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Background PACS1-Neurodevelopmental Disorder (PACS1-NDD) is an ultra-rare condition due to a recurrent mutation in the PACS1 gene. Little systematically collected data exist about the functional abilities and neurodevelopmental morbidities in children with PACS1-NDD Methods Parents of individuals with PACS1-NDD completed an on-line survey designed collaboratively by researchers, parents, and clinicians. Analyses focused on those with a confirmed R203W variant. Results Of 35 individuals with confirmed variants, 18 (51%) were female. The median age was 8 years (interquartile range 4.5–15). Seventeen (49%) had a diagnosis of epilepsy. Twelve (40%, of 30 responding to the question) reported autism and (N = 11/30, 37%) reported features of autism. Most children walked independently (N = 29/32, 91%), had a pincer grasp (N = 23/32, 72%), could feed themselves independently (N = 15/32, 47%), and used speech (N = 23/32, 72%). Sixteen of twenty-nine (55%) had simple pre-academic skills. Neither epilepsy nor autism was associated with functional abilities or other clinical features (all P > 0.05). Conclusions PACS1-NDD is a moderately-severe intellectual disability syndrome in which seizures occur but are not a defining or primary feature. Successful precision medicine clinical trials for this ultra-rare disorder must target important core features of this disorder and utilize assessment tools commensurate with the level of function in this clinical population.

Semantic spaces are used as a representation of language, capturing the meaning between linguistic units. These spaces are often built in large corpora requiring advanced equipment, specialized computational skills, and considerable effort. This project note will introduce and demonstrate the use of an accessible Shiny graphical interface allowing users to create semantic space models easily. Shiny is an R package in which one can program interactive web applications in R for others to interact with data or analyses. The advantage to Shiny applications is that naïve users can explore data without understanding the programming, and open sharing of code with the application can aid in learning the programming for one’s own use in their research. Within the application, users will be able to load popular semantic spaces or their own corpus for semantic space creation utilizing their preferred modeling technique, including LSA and TOPICS. A variety of user-friendly graphical tools, such as n-nearest neighbors or topic weighted graph, will further aid data visualization of the semantic network. Additionally, the application provides the calculation of cosine or simple co-occurrence, among other popular-relatedness values. This tool is intended for researchers who may not be programming-savvy, or as a teaching extension for psycholinguistics courses.