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For d/Deaf and hard-of-hearing prereaders who communicate predominately in spoken and/or signed English (DHH-English), Teachers of the d/Deaf (TODs) read books aloud to increase English skills, auditory-verbal comprehension, sequencing skills, verbal reasoning, background knowledge, and sight word recognition. Teachers struggle to select appropriate books for read alouds. Unfortunately, the only available book selection system for TODs serving DHH-English was developed for a different purpose than identifying storybooks to increase English skills and was based on a methodology difficult to evaluate. To help teachers select books, we created an empirically-derived difficulty-level system based on the judgments of 69 experienced TODs serving DHH-English preschoolers and kindergartener across the United States. We used 14 storybooks and asked the TODs to group these books and order these groups based on how difficult the storybooks would be for DHH-English prereaders to understand when the books were read aloud. Additionally, TODs described the groups they created and why they assigned the books in their groups. We used these descriptions in a content analysis to derive a glossary of book characteristics. Finally, we analysed these results with a multivariate technique that simultaneously analysed the glossary and ranking data to create a difficulty-level scale and to identify exemplar books. The book selection system includes a two-tiered glossary, a six-point difficulty-level scale and exemplar books for each scale level. We discuss how this empirically-derived book selection system for read alouds with DHH-English prereaders can be paired with the three existing evidence-based read-aloud interventions designed to increase oral language skills.

Purpose: Successful speech processing depends on our ability to detect and integrate multisensory cues, yet there is minimal research on multisensory speech detection and integration by children. To address this need, we studied the development of speech detection for auditory (A), visual (V), and audiovisual (AV) input. Method: Participants were 115 typically developing children clustered into age groups between 4 and 14 years. Speech detection (quantified by response times [RTs]) was determined for 1 stimulus, /buh/, presented in A, V, and AV modes (articulating vs. static facial conditions). Performance was analyzed not only in terms of traditional mean RTs but also in terms of the faster versus slower RTs (defined by the 1st vs. 3rd quartiles of RT distributions). These time regions were conceptualized respectively as reflecting optimal detection with efficient focused attention versus less optimal detection with inefficient focused attention due to attentional lapses. Results: Mean RTs indicated better detection (a) of multisensory AV speech than A speech only in 4- to 5-year-olds and (b) of A and AV inputs than V input in all age groups. The faster RTs revealed that AV input did not improve detection in any group. The slower RTs indicated that (a) the processing of silent V input was significantly faster for the articulating than static face and (b) AV speech or facial input significantly minimized attentional lapses in all groups except 6- to 7-year-olds (a peaked U-shaped curve). Apparently, the AV benefit observed for mean performance in 4- to 5-year-olds arose from effects of attention. Conclusions: The faster RTs indicated that AV input did not enhance detection in any group, but the slower RTs indicated that AV speech and dynamic V speech (mouthing) significantly minimized attentional lapses and thus did influence performance. Overall, A and AV inputs were detected consistently faster than V input; this result endorsed stimulus-bound auditory processing by these children.

