Analysis for sources of atmospheric α- and γ-HCH in gas and particle-associated phase in Dalian, China by multiple regression

Atmospheric concentrations of α- and γ-hexachlorocyclohexanes were measured once a week in Dalian throughout 2008, using a high-volume air sampler, to estimate diurnal, monthly and seasonal variations. Multiple regression analysis was used to estimate the impact of selected meteorological conditions on atmospheric concentrations of hexachlorocyclohexanes and to identify the potential source regions. Overall, α- and γ-hexachlorocyclohexanes were mainly associated with the gas phase, with an annual mean gas-phase concentration of 36±30 and 10±9.8pgm-3 respectively. On the other hand, mean particle (PM10) associated concentrations throughout the year were 1.9±2.4 and 0.46±0.43pgm-3 respectively. Gas-phase concentration of α- and γ-hexachlorocyclohexanes peaked in the autumn season whereas highest concentrations in the particle phase were measured in spring. Ratio of α-/γ-isomer ranged from 3.7 to 7.4 in the gas phase which was close to the ratio in technical hexachlorocyclohexanes (5-7). In the particle-associated phase this ratio ranged from 1.2 to 3.8, with the exception of daytime samples in spring (up to 16) and summer seasons (up to 14) and this exception could be due to the isomerization from γ- to α- in ambient air, at least partly resulted from the impact of sunlight. Regression analysis showed that, at the sampling site, concentrations of α- and γ-hexachlorocyclohexanes in the gas phase were both elevated with increasing temperature and wind speed, whereas in the particle-associated phase their concentrations tended to remain stable

Chicken IFI6 inhibits avian reovirus replication and affects related innate immune signaling pathways

Interferon-alpha inducible protein 6 (IFI6) is an important interferon-stimulated gene. To date, research on IFI6 has mainly focused on human malignant tumors, virus-related diseases and autoimmune diseases. Previous studies have shown that IFI6 plays an important role in antiviral, antiapoptotic and tumor-promoting cellular functions, but few studies have focused on the structure or function of avian IFI6. Avian reovirus (ARV) is an important virus that can exert immunosuppressive effects on poultry. Preliminary studies have shown that IFI6 expression is upregulated in various tissues and organs of specific-pathogen-free chickens infected with ARV, suggesting that IFI6 plays an important role in ARV infection. To analyze the function of avian IFI6, particularly in ARV infection, the chicken IFI6 gene was cloned, a bioinformatics analysis was conducted, and the roles of IFI6 in ARV replication and the innate immune response were investigated after the overexpression or knockdown of IFI6 in vitro. The results indicated that the molecular weight of the chicken IFI6 protein was approximately 11 kDa and that its structure was similar to that of the human IFI27L1 protein. A phylogenetic tree analysis of the IFI6 amino acid sequence revealed that the evolution of mammals and birds was clearly divided into two branches. The evolutionary history and homology of chickens are similar to those of other birds. Avian IFI6 localized to the cytoplasm and was abundantly expressed in the chicken lung, intestine, pancreas, liver, spleen, glandular stomach, thymus, bursa of Fabricius and trachea. Further studies demonstrated that IFI6 overexpression in DF-1 cells inhibited ARV replication and that the inhibition of IFI6 expression promoted ARV replication. After ARV infection, IFI6 modulated the expression of various innate immunity-related factors. Notably, the expression patterns of MAVS and IFI6 were similar, and the expression patterns of IRF1 and IFN-β were opposite to those of IFI6. The results of this study further advance the research on avian IFI6 and provide a theoretical basis for further research on the role of IFI6 in avian virus infection and innate immunity

Factors associated with residents' contract behavior with family doctors in community health service centers: A longitudinal survey from China.

