Gene editing in non-human primates may lead to valuable models for exploring the etiologies and therapeutic strategies of genetically based neurological disorders in humans. However, a monkey model of neurological disorders that closely mimics pathological and behavioral deficits in humans has not yet been successfully generated. Microcephalin 1 (MCPH1) is implicated in the evolution of the human brain, and MCPH1 mutation causes microcephaly accompanied by mental retardation. Here we generated a cynomolgus monkey (Macaca fascicularis) carrying biallelic MCPH1 mutations using transcription activator-like effector nucleases. The monkey recapitulated most of the important clinical features observed in patients, including marked reductions in head circumference, premature chromosome condensation (PCC), hypoplasia of the corpus callosum and upper limb spasticity. Moreover, overexpression of MCPH1 in mutated dermal fibroblasts rescued the PCC syndrome. This monkey model may help us elucidate the role of MCPH1 in the pathogenesis of human microcephaly and better understand the function of this protein in the evolution of primate brain size.

In this paper, we present a study on keyword selection behavior in social media analysis that is focused on particular topics, and propose a new effective strategy that considers the co-occurrence relationships between keywords and uses graph-based techniques. In particular, we used the normalized rich-club connectivity considering the weighted degree, closeness centrality, betweenness centrality and PageRank values to measure a subgroup of highly connected “rich keywords” in a keyword co-occurrence network. Community detection is subsequently applied to identify several keyword combinations that are able to accurately and comprehensively represent the researched topic. The empirical results based on four topics and comparing four existing models confirm the performance of our proposed strategy in promoting the quantity and ensuing the quality of data related to particular topics collected from social media. Overall, our findings are expected to offer useful guidelines on how to select keywords for social media-based studies and thus further increase the reliability and validity of their respective conclusions.