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Coarse particles dominated the PM fraction during windows-open while fine particles dominated during fan-on and recirculation, indicating filter effectiveness in removing coarse particles and a need for filters that limit the ingress of fine particles. Spatial variation analysis during windows-open showed that pollution hotspots make up to a third of the total route-length. PM2.5 exposure for windows-open during off-peak hours was 91% and 40% less than morning and evening peak hours, respectively. Across cities, determinants of relatively high personal exposure doses included lower car speeds, temporally longer journeys, and higher in-car concentrations. It was also concluded that car-users in the least affluent cities experienced disproportionately higher in-car PM2.5 exposures. Cities were classified into three groups according to low, intermediate and high levels of PM exposure to car commuters, allowing to draw similarities and highlight best practices.Atmospheric deposition of iron (Fe) can increase marine primary productivity, consequently affect ocean biogeochemical cycles and climate change. In this study, we develop an adaptor to generate anthropogenic Fe emission inventories for China in 2012 and 2016 via anthropogenic PM2.5 emissions from Multi-resolution Emission Inventory for China (MEIC) using local source-specific mass fractions of Fe in PM2.5. Using the generated emission inventories, we simulated Fe concentrations as well as dry deposition fluxes to China marginal seas using a WRF-CMAQ model during four campaign periods. The simulated Fe concentrations are in good agreement with observations except for those in presence of severe dust-intrusion events (NMB -13% ~ -27%), indicating a reasonably good performance of the generated Fe emissions and leaving the large underestimation of Fe concentrations mainly due to nature dust emissions. Simulated Fe concentrations over China marginal seas are in the range of 62-6.5 × 102 ng m-3, providing 2.0-12.5 μg m-2 d-1 to the seas during the study periods. We also found that inputs of total Fe in PM2.5 to the seas in presence of dust-intrusion events are 3 and 13 times larger than those in presence of haze events or on less polluted days. Due to lower Fe solubility in nature mineral aerosols than in anthropogenic aerosols, dry deposition fluxes of bioavailable Fe on haze days almost double that in dust days. The total anthropogenic emissions of Fe over China in 2012 and 2016 are estimated as 5.5 × 102 Gg and 3.3 × 102 Gg, respectively. Iron and steel industry are the dominant sources of Fe, accounting for 59-63% of the total anthropogenic Fe emissions. Geotropically, stronger emissions per area were distributed in eastern China, e.g., 2.3 to 15.4 ng m-2 s-1 in eastern China versus less then 0.4 ng m-2 s-1 in western China.Heavy metals pollutants are global concern due to their toxicities and persistence in the environment. Cd isotope signatures in soils and sediments change during weathering, and it remains unclear if Cd isotopes can effectively trace Cd sources in a riverine system. In this study, we investigate Cd concentration and its isotope compositions, as well as other heavy metals of sediments and related potential Cd sources in a riverine system. The results showed that the two river sediments evaluated were moderately polluted by Zn, Cr, and Cd, while the source samples (soil, sludge, waste, and raw materials) were seriously polluted by heavy metals derived from anthropogenic activities. According to comprehensive ecological risks, the two sediments have a moderate to low potential risk and more than half of all anthropogenic activities in the study area were at considerable or moderate potential risk. We determined that Cd pollution in river sediments was primarily derived from sewage treatment and outlets based on river flow direction and the isotope geochemical behaviors of the Cd isotope in nature conditions. This study further confirmed that analyzing Cd isotopes could be a powerful tool for tracing the source and destination of environmental Cd for multiple sources with similar Cd concentrations.Organic amendments (OAs) application is a practical strategy to improve soil organic carbon (SOC) in agriculture. The present study evaluated the impact of different OAs on the transformation of carbon and the dynamics of microorganisms in a 77-day incubation experiment. The OA treatments applied included wheat straw (U + WS), pig manure (U + PM), compost (U + CP), and improved compost (U + IC), and the no amendment group was the CK. After incubation, the SOC increased significantly in the U + WS group, but the other OA treatments had no significant effect relative to the CK. Among the OA treatments, U + CP and U + IC had lower CO2-C cumulative mineralization and the highest humification of dissolved organic carbon (DOC). U + PM had the lowest SOC content and the lowest aromatization of DOC. Redundancy analyses (RDA) showed that the CO2-C cumulative mineralization directly influenced the DOC, extracted organic carbon (EOC) and microbial biomass carbon (MBC) in all treatments. Proteobacteria positively correlated with SOC and MBC, Bacteroidetes were significantly related to DOC, and Gemmatimonadetes had a significant negative relationship with CO2-C cumulative mineralization. These results showed that U + CP and U + IC were more conducive to carbon sequestration, and U + PM was the most unfavourable during the incubation. Wheat straw played an important role in the steady improvement of the SOC.The prevalence of chronic diseases in China has increased rapidly in recent decades. Although the management rate of chronic diseases has improved, there is still no unified and effective management measure for chronic diseases at present. This highlights the importance of effectively managing chronic diseases. With the development of e-health, the ways of getting medical consultation have changed. WeChat is an extremely popular social application in China. It is easy to operate and can offer multiple functions. Many researches have reported the effectiveness of WeChat in chronic diseases management. Based on the status of WeChat application in chronic diseases management and the characteristics of WeChat technology, we firstly focused on the WeChat application on the management of chronic diseases such as hypertension, diabetes, coronary heart disease and cancer. Then we discussed the value of WeChat in chronic diseases management and analyzed the potential reasons. Lastly, we discussed the limitations of present researches. WeChat can be an effective tool for the management of chronic diseases, but the promotion of this mode needs support and efforts from various aspects to eventually realize improving public health.

