Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease in humans and results in significant morbidity and mortality. Research over the past 25 years has contributed enormous insight into this inherited disease particularly in the areas of genetics, molecular mechanisms, and pathophysiology. Our understanding continues to be limited by the heterogeneity of clinical presentations with various genetic mutations associated with HCM. Transgenic mouse models have been utilized especially studying the genotypic and phenotypic interactions. However, mice possess intrinsic cardiac and hemodynamic differences compared to humans and have limitations preventing their direct translation. Other animal models of HCM have been studied or generated in part to overcome these limitations. HCM in cats shows strikingly similar molecular, histopathological, and genetic similarities to human HCM, and offers an important translational opportunity for the study of this disease. Recently, inherited left ventricular hypertrophy in rhesus macaques was identified and collaborative investigations have been conducted to begin to develop a non-human primate HCM model. These naturally-occurring large-animal models may aid in advancing our understanding of HCM and developing novel therapeutic approaches to this disease. This review will highlight the features of HCM in humans and the relevant available and developing animal models of this condition.

This paper proposes an experimental system called IST for the facilitation of users' analogical transfer by providing image-schemas. First, by taking the radiation problem as an example, we hypothesized that a proper image-schema can promote human analogical problem solving owing to its plasticity and ascertained the hypothesis based on a cognitive experiment. We then constructed the IST system which can provide image-schemas with plasticity, by using the extended techniques of analogical mapping and of constraint-based graphics.

Since Johanson (1973), it is well known that people perceive human behavior, not only familiar ones such as walking and dancing but also unfamiliar behaviors in point-light display. While categorization among well-known behaviors has been well studied, it is not clear yet how people extract the cues that are useful for perceiving unfamiliar behaviors. We hypothesized that the hierarchical information in human structure was playing an important part for perception of unfamiliar behaviors, and examined this hypothesis by comparing performances of three kinds of models in categorization tasks of unfamiliar arm movements. While the first model was based only on local motion of the point lights, the second model used hierarchical ordering information to analyze the local motion. The third model used strict positioning following the hierarchical structure in addition to the ordering information. The comparison suggested that hierarchical ordering was important information to producing close performance to human performance.

To develop computational agents that better communicate using their own emergent language, we endow the agents with an ability to focus their attention on particular concepts in the environment. Humans often understand an object or scene as a composite of concepts and those concepts are further mapped onto words. We implement this intuition as cross-modal attention mechanisms in Speaker and Listener agents in a referential game and show attention leads to more compositional and interpretable emergent language. We also demonstrate how attention aids in understanding the learned communication protocol by investigating the attention weights associated with each message symbol and the alignment of attention weights between Speaker and Listener agents. Overall, our results suggest that attention is a promising mechanism for developing more human-like emergent language.