The evolution of language: a comparative review

For many years the evolution of language has been seen as a disreputable topic, mired in fanciful "just so stories" about language origins. However, in the last decade a new synthesis of modern linguistics, cognitive neuroscience and neo-Darwinian evolutionary theory has begun to make important contributions to our understanding of the biology and evolution of language. I review some of this recent progress, focusing on the value of the comparative method, which uses data from animal species to draw inferences about language evolution. Discussing speech first, I show how data concerning a wide variety of species, from monkeys to birds, can increase our understanding of the anatomical and neural mechanisms underlying human spoken language, and how bird and whale song provide insights into the ultimate evolutionary function of language. I discuss the ‘‘descended larynx’ ’ of humans, a peculiar adaptation for speech that has received much attention in the past, which despite earlier claims is not uniquely human. Then I will turn to the neural mechanisms underlying spoken language, pointing out the difficulties animals apparently experience in perceiving hierarchical structure in sounds, and stressing the importance of vocal imitation in the evolution of a spoken language. Turning to ultimate function, I suggest that communication among kin (especially between parents and offspring) played a crucial but neglected role in driving language evolution. Finally, I briefly discuss phylogeny, discussing hypotheses that offer plausible routes to human language from a non-linguistic chimp-like ancestor. I conclude that comparative data from living animals will be key to developing a richer, more interdisciplinary understanding of our most distinctively human trait: language

Therapeutic Impact of Cytoreductive Surgery and Irradiation of Posterior Fossa Ependymoma in the Molecular Era: A Retrospective Multicohort Analysis

Posterior fossa ependymoma comprises two distinct molecular variants termed EPN_PFA and EPN_PFB that have a distinct biology and natural history. The therapeutic value of cytoreductive surgery and radiation therapy for posterior fossa ependymoma after accounting for molecular subgroup is not known

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Attention and Intention Goals Can Mediate Disruption in Human-Computer Interaction

Part 1: Long and Short PapersInternational audienceMultitasking environments cause people to be interrupted constantly, often interfering with their ongoing tasks, activities and goals. This paper focuses on the disruption caused by interruptions and presents a disruption mediating approach for balancing the negative effects of interruptions with respect to the benefits of interruptions relevant to the user goals. Our work shows how Disruption Manager utilizing context and relationships to user goals and tasks can assess when and how to present interruptions in order to reduce their disruptiveness.The Disruption Management Framework was created to take into consideration motivations that influence people’s interruption decision process. The framework predicts the effects from interruptions using a three-layer software architecture: a knowledge layer including information about topics related to the ongoing activity, an intermediate layer including summarized information about the user tasks and their stages, and a low level layer including implicit low granularity information, such as mouse movement, context switching and windowing activity to support fail-safe disruption management when no other contextual information is available. The manager supports implicit monitoring of ongoing behaviors and categorizing possible disruptive outcome given the user and system state. The manager monitors actions and uses common sense reasoning in its model to compare communication stream topics with topics files that are active on the desktop.Experiments demonstrate that disruption manager significantly reduces the impact of interruptions and improve people’s performance in a multi-application desktop scenario with email and instant messaging. In a complex order taking activity, disruption manager yielded a 26% performance increase for tasks prioritized as being important and a 32.5% increase for urgent tasks. The evaluation shows that the modulated interruptions did not distract or troubled users. Further, subjects using the Disruption Manager were 5 times more likely to respond effectively to instant messages