Linking child adjustment difficulties with mother's maladaptive parental behavior: The mediating roles of parental cognitions and parenting stress.

Child abuse is prevalent worldwide, with most of the burden in developing countries. To reduce and prevent child abuse occurrence, many efforts are directed toward reducing maladaptive parental behaviors (MPBs), a predictor of parents' risk of engaging in child abusive behaviors. MPBs have been associated with child (e.g., behavioral difficulties) and parent characteristics (e.g., parenting stress and parental cognitions), although little research tested for mediational pathways. This study aimed to test the pathways through which child and parent characteristics are linked to MPB. Consistent with the social information processing model of parenting, we hypothesized that child behavioral difficulties would exert an indirect influence on MPB through parenting stress and that parenting stress will exert a direct and indirect effect on MPB through parental cognitions (i.e., expectations, attitudes, and attributions). This study used data from 243 mothers of children aged between 2 and 9 years in Romania. Two-stage structural equation modeling was employed to test the hypothesized model. Results support the role of child behavior, parenting stress, and parental cognitions in predicting MPB (R2 = 0.69). Significant indirect effects were found from child behavior to MPB via parenting stress and parental cognitions. Direct effects from parenting stress and parental cognitions to MPB were significant. Findings show that parenting stress and parental cognitions are important mechanisms through which child behavioral difficulties influence maladaptive parental behavior, underscoring the need to focus on these mechanisms when assessing or intervening with families at risk for child abuse.

Mouse urinary biomarkers provide signatures of maturation, diet, stress level, and diurnal rhythm.

Body fluids such as urine potentially contain a wealth of information pertaining to age, sex, social and reproductive status, physiologic state, and genotype of the donor. To explore whether urine could encode information regarding environment, physiology, and development, we compared the volatile compositions of mouse urine using solid-phase microextraction and gas chromatography-mass spectrometry (SPME-GC/MS). Specifically, we identified volatile organic compounds (VOCs) in individual urine samples taken from inbred C57BL/6J-H-2(b) mice under several experimental conditions-maturation state, diet, stress, and diurnal rhythms, designed to mimic natural variations. Approximately 1000 peaks (i.e., variables) were identified per comparison and of these many were identified as potential differential biomarkers. Consistent with previous findings, we found groups of compounds that vary significantly and consistently rather than a single unique compound to provide a robust signature. We identified over 49 new predictive compounds, in addition to identifying several published compounds, for maturation state, diet, stress, and time-of-day. We found a considerable degree of overlap in the chemicals identified as (potential) biomarkers for each comparison. Chemometric methods indicate that the strong group-related patterns in VOCs provide sufficient information to identify several parameters of natural variations in this strain of mice including their maturation state, stress level, and diet.

Variable selection using iterative reformulation of training set models for discrimination of samples: application to gas chromatography/mass spectrometry of mouse urinary metabolites.

The paper discusses variable selection as used in large metabolomic studies, exemplified by mouse urinary gas chromatography of 441 mice in three experiments to detect the influence of age, diet, and stress on their chemosignal. Partial least squares discriminant analysis (PLS-DA) was applied to obtain class models, using a procedure of 20,000 iterations including the bootstrap for model optimization and random splits into test and training sets for validation. Variables are selected using PLS regression coefficients on the training set using an optimized number of components obtained from the bootstrap. The variables are ranked in order of significance, and the overall optimal variables are selected as those that appear as highly significant over 100 different test and training set splits. Cost/benefit analysis of performing the model on a reduced number of variables is also illustrated. This paper provides a strategy for properly validated methods for determining which variables are most significant for discriminating between two groups in large metabolomic data sets avoiding the common pitfall of overfitting if variables are selected on a combined training and test set and also taking into account that different variables may be selected each time the samples are split into training and test sets using iterative procedures.