BackgroundChina adopted family doctor (FD) to help achieve "Healthy China 2030" through providing continuous, comprehensive, and life-cycle contract services. However, there is a disparity between actual and targeted FD use, as residents continue to visit specialists in large hospitals. The government implemented initiatives to improve residents' willingness to sign up with and visit their FDs. Factors that influence contract behavior are therefore significant for frontier policy research.MethodsTwo survey waves were conducted in Shanghai (2013 and 2016). The first wave included 2754 people and the second 1995 people. Exploratory factor analysis was used to synthesize "satisfaction" as a predictor of contract behavior. Pearson's chi-square, pooled and logistic regression models were used to estimate associations between influencing factors and contract behavior, and clarify variations in factors across the two waves.ResultsFour factors were extracted from 15 satisfaction items: "Treatment Environment," "Medical Technology," "Service Specification" and "Service Attitude". Consistent with descriptive analysis, longitudinal analysis showed sociodemographic characteristics (age, education, marital status, and hukou) were significant predictors of contract behavior. The odds ratio of non-communicable diseases (NCD) patients for contract behavior was 2.218 times that of residents without NCD. Contract behavior was positively correlated with awareness of FD services (OR = 21.674, 95%CI = 15.043-31.229), satisfaction with Service Attitude (OR = 1.210, 95%CI = 1.009-1.451), and visit compliance (OR = 1.959, 95%CI = 1.564-2.452). Over time, the odds ratios of the married, Shanghai hukou, NCD, and awareness of FD services declined from 0.456, 1.795, 2.492, 28.690 to 0.443, 1.678, 1.910 and 14.031 respectively, while those of age, and visit compliance increased from 1.027, 1.521 to 1.041 and 2.305 respectively. In 2016, an education-contract gradient had formed (the higher the education level, the higher probability of signing with a FD), whereas high school education had the highest odds ratio (OR = 1.163,95%CI = 0.740-1.827) in 2013. Service Attitude was the only significant satisfaction-related predictor (OR = 1.358, 95%CI = 1.001-1.842) in 2016, compared with "Treatment Environment" (OR = 1.224, 95%CI = 1.001-1.496) and "Service Specification" in 2013(OR = 1.270, 95%CI = 1.040-1.552).ConclusionsExcept for the socio-demographic variables, NCD, awareness of FD services, satisfaction and visit compliance were significant predictors of contract behavior with FDs. The effect of visit compliance had increased over time while NCD and awareness of FD services were losing impact over time. Significant satisfaction factors had also changed from "Treatment Environment" and "Service Specification" to "Service Attitude"

A novel marRAB operon contributes to the rifampicin resistance in Mycobacterium smegmatis.

The multiple-antibiotic resistance regulator (MarR) plays an important role in modulating bacterial antibiotic resistance. However, the regulatory model of the marRAB operon in mycobacteria remains to be characterized. Here we report that a MarR, encoded by Ms6508, and its marRAB operon specifically contribute to rifampicin (RIF) resistance in Mycobacterium smegmatis. We show that the MarR recognizes a conserved 21-bp palindromic motif and negatively regulates the expression of two ABC transporters in the operon, encoded by Ms6509-6510. Unlike other known drug efflux pumps, overexpression of these two ABC transporters unexpectedly increased RIF sensitivity and deletion of these two genes increased mycobacterial resistance to the antibiotic. No change can be detected for the sensitivity of recombinant mycobacterial strains to three other anti-TB drugs. Furthermore, HPLC experiments suggested that Ms6509-Ms6510 could pump RIF into the mycobacterial cells. These findings indicated that the mycobacterial MarR functions as a repressor and constitutively inhibits the expression of the marRAB operon, which specifically contributes to RIF resistance in M. smegmatis. Therefore, our data suggest a new regulatory mechanism of RIF resistance and also provide the new insight into the regulatory model of a marRAB operon in mycobacteria

Combination of Hyperspectral and Quad-Polarization SAR Images to Classify Marsh Vegetation Using Stacking Ensemble Learning Algorithm

