Predictors of Early Numeracy: Applied Measures in Two Childcare Contexts

The purpose of the current research was: (1) To assess differences in early numeracy, phonological awareness, receptive language, executive functioning, and working memory for children in two childcare settings (family and center); (2) To determine whether applied measures of phonological awareness and executive functioning could serve as predictors of numeracy performance. Children (N = 89) ranging in age from 39 to 75 months were recruited from state-licensed childcare centers and family childcare homes. Teacher ratings of executive functioning were significantly related to early number skills, phonological awareness, and receptive language, but none of the parent ratings were significantly related to the child scores. The overall model did not differ between center and family childcare children. Phonological awareness was a significant predictor of number skills for both younger and older children. Receptive language skills were the best predictor of early numeracy performance for younger children and the best predictor for older children was phonological working memory measured by a non-words repetition task. These results suggest a connection between children’s numeracy skills and a developmental change from receptive language skills to phonological working memory skills

Preschool Mathematics Performance and Executive Function: Rural-Urban Comparisons Across Time

This longitudinal study, with urban and rural preschool children, examines the relationship between executive function (EF) and mathematics. A panel of direct and indirect measures of EF were used to determine which EF measures were most predictive and a measure of mathematics assessed both numeracy and geometry skill. One hundred eighteen children, ages 39 to 68 months, and their preschool teachers were included, with assessments given twice, about six months apart. EF measures were compared by the amount of variance in mathematics skill each claimed, including the influence of a child’s age, gender, and rural-urban context. Results suggest the child’s age determines if a panel of direct EF measures is a better predictor of numeracy and geometry skills than the use of a single EF measure. Different EF measures were more strongly related to numeracy versus geometry at Time 1 and Time 2. Differences unrelated to income were found between rural and urban children on numeracy skill but not geometry skill. These results are particularly important to state and regional early childhood directors who work across urban and rural areas, legislators and policymakers, teachers and parents

Preschool Mathematics Performance and Executive Function: Rural-Urban Comparisons across Time

This longitudinal study examined the relationship between executive function (EF) and mathematics with rural and urban preschool children. A panel of direct and indirect EF measures were used to compare how well individual measures, as well as analytic approaches, predicted both numeracy and geometry skill. One hundred eighteen children, ages 39 to 68 months, were given EF and mathematics assessments twice, approximately six months apart, concurrent to their teachers completing an indirect assessment of EF for each child. Results suggest: (1) the child’s age determines if a panel of direct EF measures is a better predictor of numeracy and geometry skills than a single EF measure, (2) geometry and numeracy skill are influenced differently by contextual factors, and (3) the EF-geometry link may develop about six months later than the EF-numeracy connection. As the relationship between preschool age EF and mathematics is better understood, efforts can be made to improve the aspects of EF connected to mathematics skill, which may aid in performance

Improved eukaryotic detection compatible with large-scale automated analysis of metagenomes

Background: Eukaryotes such as fungi and protists frequently accompany bacteria and archaea in microbial communities. Unfortunately, their presence is difficult to study with “shotgun” metagenomic sequencing since prokaryotic signals dominate in most environments. Recent methods for eukaryotic detection use eukaryote-specific marker genes, but they do not incorporate strategies to handle the presence of eukaryotes that are not represented in the reference marker gene set, and they are not compatible with web-based tools for downstream analysis. Results: Here, we present CORRAL (for Clustering Of Related Reference ALignments), a tool for the identification of eukaryotes in shotgun metagenomic data based on alignments to eukaryote-specific marker genes and Markov clustering. Using a combination of simulated datasets, mock community standards, and large publicly available human microbiome studies, we demonstrate that our method is not only sensitive and accurate but is also capable of inferring the presence of eukaryotes not included in the marker gene reference, such as novel strains. Finally, we deploy CORRAL on our MicrobiomeDB.org resource, producing an atlas of eukaryotes present in various environments of the human body and linking their presence to study covariates. Conclusions: CORRAL allows eukaryotic detection to be automated and carried out at scale. Implementation of CORRAL in MicrobiomeDB.org creates a running atlas of microbial eukaryotes in metagenomic studies. Since our approach is independent of the reference used, it may be applicable to other contexts where shotgun metagenomic reads are matched against redundant but non-exhaustive databases, such as the identification of bacterial virulence genes or taxonomic classification of viral reads