ABSTRACT
Advanced parental age has been shown to increase offspring risk for a number of neuropsychiatric disorders including schizophrenia and Down's syndrome. Other psychiatric disorders have been less studied with respect to the effect of parental age on offspring risk. In this study we examine if advanced parental age increased risk for ICD-10 diagnoses. We hypothesized that advanced parental age would increase risk for offspring psychotic disorders and mental retardation but not other ICD-10 diagnoses. We examined follow-up data for 30,965 subjects treated in outpatient psychiatric facilities between 1980 and 2007. Subjects were younger than 18 years of age at their first outpatient visit. A comparison group was obtained from data on registered births in Spain from 1975. We compared parental age (maternal, paternal, combined) across diagnostic categories using ANOVA and logistic regression was used to estimate the risk of psychopathology in the offspring with advanced parental age (maternal, paternal, combined). Maternal and paternal ages were higher for subjects diagnosed with mental retardation. Risk for psychotic disorders showed a significant linear increase only with advancing maternal age, and not paternal age as is more often reported.
Subject(s)
Maternal Age , Paternal Age , Schizophrenia/epidemiology , Adolescent , Adult , Age Factors , Aged , Analysis of Variance , Chi-Square Distribution , Child , Female , Humans , International Classification of Diseases , Logistic Models , Longitudinal Studies , Male , Middle Aged , Psychotic Disorders/epidemiology , Psychotic Disorders/genetics , Retrospective Studies , Schizophrenia/genetics , Spain , Young AdultABSTRACT
In this paper, an unsupervised algorithm, called the Independent Histogram Pursuit (IHP), for segmenting dermatological lesions is proposed. The algorithm estimates a set of linear combinations of image bands that enhance different structures embedded in the image. In particular, the first estimated combination enhances the contrast of the lesion to facilitate its segmentation. Given an N-band image, this first combination corresponds to a line in N dimensions, such that the separation between the two main modes of the histogram obtained by projecting the pixels onto this line, is maximized. The remaining combinations are estimated in a similar way under the constraint of being orthogonal to those already computed. The performance of the algorithm is tested on five different dermatological datasets. The results obtained on these datasets indicate the robustness of the algorithm and its suitability to deal with different types of dermatological lesions. The boundary detection precision using k-means segmentation was close to 97%. The proposed algorithm can be easily combined with the majority of classification algorithms.
