Evaluating the Performance of Transformer-based Language Models for Neuroatypical Language.

Difficulties with social aspects of language are among the hallmarks of autism spectrum disorder (ASD). These communication differences are thought to contribute to the challenges that adults with ASD experience when seeking employment, underscoring the need for interventions that focus on improving areas of weakness in pragmatic and social language. In this paper, we describe a transformer-based framework for identifying linguistic features associated with social aspects of communication using a corpus of conversations between adults with and without ASD and neurotypical conversational partners produced while engaging in collaborative tasks. While our framework yields strong accuracy overall, performance is significantly worse for the language of participants with ASD, suggesting that they use a more diverse set of strategies for some social linguistic functions. These results, while showing promise for the development of automated language analysis tools to support targeted language interventions for ASD, also reveal weaknesses in the ability of large contextualized language models to model neuroatypical language.

Predicting pragmatic discourse features in the language of adults with autism spectrum disorder.

Individuals with autism spectrum disorder (ASD) experience difficulties in social aspects of communication, but the linguistic characteristics associated with deficits in discourse and pragmatic expression are often difficult to precisely identify and quantify. We are currently collecting a corpus of transcribed natural conversations produced in an experimental setting in which participants with and without ASD complete a number of collaborative tasks with their neurotypical peers. Using this dyadic conversational data, we investigate three pragmatic features - politeness, uncertainty, and informativeness - and present a dataset of utterances annotated for each of these features on a three-point scale. We then introduce ongoing work in developing and training neural models to automatically predict these features, with the goal of identifying the same between-groups differences that are observed using manual annotations. We find the best performing model for all three features is a feedforward neural network trained with BERT embeddings. Our models yield higher accuracy than ones used in previous approaches for deriving these features, with F1 exceeding 0.82 for all three pragmatic features.

Apparent-talker height is influenced by Mandarin lexical tone.

Apparent-talker height is determined by a talker's fundamental frequency (f0) and spectral information, typically indexed using formant frequencies (FFs). Barreda [(2017b). J. Acoust. Soc. Am. 141, 4781-4792] reports that the apparent height of a talker can be influenced by vowel-specific variation in the f0 or FFs of a sound. In this experiment, native speakers of Mandarin were presented with a series of syllables produced by talkers of different apparent heights. Results indicate that there is substantial variability in the estimated height of a single talker based on lexical tone, as well as the inherent f0 and FFs of vowel phonemes.

An update on genomic-guided therapies for pediatric solid tumors.

Currently, out of the 82 US FDA-approved targeted therapies for adult cancer treatments, only three are approved for use in children irrespective of their genomic status. Apart from leukemia, only a handful of genomic-based trials involving children with solid tumors are ongoing. Emerging genomic data for pediatric solid tumors may facilitate the development of precision medicine in pediatric patients. Here, we provide an up-to-date review of all reported genomic aberrations in the eight most common pediatric solid tumors with whole-exome sequencing or whole-genome sequencing data (from cBioPortal database, Pediatric Cancer Genome Project, Therapeutically Applicable Research to Generate Effective Treatments) and additional non-whole-exome sequencing studies. Potential druggable events are highlighted and discussed so as to facilitate preclinical and clinical research in this area.

Exome and genome sequencing of nasopharynx cancer identifies NF-κB pathway activating mutations.

Nasopharyngeal carcinoma (NPC) is an aggressive head and neck cancer characterized by Epstein-Barr virus (EBV) infection and dense lymphocyte infiltration. The scarcity of NPC genomic data hinders the understanding of NPC biology, disease progression and rational therapy design. Here we performed whole-exome sequencing (WES) on 111 micro-dissected EBV-positive NPCs, with 15 cases subjected to further whole-genome sequencing (WGS), to determine its mutational landscape. We identified enrichment for genomic aberrations of multiple negative regulators of the NF-κB pathway, including CYLD, TRAF3, NFKBIA and NLRC5, in a total of 41% of cases. Functional analysis confirmed inactivating CYLD mutations as drivers for NPC cell growth. The EBV oncoprotein latent membrane protein 1 (LMP1) functions to constitutively activate NF-κB signalling, and we observed mutual exclusivity among tumours with somatic NF-κB pathway aberrations and LMP1-overexpression, suggesting that NF-κB activation is selected for by both somatic and viral events during NPC pathogenesis.