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Background: China has the largest population and third largest number of tuberculosis cases in the world. Tuberculosis still remains a major public health concern in China. The increasing floating population has become an important part of China’s socioeconomic process and brought the potential risk for infectious disease transmission in the huge population. Both the flow of tuberculosis population in the country and the role of massive floating population in tuberculosis transmission are yet unclear.Methods: 14,027 tuberculosis flow data were derived from the new smear-positive pulmonary tuberculosis cases in China in 2012, provided by the nationwide Infectious Disease Reporting System. Spatial interaction model was used to model the tuberculosis flow and the regional socioeconomic factors.Results: The Pearl River Delta in southern China and the Yangtze River Delta along China’s east coast presented as the largest destination and concentration areas of tuberculosis inflows. Socioeconomic factors were determinants of tuberculosis flow. A 10% increase in per capita GDP was associated with 2.1% decrease in tuberculosis outflows from the provinces of origin, and 0.5% increase in tuberculosis inflows to the destinations and 18.9% increase in intraprovincial flow. Per capita net income of rural households and per capita disposable income of urban households were positively associated with tuberculosis flows. A 10% increase in per capita net income corresponded to 3.6% increase in outflows from the origin, 12.8% increase in inflows to the destinations and 47.9% increase in intraprovincial flows. Tuberculosis incidence had positive impacts on tuberculosis flows. A 10% increase in the number of tuberculosis cases corresponded to 1.1% increase in tuberculosis inflows to the destinations, 2.0% increase in outflows from the origins, and 2.2% increase in intraprovincial flows. A 10% increase in the tuberculosis incidence rate was associated with 9.9% increase in tuberculosis intraprovincial flows. In addition, the tuberculosis flow had a significant spatial dependence and positively affected tuberculosis flows in the neighboring regions.Conclusions: Tuberculosis flows had clear spatial stratified heterogeneity and spatial autocorrelation; regional socio-economic characteristics had different and statistically significant effects on tuberculosis flows in the origin and destination, and income factor played an important role among the determinants.

With the considerable increase of visible satellites for positioning, the fault detection and identification performance of Range Consensus (RANCO) algorithm for Receiver Autonomous Integrity Monitoring (RAIM) will significantly be improved. However, the calculation amount of RANCO algorithm will exponentially increase for the sharp addition of visible satellite subsets. This paper proposes a modified RANCO algorithm based on genetic algorithm (GA-RANCO) for RAIM to inhibit the exponentially expanded calculation amount. To reduce the calculation amount in searching the optimal minimal necessary subset (MNS), the preselection step is developed to speed up the convergence process of GA-RANCO. It is executed to exclude the chromosome-represented MNS for which the count of faulty satellites will exceed the upper limit of independent simultaneous satellite faults to be monitored. Mathematical simulations are introduced to determine the GA parameters, and simulation experiments under different schemes are designed to evaluate the performance of GA-RANCO algorithm. Results illustrate that the time consumption under a large number of visible satellites of GA-RANCO is much lower than that of RANCO and the faulty detection and identification performance of GA-RANCO is the same as that of RANCO.

Additional file 1. Details of the GWR analysis.

PM2.5 pollution is an environmental issue caused by multiple natural and socioeconomic factors, presenting with significant spatial disparities across mainland China. However, the determinant power of natural and socioeconomic factors and their interactive impact on PM2.5 pollution is still unclear. In the study, the GeogDetector method was used to quantify nonlinear associations between PM2.5 and potential factors. This study found that natural factors, including ecological environments and climate, were more influential than socioeconomic factors, and climate was the predominant factor (q = 0.56) in influencing PM2.5 pollution. Among all interactions of the six influencing factors, the interaction of industry and climate had the largest influence (q = 0.66). Two recognized major contaminated areas were the Tarim Basin in the northwest region and the eastern plain region; the former was mainly influenced by the warm temperate arid climate and desert, and the latter was mainly influenced by the warm temperate semi-humid climate and multiple socioeconomic factors. The findings provided an interpretation of the influencing mechanisms of PM2.5 pollution, which can contribute to more specific policies aimed at successful PM2.5 pollution control and abatement.

Commonly, the code noise and multipath error is considered to fully obey the Gaussian distribution. While in the cases with different elevation angles and orbit types, the assumption may be inappropriate. Based on an empirical study, by considering both the elevation angle and the orbit type, a new code noise and multipath distribution model is proposed to describe a more accurate code noise and multipath distribution in this paper. Actual code noise and multipath data from 10 observation stations during two months are researched, and the parameters and elevation angle range of code noise and multipath distribution model are determined. The code noise and multipath distribution model is verified to be more accurate than the model presented in the Global Navigation Satellite System Evolutionary Architecture Study report, according to the analysis on the code noise and multipath overbounding, position error overbounding, and the availability of receiver autonomous integrity monitoring. This model provides more accurate prior information for receiver autonomous integrity monitoring, especially its availability.