Component‐based multitable methods, such as multiple factor analysis (MFA), STATIS, and DiSTATIS, are routinely used to analyze multiblock data, which are now common in chemometrics and sensory evaluation studies. These blocks of data form data tables that—for example, in sensory evaluation—describe how different assessors evaluate a set of products either on a set of descriptors or on the similarity between products. To analyze these data, component‐based multitable methods extract orthogonal components explaining most of the variance of the data. However, when the data tables are heterogeneous or have complex structures, a single component space does not represent the data well and can give components that are difficult to interpret. Previous literature solved this interpretation problem by eliminating irrelevant variables—a process called sparsification—while keeping the components orthogonal. Here, we extended such methods to develop sparsification algorithms for three multitable methods, namely, "sparse MFA" (sMFA), "sparse STATIS" (sSTATIS), and "sparse DiSTATIS" (sDiSTATIS). In these sparse methods, we sparsified the data tables to identify the most informative assessors or products. In sMFA, we show how group sparsity can be used to sparsify whole tables (i.e., assessors or products), hereby greatly increasing the interpretability of sMFA's outcome. In sSTATIS and sDiSTATIS, we developed two different sparsification approaches: One approach creates subgroups of products and simplifies the components to facilitate interpretation; whereas the other approach creates subgroups of assessors and alleviates the problem of heterogeneity. We showed with three examples how these sparse methods increase interpretability of the results in sensory evaluation. Multitable data sets are becoming more and more common in chemometrics and sensory evaluation studies. The analysis of such data is complex, mostly due to the complex structure of each table combined with the complex interrelationships between tables. We present a way to sparsify three well‐known methods for analyzing multitable data: Multiple Factor Analysis, STATIS, and DiSTATIS. We show how sparsifying increases the interpretability of the results on two sensory evaluation data sets. [ABSTRACT FROM AUTHOR]

Gestures are an important social communication skill that infants and toddlers use to convey their thoughts, ideas, and intentions. Research suggests that early gesture use has important downstream impacts on developmental processes, such as language learning. However, autistic children are more likely to have challenges in their gestural development. The current study expands upon previous literature on the differences in gesture use between young autistic and non‐autistic toddlers by collecting data using a parent‐report questionnaire called the MCDI–Words and Gestures at three time points, 12, 18, and 24 months of age. Results (N = 467) showed that high‐likelihood infants who later met diagnostic criteria for ASD (n = 73 HL‐ASD) have attenuated gesture growth from 12 to 24 months for both deictic gestures and symbolic gestures when compared to high‐likelihood infants who later did not meet criteria for ASD (n = 249 HL‐Neg) and low‐likelihood infants who did not meet criteria for ASD (n = 145 LL‐Neg). Other social communicative skills, like play behaviors and imitation, were also found to be impacted in young autistic children when compared to their non‐autistic peers. Understanding early differences in social communication growth before a formal autism diagnosis can provide important insights for early intervention. Lay Summary: As infants learn to talk, they use gestures to communicate. In this study, we used a parent‐report questionnaire to look at the group level differences of gestures in the first 2 years of life by comparing infants who later developed autism to infants who did not develop autism. We found that infants who later developed autism have fewer gestures and a slower rate of gesture growth compared to infants who did not develop autism. Play behaviors and imitation skills were also impacted in young autistic children when compared to their non‐autistic peers. [ABSTRACT FROM AUTHOR]

Background: Prior work has shown that certain modifiable health, Alzheimer's disease (AD) biomarker, and demographic variables are associated with cognitive performance. However, less is known about the relative importance of these different domains of variables in predicting longitudinal change in cognition. Objective: Identify novel relationships between modifiable physical and health variables, AD biomarkers, and slope of cognitive change over two years in a cohort of older adults with mild cognitive impairment (MCI). Methods: Metrics of cardiometabolic risk, stress, inflammation, neurotrophic/growth factors, and AD pathology were assessed in 123 older adults with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (mean age = 73.9; SD = 7.6; mean education = 16.0; SD = 3.0). Partial least squares regression (PLSR)—a multivariate method which creates components that best predict an outcome—was used to identify whether these physiological variables were important in predicting slope of change in episodic memory or executive function over two years. Results: At two-year follow-up, the two PLSR models predicted, respectively, 20.0% and 19.6% of the variance in change in episodic memory and executive function. Baseline levels of AD biomarkers were important in predicting change in both episodic memory and executive function. Baseline education and neurotrophic/growth factors were important in predicting change in episodic memory, whereas cardiometabolic variables such as blood pressure and cholesterol were important in predicting change in executive function. Conclusion: These data-driven analyses highlight the impact of AD biomarkers on cognitive change and further clarify potential domain specific relationships with predictors of cognitive change. [ABSTRACT FROM AUTHOR]