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Objective: To examine the associations of excessive daytime sleepiness (EDS) and probable rapid eye movement sleep behavior disorder (pRBD), respectively, with impulsive-compulsive behaviors (ICBs) over a 5-year follow-up in patients with early Parkinson's disease (PD). Methods: The Parkinson's Progression Markers Initiative is a multicenter cohort study based on an ongoing and open-ended registry. Longitudinal associations of sleep disorders with ICB over 5-year follow-up visits were estimated using generalized linear mixed-effects models among PD participants. Results: A total of 825 PD participants were enrolled at baseline. The study sample had a median baseline age of 63.1 (interquartile range: 55.6–69.3) years and comprised 496 (61.5%) men. Among them, 201 (24.9%) had ICB at baseline. In the generalized mixed-effects models, EDS (odds ratio [OR] = 1.09, 95% confidence interval [CI] 1.05, 1.12) and RBD (OR = 1.07, 95% CI 1.03, 1.12) were substantially associated with higher odds of developing ICB over time in PD patients, after multivariate adjustment including age, gender, family history, GDS score, STAI-Y score, MDS-UPDRS part III score, LEDD, and disease duration. Consistent results were observed when stratifying by age at baseline, gender, and PD family history. Conclusions: The study findings suggest a longitudinal association between EDS and pRBD with an increased risk of developing ICB in patients with PD. The findings emphasize the significance of evaluating and addressing sleep disorders in PD patients as a potential approach to managing ICB. [ABSTRACT FROM AUTHOR]

Electrochemical C−N coupling reaction based on carbon dioxide and nitrate have been emerged as a new "green synthetic strategy" for the synthesis of urea, but the catalytic efficiency is seriously restricted by the inherent scaling relations of adsorption energies of the active sites, the improvement of catalytic activity is frequently accompanied by the decrease in selectivity. Herein, a doping engineering strategy was proposed to break the scaling relationship of intermediate binding and minimize the kinetic barrier of C−N coupling. A thus designed SrCo0.39Ru0.61O3−δ catalyst achieves a urea yield rate of 1522 μg h−1 mgcat.−1 and faradic efficiency of 34.1 % at −0.7 V versus reversible hydrogen electrode. A series of characterizations revealed that Co doping not only induces lattice distortion but also creates rich oxygen vacancies (OV) in the SrRuO3. The oxygen vacancies weaken the adsorption of *CO and *NH2 intermediates on the Co and Ru sites respectively, and the strain effects over the Co−Ru dual sites promoting the occurrence of C−N coupling of the two monomers instead of selective hydrogenating to form by‐products. This work presents an insight into molecular coupling reactions towards urea synthesis via the doping engineering on SrRuO3. [ABSTRACT FROM AUTHOR]

Aiming at the shortage of distance feature and local geometric structure information in feature extraction, a point cloud classification and segmentation network based on graph walk and graph attention was proposed. Firstly, a guided graph walk algorithm was used to supplement additional geometric information and remote feature information to the whole feature of point cloud. Secondly, the graph attention mechanism was embedded to make the model on the key areas of the point cloud and improve the feature extraction ability of the network. Finally, distance features were extracted from the initial point cloud and embedded into the network as initial residuals to avoid oversmoothing. Point cloud classification experiments were carried out on ModelNet40 dataset and ScanObjectNN dataset, and point cloud component segmentation and point cloud semantic segmentation experiments were carried out on ShapeNetPart dataset and Toronto-3D dataset, respectively. The experiment results showed that, compared with the benchmark network DGCNN, classification accuracy increased by 1.3 percentages and 5.6 percentages, respectively; The segmentation accuracy was improved by 1.2 percentages and 33.1 percentages respectively. Through the robust analysis on ModelNet40-C dataset, it was proved that the proposed network had strong robustness. [ABSTRACT FROM AUTHOR]