Long-term grid historical temperature datasets are the foundation of climate change research. Datasets developed by traditional interpolation methods usually contain data for a period of less than 50 yr, with a relatively low spatial resolution owing to the sparse distribution of stations in the historical period. In this study, the point interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (P-BSHADE) method has been used to interpolate 1-km grids of monthly surface air temperatures in the historical period of 1900–50 in China. The method can be used to remedy the station bias resulting from sparse coverage, and it considers the characteristics of spatial autocorrelation and nonhomogeneity of the temperature distribution to obtain unbiased and minimum error variance estimates. The results have been compared with those from widely used methods such as kriging, inverse distance weighting (IDW), and a combined spline with kriging (TPS-KRG) method, both theoretically and empirically. The leave-one-out cross-validation method using a real dataset was implemented. The root-mean-square error (RMSE) [mean absolute error (MAE)] for P-BSHADE is 0.98°C (0.75°C), while those for TPS-KRG, kriging, and IDW are 1.46° (1.07°), 2.23° (1.51°), and 2.64°C (1.85°C), respectively. The results of validation using a simulated dataset also present the smallest error for P-BSHADE, demonstrating its empirical superiority. In addition to its empirical superiority, the method also can produce a map of the estimated error variance, representing the uncertainty of estimation.

Rodents are the main host animals that spread plague, and Spermophilus dauricus (S. dauricus) is the most common rodent in North China. In this study, a rodent density survey was carried out in China’s Jilin Province from April to August 2005. Moran’s I and semivariogram curves were used to investigate the spatial distribution characteristics of the sampling data. We found that the spatial auto-correlation index was low and failed to generate a meaningful semivariogram curve. In this case, commonly used interpolators, such as kriging, were not suitable for mapping density over the study area. However, the Sandwich model, which is based on spatial stratified heterogeneity, could be applied to our data. Our results showed that the type of soil and land use significantly influenced the distribution of rodent density, and the interactive effect of these variables was much stronger than that of each variable alone. The Sandwich-estimated rodent density map showed that rodent density increased from the southeast to the northwest in Jilin Province. Finally, a framework of a rodent density survey using the Sandwich model was introduced.

Background As a common infectious disease, hand, foot and mouth disease (HFMD) is affected by multiple environmental and socioeconomic factors, and its pathogenesis is complex. Furthermore, the transmission of HFMD is characterized by strong spatial clustering and autocorrelation, and the classical statistical approach may be biased without consideration of spatial autocorrelation. In this paper, we propose to embed spatial characteristics into a spatiotemporal additive model to improve HFMD incidence assessment. Methods Using incidence data (6439 samples from 137 monitoring district) for Shandong Province, China, along with meteorological, environmental and socioeconomic spatial and spatiotemporal covariate data, we proposed a spatiotemporal mixed model to estimate HFMD incidence. Geo-additive regression was used to model the non-linear effects of the covariates on the incidence risk of HFMD in univariate and multivariate models. Furthermore, the spatial effect was constructed to capture spatial autocorrelation at the sub-regional scale, and clusters (hotspots of high risk) were generated using spatiotemporal scanning statistics as a predictor. Linear and non-linear effects were compared to illustrate the usefulness of non-linear associations. Patterns of spatial effects and clusters were explored to illustrate the variation of the HFMD incidence across geographical sub-regions. To validate our approach, 10-fold cross-validation was conducted. Results The results showed that there were significant non-linear associations of the temporal index, spatiotemporal meteorological factors and spatial environmental and socioeconomic factors with HFMD incidence. Furthermore, there were strong spatial autocorrelation and clusters for the HFMD incidence. Spatiotemporal meteorological parameters, the normalized difference vegetation index (NDVI), the temporal index, spatiotemporal clustering and spatial effects played important roles as predictors in the multivariate models. Efron’s cross-validation R2 of 0.83 was acquired using our approach. The spatial effect accounted for 23% of the R2, and notable patterns of the posterior spatial effect were captured. Conclusions We developed a geo-additive mixed spatiotemporal model to assess the influence of meteorological, environmental and socioeconomic factors on HFMD incidence and explored spatiotemporal patterns of such incidence. Our approach achieved a competitive performance in cross-validation and revealed strong spatial patterns for the HFMD incidence rate, illustrating important implications for the epidemiology of HFMD. Electronic supplementary material The online version of this article (10.1186/s12889-018-5169-3) contains supplementary material, which is available to authorized users.

This study investigated effect of two trellises, single crawled cordon training (SCCT) and independent long-stem pruning (ILSP) on change of total organic carbons (TOCs) in the shoots and leaves and microstructure of shoots in early October, as well as total soluble solid (TSS), total acid (TA) and maturity index (MI) of ‘Ecolly’ grape on July 21, 29 and August 24, respectively. The results proved that there was obvious difference from TOC between shoots and foliage of the two trellises. TOC level of shoots in the first, second, third and fourth, except for the fifth, from the root of the main stem labelled in ILSP was higher than those in SCCT, respectively. Level of blades followed the former. The maturity level in SCCT shoots and blades was superior to ILSP, despite total value, 3261.64 g/Kg, of TOC accumulated in ILSP shoots and leaves exceeded those, 2955.37 g/Kg, in SCCT, respectively, whcih could be observed through microstructure in various trellises of shoots. Additionally, identical TSS per shoot in SCCT was higher than that in ILSP during three consecutive seasons excluding the second and third shoots on August 24. Although SCCT revealed higher TSS than ILSP before August 9, and other results (concurrent 16.8% and 17.2%, respectively) between August 9 and 24, there were still no significant variety between trellises after August 24, MI was similar. Meanwhile, SCCT (4.54 g/L in 26 August and 3.82 g/L in 11 September) had higher TA than ILSP (4.23 g/L in 26 August and 3.62 g/L in 11 September) except before August 24. Therefore, SCCT could be proposed as a viable choice improving ripening process, quality of grape and maturation of shoots and leaves for China growers.

Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level suffer from many uncertainties. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates. To illustrate this issue, this study evaluates and compares the performance of different models for estimating plot-level forest AGB. These models include three different machine learning models [support vector machine (SVM), random forest (RF), and a radial basis function artificial neural network (RBF-ANN)], one spatial statistic model (P-BSHADE), and three combinations thereof (SVM & P-BSHADE, RF & P-BSHADE, and RBF-ANN & P-BSHADE). The results show that the root mean square error, mean absolute error, and mean relative error of all combined models are substantially smaller than those of any individual model, with the RF & P-BSHADE combined method generating the smallest values. These results indicate that a combined approach using machine learning with spatial statistics, especially the RF & P-BSHADE model, improves the accuracy of plot-level AGB models. These research results contribute to the development of accurate large-forested-landscape AGB maps.

While one has yet to be built in China, a rice supply flow network would provide essential information for stabilizing and optimizing food distribution and ensuring food security. Moreover, it is unclear how socioeconomic and natural environmental factors affect the rice supply flow. In this study, 35,768 random samples from the rice sales market were collected to build the rice flow network of China. The rice supply patterns at a regional and provincial level were revealed by the rice flow network. At the regional level, Northeast, Central, and East China are the main rice source areas, supplying almost all of China. At the provincial level, economically developed megacities, such as Beijing and Shanghai, and provinces with low temperatures like Tibet and Qinghai, rely heavily on imported rice. Determinant analysis shows that the rice supply patterns are mainly attributable to the cultivated area, primary industry employees, the permanent population, and tertiary industry proportion. These factors explain 75%, 59%, 48%, and 31%, respectively, of the rice supply spatial patterns. Furthermore, the interactions of cultivated area and temperature, cultivated area and primary industry proportion, and cultivated area and precipitation can explain 94%, 91%, and 89%, respectively, of the rice supply patterns. These findings reveal China's rice supply patterns and their determinants in recent years, providing essential information for optimizing the layout of rice production and ensuring food security in the future scenarios of climate change and socioeconomic development.

The geographical extent, magnitude, and uncertainty of global climate change have been widely discussed and have critical policy implications at both global and local scales. In this study, a new analysis of annual mean global land surface air temperature since 1880 was generated, which has greater coverage and lower uncertainty than previous distributions. The Biased Sentinel Hospitals Areal Disease Estimation (BSHADE) method, used in this study, makes a best linear unbiased estimation (BLUE) when a sample is small and biased to a spatially heterogeneous population. For the period of 1901–2010, the warming trend was found to be 0.109 °C decade−1 with 95% confidence intervals between 0.081 °C and 0.137 °C. Additionally, warming exhibited different spatial patterns in different periods. In the early 20th century (1923–1950), warming occurred mainly in the mid-high latitudes of the Northern Hemisphere, whereas in the most recent decades (1977–2014), warming was more spatially extensive across the global land surface. Compared with other common methods, the difference in results appears in the areas with few stations and in the early years, when stations had sparse coverage and were unevenly distributed. Validation, which was performed using real data that simulated the historic situation, showed a smaller error in the BSHADE estimate than in other methods. This study produced a new database with greater coverage and less uncertainty that will improve the understanding of climate dynamics on the Earth since 1880, especially in isolated areas and early periods, and will benefit the assessment of climate-change-related issues, such as the effects of human activities.

SUMMARYHand, foot and mouth disease (HFMD) risk has become an increasing concern in the Beijing–Tianjin–Hebei region, which is the biggest urban agglomeration in north-eastern Asia. In the study, spatiotemporal epidemiological features of HFMD were analysed, and a Bayesian space–time hierarchy model was used to detect local spatial relative risk (RR) and to assess the effect of meteorological factors. From 2009 to 2013, there was an obvious seasonal pattern of HFMD risk. The highest risk period was in the summer, with an average monthly incidence of 4·17/103, whereas the index in wintertime was 0·16/103. Meteorological variables influenced temporal changes in HFMD. A 1 °C rise in air temperature was associated with an 11·5% increase in HFMD (corresponding RR 1·122). A 1% rise in relative humidity was related to a 9·51% increase in the number of HFMD cases (corresponding RR 1·100). A 1 hPa increment in air pressure was related to a 0·11% decrease in HFMD (corresponding RR 0·999). A 1 h increase in sunshine was associated with a 0·28% rise in HFMD cases (corresponding RR 1·003). A 1 m/s rise in wind speed was related to a 6·2% increase in HFMD (corresponding RR 1·064). High-risk areas were mainly large cities, such as Beijing, Tianjin, Shijiazhuang and their neighbouring areas. These findings can contribute to risk control and implementation of disease-prevention policies.

