Improved customer choice predictions using ensemble methods

In this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to improved predictions. We give experimental results for two real-life marketing datasets using decision trees, ensemble versions of decision trees and the logistic regression model, which is a standard approach for this problem. The ensemble models are found to improve upon individual decision trees and outperform logistic regression.     

Working Methods Paper: Critical considerations with respect to the identification of tin species in the environment

Biogeochemical pathways of tin species in the environment are still controversial, e.g. with regard to methylation and transmethylation phenomena, owing to the fact that the identification of methylated tin-compounds is often difficult. The previous tentative identification of a mixed methylbutyltin compound in sediment and biological samples by GC/AAS after hydride generation gave an illustration of this problem. This compound was previously identified in sediments by other authors and also suspected to occur in a contaminated sediment sample from the Boyardville Marina, France. The retention time obtained by GC/AAS corresponded to the actual retention time of a mixed methylbutyltin calibrant. However, additional checks demonstrated that the compound detected was actually monophenyltin. This evidence was produced by a thorough analysis of a selected sediment sample by alternative techniques such as GC/AAS and GC/AES after pentylation, GC/FPD and GC/MS. The results presented highlight the need for a full identification of compounds to avoid mis-interpretation.     

Complex II from a structural perspective

The super-macromolecular complex, succinate:quinone oxidoreductase (SQR, Complex II, succinate dehydrogenase) couples the oxidation of succinate in the matrix / cytoplasm to the reduction of quinone in the membrane. This function directly connects the Krebs cycle and the aerobic respiratory chain. Until the recent first report of the structure of SQR from Escherichia coli (E. coli) the structure-function relationships in SQR have been inferred from the structures of the homologous QFR, which catalyses the same reaction in the opposite direction. The structure of SQR from E. coli, analogous to the mitochondrial respiratory Complex II, has provided new insight into SQR's molecular design and mechanism, revealing the electron transport pathway through the enzyme. Comparison of the structures of SQR, QFR and other related flavoproteins shows how common amino acid residues at the interface of two domains facilitate the inter-conversion of succinate and fumarate. Additionally, the structure has provided a possible explanation as to why certain organisms utilise both SQR and QFR despite the fact that both can catalyse the inter-conversion of succinate and fumarate, in vitro and in vivo. Here we review how this structure has advanced our knowledge of this important enzyme and compare the structural information to other members of the Complex II superfamily and related flavoproteins.