Combinations of multi-sensor remote sensing images and machine learning have attracted much attention in recent years due to the spectral similarity of wetland plant canopy. However, the integration of hyperspectral and quad-polarization synthetic aperture radar (SAR) images for classifying marsh vegetation has still been faced with the challenges of using machine learning algorithms. To resolve this issue, this study proposed an approach to classifying marsh vegetation in the Honghe National Nature Reserve, northeast China, by combining backscattering coefficient and polarimetric decomposition parameters of C-band and L-band quad-polarization SAR data with hyperspectral images. We further developed an ensemble learning model by stacking Random Forest (RF), CatBoost and XGBoost algorithms for marsh vegetation mapping and evaluated its classification performance of marsh vegetation between combinations of hyperspectral and full-polarization SAR data and any of the lone sensor images. Finally, this paper explored the effect of different polarimetric decomposition methods and wavelengths of radar on classifying wetland vegetation. We found that a combination of ZH-1 hyperspectral images, C-band GF-3, and L-band ALOS-2 quad-polarization SAR images achieved the highest overall classification accuracy (93.13%), which was 5.58–9.01% higher than that only using C-band or L-band quad-polarization SAR images. This study confirmed that stacking ensemble learning provided better performance than a single machine learning model using multi-source images in most of the classification schemes, with the overall accuracy ranging from 77.02% to 92.27%. The CatBoost algorithm was capable of identifying forests and deep-water marsh vegetation. We further found that L-band ALOS-2 SAR images achieved higher classification accuracy when compared to C-band GF-3 polarimetric SAR data. ALOS-2 was more sensitive to deep-water marsh vegetation classification, while GF-3 was more sensitive to shallow-water marsh vegetation mapping. Finally, scattering model-based decomposition provided important polarimetric parameters from ALOS-2 SAR images for marsh vegetation classification, while eigenvector/eigenvalue-based and two-component decompositions produced a great contribution when using GF-3 SAR images

Combination of Hyperspectral and Quad-Polarization SAR Images to Classify Marsh Vegetation Using Stacking Ensemble Learning Algorithm

Combinations of multi-sensor remote sensing images and machine learning have attracted much attention in recent years due to the spectral similarity of wetland plant canopy. However, the integration of hyperspectral and quad-polarization synthetic aperture radar (SAR) images for classifying marsh vegetation has still been faced with the challenges of using machine learning algorithms. To resolve this issue, this study proposed an approach to classifying marsh vegetation in the Honghe National Nature Reserve, northeast China, by combining backscattering coefficient and polarimetric decomposition parameters of C-band and L-band quad-polarization SAR data with hyperspectral images. We further developed an ensemble learning model by stacking Random Forest (RF), CatBoost and XGBoost algorithms for marsh vegetation mapping and evaluated its classification performance of marsh vegetation between combinations of hyperspectral and full-polarization SAR data and any of the lone sensor images. Finally, this paper explored the effect of different polarimetric decomposition methods and wavelengths of radar on classifying wetland vegetation. We found that a combination of ZH-1 hyperspectral images, C-band GF-3, and L-band ALOS-2 quad-polarization SAR images achieved the highest overall classification accuracy (93.13%), which was 5.58–9.01% higher than that only using C-band or L-band quad-polarization SAR images. This study confirmed that stacking ensemble learning provided better performance than a single machine learning model using multi-source images in most of the classification schemes, with the overall accuracy ranging from 77.02% to 92.27%. The CatBoost algorithm was capable of identifying forests and deep-water marsh vegetation. We further found that L-band ALOS-2 SAR images achieved higher classification accuracy when compared to C-band GF-3 polarimetric SAR data. ALOS-2 was more sensitive to deep-water marsh vegetation classification, while GF-3 was more sensitive to shallow-water marsh vegetation mapping. Finally, scattering model-based decomposition provided important polarimetric parameters from ALOS-2 SAR images for marsh vegetation classification, while eigenvector/eigenvalue-based and two-component decompositions produced a great contribution when using GF-3 SAR images

