An Analysis of Carrying Behaviour in the Common Marmoset (<i>Callithrix jacchus</i>)

Marmosets (Callithrix jacchus) give birth to infants, usually twins, and the mother can become pregnant again almost immediately, so the female is lactating, to feed the newly born infants, while pregnant. These conditions impose extreme physiological demands upon the mother; all the adult and subadult members of the family help to care for the infants, in many instances, from the day of birth. The adult males and adolescents play a very important role in the care of the infants and in their socialisation. The effects of carrying infants on the behaviour of the mother and the other caregivers were investigated. Identified members of families of marmosets were observed in the first two weeks after the infant's birth, and again in the fifth to sixth weeks of their life. The movements of the caregivers were quantified, as were the duration of the carrying bouts. Carrying one infant and carrying as a pair were scored individually. Carrying infants was found to have a profound effect upon their mobility and activity levels. The adults, and subadult siblings, tended to carry the infants as a pair rather than consecutively. In this way the caregivers, although burdened by the infants, may be maximising the time spent in enforced in activity by carrying the infants together

The Choice between fixed and random effects models: some considerations for educational research.

We discuss the use of fixed and random effects models in the context of educational research and set out the assumptions behind the two modelling approaches. To illustrate the issues that should be considered when choosing between these approaches, we analyse the determinants of pupil achievement in primary school, using data from the Avon Longitudinal Study of Parents and Children. We conclude that a fixed effects approach will be preferable in scenarios where the primary interest is in policy-relevant inference about the effects of individual characteristics, but the process through which pupils are selected into schools is poorly understood or the data are too limited to adjust for the effects of selection. In this context, the robustness of the fixed effects approach to the random effects assumption is attractive, and educational researchers should consider using it, even if only to assess the robustness of estimates obtained from random effects models. On the other hand, when the selection mechanism is fairly well understood and the researcher has access to rich data, the random effects model should naturally be preferred because it can produce policy-relevant estimates while allowing a wider range of research questions to be addressed. Moreover, random effects estimators of regression coefficients and shrinkage estimators of school effects are more statistically efficient than those for fixed effects.fixed effects, random effects, multilevel modelling, education, pupil achievement

The Choice Between Fixed and Random Effects Models: Some Considerations for Educational Research

We discuss fixed and random effects models in the context of educational research and set out the assumptions behind the two approaches. To illustrate the issues, we analyse the determinants of pupil achievement in primary school, using data from the Avon Longitudinal Study of Parents and Children. We conclude that a fixed effects approach will be preferable in scenarios where the primary interest is in policy-relevant inference of the effects of individual characteristics, but the process through which pupils are selected into schools is poorly understood or the data are too limited to adjust for the effects of selection. In this context, the robustness of the fixed effects approach to the random effects assumption is attractive, and educational researchers should consider using it, even if only to assess the robustness of estimates obtained from random effects models. When the selection mechanism is fairly well understood and the researcher has access to rich data, the random effects model should be preferred because it can produce policy-relevant estimates while allowing a wider range of research questions to be addressed. Moreover, random effects estimators of regression coefficients and shrinkage estimators of school effects are more statistically efficient than those for fixed effects.fixed effects, random effects, multilevel modelling, education, pupil achievement

Are current ecological restoration practices capturing natural levels of genetic diversity? A New Zealand case study using AFLP and ISSR data from mahoe (Melicytus ramiflorus)

Sourcing plant species of local provenance (eco-sourcing) has become standard practice in plant community restoration projects. Along with established ecological restoration practices, knowledge of genetic variation in existing and restored forest fragments is important for ensuring the maintenance of natural levels of genetic variation and connectivity (gene flow) among populations. The application of restoration genetics often employs anonymous ‘fingerprinting’ markers in combination with limited sample sizes due to financial constraints. Here, we used two such marker systems, AFLPs and ISSRs, to estimate population-level genetic variation of a frequently used species in restoration projects in New Zealand, māhoe (Melicytus ramiflorus, Violaceae). We examined two rural and two urban forest fragments, as potential local source populations, to determine whether the māhoe population at the recently (re)constructed ecosystem at Waiwhakareke Natural Heritage Park (WNHP), Hamilton, New Zealand reflects the genetic variation observed in these four potential source populations. Both marker systems produced similar results and indicated, even with small population sizes, that levels of genetic variation at WNHP were comparable to in situ populations. However, the AFLPs did provide finer resolution of the population genetic structure than ISSRs. ISSRs, which are less expensive and technically less demanding to generate than AFLPs, may be sufficient for restoration projects where only a broad level of genotypic resolution is required. We recommend the use of AFLPs when species with a high conservation status are being used due to the greater resolution of this technique

