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1.
Am J Med Genet A ; 194(2): 160-173, 2024 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-37768187

RESUMEN

Rett syndrome (RTT) is a progressive neurodevelopmental disorder, and pathogenic Methyl-CpG-binding Protein 2 (MECP2) variants are identified in >95% of individuals with typical RTT. Most of RTT-causing variants in MECP2 are de novo and usually on the paternally inherited X chromosome. While paternal age has been reported to be associated with increased risk of genetic disorders, it is unknown whether parental age contributes to the risk of the development of RTT. Clinical data including parental age, RTT diagnostic status, and clinical severity are collected from 1226 participants with RTT and confirmed MECP2 variants. Statistical analyses are performed using Student t-test, single factor analysis of variance (ANOVA), and multi-factor regression. No significant difference is observed in parental ages of RTT probands compared to that of the general population. A small increase in parental ages is observed in participants with missense variants compared to those with nonsense variants. When we evaluate the association between clinical severity and parental ages by multiple regression analysis, there is no clear association between clinical severity and parental ages. Advanced parental ages do not appear to be a risk factor for RTT, and do not contribute to the clinical severity in individuals with RTT.


Asunto(s)
Síndrome de Rett , Humanos , Síndrome de Rett/diagnóstico , Síndrome de Rett/epidemiología , Síndrome de Rett/genética , Mutación , Proteína 2 de Unión a Metil-CpG/genética , Cromosomas Humanos X , Padres
2.
PLoS One ; 17(3): e0264988, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35324943

RESUMEN

A combination of machine learning and expert analyst review was used to detect odontocete echolocation clicks, identify dominant click types, and classify clicks in 32 years of acoustic data collected at 11 autonomous monitoring sites in the western North Atlantic between 2016 and 2019. Previously-described click types for eight known odontocete species or genera were identified in this data set: Blainville's beaked whales (Mesoplodon densirostris), Cuvier's beaked whales (Ziphius cavirostris), Gervais' beaked whales (Mesoplodon europaeus), Sowerby's beaked whales (Mesoplodon bidens), and True's beaked whales (Mesoplodon mirus), Kogia spp., Risso's dolphin (Grampus griseus), and sperm whales (Physeter macrocephalus). Six novel delphinid echolocation click types were identified and named according to their median peak frequencies. Consideration of the spatiotemporal distribution of these unidentified click types, and comparison to historical sighting data, enabled assignment of the probable species identity to three of the six types, and group identity to a fourth type. UD36, UD26, and UD28 were attributed to Risso's dolphin (G. griseus), short-finned pilot whale (G. macrorhynchus), and short-beaked common dolphin (D. delphis), respectively, based on similar regional distributions and seasonal presence patterns. UD19 was attributed to one or more species in the subfamily Globicephalinae based on spectral content and signal timing. UD47 and UD38 represent distinct types for which no clear spatiotemporal match was apparent. This approach leveraged the power of big acoustic and big visual data to add to the catalog of known species-specific acoustic signals and yield new inferences about odontocete spatiotemporal distribution patterns. The tools and call types described here can be used for efficient analysis of other existing and future passive acoustic data sets from this region.


Asunto(s)
Delfines , Ecolocación , Acústica , Animales , Aprendizaje Automático , Cachalote , Vocalización Animal , Ballenas
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