Can improving working memory prevent academic difficulties? A school based randomised controlled trial.

BACKGROUND: Low academic achievement is common and is associated with adverse outcomes such as grade repetition, behavioural disorders and unemployment. The ability to accurately identify these children and intervene before they experience academic failure would be a major advance over the current 'wait to fail' model. Recent research suggests that a possible modifiable factor for low academic achievement is working memory, the ability to temporarily store and manipulate information in a 'mental workspace'. Children with working memory difficulties are at high risk of academic failure. It has recently been demonstrated that working memory can be improved with adaptive training tasks that encourage improvements in working memory capacity. Our trial will determine whether the intervention is efficacious as a selective prevention strategy for young children at risk of academic difficulties and is cost-effective. METHODS/DESIGN: This randomised controlled trial aims to recruit 440 children with low working memory after a school-based screening of 2880 children in Grade one. We will approach caregivers of all children from 48 participating primary schools in metropolitan Melbourne for consent. Children with low working memory will be randomised to usual care or the intervention. The intervention will consist of 25 computerised working memory training sessions, which take approximately 35 minutes each to complete. Follow-up of children will be conducted at 6, 12 and 24 months post-randomisation through child face-to-face assessment, parent and teacher surveys and data from government authorities. The primary outcome is academic achievement at 12 and 24 months, and other outcomes include child behaviour, attention, health-related quality of life, working memory, and health and educational service utilisation. DISCUSSION: A successful start to formal learning in school sets the stage for future academic, psychological and economic well-being. If this preventive intervention can be shown to be efficacious, then we will have the potential to prevent academic underachievement in large numbers of at-risk children, to offer a ready-to-use intervention to the Australian school system and to build international research partnerships along the health-education interface, in order to carry our further studies of effectiveness and generalisability.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation

Patterns of genetic diversity of the Hawaiian spinner dolphin (Stenella longirostris)

Volume: 543Start Page: 65End Page: 7

Predictive modeling of spinner dolphin (Stenella longirostris) resting habitat in the main Hawaiian Islands.

Predictive habitat models can provide critical information that is necessary in many conservation applications. Using Maximum Entropy modeling, we characterized habitat relationships and generated spatial predictions of spinner dolphin (Stenella longirostris) resting habitat in the main Hawaiian Islands. Spinner dolphins in Hawai'i exhibit predictable daily movements, using inshore bays as resting habitat during daylight hours and foraging in offshore waters at night. There are growing concerns regarding the effects of human activities on spinner dolphins resting in coastal areas. However, the environmental factors that define suitable resting habitat remain unclear and must be assessed and quantified in order to properly address interactions between humans and spinner dolphins. We used a series of dolphin sightings from recent surveys in the main Hawaiian Islands and a suite of environmental variables hypothesized as being important to resting habitat to model spinner dolphin resting habitat. The model performed well in predicting resting habitat and indicated that proximity to deep water foraging areas, depth, the proportion of bays with shallow depths, and rugosity were important predictors of spinner dolphin habitat. Predicted locations of suitable spinner dolphin resting habitat provided in this study indicate areas where future survey efforts should be focused and highlight potential areas of conflict with human activities. This study provides an example of a presence-only habitat model used to inform the management of a species for which patterns of habitat availability are poorly understood

Location of the study site in the Hawaiian Archipelago.

<p>Location of the study site in the Hawaiian Archipelago.</p

Model gain shown for selected bays on the islands of Kaua'i, O'ahu, Moloka'i and Maui.

<p>Model gain shown for selected bays on the islands of Kaua'i, O'ahu, Moloka'i and Maui.</p

Fractional predicted area and <i>p</i>-values of binomial tests from the Maxent model of spinner dolphin resting habitat for the equal sensitivity-specificity threshold and for fixed thresholds of 1, 5 and 10.

<p>Fractional predicted area and <i>p</i>-values of binomial tests from the Maxent model of spinner dolphin resting habitat for the equal sensitivity-specificity threshold and for fixed thresholds of 1, 5 and 10.</p