pylori pathogenesis and identifying new therapeutic targets. We collected 1011 well-characterized clinical strains from 50 countries and generated high-quality genome sequences. We analysed core genome diversity and population structure of the HpGP dataset and 255 worldwide reference genomes to outline the ancestral contribution to Eurasian, African, and American populations. We found evidence of substantial contribution of population hpNorthAsia and subpopulation hspUral in Northern European H. pylori. The genomes of H. pylori isolated from northern and southern Indigenous Americans differed in that bacteria isolated in northern Indigenous communities were more similar to North Asian H. pylori while the southern had higher relatedness to hpEastAsia. Notably, we also found a highly clonal yet geographically dispersed North American subpopulation, which is negative for the cag pathogenicity island, and present in 7% of sequenced US genomes. We expect the HpGP dataset and the corresponding strains to become a major asset for H. pylori genomics

ESTIMACIÓN DE LA FRECUENCIA FUNDAMENTAL DE SEÑALES DE VOZ USANDO TRANSFROMADA WAVELET.

We often use the analysis way local maxims, which are present trough the scales of decomposition in the neighbourhood of the Glottal Closure Instant (GCI) for the estimation of the fundamental frequency of speech signal. These methods use the correlation of the local maxima position for various scales of decomposition. This is not simple because there are many local maxims in the speech waveform and, therefore, the scales that correspond to high frequencies are easily affected by noise. A new method is proposed, based on the determination and correlation of distances for each decomposition scale, which works on white noise perturbations. Its achievement is compared respect to the Keele Pitch Database with the Simplified Inverse Filtering Tracking method which is a method commonly used in commercial systems.En la estimación de la frecuencia fundamental de señales de voz usando transformada Wavelet es común usar el hecho de que ocurren máximos locales a través de las escalas de descomposición en la vecindad del instante de cierre glótico (Glottal Closure Instant-GCI). Dichos métodos se basan en la correlación de las posiciones de los máximos locales para varias escalas de descomposición; pero ello no es tan simple porque existen muchos máximos locales en una señal de voz y, además, las escalas correspondientes a las frecuencias altas son fácilmente afectadas por el ruido. Se propone un método basado en la determinación y correlación de las distancias para cada escala de descomposición, el cual funciona ante perturbaciones de ruido blanco gausiano. Su desempeño se compara respecto a la base de datos Keele Pitch Database con el método SIFT(Simplified Inverse Filtering Tracking) el cual es un método de estimación de la frecuencia fundamental comúnmente usado en sistemas comerciales

On impact and volcanism across the Cretaceous-Paleogene boundary.

The cause of the end-Cretaceous mass extinction is vigorously debated, owing to the occurrence of a very large bolide impact and flood basalt volcanism near the boundary. Disentangling their relative importance is complicated by uncertainty regarding kill mechanisms and the relative timing of volcanogenic outgassing, impact, and extinction. We used carbon cycle modeling and paleotemperature records to constrain the timing of volcanogenic outgassing. We found support for major outgassing beginning and ending distinctly before the impact, with only the impact coinciding with mass extinction and biologically amplified carbon cycle change. Our models show that these extinction-related carbon cycle changes would have allowed the ocean to absorb massive amounts of carbon dioxide, thus limiting the global warming otherwise expected from postextinction volcanism