Modeling within-household associations in household panel studies

Household panel data provide valuable information about the extent of similarity in coresidents' attitudes and behaviours. However, existing analysis approaches do not allow for the complex association structures that arise due to changes in household composition over time. We propose a flexible marginal modeling approach where the changing correlation structure between individuals is modeled directly and the parameters estimated using second-order generalized estimating equations (GEE2). A key component of our correlation model specification is the 'superhousehold', a form of social network in which pairs of observations from different individuals are connected (directly or indirectly) by coresidence. These superhouseholds partition observations into clusters with nonstandard and highly variable correlation structures. We thus conduct a simulation study to evaluate the accuracy and stability of GEE2 for these models. Our approach is then applied in an analysis of individuals' attitudes towards gender roles using British Household Panel Survey data. We find strong evidence of between-individual correlation before, during and after coresidence, with large differences among spouses, parent-child, other family, and unrelated pairs. Our results suggest that these dependencies are due to a combination of non-random sorting and causal effects of coresidence

Revisiting fixed- and random-effects models: some considerations for policy-relevant education research

The use of fixed (FE) and random effects (RE) in two-level hierarchical linear regression is discussed in the context of education research. We compare the robustness of FE models with the modelling flexibility and potential efficiency of those from RE models. We argue that the two should be seen as complementary approaches. We then compare both modelling approaches in our empirical examples. Results suggest a negative effect of special educational needs (SEN) status on educational attainment, with selection into SEN status largely driven by pupil level rather than school-level factors

Selection of Preprocessing Methodology for Multivariate Regression of Cellular FTIR and Raman Spectra in Radiobiological Analyses

Vibrational spectra of biological species suffer from the influence of many extraneous interfering factors that require removal through preprocessing before analysis. The present study was conducted to optimise the preprocessing methodology and variable subset selection during regression of and confocal Raman microspectroscopy (CRM) and Fourier Transform Infrared microspectroscopy (FTIRM) spectra against ionizing radiation dose. Skin cells were γ-irradiated in-vitro and their Raman and FTIRM spectra were used to retrospectively predict the radiation dose using linear and nonlinear partial least squares (PLS) regression algorithms in addition to support vector regression (SVR). The optimal preprocessing methodology (which comprised combinations of spectral filtering, baseline subtraction, scaling and normalization options) was selected using a genetic algorithm (GA) with the root mean squared error of prediction (RMSEP) used as the fitness criterion for selection of the preprocessing chromosome (where this was calculated on an independent set of test spectra randomly selected from the dataset on each pass of the algorithm). The results indicated that GA selection of the optimal preprocessing methodology substantially improved the predictive capacity of the regression algorithms over baseline methodologies, although the optimal preprocessing chromosomes were similar for various regression algorithms, suggesting an optimal preprocessing methodology for radiobiological analyses with biospectroscopy. Feature selection of both FTIRM and CRM spectra using genetic algorithms and multivariate regression provided further decreases in RMSEP, but only with non-linear multivariate regression algorithms

Right from the start: protocol for a pilot study for a randomised trial of the New Baby Programme for improving outcomes for children born to socially vulnerable mothers:protocol for a pilot study for a randomised trial of the New Baby Programme for improving outcomes for children born to socially vulnerable mothers

Background: Children born to mothers who experience social complexity (e.g. substance misuse, intimate partner violence, mental ill health, a history of maltreatment) are at increased risk for a range of adverse outcomes at birth and during development. Home visiting programmes have been advocated as a strategy for improving outcomes for disadvantaged mothers and children, such as the Nurse-Family Partnership for young, socially disadvantaged first-time mothers. However, no evidence-based programme is available for multiparous women or older first-time mothers. The New Baby Programme was developed in Northern Ireland. It augments the universal health visiting service available in the UK with a content designed to promote maternal health and well-being in pregnancy, maximise secure attachments of children and parents and enhance sensitive parenting and infant cognitive development.Methods/Design: This pilot study is designed to investigate whether it is possible to recruit and retain socially vulnerable mothers in a randomised trial that compares the effects of the New Baby Programme with standard antenatal and postnatal care. Feasibility issues include the referral/recruitment pathway (including inclusion and exclusion criteria), the consent and randomisation, the ability to maintain researcher blinding, the acceptability of the intervention to participants, and the feasibility and acceptability of the outcome measures. The results of the study will inform a definitive phase-3 RCT.Discussion: Trials of complex social interventions often encounter challenges that lead to the trial being abandoned (e.g. because of problems in recruitment) or present considerable analytic challenges relating to dropout, attrition and bias. This pilot study aims to maximise the chances of successful implementation.Trial registration: ISRCTN35456296 retrospectively registered.</p