Background Malaria remains an important public health concern in China and is particularly serious in Yunnan, a China’s provincial region of high malaria burden with an incidence of 1.79/105 in 2012. This study aims to examine the epidemiologic profile and spatiotemporal aspects of epidemics of malaria, and to examine risk factors which may influence malaria epidemics in Yunnan Province. Methods The data of malaria cases in 2012 in 125 counties of Yunnan Province was used in this research. The epidemical characteristics of cases were revealed, and time and space clusters of malaria were detected by applying scan statistics method. In addition, we applied the geographically weighted regression (GWR) model in identifying underlying risk factors. Results There was a total of 821 cases of malaria, and male patients accounted for 83.9% (689) of the total cases. The incidence in the group aged 20–30 years was the highest, at 3.00/105. The majority (84.1%) of malaria cases occurred in farmers and migrant workers, according to occupation statistics. On a space-time basis, epidemics of malaria of varying severity occurred in the summer and autumn months, and the high risk regions were mainly distributed in the southwest counties. Annual average temperature, annual cumulative rainfall, rice yield per square kilometer and proportion of rural employees mainly showed a positive association with the malaria incidence rate, according to the GWR model. Conclusions Malaria continues to be one of serious public health issues in Yunnan Province, especially in border counties in southwestern Yunnan. Temperature, precipitation, rice cultivation and proportion of rural employees were positively associated with malaria incidence. Individuals, and disease prevention and control departments, should implement more stringent preventative strategies in locations with hot and humid environmental conditions to control malaria.

BackgroundBacillary dysentery remains a worldwide public health problem, which has been found to have spatial–temporal heterogeneity, however most studies have only focused on the disease from either a time or space perspective, the spatial–temporal association between them has been still unclear. MethodIn this study, the Bayesian space–time hierarchy model was used to identify the spatial-temporal patterns of this disease in Shandong province, China. And then GeoDetector was used to quantify the determinant power of meteorological factors and their interactive effect among different regions in Shandong. ResultsThe results indicated that, temporally, the incidence peaked in summer. Geographically, the hot spots were distributed discretely among three regions, among which the effect of meteorological factors on this disease exist significant discrepancy. The most important two dominant factors of eastern coastal region were wind speed and average temperature, with determinant powers of 28% and 25%, respectively. The first two dominant factors of western inland region were average temperature and precipitation, with determinant powers of 47% and 32%, respectively. The first two dominant factors of middle region were average temperature and wind speed, with determinant powers of 66% and 48%, respectively. ConclusionsThese findings suggest that in a hot and humid environment would boost the transmission of bacillary dysentery, which can be served as a suggestion and basis for the surveillance and will be helpful for this disease control and implementing disease-prevention policies.

This study quantified influences of interactions between anthropogenic and natural factors on trace element accumulation and pollution risk in karst soils at regional and local scales and identified the dominant interacting factors. A total of 513 soil samples were collected from Hechi, southern China to measure concentrations of arsenic (As), cadmium (Cd), chromium (Cr), mercury (Hg), and lead (Pb), which were compared with published background values. Descriptive statistics and occurrence characteristics were developed with geostatistical methods and the comprehensive pollution risk was calculated using the Nemerow pollution index (NPI). Geo-detector models were used to further examine and quantify the influence of 14 factors (5 anthropogenic and 9 natural) on trace element concentrations and NPI, both individually and interacting with the other 13 factors. The results clearly demonstrate that anthropogenic factors interact with natural factors to enhance nonlinearly and significantly trace element accumulation in karst soils. Watershed was the natural factor that most enhanced trace element accumulation when interacting with anthropogenic factors. Land use and smelting industry were the anthropogenic factors that most enhanced trace element accumulation when interacting with natural factors. Land use-watershed interaction accounted for 56% of Cd accumulation and smelting industry-watershed interaction for 19% of As accumulation. Land use-watershed, land use-lithology, and pH-watershed interactions accounted for 51%, 19%, and 15%, respectively of NPI values. The findings indicate that changing land use and reducing pollutant discharge from the smelting industry should be considered.

Background Hand, foot and mouth disease (HFMD) is a common infectious disease whose mechanism of transmission continues to remain a puzzle for researchers. The measurement and prediction of the HFMD incidence can be combined to improve the estimation accuracy, and provide a novel perspective to explore the spatiotemporal patterns and determinant factors of an HFMD epidemic. Methods In this study, we collected weekly HFMD incidence reports for a total of 138 districts in Shandong province, China, from May 2008 to March 2009. A Kalman filter was integrated with geographically weighted regression (GWR) to estimate the HFMD incidence. Spatiotemporal variation characteristics were explored and potential risk regions were identified, along with quantitatively evaluating the influence of meteorological and socioeconomic factors on the HFMD incidence. Results The results showed that the average error covariance of the estimated HFMD incidence by district was reduced from 0.3841 to 0.1846 compared to the measured incidence, indicating an overall improvement of over 50% in error reduction. Furthermore, three specific categories of potential risk regions of HFMD epidemics in Shandong were identified by the filter processing, with manifest filtering oscillations in the initial, local and long-term periods, respectively. Amongst meteorological and socioeconomic factors, the temperature and number of hospital beds per capita, respectively, were recognized as the dominant determinants that influence HFMD incidence variation. Conclusions The estimation accuracy of the HFMD incidence can be significantly improved by integrating a Kalman filter with GWR and the integration is effective for exploring spatiotemporal patterns and determinants of an HFMD epidemic. Our findings could help establish more accurate HFMD prevention and control strategies in Shandong. The present study demonstrates a novel approach to exploring spatiotemporal patterns and determinant factors of HFMD epidemics, and it can be easily extended to other regions and other infectious diseases similar to HFMD.

