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Ten square-based pyramidal molybdenum complexes with different sulfur donor ligands, that is, a variety of dithiolenes and sulfides, were prepared, which mimic coordination motifs of the molybdenum cofactors of molybdenum-dependent oxidoreductases. The model compounds were investigated by Mo K-edge X-ray absorption spectroscopy (XAS) and (with one exception) their molecular structures were analyzed by X-ray diffraction to derive detailed information on bond lengths and geometries of the first coordination shell of molybdenum. Only small variations in Mo=O and Mo-S bond lengths and their respective coordination angles were observed for all complexes including those containing Mo(CO)(2) or Mo(mu-S)(2)Mo motifs. XAS analysis (edge energy) revealed higher relative oxidation levels in the molybdenum ion in compounds with innocent sulfur-based ligands relative to those in dithiolene complexes, which are known to exhibit noninnocence, that is, donation of substantial electron density from ligand to metal. In addition, longer average Mo-S and Mo=O bonds and consequently lower.(Mo=O) stretching frequencies in the IR spectra were observed for complexes with dithiolene-derived ligands. The results emphasize that the noninnocent character of the dithiolene ligand influences the electronic structure of the model compounds, but does not significantly affect their metal coordination geometry, which is largely determined by the Mo(IV) or (V) ion itself. The latter conclusion also holds for the molybdenum site geometries in the oxidized Mo-VI cofactor of DMSO reductase and the reduced Mo-IV cofactor of arsenite oxidase. The innocent behavior of the dithiolene molybdopterin ligands observed in the enzymes is likely to be related to cofactor-protein interactions.
HARE
(2023)
Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data collection, preparation, and processing steps, resulting in a heterogeneous and fragmented process. To address these challenges, we present a comprehensive framework, HARE, which seamlessly integrates all necessary steps. HARE offers synchronized data collection and labeling, integrated pose estimation for data anonymization, a multimodal classification approach, and a novel method for determining optimal sensor placement to enhance classification results. Additionally, our framework incorporates real-time activity recognition with on-device model adaptation capabilities. To validate the effectiveness of our framework, we conducted extensive evaluations using diverse datasets, including our own collected dataset focusing on nursing activities. Our results show that HARE’s multimodal and on-device trained model outperforms conventional single-modal and offline variants. Furthermore, our vision-based approach for optimal sensor placement yields comparable results to the trained model. Our work advances the field of sensor-based human activity recognition by introducing a comprehensive framework that streamlines data collection and classification while offering a novel method for determining optimal sensor placement.