73 research outputs found

    Indenyl hapticity in (η-indenyl-RhL2) and Cr(CO)3(μ-η:η-indenyl-RhL2) complexes. A 1H, 13C and 103Rh NMR spectroscopic study

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    A series of indenyl- and (Cr(CO)3)indenyl-RhL2 complexes (L2 = COD, (CO)2) bearing substituents on both the six- and five-membered ring have been synthesized and fully characterized, and their H-1, C-13 and Rh-103 NMR spectra recorded. The changes of the spectral parameters caused by the introduction of the Cr(CO)3 unit suggest significant modifications of the electronic distribution in the indenyl moiety induced in the ground state. The increased reactivity in the ligand exchange reactions ('extra-indenyl effect') and the strong modifications of the catalytic and spectroscopic properties of the Rh center itself indicate a substantial weakening of the coordinative bond between rhodium and the indenyl moiety in the heterobimetallic species as expected on going from an eta5 towards a more pronounced eta3 coordination mode

    Classification of major depressive disorder using vertex-wise brain sulcal depth, curvature, and thickness with a deep and a shallow learning model

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    Abstract Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing 7012 participants from 31 sites (N = 2772 MDD and N = 4240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible. Future studies are needed to determine whether more sophisticated integration of information from other MRI modalities such as fMRI and DWI will lead to a higher performance in this diagnostic task

    Achieving Business Practicability of Model-Driven Cross-Platform Apps

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    -Due to the incompatibility of mobile device platforms such as Android and iOS, apps have to be developed separately for each target platform. Cross-platform development approaches based on Web technology have significantly improved over the last years. However, since they do not lead to native apps, these frameworks are not feasible for all kinds of business apps. Moreover, the way apps are developed is cumbersome. Advanced cross-platform approaches such as MD2, which is based on model-driven development (MDSD) techniques, are a much more powerful yet less mature choice. We discuss business implications of MDSD for apps and introduce MD2 as our proposed solution to fulfill typical requirements. Moreover, we highlight a business-oriented enhancement that further increases MD2's business practicability. We generalize our findings and sketch the path towards more versatile MDSD of app

    Achieving Business Practicability of Model-Driven Cross-Platform Apps

    No full text
    Due to the incompatibility of mobile device platforms such as Android and iOS, apps have to be developed separately for each target platform. Cross-platform development approaches based on Web technology have significantly improved over the last years. However, since they do not lead to native apps, these frameworks are not feasible for all kinds of business apps. Moreover, the way apps are developed is cumbersome. Advanced cross-platform approaches such as MD2, which is based on model-driven development (MDSD) techniques, are a much more powerful yet less mature choice. We discuss business implications of MDSD for apps and introduce MD2 as our proposed solution to fulfill typical requirements. Moreover, we highlight a business-oriented enhancement that further increases MD2's business practicability. We generalize our findings and sketch the path towards more versatile MDSD of apps
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