Multivariate statistical analysis on the kaolinite/chlorite ratios from 20 South Atlantic sediment cores

Multivariate statistical analysis on the kaolinite/chlorite ratios from 20 South Atlantic sediment cores allowed for the extraction of two processes controlling the fluctuations of the kaolinite/chlorite ratio during the last 130,000 yrs, (1) the relative strength of North Atlantic Deep Water (NADW) inflow into the South Atlantic Ocean and (2) the influx of aeolian sediments from the south African continent. The NADW fluctuation can be traced in the entire deep South Atlantic while the dust signal is restricted to the vicinity of South Africa. Our data indicate that NADW formation underwent significant changes in response to glacial/interglacial climate changes with enhanced export to the Southern Hemisphere during interglacials. The most pronounced phases with Enhanced South African Dust Export (ESADE) occurred during cold Marine Isotope Stage (MIS) 5d and across the Late Glacial/Holocene transition from 16 ka to 4 ka (MIS 2 to 1). This particular pattern is attributed to the interaction of Antarctic Sea Ice extent, the position of the westerlies and the South African monsoon system.

Recruitment patterns and processes of coastal fish species in a temperate rocky reef

Tese de Doutoramento, Ciências do Mar, da Terra e do Ambiente, Ramo: Ciências do Mar, Especialização em Ecologia Marinha, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2016

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.