No abstract

Most research studies reported that oxygen vacancies on transition metal oxides are crucial for improving the catalytic oxidation activities. However, few studies investigated the coexistence effect of metal defects and oxygen vacancies on the performances and mechanisms of catalysts. Herein, we present a facile approach for synthesizing a Co3–x O4–y catalyst with both cobalt defects and oxygen vacancies simultaneously, exhibiting significant thermo-/photothermo-catalytic performance for toluene oxidation (300 ppm, GHSV = 72,000 mL g–1 h–1) with over 90% toluene conversion in 10 min of light illumination (with a corresponding light-to-heat conversion temperature of ∼180 °C). Furthermore, the synchrotron radiation X-ray absorption spectroscopy, O2 temperature-programmed desorption, and density functional theory calculations demonstrate that cobalt defects are conducive to the formation of oxygen vacancies. In situ DRIFTS studies reveal that the as-formed surface-reactive oxygen species can be replenished continuously during the reaction progress, and the coexisting oxygen vacancies can accelerate the conversion of intermediates, thereby promoting the toluene degradation.

Identification of glomerular lesions and structures is a key point for pathological diagnosis, treatment instructions, and prognosis evaluation in kidney diseases. These time‐consuming tasks require a more accurate and reproducible quantitative analysis method. We established derivation and validation cohorts composed of 400 Chinese patients with immunoglobulin A nephropathy (IgAN) retrospectively. Deep convolutional neural networks and biomedical image processing algorithms were implemented to locate glomeruli, identify glomerular lesions (global and segmental glomerular sclerosis, crescent, and none of the above), identify and quantify different intrinsic glomerular cells, and assess a network‐based mesangial hypercellularity score in periodic acid–Schiff (PAS)‐stained slides. Our framework achieved 93.1% average precision and 94.9% average recall for location of glomeruli, and a total Cohen's kappa of 0.912 [95% confidence interval (CI), 0.892–0.932] for glomerular lesion classification. The evaluation of global, segmental glomerular sclerosis, and crescents achieved Cohen's kappa values of 1.0, 0.776, 0.861, and 95% CI of (1.0, 1.0), (0.727, 0.825), (0.824, 0.898), respectively. The well‐designed neural network can identify three kinds of intrinsic glomerular cells with 92.2% accuracy, surpassing the about 5–11% average accuracy of junior pathologists. Statistical interpretation shows that there was a significant difference (P value < 0.0001) between this analytic renal pathology system (ARPS) and four junior pathologists for identifying mesangial and endothelial cells, while that for podocytes was similar, with P value = 0.0602. In addition, this study indicated that the ratio of mesangial cells, endothelial cells, and podocytes within glomeruli from IgAN was 0.41:0.36:0.23, and the performance of mesangial score assessment reached a Cohen's kappa of 0.42 and 95% CI (0.18, 0.69). The proposed computer‐aided diagnosis system has feasibility for quantitative analysis and auxiliary recognition of glomerular pathological features. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.