Interest in assessing the effects of temperature on hand, foot, and mouth disease (HFMD) has increased. However, little evidence is available on spatial heterogeneity in relationship to temperature and HFMD in metropolitan (capital city and municipal districts) and other areas where economic levels are significantly different. In this study, the Bayesian space-time hierarchy model was applied to identify the spatiotemporal heterogeneity of HFMD. GeoDetector was then used to quantify the determinant power of temperature to the disease in regions where the economic level has significant spatial heterogeneity. There was significant spatial heterogeneity in the influence of temperature on the incidence of HFMD in metropolitan and other areas. In metropolitan areas, where the disease risk is higher (hot spots), the HFMD incidence was higher alongside an increase in average temperature. However, in non-metropolitan areas, where the disease risk is lower (cold spots), there was an approximately S-shaped relationship between the temperature and the HFMD risk. More specifically, when the temperature was >25 °C, the HFMD incidence no longer increased monotonically with the increasing temperature. There was significant spatial heterogeneity in the effects of temperature on the HFMD incidence in metropolitan and non-metropolitan areas. This finding may serve as a suggestion and basis for the surveillance and control of this disease and it is conducive to the rational allocation of medical resources in different areas.

BACKGROUND In China, the cases of newly diagnosed HIV/AIDS in men who have sex with men (MSM) have increased more than tenfold since 2006. However, the MSM population size, geographical distribution, and migration patterns are largely unknown. OBJECTIVE Our aim is to estimate the number, spatial distribution, and migration of MSM populations in mainland China using big data from social networking. METHODS We collected 85 days of data on online users of a social networking MSM app in mainland China. Daily online MSM users and their migration across the country were investigated during a holiday period and a nonholiday period. Using the capture-mark-recapture model, we designed an experiment consisting of two independent samples to estimate the total provincial MSM population. RESULTS The estimate of MSM in mainland China was 8,288,536 (95% CI 8,274,931-8,302,141), accounting for 1.732% (95% CI 1.729%-1.734%) of adult men aged 18 to 64 years. The average daily number of MSM social networking online across mainland China was 1,198,682 during the nonholiday period. The five provinces (including municipalities) with the highest average number of daily online MSM numbers were Guangdong (n=141,712), Jiangsu (n=90,710), Zhejiang (n=72,212), Shandong (n=68,065), and Beijing (n=66,057). The proportion of daily online MSM among adult men in different cities varied from 0.04% to 0.96%, with a mean of 0.20% (SD 0.14%). Three migrating centers—Guangdong, Beijing, and the Yangtze River Delta (Shanghai-Zhejiang-Jiangsu)—accounted for 57.23% of MSM migrants in the county. CONCLUSIONS The percentage of MSM among adult men in mainland China is at the middle level compared with other Asia and Pacific countries. However, the number of MSM is very large, and the distribution is uneven. Both MSM distribution and migration are highly affected by socioeconomic status.

Beijing–Tianjin–Hebei is the largest urban agglomeration in northern China, but the spatiotemporal patterns and risk factors concerning hepatitis B virus (HBV) incidence in this area have been unclear. The present study aimed to reveal the spatiotemporal epidemiological features of HBV infection and quantify the association between HBV infection and socio-economic risk factors. The data on HBV cases in Beijing–Tianjin–Hebei from 2007 to 2012 was collected for each county. The Bayesian space–time hierarchy model and the GeoDetector method were used to reveal spatiotemporal patterns and detect risk factors. High-risk regions were mainly distributed in the underdeveloped rural areas in the north and mid-south of the study region, while low-risk regions were mainly distributed in the urban and western areas. The HBV annual incidence rate decreased substantially over the 6-year period, dropping from 7.34/105 to 5.51/105. Compared with this overall trend, 38.5% of high-risk counties showed a faster decrease, and 35.9% of high-risk counties exhibited a slower decrease. Meanwhile, 29.7% of low-risk counties had a faster decrease, and 44.6% of low-risk counties exhibited a slower decrease. Socio-economic factors were strongly associated with the spatiotemporal patterns and variation. The population density and gross domestic product per capita were negatively associated with HBV transmission, with determinant powers of 0.17 and 0.12, respectively. The proportion of primary industry and the number of healthcare workers were positively associated with the disease incidence, with determinant powers of 0.11 and 0.8, respectively. The interactive effect between population density and the other factors exerted a greater influence on HBV transmission than that of these factors measured independently.

Metabolites have essential roles in microbial communities, including as mediators of nutrient and energy exchange, cell-to-cell communication, and antibiosis. However, detecting and quantifying metabolites and other chemicals in samples having extremes in salt or mineral content using liquid chromatography-mass spectrometry (LC-MS)-based methods remains a significant challenge. Here we report a facile method based on in situ chemical derivatization followed by extraction for analysis of metabolites and other chemicals in hypersaline samples, enabling for the first time direct LC-MS-based exo-metabolomics analysis in sample matrices containing up to 2 molar total dissolved salts. The method, MetFish, is applicable to molecules containing amine, carboxylic acid, carbonyl, or hydroxyl functional groups, and can be integrated into either targeted or untargeted analysis pipelines. In targeted analyses, MetFish provided limits of quantification as low as 1 nM, broad linear dynamic ranges (up to 5-6 orders of magnitude) with excellent linearity, and low median inter-day reproducibility (e.g. 2.6%). MetFish was successfully applied in targeted and untargeted exo-metabolomics analyses of microbial consortia, quantifying amino acid dynamics in the exo-metabolome during community succession; in situ in a native prairie soil, whose exo-metabolome was isolated using a hypersaline extraction; and in input and produced fluids from a hydraulically fractured well, identifying dramatic changes in the exo-metabolome over time in the well.

