[Temporal and Spatial Variation in O3 Concentration Near the Surface of Shandong Peninsula and Analysis of Potential Source Areas].

The purpose of this study was to explore the temporal and spatial distribution characteristics and potential sources of ozone (O3) in the Shandong Peninsula over a long period of time based on the analysis of the temporal and spatial changes in O3 concentration in Shandong Peninsula from 2005 to 2020. We used wavelet analysis, the entropy weight method, and correlation analysis to discuss O3 and its influencing factors and researched the potential sources of O3 in Shandong Peninsula. The results showed that:① in terms of the time pattern, the near-surface O3 in Shandong Peninsula showed a "triple peak" trend from 2005 to 2020, reaching the maximum value of[(40.48±7.64) µg·m-3] in 2010 and a minimum value of[(36.63±5.61) µg·m-3] in 2013. The season was expressed as:summer[(42.49±1.7) µg·m-3]>spring[(40.65±0.6) µg·m-3]>autumn[(36.47±0.7) µg·m-3]>winter[(36.46±0.3) µg·m-3]. ② In terms of the spatial pattern, the O3 concentration of Shandong Peninsula gradually increased with the increase in latitude from 2005 to 2020, showing the characteristics of high concentrations in the east and west and low in the middle region. During the 16-year evolution of the O3 concentration, there was a 1.5 a main oscillation period. ③The analysis of meteorological conditions revealed that O3 concentration was positively correlated with temperature, precipitation, relative humidity, and sunshine hours, whereas pressure and wind speed were negatively correlated. In the analysis of social factors, soot (dust) emissions were the most obvious factor affecting the third indicator, with a weight of 0.25. ④ Through simulating the trajectory of airflow from different regions (Ji'nan and Qingdao), it was found that the ocean airflow contributed 10.69% to Jinan and 48.94% to Qingdao. There was 64.04% of the long-distance air mass transmission path coming from the northwest, and 43.69% of the short-distance air mass transmission path was from the Bohai Sea and the Yellow Sea, followed by Shandong Province with 21.01%. ⑤ The analysis of potential sources of O3 showed that the potential sources of Ji'nan were mainly distributed in Jinzhou, Liaoning Province, northern Jiangsu Province, Hubei Province, and Anhui Province, with a WPSCF value >0.6, and Qingdao's WPSCF value of >0.6 was mainly distributed in the Yellow Sea area. The O3 contribution of Jining City, Linyi City, Xuzhou City, Huaibei City, and Lianyungang City was >40 µg·m-3. The area with >45 µg·m-3 in Qingdao was mainly in the Yellow Sea. Through the analysis of potential sources in the Shandong Peninsula, particular attention should be paid to the supply of industrial sources in the surrounding areas and the marine sources provided by marine air pollution.

Automatic Assessment of Mitral Regurgitation Severity Using the Mask R-CNN Algorithm with Color Doppler Echocardiography Images.

Accurate assessment of mitral regurgitation (MR) severity is critical in clinical diagnosis and treatment. No single echocardiographic method has been recommended for MR quantification thus far. We sought to define the feasibility and accuracy of the mask regions with a convolutional neural network (Mask R-CNN) algorithm in the automatic qualitative evaluation of MR using color Doppler echocardiography images. The authors collected 1132 cases of MR from hospital A and 295 cases of MR from hospital B and divided them into the following four types according to the 2017 American Society of Echocardiography (ASE) guidelines: grade I (mild), grade II (moderate), grade III (moderate), and grade IV (severe). Both grade II and grade III are moderate. After image marking with the LabelMe software, a method using the Mask R-CNN algorithm based on deep learning (DL) was used to evaluate MR severity. We used the data from hospital A to build the artificial intelligence (AI) model and conduct internal verification, and we used the data from hospital B for external verification. According to severity, the accuracy of classification was 0.90, 0.89, and 0.91 for mild, moderate, and severe MR, respectively. The Macro F1 and Micro F1 coefficients were 0.91 and 0.92, respectively. According to grading, the accuracy of classification was 0.90, 0.87, 0.81, and 0.91 for grade I, grade II, grade III, and grade IV, respectively. The Macro F1 and Micro F1 coefficients were 0.89 and 0.89, respectively. Automatic assessment of MR severity is feasible with the Mask R-CNN algorithm and color Doppler electrocardiography images collected in accordance with the 2017 ASE guidelines, and the model demonstrates reasonable performance and provides reliable qualitative results for MR severity.