It remains controversial regarding the optimal type of fixation implant for the treatment of femoral neck fractures (FNFs). Biomechanical rational for implant choices can benefit from the integration of finite element analysis (FEA) in device evaluation and design improvement. In this study, we aim to evaluate biomechanical performance of several internal fixation implants for Pauwels type III FNFs under physiological loading conditions using FEA, as well as to assess the biomechanical contribution of medial buttress plate (MBP) augmentation.

Several fixation styles for FNFs have been analyzed numerically by the finite element method. Five groups of models were developed with different FNFs fixation implants, including dynamic hip screw (DHS), cannulated screws (CSs), proximal femoral nail antirotation (PFNA), DHS with MBP augmentation (DHS+MBP), and CSs with MBP (CSs+MBP). For each group, four FE models were established to evaluate strain in bone and stress in devices during walking and stair climbing coical treatment of FNFs.

Compared to the other fixation styles, the PFNA showed biomechanical advantages of decreasing risk of implant failure and bone yielding. The MBP augmentation provided an additional load path to bridge fracture fragments, which reduced failure risk of DHS and CSs, especially during dynamic loading scenarios. Although further studies are needed for patients with other types of FNFs, our findings may provide valuable references for device design optimization in terms of complex physiological loadings, as well as for clinical decision making in surgical treatment of FNFs.

Limited-channel EEG research in neonates is hindered by lack of open, accessible analytic tools. To overcome this limitation, we have created the Washington University-Neonatal EEG Analysis Toolbox (WU-NEAT), containing two of the most commonly used tools, provided in an open-source, clinically-validated package running within MATLAB.

The first algorithm is the amplitude-integrated EEG (aEEG), which is generated by filtering, rectifying and time-compressing the original EEG recording, with subsequent semi-logarithmic display. The second algorithm is the spectral edge frequency (SEF), calculated as the critical frequency below which a user-defined proportion of the EEG spectral power is located. The aEEG algorithm was validated by three experienced reviewers. Reviewers evaluated aEEG recordings of fourteen preterm/term infants, displayed twice in random order, once using a reference algorithm and again using the WU-NEAT aEEG algorithm. Using standard methodology, reviewers assigned a background pattern claikely to become outdated as technology changes, thereby facilitating future collaborative research in neonatal EEG.

Currently, it is challenging to detect acute ischemic stroke (AIS)-related changes on computed tomography (CT) images. Therefore, we aimed to develop and evaluate an automatic AIS detection system involving a two-stage deep learning model.

We included 238 cases from two different institutions. AIS-related findings were annotated on each of the 238 sets of head CT images by referring to head magnetic resonance imaging (MRI) images in which an MRI examination was performed within 24h following the CT scan. These 238 annotated cases were divided into a training set including 189 cases and test set including 49 cases. Subsequently, a two-stage deep learning detection model was constructed from the training set using the You Only Look Once v3 model and Visual Geometry Group 16 classification model. Then, the two-stage model performed the AIS detection process in the test set. To assess the detection model's results, a board-certified radiologist also evaluated the test set head CT images with and without the aid of the detection model. The sensitivity of AIS detection and number of false positives were calculated for the evaluation of the test set detection results. The sensitivity of the radiologist with and without the software detection results was compared using the McNemar test. buy AZD3514 A p-value of less than 0.05 was considered statistically significant.

For the two-stage model and radiologist without and with the use of the software results, the sensitivity was 37.3%, 33.3%, and 41.3%, respectively, and the number of false positives per one case was 1.265, 0.327, and 0.388, respectively. On using the two-stage detection model's results, the board-certified radiologist's detection sensitivity significantly improved (p-value=0.0313).

Our detection system involving the two-stage deep learning model significantly improved the radiologist's sensitivity in AIS detection.

Our detection system involving the two-stage deep learning model significantly improved the radiologist's sensitivity in AIS detection.