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High concentrations of organic aerosol (OA) occur in Asian countries, leading to great health burdens. Clean air actions have resulted in significant emission reductions of air pollutants in China. However, long-term nation-wide trends in OA and their causes remain unknown. Here, we present both observational and model evidence demonstrating widespread decreases with a greater reduction in primary OA than in secondary OA (SOA) in China during the period of 2013 to 2020. Most of the decline is attributed to reduced residential fuel burning while the interannual variability in SOA may have been driven by meteorological variations. We find contrasting effects of reducing NOx and SO2 on SOA production which may have led to slight overall increases in SOA. Our findings highlight the importance of clean energy replacements in multiple sectors on achieving air-quality targets because of high OA precursor emissions and fluctuating chemical and meteorological conditions.Clean air actions affect air quality greatly. Here, the authors report widespread decreases in organic aerosol (OA) in China from 2013 to 2020 with primary OA decreasing more than secondary OA. However, further reductions are challenging. [ABSTRACT FROM AUTHOR]

CO2 emission inventory provides fundamental data for climate research and emission mitigation. Currently, most global CO2 emission inventories were developed with energy statistics from International Energy Agency (IEA) and were available at country level with limited source categories. Here, as the first step toward a high-resolution and dynamic updated global CO2 emission database, we developed a data-driven approach to construct seamless and highly-resolved energy consumption data cubes for 208 countries/territories, 797 sub-country administrative divisions in 29 countries, 42 fuel types, and 52 sectors, with the fusion of activity data from 24 international statistics and 65 regional/local statistics. Global CO2 emissions from fossil fuel combustion and cement production in 1970–2021 were then estimated with highly-resolved source category (1,484 of total) and sub-country information (797 of total). Specifically, 73% of global CO2 emissions in 2021 were estimated with sub-country information, providing considerably improved spatial resolution for global CO2 emission accounting. With the support of detailed information, the dynamics of global CO2 emissions across sectors and fuel types were presented, representing the evolution of global economy and progress of climate mitigation. Remarkable differences of sectoral contribution were found across sub-country administrative divisions within a given country, revealing the uneven distribution of energy and economic structure among different regions. Our estimates were generally consistent with existing databases at aggregated level for global total or large emitters, while large discrepancies were observed for middle and small emitters. Our database, named the Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC) is publicly available through http://meicmodel.org.cn with highly-resolved information and timely update, which provides an independent carbon emission accounting data source for climate research. [ABSTRACT FROM AUTHOR]

Photocatalytic two-electron oxygen reduction to produce high-value hydrogen peroxide (H2O2) is gaining popularity as a promising avenue of research. However, structural evolution mechanisms of catalytically active sites in the entire photosynthetic H2O2 system remains unclear and seriously hinders the development of highly-active and stable H2O2 photocatalysts. Herein, we report a high-loading Ni single-atom photocatalyst for efficient H2O2 synthesis in pure water, achieving an apparent quantum yield of 10.9% at 420 nm and a solar-to-chemical conversion efficiency of 0.82%. Importantly, using in situ synchrotron X-ray absorption spectroscopy and Raman spectroscopy we directly observe that initial Ni-N3 sites dynamically transform into high-valent O1-Ni-N2 sites after O2 adsorption and further evolve to form a key *OOH intermediate before finally forming HOO-Ni-N2. Theoretical calculations and experiments further reveal that the evolution of the active sites structure reduces the formation energy barrier of *OOH and suppresses the O=O bond dissociation, leading to improved H2O2 production activity and selectivity. Here, the authors explore how Ni single-atom sites on carbon nitride evolve under photocatalytic conditions. They show that this evolution plays a pivotal role in enhancing photocatalytic H2O2 production. [ABSTRACT FROM AUTHOR]

The aqueous ethanol extract of Periplaneta americana (PAE) dramatically stimulates wound healing by promoting the migration of keratinocytes. The aims of this study were to identify the active oligopeptides present in PAE that promote wound healing and to elucidate their underlying molecular mechanisms. For the detection of cell migration and proliferation in vitro, Transwell assays, wound healing assays, and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays were performed on the human keratinocyte cell line HaCaT. Western blot analysis was performed on the HaCaT cells to detect the protein levels of SMAD family member 3 (Smad3) and phosphorylated Smad3 (P-Smad3). Immunofluorescence analysis was performed on the HaCaT cells to detect the degree of Smad3 translocation into the nucleus. Three oligopeptides were purified from PAE via a series of chromatographic techniques, and their individual effects on HaCaT cell migration were evaluated using wound healing assays. The purification techniques employed were gel filtration chromatography, C8 reversed-phase column chromatography, and reversed-phase high-performance liquid chromatography (RP-HPLC). The amino acid sequences of the oligopeptides (LAKF, PYAHF, and PQLSY) were identified via HPLC–tandem mass spectrometry (HPLC–MS/MS) and Edman degradation. The oligopeptide with the PYAHF sequence significantly promoted HaCaT cell migration. In addition, this oligopeptide promoted the nuclear translocation of P-Smad3 by activating the Smad3 signaling pathway. The findings of this study provide a foundation for the future development of peptide drugs for promoting wound healing. [ABSTRACT FROM AUTHOR]

Recently, leveraging on the development of end-to-end convolutional neural networks, deep stereo matching networks have achieved remarkable performance far exceeding traditional approaches. However, state-of-the-art stereo frameworks still have difficulties at finding correct correspondences in texture-less regions, detailed structures, small objects and near boundaries, which could be alleviated by geometric clues such as edge contours and corresponding constraints. To improve the quality of disparity estimates in these challenging areas, we propose an effective multi-task learning network, EdgeStereo, composed of a disparity estimation branch and an edge detection branch, which enables end-to-end predictions of both disparity map and edge map. To effectively incorporate edge cues, we propose the edge-aware smoothness loss and edge feature embedding for inter-task interactions. It is demonstrated that based on our unified model, edge detection task and stereo matching task can promote each other. In addition, we design a compact module called residual pyramid to replace the commonly-used multi-stage cascaded structures or 3-D convolution based regularization modules in current stereo matching networks. By the time of the paper submission, EdgeStereo achieves state-of-art performance on the FlyingThings3D dataset, KITTI 2012 and KITTI 2015 stereo benchmarks, outperforming other published stereo matching methods by a noteworthy margin. EdgeStereo also achieves comparable generalization performance for disparity estimation because of the incorporation of edge cues. [ABSTRACT FROM AUTHOR]

Bird detection in LR images is essential for the applications of unmanned aerial vehicles. It is still a challenging task because traditional discriminative features in high-resolution (HR) usually disappear in low-resolution (LR) images. Although recent advances in single image super-resolution (SISR) and object detection algorithms have offered unprecedented potential for computer-automated reconstructing LR images and detecting various objects, these algorithms are mainly evaluated using synthetic datasets. It is unclear how these algorithms would perform on bird images acquired in the wild and how we could gauge the progress in the real-time bird detection. This paper presents a novel bird detection framework in LR aerial images using deep neural networks (DNN). We collect a dataset named BIRD-50 and a public dataset named CUB-200 of real bird images with different scale low-resolutions. Using these datasets, we introduce a novel DNN based framework for bird detection in reconstructed HR images, which exploits the mapping function from LR to HR aerial image and detects the birds by the state-of-the-art object feature extraction and localization methods. By systematically analyzing the influence of the resolution reduction on the bird detection, the experimental results indicate that our approach has produced significantly improved detection precision for bird detection by the inclusion of SISR algorithms. [ABSTRACT FROM AUTHOR]