Mutations disrupting the kinase domain of IKKα lead to immunodeficiency and immune dysregulation in humans.

IKKα, encoded by CHUK, is crucial in the non-canonical NF-κB pathway and part of the IKK complex activating the canonical pathway alongside IKKβ. The absence of IKKα causes fetal encasement syndrome in humans, fatal in utero, while an impaired IKKα-NIK interaction was reported in a single patient and causes combined immunodeficiency. Here, we describe compound heterozygous variants in the kinase domain of IKKα in a female patient with hypogammaglobulinemia, recurrent lung infections, and Hay-Wells syndrome-like features.

Novel Z-DNA binding domains in giant viruses.

Z-nucleic acid structures play vital roles in cellular processes and have implications in innate immunity due to their recognition by Zα domains containing proteins (Z-DNA/Z-RNA binding proteins, ZBPs). Although Zα domains have been identified in six proteins, including viral E3L, ORF112, and I73R, as well as, cellular ADAR1, ZBP1, and PKZ, their prevalence across living organisms remains largely unexplored. In this study, we introduce a computational approach to predict Zα domains, leading to the revelation of previously unidentified Zα domain-containing proteins in eukaryotic organisms, including non-metazoan species.

Predicting Autism from Head Movement Patterns during Naturalistic Social Interactions.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized in part by difficulties in verbal and nonverbal social communication. Evidence indicates that autistic people, compared to neurotypical peers, exhibit differences in head movements, a key form of nonverbal communication. Despite the crucial role of head movements in social communication, research on this nonverbal cue is relatively scarce compared to other forms of nonverbal communication, such as facial expressions and gestures.

Comparison of Human Experts and AI in Predicting Autism from Facial Behavior.

Advances in computational behavior analysis via artificial intelligence (AI) promise to improve mental healthcare services by providing clinicians with tools to assist diagnosis or measurement of treatment outcomes. This potential has spurred an increasing number of studies in which automated pipelines predict diagnoses of mental health conditions. However, a fundamental question remains unanswered: How do the predictions of the AI algorithms correspond and compare with the predictions of humans? This is a critical question if AI technology is to be used as an assistive tool, because the utility of an AI algorithm would be negligible if it provides little information beyond what clinicians can readily infer.