No abstract

Pogo transposable element derived with ZNF domain (POGZ) has been identified as one of the most recurrently de novo mutated genes in patients with neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), intellectual disability and White-Sutton syndrome; however, the neurobiological basis behind these disorders remains unknown. Here, we show that POGZ regulates neuronal development and that ASD-related de novo mutations impair neuronal development in the developing mouse brain and induced pluripotent cell lines from an ASD patient. We also develop the first mouse model heterozygous for a de novo POGZ mutation identified in a patient with ASD, and we identify ASD-like abnormalities in the mice. Importantly, social deficits can be treated by compensatory inhibition of elevated cell excitability in the mice. Our results provide insight into how de novo mutations on high-confidence ASD genes lead to impaired mature cortical network function, which underlies the cellular pathogenesis of NDDs, including ASD.

This paper proposes a novel feature extraction framework from mutli-party multimodal conversation for inference of personality traits and emergent leadership. The proposed framework represents multi modal features as the combination of each participant's nonverbal activity and group activity. This feature representation enables to compare the nonverbal patterns extracted from the participants of different groups in a metric space. It captures how the target member outputs nonverbal behavior observed in a group (e.g. the member speaks while all members move their body), and can be available for any kind of multiparty conversation task. Frequent cooccurrent events are discovered using graph clustering from multimodal sequences. The proposed framework is applied for the ELEA corpus which is an audio visual dataset collected from group meetings. We evaluate the framework for binary classification task of 10 personality traits. Experimental results show that the model trained with co-occurrence features obtained higher accuracy than previously related work in 8 out of 10 traits. In addition, the cooccurrence features improve the accuracy from 2% up to 17%.

Schizophrenia is a chronic psychiatric disorder with complex genetic and environmental origins. While many antipsychotics have been demonstrated as effective in the treatment of schizophrenia, a substantial number of schizophrenia patients are partially or fully unresponsive to the treatment. Clozapine is the most effective antipsychotic drug for treatment-resistant schizophrenia; however, clozapine has rare but serious side-effects. Furthermore, there is inter-individual variability in the drug response to clozapine treatment. Therefore, the identification of the molecular mechanisms underlying the action of clozapine and drug response predictors is imperative. In the present study, we focused on a pair of monozygotic twin cases with treatment-resistant schizophrenia, in which one twin responded well to clozapine treatment and the other twin did not. Using induced pluripotent stem (iPS) cell-based technology, we generated neurons from iPS cells derived from these patients and subsequently performed RNA-sequencing to compare the transcriptome profiles of the mock or clozapine-treated neurons. Although, these iPS cells similarly differentiated into neurons, several genes encoding homophilic cell adhesion molecules, such as protocadherin genes, showed differential expression patterns between these two patients. These results, which contribute to the current understanding of the molecular mechanisms of clozapine action, establish a new strategy for the use of monozygotic twin studies in schizophrenia research.

Group discussions are used widely when generating new ideas and forming decisions as a group. Therefore, it is assumed that giving social influence to other members through facilitating the discussion is an important part of discussion skill. This study focuses on influential statements that affect discussion flow and highly related to facilitation, and aims to establish a model that predicts influential statements in group discussions. First, we collected a multimodal corpus using different group discussion tasks; in-basket and case-study. Based on schemes for analyzing arguments, each utterance was annotated as being influential or not. Then, we created classification models for predicting influential utterances using prosodic features as well as attention and head motion information from the speaker and other members of the group. In our model evaluation, we discovered that the assessment of each participant in terms of discussion facilitation skills by experienced observers correlated highly to the number of influential utterances by a given participant. This suggests that the proposed model can predict influential statements with considerable accuracy, and the prediction results can be a good predictor of facilitators in group discussions.