Publications

Abstract

Importance: Screen time has become an integral part of children's daily lives. Nevertheless, the developmental consequences of screen exposure in young children remain unclear.

Objective: To investigate the screen time trajectory from 6 to 72 months of age and its association with children's development at age 72 months in a prospective birth cohort.

Design, setting, and participants: Women in Shanghai, China, who were at 34 to 36 gestational weeks and had an expected delivery date between May 2012 and July 2013 were recruited for this cohort study. Their children were followed up at 6, 9, 12, 18, 24, 36, 48, and 72 months of age. Children's screen time was classified into 3 groups at age 6 months: continued low (ie, stable amount of screen time), late increasing (ie, sharp increase in screen time at age 36 months), and early increasing (ie, large amount of screen time in early stages that remained stable after age 36 months). Cognitive development was assessed by specially trained research staff in a research clinic. Of 262 eligible mother-offspring pairs, 152 dyads had complete data regarding all variables of interest and were included in the analyses. Data were analyzed from September 2019 to November 2021.

Exposures: Mothers reported screen times of children at 6, 9, 12, 18, 24, 36, 48, and 72 months of age.

Main outcomes and measures: The cognitive development of children was evaluated using the Wechsler Intelligence Scale for Children, 4th edition, at age 72 months. Social-emotional development was measured by the Strengths and Difficulties Questionnaire, which was completed by the child's mother. The study described demographic characteristics, maternal mental health, child's temperament at age 6 months, and mental development at age 12 months by subgroups clustered by a group-based trajectory model. Group difference was examined by analysis of variance.

Results: A total of 152 mother-offspring dyads were included in this study, including 77 girls (50.7%) and 75 boys (49.3%) (mean [SD] age of the mothers was 29.7 [3.3] years). Children's screen time trajectory from age 6 to 72 months was classified into 3 groups: continued low (110 [72.4%]), late increasing (17 [11.2%]), and early increasing (25 [16.4%]). Compared with the continued low group, the late increasing group had lower scores on the Full-Scale Intelligence Quotient (β coefficient, -8.23; 95% CI, -15.16 to -1.30; P < .05) and the General Ability Index (β coefficient, -6.42; 95% CI, -13.70 to 0.86; P = .08); the early increasing group presented with lower scores on the Full-Scale Intelligence Quotient (β coefficient, -6.68; 95% CI, -12.35 to -1.02; P < .05) and the Cognitive Proficiency Index (β coefficient, -10.56; 95% CI, -17.23 to -3.90; P < .01) and a higher total difficulties score (β coefficient, 2.62; 95% CI, 0.49-4.76; P < .05).

Conclusions and relevance: This cohort study found that excessive screen time in early years was associated with poor cognitive and social-emotional development. This finding may be helpful in encouraging awareness among parents of the importance of onset and duration of children's screen time.

Abstract

Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP) markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three different data sets were provided for the Genetic Analysis Workshop 15, thus allowing examination of various novel and existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene by environment interaction. The approach most often applied in this presentation group was random forests because of its simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs. Machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise and higher-order interactions compared with standard statistical approaches and can also have a high capability for classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are required.

Abstract

This paper constitutes a research proposal based on Rousse-Malpalt’s
(2019) dissertation, which extensively examines the effectiveness of the
Accelerative Integrated Method (AIM) in second language (L2) learning.
Although it has been found that AIM is a greatly effective method in comparison with non-implicit teaching methods, the reasons behind its success and effectiveness are yet unknown. As Semantic Memory (SM) is the component of memory responsible for the conceptualization and storage of knowledge, this paper sets to propose an investigation of its role in the learning process of AIM and provide with insights as to why the embodied experience of learning with AIM is more effective than others. The tasks proposed for administration take into account the factors of gestures being related to a learner’s memorization process and Semantic Memory. Lastly, this paper provides with a future research idea about the learning mechanisms of sign languages in people with hearing deficits and healthy population, aiming to indicate which brain mechanisms benefit from the teaching method of AIM and reveal important brain functions for SLA via AIM.

Abstract

The interplay between categorical and continuous aspects of the speech signal remains central and yet controversial in the fields of phonetics and phonology. The division between phonological abstractions and phonetic variations has been particularly relevant to the unraveling of diverse communicative functions of pitch in the domain of prosody. Pitch influences vocal communication in two major but fundamentally different ways, and lexical and intonational tones exquisitely capture these functions. Lexical tone contrasts convey lexical meanings as well as derivational meanings at the word level and are grammatically encoded as discrete structures. Intonational tones, on the other hand, signal post-lexical meanings at the phrasal level and typically allow gradient pragmatic variations. Since categorical and gradient uses of pitch are ubiquitous and closely intertwined in their physiological and psychological processes, further research is warranted for a more detailed understanding of their structural and functional characterisations. This Research Topic addresses this matter from a wide range of perspectives, including first and second language acquisition, speech production and perception, structural and functional diversity, and working with distinct languages and experimental measures. In the following, we provide a short overview of the contributions submitted to this topic

Abstract

The Corpus NGT