In order to study effect of oak sawdust on the quality of Kyoho wine immersed by fermented Cabernet Sauvignon residual, Kyoho wine aged without oak sawdust (KWO), including KWO1 and KWO2 according to immersed orders by fermented Cabernet Sauvignon residual, was taken as control to compare the effect of oak sawdust on quality of Kyoho wine (KO), including KO1 and KO2 according to aged orders by oak sawdust. During the 15 days of aging, physical and chemical indicators, such as chroma, tonality, and total phenol in wine were determined simultaneously by using a spectrophotometer, including tannin content by KMnO4 titration, once every 3 days. The results showed that the chromaticities of Kyoho wine were 3.21, 3.02, 4.46, and 3.71 for KO1, KO2, KWO1, and KWO2, respectively. Similarly, the hues were in turn 0.73, 0.68, 0.97, and 0.72, respectively. Tannin contents were 1601.5 mg/L, 1517.3 mg/L, 337.2 mg/L, and 115.6 mg/L; total phenol contents were 277.67 mg/L, 222.1 mg/L, 64 mg/L, and 79.8 mg/L. Therefore, the contents of tannin and total phenol from KO1 wine were all the highest values. The chroma and tone of the four types of wine showed an upward trend of “S.” The chromaticity and tone were the lowest for the KO2 wine and the highest for the KWO1 wine with the larger difference between KO2 and KWO1.

The turbidity and precipitation affect seriously sensory quality of chaenomeles juice and wine. In order to reduce the harmful effects of turbid chaenomeles juice on fermentation and to ensure the sensory quality of fruit wine, pectinase was used to clarify chaenomeles juice in this experiment. The gradient of pectinase dosage was designed into 20, 25, 30, 35, 40 and 45 mg/L. The optimum dosage of pectinase selected was used for clarifying chaenomeles juice. chemical clarification method (bentonite and chitosan) was employed to clarify and stabilize the raw wine, improve its appearance and reduce the risk of diseases in the storage process, by designing the gradient of bentonite dosage, 0.8, 0.9, 1.0, 1.1, 1.2 and 1.3 g/L to select the most suitable amount of bentonite, whereas the gradient of chitosan for 1.2, 1.5, 1.8, 2.1, 2.4 and 2.7 g/L to the same effect. The optimum dosage of bentonite and chitosan were applied to chaenomeles wine to analyze and compare the clarifying effect of both two to wine, while the clarification indexes, such as anthocyanin content, turbidity, chroma and light transmittance were determined. The best dosage of pectinase was selected as 35 mg/L, the color 2.425, the light transmittance 54.7% and the best clarification effect of chaenomeles juice was obtained after clarifying 5 h. The best clarifying effect was obtained under the light permeability 29.3% and turbidity 90.04 NTU in chaenomeles wine through the best bentonite dosage of 1.0 g/L after clarifiing 12 h. While the best dosage of chitosan to clarify the chaenomeles wine was 1.8 g/L, which made the light permeability 9.80% and the turbidity 199.2 NTU after 12 h. The chaenomeles juice treated with 35 mg/L pectinase had higher the clarification efficiency and effect, and the color remained intact. While the clarifying effect of 1.0 g/L bentonite solution on chaenomeles liquor was better than that of 1.8 g/L chitosan treatment.

Additional file 1: Figure S1. QQ plot of HFMD incidence for the 22 counties in Ningxia and for the time period from 2009 to 2013. In each plot, points located on the reference red line indicate a perfect agreement with data and function.

In order to analyze the fruit ripening quality of Merlot grapes in the blocks of 5-1, 5-2 and 5-3 in Shihezi region, various physicochemical indexes of Merlot grapes in different blocks were measured from August 23 to September 7, 2017 and 2018, respectively. The results showed that as the grapes maturation, the sugar accumulation showed an upward trend. The total sugar content of the three plots during the ripening period was 194.2-259.1 g/L (measured by glucose), and the total sugar of 5-1 was the highest, 259.1 g/L. The total acid content showed a downward trend, ranging from 9.3 to 4.6 g/L (measured by tartaric acid), the highest value in 5-1 was 5.3 g/L when was harvested, and the fastest decline of total acid belonged to 5-3 plots. PH was generally on the rise from 3.39 to 3.83, meanwhile increase of 5-3 grapes was the fastest compared with other two, with a small difference between 5-1 and 5-2. Tannin continued to decrease with the change range between 6.24 and 1.04 mg/g, and tannin in block 5-2 decreased the fastest with both 2.08 mg/g of 5-1 and 5-3. The content of total phenol decreased after increase. 5-3 grape increased the fastest and also decreased the fastest block from 46.25 mg/L to 104.779 mg/L to 44.118 mg/L. 5-1 total phenol rose in the second place, but decreased in the slowest, and finally remained at 60.662 mg/L. The content of anthocyan in 5-1 had been on an upward trend, rising to the highest level of 111 mg/L eventually, 5-2 and 5-3 rapidly rising and then slightly decreasing. The chroma was also decreased after increase, and the fastest rising belonged to 5-1 from the lowest point of 9.563 to the highest point of 16.186, and fell slowly to 14.363. 5-3 rose to 12.75 with the slowest speed and fall to 11.05 with the fastest rate. Through comprehensive analysis of the indicators of the three plots combined with the growth environment, the fruit quality of the plot 5-1 was relatively good, which was the most suitable growth and ripening of Merlot grapes.

