Phenotyping progenies for complex architectural traits: a strategy for 1-year-old apple trees (Malus x domestica Borkh.)

International audienceThe aim of this study was to define a methodology for describing architectural traits in a quantitative way on tree descendants. Our strategy was to collect traits related to both tree structural organization, resulting from growth and branching, and tree form and then to select among these traits relevant descriptors on the basis of their genetic parameters. Because the complexity of tree architecture increases with tree age, we chose to describe the trees in the early stages of development. The study was carried out on a 1-year-old apple progeny derived from two parent cultivars with contrasted architecture. A large number of variables were collected at different positions and scales within the trees. Broad-sense heritability and genetic correlations were estimated and the within tree variability was analyzed for variables measured on long sylleptic axillary shoots (LSAS). These results were combined to select heritable and not correlated variables. Finally, the selection of variables proposed combines topological with geometric traits measured on both trunks and LSAS: (1) on the trunk, mean internode length, and number of sylleptic axillary shoots; (2) on axillary shoots, conicity, bending, and number of sylleptic axillary shoots born at order 3. The trees of the progeny were partitioned on the basis of these variables. The putative agronomic interest of the selected variables with respect to the subsequent tree development is discussed

Modeling heterogeneity for bivariate survival data by the Weibull distribution

Bivariate Weibull, Frailty, Heterogeneity, Parametric regression, Survival times,

Conceptualisation of self-management intervention for people with early stage dementia

Dementia is a major challenge for health and social care services. People living with dementia in the earlier stages experience a "care-gap". Although they may address this gap in care, self-management interventions have not been provided to people with dementia. It is unclear how to conceptualise self-management for this group and few published papers address intervention design. Initial focusing work used a logic mapping approach, interviews with key stakeholders, including people with dementia and their family members. An initial set of self-management targets were identified for potential intervention. Self-management for people living with dementia was conceptualised as covering five targets: (1) relationship with family, (2) maintaining an active lifestyle, (3) psychological wellbeing, (4) techniques to cope with memory changes, and (5) information about dementia. These targets were used to focus literature reviewing to explore an evidence base for the conceptualisation. We discuss the utility of the Corbin and Strauss (Unending work and care: managing chronic illness at home. Jossey-Bass, Oxford, 1988) model of self-management, specifically that self-management for people living with dementia should be conceptualised as emphasising the importance of "everyday life work" (targets 1 and 2) and "biographical work" (target 3), with inclusion of but less emphasis on specific "illness work" (targets 4, 5). We argue that self-management is possible for people with dementia, with a strengths focus and emphasis on quality of life, which can be achieved despite cognitive impairments. Further development and testing of such interventions is required to provide much needed support for people in early stages of dementia

Computation of reference Bayesian inference for variance components in longitudinal studies

Bayesian, GLMM, Jeffreys’ prior, PQL, Reference prior, Uniform shrinkage prior, Variance component,

Accuracy and precision of variance components in occupational posture recordings : a simulation study of different data collection strategies

Background: Information on exposure variability, expressed as exposure variance components, is of vital use in occupational epidemiology, including informed risk control and efficient study design. While accurate and precise estimates of the variance components are desirable in such cases, very little research has been devoted to understanding the performance of data sampling strategies designed specifically to determine the size and structure of exposure variability. The aim of this study was to investigate the accuracy and precision of estimators of between-subjects, between-days and within-day variance components obtained by sampling strategies differing with respect to number of subjects, total sampling time per subject, number of days per subject and the size of individual sampling periods. Methods: Minute-by-minute values of average elevation, percentage time above 90 degrees and percentage time below 15 degrees were calculated in a data set consisting of measurements of right upper arm elevation during four full shifts from each of 23 car mechanics. Based on this parent data, bootstrapping was used to simulate sampling with 80 different combinations of the number of subjects (10, 20), total sampling time per subject (60, 120, 240, 480 minutes), number of days per subject (2, 4), and size of sampling periods (blocks) within days (1, 15, 60, 240 minutes). Accuracy (absence of bias) and precision (prediction intervals) of the variance component estimators were assessed for each simulated sampling strategy. Results: Sampling in small blocks within days resulted in essentially unbiased variance components. For a specific total sampling time per subject, and in particular if this time was small, increasing the block size resulted in an increasing bias, primarily of the between-days and the within-days variance components. Prediction intervals were in general wide, and even more so at larger block sizes. Distributing sampling time across more days gave in general more precise variance component estimates, but also reduced accuracy in some cases. Conclusions: Variance components estimated from small samples of exposure data within working days may be both inaccurate and imprecise, in particular if sampling is laid out in large consecutive time blocks. In order to estimate variance components with a satisfying accuracy and precision, for instance for arriving at trustworthy power calculations in a planned intervention study, larger samples of data will be required than for estimating an exposure mean value with a corresponding certainty