LINKAGE RELATIONSHIPS AMONG PROTEIN-CODING LOCI IN FISHES OF THE GENUS XIPHOPHORUS

Inbred strains of three species of fishes of the genus Xiphophorus (platyfish and swordtails) were crossed to produce intra- and interspecific F(,1) hybrids, which were then backcrossed to one or both parental stocks. Backcross hybrids were used for the analysis of segregation and linkage of 33 protein-coding loci (whose products were visualized by starch gel electrophoresis) and a sex-linked pigment pattern gene. Segregation was Mendelian for all loci with the exception of one instance of segregation distortion. Six linkage groups of enzyme-coding loci were established: LG I, ADA --6%-- G(,6)PD --24%-- 6PGD; LG II, Est-2 --27%-- Est-3 --0%-- Est-5 --23%-- LDH-1 --16%-- MPI; LG III, AcPh --38%-- G(,3)PD-1 (GUK-2 --14%-- G(,3)PD-1 is also in LG III, but the position of GUK-2 with respect to AcPh has not yet been determined); LG IV, GPI-1 --41%-- IDH-1; LG V, Est-1 --38%-- MDH-2; and LG VI, P1P --7%-- UMPK-1 (P1P is a plasma protein, very probably transferrin).^ Sex-specific recombination appeared absent in LG II and LG IV locus pairs; significantly higher male recombination was demonstrated in LG I but significantly higher female recombination was detected in LG V. Only one significant population-specific difference in recombination was detected, in the G(,6)PD - 6PGD region of LG I; the notable absence of such effects implies close correspondence of the genomes of the species used in the study. Two cases of possible evolutionary conservation of linkage groups in fishes and mammals were described, involving the G(,6)PD - 6PGD linkage in LG I and the cluster of esterase loci in LG II. One clear case of divergence was observed, that of the linkage of ADA in LG I. It was estimated that a minimum of (TURN)50% of the Xiphophorus genome was marked by the loci studied. Therefore, the prior probability that a new locus will assort independently from the markers already established is estimated to be less than 0.5. A maximum of 21 of the 24 pairs of chromosomes could be marked with at least one locus.^ Only the two LG V loci showed a significant association with a postulated gene controlling the severity of a genetically controlled melanoma caused by abnormal proliferation of macromelanophore pigment pattern cells. The independence of melanotic severity from all other informative markers implies that one or at most a few major genes are involved in control of melanotic severity in this system. ^

‘The best thing I’ve ever done is ring these people…’: Making a Difference through Family Support

Effective family support strategies offer early intervention and help for families and children at risk of experiencing social exclusion and maltreatment. This paper reports a study which evaluated client outcomes from participation in an Intensive Family Support Service by comparing views of workers and service users on perceived benefits. It profiles the characteristics and circumstances of families recruited to service, services and interventions delivered and the potential of IFSS to lead to safe and positive outcomes for children and families. Findings discussed highlight the individualized and collaborative approach and the high degree of engagement with service users that facilitated gains in the domains of child and family functioning targeted. Implications of the findings for policy and practice in responding to vulnerable families and children are discussed.

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^