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The determination of the spin state of iron-bearing compounds at high pressure and temperature is crucial for our understanding of chemical and physical properties of the deep Earth. Studies on the relationship between the coordination of iron and its electronic spin structure in iron-bearing oxides, silicates, carbonates, iron alloys, and other minerals found in the Earth's mantle and core are scarce because of the technical challenges to simultaneously probe the sample at high pressures and temperatures. We used the unique properties of a pulsed and highly brilliant x-ray free electron laser (XFEL) beam at the High Energy Density (HED) instrument of the European XFEL to x-ray heat and probe samples contained in a diamond anvil cell. We heated and probed with the same x-ray pulse train and simultaneously measured x-ray emission and x-ray diffraction of an FeCO3 sample at a pressure of 51 GPa with up to melting temperatures. We collected spin state sensitive Fe K beta(1,3) fluorescence spectra and detected the sample's structural changes via diffraction, observing the inverse volume collapse across the spin transition. During x-ray heating, the carbonate transforms into orthorhombic Fe4C3O12 and iron oxides. Incipient melting was also observed. This approach to collect information about the electronic state and structural changes from samples contained in a diamond anvil cell at melting temperatures and above will considerably improve our understanding of the structure and dynamics of planetary and exoplanetary interiors.
Polygenic risk scores (PRS) aggregating results from genome-wide association studies are the state of the art in the prediction of susceptibility to complex traits or diseases, yet their predictive performance is limited for various reasons, not least of which is their failure to incorporate the effects of gene-gene interactions. Novel machine learning algorithms that use large amounts of data promise to find gene-gene interactions in order to build models with better predictive performance than PRS. Here, we present a data preprocessing step by using data-mining of contextual information to reduce the number of features, enabling machine learning algorithms to identify gene-gene interactions. We applied our approach to the Parkinson's Progression Markers Initiative (PPMI) dataset, an observational clinical study of 471 genotyped subjects (368 cases and 152 controls). With an AUC of 0.85 (95% CI = [0.72; 0.96]), the interaction-based prediction model outperforms the PRS (AUC of 0.58 (95% CI = [0.42; 0.81])). Furthermore, feature importance analysis of the model provided insights into the mechanism of Parkinson's disease. For instance, the model revealed an interaction of previously described drug target candidate genes TMEM175 and GAPDHP25. These results demonstrate that interaction-based machine learning models can improve genetic prediction models and might provide an answer to the missing heritability problem.