Department of Analytical Chemistry and Pharmaceutical Technology, Pharmaceutical Institute, Vrije Universiteit Brussel-VUB, Laarbeeklaan 103, B-1090 Brussels, Belgium.
Published: June 2006
AI Article Synopsis
Article Abstract
The use of classification trees for modeling and predicting the passage of molecules through the blood-brain barrier was evaluated. The models were built and evaluated using a data set of 147 molecules extracted from the literature. In the first step, single classification trees were built and evaluated for their predictive abilities. In the second step, attempts were made to improve the predictive abilities using a set of 150 classification trees in a boosting approach. Two boosting algorithms, discrete and real adaptive boosting, were used and compared. High-predictive classification trees were obtained for the data set used, and the models could be improved with boosting. In the context of this research, discrete adaptive boosting gives slightly better results than real adaptive boosting.
Introduction: Psychotropic medication (PM) use in behavioral-variant frontotemporal dementia (bvFTD) is higher than in other dementias. However, no information exists on whether PM use differs between sporadic and genetic bvFTD.
Methods: We analyzed data from sporadic and genetic bvFTD participants with PM prescriptions in the Advancing Research and Treatment in Frontotemporal Lobar Degeneration/Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects study.
Sequencing-based microbial count data analysis is a challenging task due to the presence of numerous non-biological zeros, which can impede downstream analysis. To tackle this issue, we introduce two novel approaches, PhyImpute and UniFracImpute, which leverage similar microbial samples to identify and impute non-biological zeros in microbial count data. Our proposed methods utilize the probability of non-biological zeros and phylogenetic trees to estimate sample-to-sample similarity, thus addressing this challenge.
Poly(butylene succinate-co-adipate) (PBSA), a biodegradable plastic, is significantly colonized and degraded by soil microbes under natural field conditions, especially by fungal plant pathogens, raising concerns about potential economic losses. This study hypothesizes that the degradation of biodegradable plastics may increase the presence and abundance of plant pathogens by serving as an additional carbon source, ultimately posing a risk to forest ecosystems. We investigated (i) fungal plant pathogens during the exposure of PBSA in European broadleaved and coniferous forests (two forest types), with a specific focus on potential risk to tree health, and (ii) the response of such fungi to environmental factors, including tree species, soil pH, nutrient availability, moisture content, and the physicochemical properties of leaf litter layer.
Dependent samples, in which repeated measurements are made on the same subjects, eliminate potential differences among the subjects. In k-dependent samples, missing data can occur for various reasons. The Skillings-Mack test is used instead of the Friedman test for k-dependent samples with missing observations that are non-normally distributed.
