Dataset Information

Unraveling middle childhood attachment-related behavior sequences using a micro-coding approach.

ABSTRACT: Attachment theory states that children learn to trust in their parent's availability and support if they repeatedly experience that their parents respond sensitively to their needs during distress. Attachment is thus developed and shaped by day-to-day interactions, while at the same time, each interaction is a momentary expression of the attachment relation. How attachment-related behaviors of mother and child follow upon each other during interactions in middle childhood, and how these sequences differ in function of attachment quality, has hardly been studied up to now. To fill this gap, we analyzed the micro-coded interaction of 55 mother-child dyads (27 girls, 28 boys, mean age: 10.3) after a standardized stress-induction. Results reveal that all mother-child dyads show a loop between positive mother and child behaviors. This pattern is complemented with a loop of negative mother and child behaviors in low-trust and more avoidantly attached children: these children tend to handle negative mother behavior less well as they show more negative behavior and less positive behavior in response to negative maternal behavior. More anxiously attached children also show less positive behavior, but react positively on collaborative interactions. The micro-coded interactions thus reveal important insights that inform practitioners and advance attachment theory.

SUBMITTER: Bodner N

PROVIDER: S-EPMC6818776 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Unraveling middle childhood attachment-related behavior sequences using a micro-coding approach.

Bodner Nadja N Bosmans Guy G Sannen Jasmien J Verhees Martine M Ceulemans Eva E

PloS one 20191029 10

Attachment theory states that children learn to trust in their parent's availability and support if they repeatedly experience that their parents respond sensitively to their needs during distress. Attachment is thus developed and shaped by day-to-day interactions, while at the same time, each interaction is a momentary expression of the attachment relation. How attachment-related behaviors of mother and child follow upon each other during interactions in middle childhood, and how these sequence ...[more]

PMID: 31661519

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Project description:Cultural psychology assumes that the ecocultural conditions of a particular setting shape children's pathways, resulting in multiple adaptive solutions to universal developmental tasks. While the adaptivity of attachment and children's psychosocial development during the early years has been thoroughly investigated, attachment research during middle childhood continues to reflect Western ideals of family. Adhering to ideas of monotropy, most studies only focus on parental attachment figures. However, this restricted empirical perspective does not only result in a Eurocentric bias, it also neglects theoretical reflections on the growing complexity of attachment during middle childhood, thus only considering a limited selection of all individuals contributing to the children's feeling of security, even in Western settings. To investigate the variability and adaptivity of attachment during middle childhood, this study assessed children's attachment figures in two extreme settings of development, introducing an exhaustive network perspective on attachment during this developmental stage. Children of the Cameroonian Nseh (N = 11) and German children from Bad Nauheim (N = 11) identified and differentiated all individuals contributing to their attachment need in an exploratory and transdisciplinary approach. The socio-structural composition of children's attachment networks follows the context-specific systems of care and concepts of interconnectedness and the ecological features of each setting, resulting in marked differences between both contexts. The functional composition, however, reflects children's preoccupation with similar developmental challenges across settings. Same-aged peers contribute to the children's feeling of safety in both settings, thereby deviating from previous reflections on their subordinate relevance during middle childhood. Overall, these results support the adaptiveness of children's attachment patterns while also demonstrating universal trends across contexts. They highlight the collective nature of attachment during middle childhood that exceeds the impact of individual dyads. Thus, broad and context-sensitive research strategies become a necessary addition to attachment research in order to generate an exhaustive understanding for children's development across cultural contexts.

Project description:BackgroundMicroRNAs (miRNAs) are endogenous ?22 nt RNAs that are identified in many species as powerful regulators of gene expressions. Experimental identification of miRNAs is still slow since miRNAs are difficult to isolate by cloning due to their low expression, low stability, tissue specificity and the high cost of the cloning procedure. Thus, computational identification of miRNAs from genomic sequences provide a valuable complement to cloning. Different approaches for identification of miRNAs have been proposed based on homology, thermodynamic parameters, and cross-species comparisons.ResultsThe present paper focuses on the integration of miRNA classifiers in a meta-classifier and the identification of miRNAs from metagenomic sequences collected from different environments. An ensemble of classifiers is proposed for miRNA hairpin prediction based on four well-known classifiers (Triplet SVM, Mipred, Virgo and EumiR), with non-identical features, and which have been trained on different data. Their decisions are combined using a single hidden layer neural network to increase the accuracy of the predictions. Our ensemble classifier achieved 89.3% accuracy, 82.2% f-measure, 74% sensitivity, 97% specificity, 92.5% precision and 88.2% negative predictive value when tested on real miRNA and pseudo sequence data. The area under the receiver operating characteristic curve of our classifier is 0.9 which represents a high performance index.The proposed classifier yields a significant performance improvement relative to Triplet-SVM, Virgo and EumiR and a minor refinement over MiPred.The developed ensemble classifier is used for miRNA prediction in mine drainage, groundwater and marine metagenomic sequences downloaded from the NCBI sequence reed archive. By consulting the miRBase repository, 179 miRNAs have been identified as highly probable miRNAs. Our new approach could thus be used for mining metagenomic sequences and finding new and homologous miRNAs.ConclusionsThe paper investigates a computational tool for miRNA prediction in genomic or metagenomic data. It has been applied on three metagenomic samples from different environments (mine drainage, groundwater and marine metagenomic sequences). The prediction results provide a set of extremely potential miRNA hairpins for cloning prediction methods. Among the ensemble prediction obtained results there are pre-miRNA candidates that have been validated using miRbase while they have not been recognized by some of the base classifiers.

Dataset Information

Unraveling middle childhood attachment-related behavior sequences using a micro-coding approach.

Publications

Unraveling middle childhood attachment-related behavior sequences using a micro-coding approach.

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