Effects of two different memory training approaches in very pretermborn children

Background: Little research has been conducted to assess the effect of using memory training with school aged children who were born very preterm. This study aimed to determine whether two types of memory training approaches resulted in an improvement of trained functions and/or a generalization of the training effect to non-trained cognitive domains. Methods: Sixty-eight children born very preterm (7-12 years) were randomly allocated to a group undertaking memory strategy training (n=23), working memory training (n=22), or a waiting control group (n=23). Neuropsychological assessment was performed before and immediately after the training or waiting period, and at a six-month follow-up. Results: In both training groups, significant improvement of different memory domains occurred immediately after training (near transfer). Improvement of non-trained arithmetic performance was observed after strategy training (far transfer). At a six-month follow-up assessment, children in both training groups demonstrated better working memory, and their parents rated their memory functions to be better than controls. Performance level before the training was negatively associated with the training gain. Conclusions: These results highlight the importance of cognitive interventions, in particular the teaching of memory strategies, in very preterm-born children at early school age to strengthen cognitive performance and prevent problems at school.

Impact of above- and below-ground invertebrates on temporal and spatial stability of grassland of different diversity

1. Recent theoretical studies suggest that the stability of ecosystem processes is not governed by diversity per se, but by multitrophic interactions in complex communities. However, experimental evidence supporting this assumption is scarce.2. We investigated the impact of plant diversity and the presence of above- and below-ground invertebrates on the stability of plant community productivity in space and time, as well as the interrelationship between both stability measures in experimental grassland communities.3. We sampled above-ground plant biomass on subplots with manipulated above- and below-ground invertebrate densities of a grassland biodiversity experiment (Jena Experiment) 1, 4 and 6 years after the establishment of the treatments to investigate temporal stability. Moreover, we harvested spatial replicates at the last sampling date to explore spatial stability.4. The coefficient of variation of spatial and temporal replicates served as a proxy for ecosystem stability. Both spatial and temporal stability increased to a similar extent with plant diversity. Moreover, there was a positive correlation between spatial and temporal stability, and elevated plant density might be a crucial factor governing the stability of diverse plant communities.5. Above-ground insects generally increased temporal stability, whereas impacts of both earthworms and above-ground insects depended on plant species richness and the presence of grasses. These results suggest that inconsistent results of previous studies on the diversity–stability relationship have in part been due to neglecting higher trophic-level interactions governing ecosystem stability.6. Changes in plant species diversity in one trophic level are thus unlikely to mirror changes in multitrophic interrelationships. Our results suggest that both above- and below-ground invertebrates decouple the relationship between spatial and temporal stability of plant community productivity by differently affecting the homogenizing mechanisms of plants in diverse plant communities.7.Synthesis. Species extinctions and accompanying changes in multitrophic interactions are likely to result not only in alterations in the magnitude of ecosystem functions but also in its variability complicating the assessment and prediction of consequences of current biodiversity loss.

On ANOVA Decompositions of Kernels and Gaussian Random Field Paths

The FANOVA (or “Sobol’-Hoeffding”) decomposition of multivariate functions has been used for high-dimensional model representation and global sensitivity analysis. When the objective function f has no simple analytic form and is costly to evaluate, computing FANOVA terms may be unaffordable due to numerical integration costs. Several approximate approaches relying on Gaussian random field (GRF) models have been proposed to alleviate these costs, where f is substituted by a (kriging) predictor or by conditional simulations. Here we focus on FANOVA decompositions of GRF sample paths, and we notably introduce an associated kernel decomposition into 4 d 4d terms called KANOVA. An interpretation in terms of tensor product projections is obtained, and it is shown that projected kernels control both the sparsity of GRF sample paths and the dependence structure between FANOVA effects. Applications on simulated data show the relevance of the approach for designing new classes of covariance kernels dedicated to high-dimensional kriging.