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Liquid–liquid phase separation (LLPS) underlies diverse biological processes. Because most LLPS studies were performed in vitro using recombinant proteins or in cells that overexpress protein, the physiological relevance of LLPS for endogenous protein is often unclear. PERIOD, the intrinsically disordered domain-rich proteins, are central mammalian circadian clock components and interact with other clock proteins in the core circadian negative feedback loop. Different core clock proteins were previously shown to form large complexes. Circadian clock studies often rely on experiments that overexpress clock proteins. Here, we show that when Per2 transgene was stably expressed in cells, PER2 protein formed nuclear phosphorylation-dependent slow-moving LLPS condensates that recruited other clock proteins. Super-resolution microscopy of endogenous PER2, however, revealed formation of circadian-controlled, rapidly diffusing nuclear microbodies that were resistant to protein concentration changes, hexanediol treatment, and loss of phosphorylation, indicating that they are distinct from the LLPS condensates caused by protein overexpression. Surprisingly, only a small fraction of endogenous PER2 microbodies transiently interact with endogenous BMAL1 and CRY1, a conclusion that was confirmed in cells and in mice tissues, suggesting an enzyme-like mechanism in the circadian negative feedback process. Together, these results demonstrate that the dynamic interactions of core clock proteins are a key feature of mammalian circadian clock mechanism and the importance of examining endogenous proteins in LLPS and circadian clock studies.

 

Video programs are important, accessible educational resources for young children, especially those from an under-resourced backgrounds. These programs’ potential can be amplified if children are allowed to socially interact with media characters during their video watching. This paper presents the design and empirical investigation of interactive science-focused videos in which the main character, powered by a conversational agent, engaged in contingent conversation with children by asking children questions and providing responsive feedback. We found that children actively interacted with the media character in the conversational videos and their parents spontaneously provided support in the process. We also found that the children who watched the conversational video performed better in the immediate, episode-specific science assessment compared to their peers who watched the broadcast, non-interactive version of the same episode. Several design implications are discussed for using conversational technologies to better support child active learning and parent involvement in video watching. 

Acid-base homeostasis is a fundamental property of living cells and its persistent disruption in human cells can lead to a wide range of diseases. We have conducted computational modeling and analysis of transcriptomic data of 4750 human tissue samples of nine cancer types in the TCGA database. Built on our previous study, we have quantitatively estimated the (average) production rate of OH− by cytosolic Fenton reactions, which continuously disrupt the intracellular pH homeostasis. Our predictions indicate that all or a subset of 43 reprogrammed metabolisms (RMs) are induced to produce net protons (H+) at comparable rates of Fenton reactions to keep the intracellular pH stable. We have then discovered that a number of well-known phenotypes of cancers, including increased growth rate, metastasis rate and local immune cell composition, can be naturally explained in terms of the Fenton reaction level and the induced RMs. This study strongly suggests the possibility to have a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors. In addition, strong evidence is provided to demonstrate that a popular view of that Na+/H+ exchangers, along with lactic acid exporters and carbonic anhydrases are responsible for the intracellular alkalization and extracellular acidification in cancer may not be justified. 

Despite its benefits for children’s skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions. While recent advances made AI generation of questions from stories possible, the fully-automated approach excludes parent involvement, disregards educational goals, and underoptimizes for child engagement. Informed by need-finding interviews and participatory design (PD) results, we developed StoryBuddy, an AI-enabled system for parents to create interactive storytelling experiences. StoryBuddy’s design highlighted the need for accommodating dynamic user needs between the desire for parent involvement and parent-child bonding and the goal of minimizing parent intervention when busy. The PD revealed varied assessment and educational goals of parents, which StoryBuddy addressed by supporting configuring question types and tracking child progress. A user study validated StoryBuddy’s usability and suggested design insights for future parent-AI collaboration systems. 

Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets carefully designed to serve this purpose. In particular, existing datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. Drawing on the reading education research, we introduce FairytaleQA, a dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. Our dataset is valuable in two folds: First, we ran existing QA models on our dataset and confirmed that this annotation helps assess models’ fine-grained learning skills. Second, the dataset supports question generation (QG) task in the education domain. Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.