Psychological profile in children and adolescents with severe course Juvenile idiopathic arthritis

Objective. Juvenile Idiopathic Arthritis (JIA) is the most common chronic pediatric rheumatic disease. It is recognized that only reliance on clinical signs of disease outcome is inadequate for understanding the impact of illness and its treatment on child's life and functioning. There is a need for a multidisciplinary and holistic approach to children with arthritis which considers both physical and emotional functioning. This study investigated the psychosocial functioning of children and adolescent with JIA and the disease-related changes in their family. Methods. The sample consisted of 33 hospitalized patients, aged 6-16 years. Both parents and the children were given a number of questionnaire to fill out. Clinical information was extracted from the interviews. Results. Self-reported psychological functioning (depression, anxiety, and behavior) was not different from the normal population; however significant psychological suffering was detected by the clinical interview. Conclusions. Children and adolescents with JIA do not show overt psychopathology by structured assessment; nevertheless a more clinically oriented holistic approach confirms JIA as a disrupting event causing relevant changes in the quality of life of the affected families. Copyright © 2012 Emanuela Russo et al

Building Roadmaps of Local Minima of Visual Models

Getting trapped in suboptimal local minima is a perennial problem in model based vision, especially in applications like monocular human body tracking where complex nonlinear parametric models are repeatedly fitted to ambiguous image data. We show that the trapping problem can be attacked by building `roadmaps' of nearby minima linked by transition pathways --- paths leading over low `cols' or `passes' in the cost surface, found by locating the transition state (codimension-1 saddle point) at the top of the pass and then sliding downhill to the next minimum. We know of no previous vision or optimization work on numerical methods for locating transition states, but such methods do exist in computational chemistry, where transitions are critical for predicting reaction parameters. We present two families of methods, originally derived in chemistry, but here generalized, clarified and adapted to the needs of model based vision: eigenvector tracking is a modified form of damped Newton minimization, while hypersurface sweeping sweeps a moving hypersurface through the space, tracking minima within it. Experiments on the challenging problem of estimating 3D human pose from monocular images show that our algorithms find nearby transition states and minima very efficiently, but also underline the disturbingly large number of minima that exist in this and similar model based vision problems

Reducing fall risk with combined motor and cognitive training in elderly fallers

Falling is a major clinical problem in elderly people, demanding effective solutions. At present, the only effective intervention is motor training of balance and strength. Executive function-based training (EFt) might be effective at preventing falls according to evidence showing a relationship between executive functions and gait abnormalities. The aim was to assess the effectiveness of a motor and a cognitive treatment developed within the EU co-funded project I-DONT-FALL