Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium
(2017)
Investigations of the micro- and nanostructures and chemical composition of the sponge skeletons as examples for natural structural biocomposites are of fundamental scientific relevance. Recently, we show that some demosponges (Verongula gigantea, Aplysina sp.) and glass sponges (Farrea occa, Euplectella aspergillum) possess chitin as a component of their skeletons. The main practical approach we used for chitin isolation was based on alkali treatment of corresponding external layers of spicules sponge material with the aim of obtaining alkali-resistant compounds for detailed analysis. Here, we present a detailed study of the structural and physicochemical properties of spicules of the glass sponge Rossella fibulata. The structural similarity of chitin derived from this sponge to invertebrate alpha chitin has been confirmed by us unambiguously using physicochemical and biochemical methods. This is the first report of a silica-chitin composite biomaterial found in Rossella species. Finally, the present work includes a discussion related to strategies for the practical application of silica-chitin-based composites as biomaterials.
Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.