RETRACTED ARTICLE: Removal of heavy metals from plating wastewater by using complexation-ultrafiltration process

Complexation-ultrafiltration process was employed to remove Ni( II ), Cu( II ), Zn( II ) and Cr( III ) from plating wastewater. Polyacrylic acid sodium (PAASS) as a water-soluble polymer was used for complexing the cationic forms of the heavy metals before filtration. The size of the complex has to be larger than the selected membranes so the complex can be retained. Permeate water is then purified from the heavy metals. Filtration experiments were performed with ultrafiltration membrane system, equipped with a polysulfone memebrane with a 10,000 Da cut-off. Different parameters, affecting the percentage rejection of the metals, such as pH, loading ratio, and permeate flux (FVC) have been investigated. The concentrated retentates were used further for the decomplexation, after the decomplexation, diafiltration experiments were carried out at pH 1.5 using dilute H2SO4 solution. All the four metals were found to be extracted from the metal-PAASS complexes with satisfaction. It was also found that the complexing performance of the PAASS regenerated did not change compared with the fresh PAASS. ? 2011 IEEE

Longitudinal Evaluation on the Operation Index Applied to Public Hospitals in Pudong New District of Shanghai, China

The public hospital reform has lasted 5 years in China; however, the operation development status and trends of public hospitals have not been systematically evaluated in Pudong New District. We first applied the technology of longitudinal index to assess the development of public hospitals there. The quantitative data were mainly gathered by taking health statistics database from 2009 to 2014. The results showed that overall operating index presented a down-up trend, with the highest point in 2014 and the lowest point in 2012. Overall operating index, development foundation index, and management condition index were found to be statistically different ( P = .010, P = .016, P = .031) in different years, whereas the service operation index and financial risk index were not so ( P = .543, P = .228). Moreover, the results demonstrated that no obvious difference was observed in the overall operating index between the general and specialized hospitals ( P = .327), which was the same in the 4 first-class indexes. However, there were statistical differences in the overall operating index and development foundation index among these 5 years ( P = .018, P = .036), but none in the service operation index, management condition index, and financial risk index ( P = .503, P = .062, P = .177). No interaction effects were discovered between year and hospital categories in the current study ( P = .673, P = .375, P = .885, P = .152, P = .288)

Molecular characterization of emerging chicken and turkey parvovirus variants and novel strains in Guangxi, China

Abstract Avian parvoviruses cause several enteric poultry diseases that have been increasingly diagnosed in Guangxi, China, since 2014. In this study, the whole-genome sequences of 32 strains of chicken parvovirus (ChPV) and 3 strains of turkey parvovirus (TuPV) were obtained by traditional PCR techniques. Phylogenetic analyses of 3 genes and full genome sequences were carried out, and 35 of the Guangxi ChPV/TuPV field strains were genetically different from 17 classic ChPV/TuPV reference strains. The nucleotide sequence alignment between ChPVs/TuPVs from Guangxi and other countries revealed 85.2–99.9% similarity, and the amino acid sequences showed 87.8–100% identity. The phylogenetic tree of these sequences could be divided into 6 distinct ChPV/TuPV groups. More importantly, 3 novel ChPV/TuPV groups were identified for the first time. Recombination analysis with RDP 5.0 revealed 15 recombinants in 35 ChPV/TuPV isolates. These recombination events were further confirmed by Simplot 3.5.1 analysis. Phylogenetic analysis based on full genomes showed that Guangxi ChPV/TuPV strains did not cluster according to their geographic origin, and the identified Guangxi ChPV/TuPV strains differed from the reference strains. Overall, whole-genome characterizations of emerging Guangxi ChPV and TuPV field strains will provide more detailed insights into ChPV/TuPV mutations and recombination and their relationships with molecular epidemiological features