• Digital Humanities and Cultural Heritage
• Publications close
• Research data close
• Other research products close
• UG close
• bioRxiv close

AbstractThe explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource,stdpopsim, that attempts to rectify this situation.Stdpopsimis a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating howstdpopsimcan be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

ABSTRACTIn the US, the normal, oral temperature of adults is, on average, lower than the canonical 37°C established in the 19th century. We postulated that body temperature has decreased over time. Using measurements from three cohorts--the Union Army Veterans of the Civil War (N=23,710; measurement years 1860-1940), the National Health and Nutrition Examination Survey I (N=15,301; 1971-1975), and the Stanford Translational Research Integrated Database Environment (N=150,280; 2007-2017)--we determined that mean body temperature in men and women, after adjusting for age, height, weight and, in some models date and time of day, has decreased monotonically by 0.03°C per birth decade. A similar decline within the Union Army cohort as between cohorts, makes measurement error an unlikely explanation. This substantive and continuing shift in body temperature—a marker for metabolic rate—provides a framework for understanding changes in body habitus and human longevity over the last 200 years.

The extent to which prehistoric migrations of farmers influenced the genetic pool of western North Africans remains unclear. Archaeological evidence suggests that the Neolithization process may have happened through the adoption of innovations by local Epipaleolithic communities or by demic diffusion from the Eastern Mediterranean shores or Iberia. Here, we present an analysis of individuals’ genome sequences from Early and Late Neolithic sites in Morocco and from Early Neolithic individuals from southern Iberia. We show that Early Neolithic Moroccans (∼5,000 BCE) are similar to Later Stone Age individuals from the same region and possess an endemic element retained in present-day Maghrebi populations, confirming a long-term genetic continuity in the region. This scenario is consistent with Early Neolithic traditions in North Africa deriving from Epipaleolithic communities that adopted certain agricultural techniques from neighboring populations. Among Eurasian ancient populations, Early Neolithic Moroccans are distantly related to Levantine Natufian hunter-gatherers (∼9,000 BCE) and Pre-Pottery Neolithic farmers (∼6,500 BCE). Late Neolithic (∼3,000 BCE) Moroccans, in contrast, share an Iberian component, supporting theories of trans-Gibraltar gene flow and indicating that Neolithization of North Africa involved both the movement of ideas and people. Lastly, the southern Iberian Early Neolithic samples share the same genetic composition as the Cardial Mediterranean Neolithic culture that reached Iberia ∼5,500 BCE. The cultural and genetic similarities between Iberian and North African Neolithic traditions further reinforce the model of an Iberian migration into the Maghreb. 5,601 6774 9,58 6779 SCIE Q1

The N400 component of the event-related brain potential has aroused much interest because it is thought to provide an online measure of meaning processing in the brain. This component, however, has been hard to capture within traditional approaches to language processing. Here, we show that a neural network that eschews these traditions can capture a wide range of findings on the factors that affect the amplitude of the N400. The model simulates the N400 as the change induced by an incoming word in an initial, implicit probabilistic representation of the situation or event described by the linguistic input, captured by the hidden unit activation pattern in a neural network. We further propose a new learning rule in which the process underlying the N400 drives implicit learning in the network. The model provides a unified account of a large body of findings and connects human language processing with successful deep learning approaches to language processing.

AbstractProduction and comprehension have long been viewed as inseparable components of language. The study of vision, by contrast, has centered almost exclusively on comprehension. Here we investigate drawing — the most basic form of visual production. How do we convey concepts in visual form, and how does refining this skill, in turn, affect recognition? We developed an online platform for collecting large amounts of drawing and recognition data, and applied a deep convolutional neural network model of visual cortex trained only on natural images to explore the hypothesis that drawing recruits the same abstract feature representations that support natural visual object recognition. Consistent with this hypothesis, higher layers of this model captured the abstract features of both drawings and natural images most important for recognition, and people learning to produce more recognizable drawings of objects exhibited enhanced recognition of those objects. These findings could explain why drawing is so effective for communicating visual concepts, they suggest novel approaches for evaluating and refining conceptual knowledge, and they highlight the potential of deep networks for understanding human learning.