Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge.

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods

Designing bifunctional alkene isomerization catalysts using predictive modelling

Controlling the isomerization of alkenes is important for the manufacturing of fuel additives, fine-chemicals and pharmaceuticals. But even if isomerization seems to be a simple unimolecular process, the factors that govern catalyst performance are far from clear. Here we present a set of models that describe selectivity and activity, enabling the rational design and synthesis of alkene isomerization catalysts. The models are based on simple molecular descriptors, with a low computational cost, and are tested and validated on a set of eleven known Ru-imidazol-phosphine complexes and two new ones. Despite their simplicity, these models show good predictive power, with R2 values of 0.60–0.85. Using a combination of principal components analysis (PCA) and partial least squares (PLS) regression, we construct a “catalyst map”, that captures trends in reactivity and selectivity as a function of electrostatic charge on the N* atom, EHOMO, polar surface area and the optimal mass substituents on P/distance Ru–P ratio. In addition to indicating “good regions” in the catalyst space, these models also give insight into mechanistic steps. For example, we find that the electrostatic charge on N*, EHOMO and polar surface area are crucial in the rate-limiting step, whereas the optimal mass of substituents on P/distance Ru–P is correlated with the product selectivity

Estimating CDKN2A mutation carrier probability among global familial melanoma cases using GenoMELPREDICT

Background: Although rare in the general population, highly penetrant germline mutations in CDKN2A are responsible for 5%-40% of melanoma cases reported in melanoma-prone families. We sought to determine whether MELPREDICT was generalizable to a global series of families with melanoma and whether performance improvements can be achieved.Methods: In total, 2116 familial melanoma cases were ascertained by the international GenoMEL Consortium. We recapitulated the MELPREDICT model within our data (GenoMELPREDICT) to assess performance improvements by adding phenotypic risk factors and history of pancreatic cancer. We report areas under the curve (AUC) with 95% confidence intervals (CIs) along with net reclassification indices (NRIs) as performance metrics.Results: MELPREDICT performed well (AUC 0.752, 95% CI 0.730-0.775), and GenoMELPREDICT performance was similar (AUC 0.748, 95% CI 0.726-0.771). Adding a reported history of pancreatic cancer yielded discriminatory improvement (P < .0001) in GenoMELPREDICT (AUC 0.772, 95% CI 0.750-0.793, NRI 0.40). Including phenotypic risk factors did not improve performance.Conclusion: The MELPREDICT model functioned well in a global data set of familial melanoma cases. Adding pancreatic cancer history improved model prediction. GenoMELPREDICT is a simple tool for predicting CDKN2A mutational status among melanoma patients from melanoma-prone families and can aid in directing these patients to receive genetic testing or cancer risk counseling.Hereditary cancer genetic