23 research outputs found

    Psychological complications of childhood chronic physical illness in Nigerian children and their mothers: the implication for developing pediatric liaison services

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    <p>Abstract</p> <p>Background</p> <p>Pediatric liaison services attending to the psychological health needs of children with chronic physical illness are limited or virtually non-existent in Nigeria and most sub-Saharan African countries, and psychological problems complicate chronic physical illness in these children and their mothers. There exist needs to bring into focus the public health importance of developing liaison services to meet the psychological health needs of children who suffer from chronic physical illness in this environment. Sickle cell disease (SCD) and juvenile diabetes mellitus (JDM) are among the most common chronic physical health conditions in Nigerian children. This study compared the prevalence and pattern of emotional disorders and suicidal behavior among Nigerian children with SCD, JDM and a group of healthy children. Psychological distress in the mothers of these children that suffer chronic physical illness was also compared with psychological distress in mothers of healthy control children.</p> <p>Methods</p> <p>Forty-five children aged 9 to 17 years were selected for each group of SCD, JDM and controls. The SCD and JDM groups were selected by consecutive clinic attendance and the healthy children who met the inclusion criteria were selected from neighboring schools. The Youth version of the Computerized Diagnostic Interview Schedule for Children, version IV (C- DISC- IV) was used to assess for diagnosis of emotional disorders in these children. Twelve-item General Health Questionnaire (GHQ – 12) was used to assess for psychological distress in mothers of these children and healthy control children.</p> <p>Results</p> <p>Children with JDM were significantly more likely to experience DSM – IV emotional disorders than children with SCD and the healthy group (p = 0.005), while children with JDM and SCD were more likely to have 'intermediate diagnoses' of emotional disorders (p = 0.0024). Children with SCD and JDM had higher rates of suicidal ideation when compared to healthy control children and a higher prevalence of maternal psychological distress was found in their mothers when compared to the mothers of healthy children (p = 0.035).</p> <p>Conclusion</p> <p>The higher prevalence of emotional disorders and suicidal ideation among children with SCD and JDM points to a need for development of liaison services in pediatric facilities caring for children with chronic physical illness to ensure holistic approach to their care. The proposed liaison services would also be able to provide family support interventions that would address the psychological distress experienced by the mothers of these children.</p

    Multilocus ISSR Markers Reveal Two Major Genetic Groups in Spanish and South African Populations of the Grapevine Fungal Pathogen Cadophora luteo-olivacea

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    Cadophora luteo-olivacea is a lesser-known fungal trunk pathogen of grapevine which has been recently isolated from vines showing decline symptoms in grape growing regions worldwide. In this study, 80 C. luteo-olivacea isolates (65 from Spain and 15 from South Africa) were studied. Inter-simple-sequence repeat-polymerase chain reaction (ISSR-PCR) generated 55 polymorphic loci from four ISSR primers selected from an initial screen of 13 ISSR primers. The ISSR markers revealed 40 multilocus genotypes (MLGs) in the global population. Minimum spanning network analysis showed that the MLGs from South Africa clustered around the most frequent genotype, while the genotypes from Spain were distributed all across the network. Principal component analysis and dendrograms based on genetic distance and bootstrapping identified two highly differentiated genetic clusters in the Spanish and South African C. luteo-olivacea populations, with no intermediate genotypes between these clusters. Movement within the Spanish provinces may have occurred repeatedly given the frequent retrieval of the same genotype in distant locations. The results obtained in this study provide new insights into the population genetic structure of C. luteo-olivacea in Spain and highlights the need to produce healthy and quality planting material in grapevine nurseries to avoid the spread of this fungus throughout different grape growing regions

    Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance

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    International audienceIn this work we introduce Deforming Autoencoders, a generative model for images that disentangles shape from appearance in an unsupervised manner. As in the deformable template paradigm, shape is represented as a deformation between a canonical coordinate system ('template') and an observed image, while appearance is modeled in 'canonical', template, coordinates, thus discarding variability due to deformations. We introduce novel techniques that allow this approach to be deployed in the setting of autoencoders and show that this method can be used for unsupervised group-wise image alignment. We show experiments with expression morphing in humans, hands, and digits, face manipulation, such as shape and appearance interpolation, as well as unsupervised landmark local-ization. A more powerful form of unsupervised disentangling becomes possible in template coordinates, allowing us to successfully decompose face images into shading and albedo, and further manipulate face images. Latent Representation Input Image Generated Deformation Generated Texture Decoder Decoder Spatial Warping Reconstructed Image Encoder Fig. 1. Deforming Autoencoders follow the deformable template paradigm and model image generation through a cascade of appearance (or, 'texture') synthesis in a canonical coordinate system and a spatial deformation that warps the texture to the observed image coordinates. By keeping the latent vector for texture short the network is forced to model shape variability through the deformation branch, so as to minimize a reconstruction loss. This allows us to train a deep gen-erative image model that disentangles shape and appearance in an entirely unsupervised manner
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