The color of red wine will change constantly during fermentation and storage, which is related to the yeast introduced. Wild yeast has a strong adaptability to the environment, and effect of which on the color change of red wine is more obvious. In order to explore the molecular mechanism of wild yeast on color change of red wine, this paper summarized the sources and species of wild yeast including influence on production capacity of anthocyanin. The variation of wine color regulated by yeast was related to the genetic material of parent of hybrid strain producing pigment, chromogenic enzyme and storage temperature. Proanthocyanins and yeasts could promote each other. Different yeasts could produce anthocyanins with similar species but greater differences. Therefore, wine color was controlled by various yeasts. Isotopic labeling technology could detect the obvious symbiotic relationship between yeast and anthocyanin, and track accurately and timely the change of wine color. Metabolism of wild yeast and instability of anthocyanin could co-lead to discoloration of wine, however the molecular mechanism of colour fading was not clear nowadays. Therefore, there was an urgent need to screen the wild yeast with the safety and stability to wine color.The color of red wine will change constantly during fermentation and storage, which is related to the yeast introduced. Wild yeast has a strong adaptability to the environment, and effect of which on the color change of red wine is more obvious. In order to explore the molecular mechanism of wild yeast on color change of red wine, this paper summarized the sources and species of wild yeast including influence on production capacity of anthocyanin. The variation of wine color regulated by yeast was related to the genetic material of parent of hybrid strain producing pigment, chromogenic enzyme and storage temperature. Proanthocyanins and yeasts could promote each other. Different yeasts could produce anthocyanins with similar species but greater differences. Therefore, wine color was controlled by various yeasts. Isotopic labeling technology could detect the obvious symbiotic relationship between yeast and anthocyanin, and track accurately and timely the change of wine color. Metabolism of wild yeast and i...

The ionospheric delay has a very important influence on the positioning accuracy of satellite navigation. It can be effectively reduced by establishing an accurate and reasonable ionospheric correction model. At present, Klobuchar parameter model is widely used in single-frequency receiver, but the correction rate of this model can only reach about 60%, which can not meet the need of high precision navigation and positioning. Through the research and analysis of the ionospheric error data of the Klobuchar parameter model, it is found that there are some periodic phenomena objectively. Aiming at the error information which cannot be represented by definite mathematical model, a TS (Takagi-Sugeno) fuzzy neural network prediction model applied to Klobuchar ionospheric delay error is established by combining TS fuzzy theory with neural network. The simulation results show that the model has good fitting ability and prediction effect on the Klobuchar ionospheric delay error. Using this model to provide error compensation for the ionospheric delay can reduce the error by about 20%. It is of great significance to improve the accuracy of navigation and positioning.

The purpose of this experiment was to study the effect of corn starch’s transformation rate on the quality of papaya juice and discuss the its role in the fermentation process of papaya juice. Papaya grown in Lincang was taken as experimental material. During the period of 2017-2018, five repetitive experiments on gelatinization, liquefaction and saccharification of maize starch were carried out, respectively. Pure saccharified liquid, fermentable sugar used by yeast, was extracted by coarse filtration of gauze and filtration of suction filter. In 2017, the activity conditions of alpha-amylase and glucoamylase were determined. Four different saccharification methods including double enzymatic method, acid hydrolysis method, enzymatic hydrolysis method and acid enzymatic method were selected for experiments, respectively. The optimum method on saccharification of corn starch was determined according to the sugar content and yield of saccharified solution. The saccharified liquid obtained was added to peeled and non-peeled papaya juices for fermentation, respectively. During the experiment, the quality indexes, such as total sugar, total acid, tannin, anthocyanin and chroma were determined, respectively. In 2018, the transformation experiments of corn starch in corn flour, fresh corn granules and mature corn granules were meanwhile carried out. The best raw material for saccharification was selected through the total sugar content and the extraction rate of saccharification solution. The optimum conditions of enzyme activity were determined as pH 6, temperature 70 °C in α-amylase, pH 5 and temperature 60 °C in saccharifying enzyme. The yield of saccharified solution of mature corn granules was 60%, the sugar content was 193.64 g/L. The double enzymatic saccharification obtained 46 g/L sugar content and 46.60% yield of saccharified solution. Therefore, mature maize granules with double enzymes method were the best choice for saccharification experiments, which increased not only the sugar content and the yield of saccharified solution, but also made the operation simple and filter easy. The saccharified liquid extracted by mature corn granules combining with double enzymatic method could promote the fermentation of papaya wine, which, on the one hand, could protect the color of papaya wine, on the other hand, improve the flavor of papaya wine, and make papaya wine with unique honey and caramel flavor.

Ensuring the suitability of urban development land is essential for delineating spatial growth boundaries and urban spatial layouts. However, the significant impact of subjective uncertainty on the suitability evaluation process significantly reduces the reliability of the evaluation results. Thus, in this study, we developed a new method to address this issue and improve the accuracy of the evaluation results. Zhengzhou in China was considered as the research area and the data utilized were obtained from the following primary sources: Landsat TM/ETM/OLI image data, land use data, digital elevation model data, spatial primary geographical data, and digital map data. A new method for evaluating the suitability of urban development land was developed by combining logistic regression, principal component analysis, kriging interpolation, K-means, and the Geodetector method to evaluate and classify the suitability of urban development land in Zhengzhou City during 2013. By using logistic regression, we could accurately evaluate the effects of a single factor, thereby avoiding subjective assessments. The principal component can be used to reduce the dimensions of the evaluation results for a single factor where the weight of the principal component is determined by using the cumulative contribution rate in order to obtain the comprehensive evaluation result. Kriging interpolation can be used to predict the evaluation results for the grid surface by using the principal component to comprehensively evaluate the sample points. K-means can be used to automatically classify the evaluation results for the grid surface. Geodetector was used to detect the spatial differentiation of the results in order to confirm the validity of the spatial partition results. These methods can avoid interference due to human factors and yield more objective and accurate evaluation results. The results indicated that the proposed evaluation method can avoid the subjective influence of the evaluation index classification and the determination of the index weight to obtain extremely accurate evaluations and high effectiveness. The suitability grading and evaluation values were highly consistent with the spatial pattern, thereby demonstrating the applicability of the evaluation results. The method and evaluation results may provide a scientific reference to support decisions regarding land resource allocation during urban development.

