A three-dimensional (3D) nitrogen-doped carbon nanotubes/graphene (NCNTs/G) composite was prepared by pyrolysis of pyridine over a graphene-sheet-supported Ni catalyst. The morphology and structure of the NCNTs/G composite was investigated by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and Raman spectroscopy. Tangled NCNTs with lengths of several hundred nanometers are sparsely, but tighly, distributed on graphene sheets, forming quasi-aligned NCNT arrays. The N content in the NCNTs/G measured by X-ray photoelectron spectroscopy is about 6.6 at. %. The NCNTs/G shows a higher activity and selectivity to the oxygen reduction reaction in alkaline electrolyte compared with undoped CNTs/G, as demonstrated by cyclic voltammetry, rotating disk electrode, and rotating ring-disk electrode measurements. The results indicate that the 3D NCNTs/G composite has potential application in fuel cells.

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As one of the major sources of pollutions in the environments, effluents from municipal wastewater recently became a hot topic. This study quantified monthly county-level releases of five heavy metals, i.e., lead (Pb), cadmium (Cd), chromium (Cr), arsenic (As), and mercury (Hg), from municipal wastewater into the environment in the Heilongjiang Province of China, based on sampling, measurement, and modeling tools. Wastewater samples were collected from 27 municipal wastewater treatment plants (MWTPs) in 15 county-level cities of Heilongjiang every month from 2015 to 2017. The concentrations of five heavy metals were analyzed in both influents (Pb: 160 ± 100 μg/L; Cd: 15 ± 9.0 μg/L; Cr: 170 ± 64 μg/L; Hg: 0.67 ± 1.5 μg/L; As: 6.2 ± 4.8 μg/L) and effluents (Pb: 45 ± 15 μg/L; Cd: 5.2 ± 5.1 μg/L; Cr: 57 ± 13 μg/L; Hg: 0.28 ± 0.12 μg/L; As: 2.6 ± 1.4 μg/L). The removal ratios of the five heavy metals ranged from 50% to 67%. Inflow fluxes of Pb, Cr, and Cd displayed increasing trends first then decreased after reaching a maximum value, whereas those of Hg and Pb remained stable. Material flow analysis reveals that constructions of MWTPs are conducive to significantly reduce the releases of heavy metals from urban areas into the aquatic environment in the study area. Additionally, municipal wastewater sludge (used as fertilizer or spread on the land) could be a significant source of heavy metals in the land.

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Metformin, the unique first choice medicine for type 2 diabetes (T2D) in the US and Europe, was not widely adopted in China until recently. We measured metformin in wastewater samples, collected in major cities in China every 2 years from 2014 to 2020, to understand spatial and temporal trends of metformin use in T2D treatment in China. The estimated metformin use (mg/day/person) increased significantly from 1.6 ± 1.8 in 2014 to 4.2 ± 3.6 in 2016, then to 7.0 ± 8.8 in 2018, and 9.2 ± 9.0 in 2020. Metformin use increased nationally and in all seven geographical regions. Our findings reflect the broader uptake of metformin for treatment of T2D in China in accordance with expert recommendations. There was higher metformin use in the North than in the South of China, reflecting similar spatial trends in obesity rate in China. Wastewater analysis is likely to be a cost-effective way to monitor T2D treatment across China in the future.

The efficient and automatic detection of chest abnormalities is vital for the auxiliary diagnosis of medical images. Many studies utilize computer vision and deep learning approaches involving symmetry and asymmetry concepts to detect chest abnormalities, and achieve promising findings. However, an accurate instance-level and multi-label detection of abnormalities in chest X-rays remains a significant challenge. Here, a novel anomaly detection method for symmetric chest X-rays using dual-attention and multi-scale feature fusion is proposed. Three aspects of our method should be noted in comparison with the previous approaches. We improved the deep neural network with channel-dimensional and spatial-dimensional attention to capture the abundant contextual features. We then used an optimized multi-scale learning framework for feature fusion to adapt to the scale variation in the abnormalities. Considering the influence of the data imbalance and other factors, we introduced a seesaw loss function to flexibly adjust the sample weights and enhance the model learning efficiency. The rigorous experimental evaluation of a public chest X-ray dataset with fourteen different types of abnormalities demonstrates that our model has a mean average precision of 0.362 and outperforms existing methods.