Different environmental variables predict body and brain size evolution in Homo

Increasing body and brain size constitutes a key macro-evolutionary pattern in the hominin lineage, yet the mechanisms behind these changes remain debated. Hypothesized drivers include environmental, demographic, social, dietary, and technological factors. Here we test the influence of environmental factors on the evolution of body and brain size in the genus Homo over the last one million years using a large fossil dataset combined with global paleoclimatic reconstructions and formalized hypotheses tested in a quantitative statistical framework. We identify temperature as a major predictor of body size variation within Homo, in accordance with Bergmann’s rule. In contrast, net primary productivity of environments and long-term variability in precipitation correlate with brain size but explain low amounts of the observed variation. These associations are likely due to an indirect environmental influence on cognitive abilities and extinction probabilities. Most environmental factors that we test do not correspond with body and brain size evolution, pointing towards complex scenarios which underlie the evolution of key biological characteristics in later Homo.Introduction Results - Approach of power analysis and linear regressions - Power analysis of synthetic data - Analysis of fossil data Discussion Methods - Body and brain size database - Climate reconstructions - Synthetic datasets and power analysi

SEMIC: an efficient surface energy and mass balance model applied to the Greenland ice sheet

We present SEMIC, a Surface Energy and Mass balance model of Intermediate Complexity for snow- and ice-covered surfaces such as the Greenland ice sheet. SEMIC is fast enough for glacial cycle applications, making it a suitable replacement for simpler methods such as the positive degree day (PDD) method often used in ice sheet modelling. Our model explicitly calculates the main processes involved in the surface energy and mass balance, while maintaining a simple interface and requiring minimal data input to drive it. In this novel approach, we parameterise diurnal temperature variations in order to more realistically capture the daily thaw–freeze cycles that characterise the ice sheet mass balance. We show how to derive optimal model parameters for SEMIC specifically to reproduce surface characteristics and day-to-day variations similar to the regional climate model MAR (Modèle Atmosphérique Régional, version 2) and its incorporated multilayer snowpack model SISVAT (Soil Ice Snow Vegetation Atmosphere Transfer). A validation test shows that SEMIC simulates future changes in surface temperature and surface mass balance in good agreement with the more sophisticated multilayer snowpack model SISVAT included in MAR. With this paper, we present a physically based surface model to the ice sheet modelling community that is general enough to be used with in situ observations, climate model, or reanalysis data, and that is at the same time computationally fast enough for long-term integrations, such as glacial cycles or future climate change scenarios

The significance of motivation in student-centred learning : a reflective case study

The theoretical underpinnings of student-centred learning suggest motivation to be an integral component. However, lack of clarification of what is involved in motivation in education often results in unchallenged assumptions that fail to recognise that what motivates some students may alienate others. This case study, using socio-cognitive motivational theory to analyse previously collected data, derives three fuzzy propositions which, collectively, suggest that motivation interacts with the whole cycle of episodes in the teachinglearning process. It argues that the development of the higherlevel cognitive competencies that are implied by the term, student-centred learning, must integrate motivational constructs such as goal orientation, volition, interest and attributions into pedagogical practices

An Extended, Boolean Model of the Septation Initiation Network in S.Pombe Provides Insights into Its Regulation.

Cytokinesis in fission yeast is controlled by the Septation Initiation Network (SIN), a protein kinase signaling network using the spindle pole body as scaffold. In order to describe the qualitative behavior of the system and predict unknown mutant behaviors we decided to adopt a Boolean modeling approach. In this paper, we report the construction of an extended, Boolean model of the SIN, comprising most SIN components and regulators as individual, experimentally testable nodes. The model uses CDK activity levels as control nodes for the simulation of SIN related events in different stages of the cell cycle. The model was optimized using single knock-out experiments of known phenotypic effect as a training set, and was able to correctly predict a double knock-out test set. Moreover, the model has made in silico predictions that have been validated in vivo, providing new insights into the regulation and hierarchical organization of the SIN

Towards optimal 1.5° and 2 °C emission pathways for individual countries: A Finland case study

© 2019 Nationally Determined Contributions (NDCs) submitted so far under the Paris Agreement are not in line with its long-term temperature goal. To bridge this gap, countries are required to provide regular updates and enhancements of their long-term targets and strategies, based on scientific assessments. The goal of this paper is to demonstrate a policy-support approach for evaluating NDCs and guiding enhanced ambition. The approach rests on deriving national targets in line with the Paris Agreement by downscaling regional results of Integrated Assessment Models (IAMs) to the country level. The method of downscaling relies on a reduced complexity IAM: SIAMESE (Simplified Integrated Assessment Model with Energy System Emulator). We apply the approach to an EU28 member state – Finland – with the aim of providing useful insights for policy makers to consider cost-effective mitigation options. Results over the historical period confirm that our approach is valid when national policies are similar to those across the larger IAM region, but must include country-specific circumstances. Strengths and limitations of the approach are discussed. We assess the remaining carbon budget and analyse the different implications of 2 °C and 1.5 °C global warming limits for the emissions pathway and energy mix in Finland over the 21st century

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multidrug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDR-TB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e. the Moran’s coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird (spatial resolution = 0.61 m) data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centres, using a 10 m2 grid-based algorithm. Geographical information system (GIS)- gridded measurements of each health centre were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDRTB covariates. Pearson’s correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS® module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases, using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centres and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran’s coefficient into uncorrelated, orthogonal map pattern components which revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations