Clinical implications of gait analysis in the rehabilitation of adult patients with "Prader-Willi" Syndrome: a cross-sectional comparative study ("Prader-Willi" Syndrome vs matched obese patients and healthy subjects)

<p>Abstract</p> <p>Background</p> <p>Being severely overweight is a distinctive clinical feature of Prader-Willi Syndrome (PWS). PWS is a complex multisystem disorder, representing the most common form of genetic obesity. The aim of this study was the analysis of the gait pattern of adult subjects with PWS by using three-Dimensional Gait Analysis. The results were compared with those obtained in a group of obese patients and in a group of healthy subjects.</p> <p>Methods</p> <p>Cross-sectional, comparative study: 19 patients with PWS (11 males and 8 females, age: 18–40 years, BMI: 29.3–50.3 kg/m²); 14 obese matched patients (5 males and 9 females, age: 18–40 years, BMI: 34.3–45.2 kg/m²); 20 healthy subjects (10 males and 10 females, age: 21–41 years, BMI: 19.3–25.4 kg/m²). Kinematic and kinetic parameters during walking were assessed by an optoelectronic system and two force platforms.</p> <p>Results</p> <p>PWS adult patients walked slower, had a shorter stride length, a lower cadence and a longer stance phase compared with both matched obese, and healthy subjects. Obese matched patients showed spatio-temporal parameters significantly different from healthy subjects.</p> <p>Furthermore, Range Of Motion (ROM) at knee and ankle, and plantaflexor activity of PWS patients were significantly different between obese and healthy subjects. Obese subjects revealed kinematic and kinetic data similar to healthy subjects.</p> <p>Conclusion</p> <p>PWS subjects had a gait pattern significantly different from obese patients. Despite that, both groups had a similar BMI. We suggest that PWS gait abnormalities may be related to abnormalities in the development of motor skills in childhood, due to precocious obesity. A tailored rehabilitation program in early childhood of PWS patients could prevent gait pattern changes.</p

Monitoring credit risk in the social economy sector by means of a binary goal programming model

The final publication is available at Springer via http://dx.doi.org/10.1007/s11628-012-0173-7Monitoring the credit risk of firms in the social economy sector presents a considerable challenge, since it is difficult to calculate ratings with traditional methods such as logit or discriminant analysis, due to the relatively small number of firms in the sector and the low default rate among cooperatives. This paper intro- duces a goal programming model to overcome such constraints and to successfully manage credit risk using economic and financial information, as well as expert advice. After introducing the model, its application to a set of Spanish cooperative societies is described.García García, F.; Guijarro Martínez, F.; Moya Clemente, I. (2013). Monitoring credit risk in the social economy sector by means of a binary goal programming model. 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Factors underlying age-related changes in discrete aiming

Age has a clear impact on one’s ability to make accurate goal-directed aiming movements. Older adults seem to plan slower and shorter-ranged initial pulses towards the target, and rely more on sensory feedback to ensure endpoint accuracy. Despite the fact that these age-related changes in manual aiming have been observed consistently, the underlying mechanism remains speculative. In an attempt to isolate four commonly suggested underlying factors, young and older adults were instructed to make discrete aiming movements under varying speed and accuracy constraints. Results showed that older adults were physically able to produce fast primary submovements and that they demonstrated similar movement-programming capacities as young adults. On the other hand, considerable evidence was found supporting a decreased visual feedback-processing efficiency and the implementation of a play-it-safe strategy in older age. In conclusion, a combination of the latter two factors seems to underlie the age-related changes in manual aiming behaviour

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Ancestral Inference and the Study of Codon Bias Evolution: Implications for Molecular Evolutionary Analyses of the Drosophila melanogaster Subgroup

Reliable inference of ancestral sequences can be critical to identifying both patterns and causes of molecular evolution. Robustness of ancestral inference is often assumed among closely related species, but tests of this assumption have been limited. Here, we examine the performance of inference methods for data simulated under scenarios of codon bias evolution within the Drosophila melanogaster subgroup. Genome sequence data for multiple, closely related species within this subgroup make it an important system for studying molecular evolutionary genetics. The effects of asymmetric and lineage-specific substitution rates (i.e., varying levels of codon usage bias and departures from equilibrium) on the reliability of ancestral codon usage was investigated. Maximum parsimony inference, which has been widely employed in analyses of Drosophila codon bias evolution, was compared to an approach that attempts to account for uncertainty in ancestral inference by weighting ancestral reconstructions by their posterior probabilities. The latter approach employs maximum likelihood estimation of rate and base composition parameters. For equilibrium and most non-equilibrium scenarios that were investigated, the probabilistic method appears to generate reliable ancestral codon bias inferences for molecular evolutionary studies within the D. melanogaster subgroup. These reconstructions are more reliable than parsimony inference, especially when codon usage is strongly skewed. However, inference biases are considerable for both methods under particular departures from stationarity (i.e., when adaptive evolution is prevalent). Reliability of inference can be sensitive to branch lengths, asymmetry in substitution rates, and the locations and nature of lineage-specific processes within a gene tree. Inference reliability, even among closely related species, can be strongly affected by (potentially unknown) patterns of molecular evolution in lineages ancestral to those of interest

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives