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Examining Concurrent Associations Between Gesture Use, Developmental Domains, and Autistic Traits in Toddlers With Down Syndrome.

J Speech Lang Hear Res

January 2025

Down Syndrome Program, Division of Developmental Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, MA.

Purpose: Toddlers with Down syndrome (DS) showcase comparable or higher rates of gestures than chronological age- and language-matched toddlers without DS. Little is known about how gesture use in toddlers with DS relates to multiple domains of development, including motor, pragmatics, language, and visual reception (VR) skills. Unexplored is whether gesture use is a good marker of social communication skills in DS or if gesture development might be more reliably a marker of motor, language, pragmatics, or VR skills.

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Technoference, namely parental screen use in the presence of a child, is a widespread phenomenon that has negative effects on parent-child interaction and communication. When parents use screens around their children there are fewer interactions and parents are less contingent and responsive to the child. Additionally, children show more negative behaviors, such as whining, frustration, and outbursts.

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Introduction: Children's early language and communication skills are efficiently measured using parent report, for example, communicative development inventories (CDIs). These have scalable potential to determine risk of later language delay, and associations between delay and risk factors such as prematurity and poverty. However, there may be measurement difficulties in parent reports, including anomalous directions of association between child age/socioeconomic status and reported language.

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Despite increased attempts to express equality in speech, biases often leak out through subtle linguistic cues. For example, the subject-complement statement (SCS, "Girls are as good as boys at math") is used to advocate for equality but often reinforces gender stereotypes (boys are the standard against which girls are judged). We ask whether stereotypes conveyed by SCS can be counteracted by gesture.

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Automatic Sign Language Recognition Systems (ASLR) offers smooth communication between hearing-impaired and normal-hearing individuals, enhancing educational opportunities for impaired. However, it struggles with "curse of dimensionality" due to excessive features resulting in prolonged training time and exhaustive computational demand. This paper proposes technique that integrates machine learning and swarm intelligence to effectively address this issue.

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