Role transformation of fecundity and viability: The leading cause of fitness costs associated with beta-cypermethrin resistance in Musca domestica.

Fitness is closely associated with the development of pesticide resistance in insects, which determines the control strategies employed to target species and the risks of toxicity faced by non-target species. After years of selections with beta-cypermethrin in laboratory, a strain of housefly was developed that was 684,521.62-fold resistant (CRR) compared with the susceptible strain (CSS). By constructing ≤ 21 d and ≤ 30 d life tables, the differences in life history parameters between CSS and CRR were analyzed. The total production numbers of all the detected development stages in CRR were lower than in CSS. Except for the lower mortality of larvae, all the other detected mortalities in CRR were higher than in CSS. ♀:♂ and normal females of CRR were also lower than those of CSS. For CRR, the relative fitness was 0.25 in the ≤ 21 d life table and 0.24 in the ≤ 30 d life table, and a lower intrinsic rate of increase (rm) and net reproductive rate (Ro) were detected. Based on phenotype correlation and structural equation model (SEM) analyses, fecundity and viability were the only directly positive fitness components affecting fitness in CRR and CSS, and the other components played indirect roles in fitness. The variations of the relationships among fitness, fecundity and viability seemed to be the core issue resulting in fitness differences between CRR and CSS. The interactions among all the detected fitness components and the mating frequency-time curves appeared to be distinctly different between CRR and CSS. In summary, fecundity and its related factors separately played direct and indirect roles in the fitness costs of a highly beta-cypermethrin-resistant housefly strain.

[Application of Landsat ETM+ in monitoring of desertification in agro-pastoral ecotone of northern China].

Agro-pastoral ecotone of northern China is a transitional and interlaced zone of agricultural cultivation region and grazing region The ecotone is a complex containing several ecosystems. Soil desertification has become a serious problem that endangered sustainable development in the ecotone. The area of desertification land has been increasing year after year in agro-pastoral ecotone of northern China. This problem concerns the ecological environment, economic development and living quality of people in northern and central eastern of China. For these reasons, ecotone has recently become a focus of research of restoration ecology and global climate change. Remote sensing monitoring of desertification land is a key technique to collect the status and development of sandy land, providing scientific bases for the national desertification control. Landsat ETM+ is an advanced multispectral remote sensing system for the research of regional scale and has been widely used in many fields, such as geologic surveys, mapping, vegetation monitoring, etc. In the present, the authors introduce that spectral characteristics, desertification information extraction, desertification classification and development analyses in detail, and summarizes the study progresses discusses the problems and trends.

Peroxisomal proteomics, a new tool for risk assessment of peroxisome proliferating pollutants in the marine environment.

In an attempt to improve the detection of peroxisome proliferation as a biomarker in environmental pollution assessment, we have applied a novel approach based on peroxisomal proteomics. Peroxisomal proteins from digestive glands of mussels Mytilus galloprovincialis were analyzed using 2-DE and MS. We have generated a reference 2-DE map from samples obtained in a well-studied reference area and compared this with peroxisomal proteomes from other sequenced genomes. In addition, by comparing 2-DE maps from control samples with samples obtained in a polluted area, we have characterized the peroxisome proliferation expression pattern associated with exposure to a polluted environment. Over 100 spots were reproducibly resolved per 2-DE map; 55 differentially expressed spots were quantitatively detected and analyzed, and 14 of these showed an increase in protein expression of more than fourfold. Epoxide hydrolase, peroxisomal antioxidant enzyme, and sarcosine oxidase (SOX) have been identified by ESI MS/MS, and acyl-CoA oxidase, multifunctional protein, and Cu,Zn-superoxide dismutase were immunolocalized by Western blotting. Our results indicate that a peroxisomal protein pattern associated to marine pollutant exposure can be generated, and this approach may have a greater potential as biomarker than traditional, single-protein markers.