Bacillary dysentery is a global public health problem that exhibits manifest spatiotemporal heterogeneity. However, long-term variations and regional determinant factors remain unclear. In this study, the Bayesian space-time hierarchy model was used to identify the long-term spatiotemporal heterogeneity of the incidence of bacillary dysentery and quantify the associations of meteorological factors with the incidence of bacillary dysentery in northern and southern China from 2013 to 2017. GeoDetector was used to quantify the determinant powers of socioeconomic factors in the two regions. The results showed that the incidence of bacillary dysentery peaked in summer (June to August), indicating temporal seasonality. Geographically, the hot spots (high-risk areas) were distributed in northwestern China (Xinjiang, Gansu, and Ningxia) and northern China (including Beijing, Tianjin, and Hebei), whereas the cold spots (low-risk areas) were concentrated in southeastern China (Jiangsu, Zhejiang, Fujian, and Guangdong). Moreover, significant regional differences were found among the meteorological and socioeconomic factors. Average temperature was the dominant meteorological factor in both northern and southern China. In northern and southern China, a 1 °C increase in the average temperature led to an increase of 1.01% and 4.26% in bacillary dysentery risk, respectively. The dominant socioeconomic factors in northern and southern China were per capita gross domestic product and the number of health technicians, with q statistic values of 0.81 and 0.49, respectively. These findings suggest that hot, moist, and overcrowded environments or poor health conditions increase the risk of bacillary dysentery. This study provides suggestions and serves as a basis for surveillance efforts. Further, the suggestions may aid in the control of bacillary dysentery and in the implementation of disease prevention policies.

Aeolian dust can influence the climate, air quality, human health, and ecosystems. Dust events in Northern China are the main contributors to dust aerosols in the world, but the impacts of meteorological and anthropogenic factors and their interactions on dust events remain unclear. This study analyzed the spatial and temporal variations of dust event frequencies and quantitatively investigated the impacts of meteorological conditions, anthropogenic factors, and their interactions on dust events using the geographical detector model (GeoDetector) in Northern China. Results revealed that the dust event frequency significantly decreased by 0.006 times yr−1 per site during 1980–2007. At the regional scale, there were large seasonal variations in the effects of meteorological conditions and anthropogenic factors on dust events. Strong winds and soil surface conditions are main drivers of dust events in spring. In summer and autumn, anthropogenic factors have significant impacts on the occurrence of dust events, but the frozen period and relative humidity are major impacting factors in winter. Effects of natural and anthropogenic factors on dust events showed great spatial and seasonal disparities over different vegetation regions. Interactions between two factors enhanced their impacts on the occurrence of dust events. There are also large spatial and seasonal variations in the primary interactions on dust events over different vegetation regions. The findings could help us to better understand the relative importance of various factors on dust events, which has important implications for improving the prediction of dust emission models and developing desertification control strategies.

City-level CO2 emission scenarios are important for cities’ policies of emission reduction. However, current studies do not reveal the macro patterns of the evolution of cities. This work uses the evolution-based city emission scenario (ECES) model, which tracks the city evolution patterns by probability methods based on multiple cities’ emissions of different periods, to reveal the underlying evolution rules of cities’ CO2 emissions. By the K-means clustering method, five clusters of cities are divided, and the evolution patterns of the city clusters are analyzed. Based on the maximum evolution probability, we discover the city evolution chains that reflect the common pattern of city development. We also propose two indicators for the estimation of emission intensity in 2025 in the natural evolution scenario. Policy implications are then discussed, including optimizing the low-carbon development pathway of cities, cooperate with similar cities.

Geminal dicationic ionic liquids (ILs) often display higher thermal stabilities, viscosities, and densities compared to traditional monocationic ILs. Also, dicationic ILs have advantages in terms of tuning of their physicochemical properties by different structural modifications. They can be considered as a combination of three structural moieties: (1) cationic head groups, (2) an alkane linker chain, and (3) the associated anions. Two types of each imidazolium, pyrrolidinium, and phosphonium cations were joined by different alkane linkages (C6, C9, and C12) to develop 18 different dications. These dications were paired with two different anions (NTf2– and PFOS–) to synthesize 36 different dicationic ILs. The effect of variations in the structural moieties of these related ILs on their physicochemical properties, including melting points, densities, viscosities, solubilities, and thermal stabilities, were evaluated. ILs synthesized in this study displayed TGA thermal stabilities in the range 330–467 °C. A...

In this paper, two methods are proposed, namely the unified processing method and the distributed processing method, to process the global navigation satellite system observation data in integrated navigation simulation which uses the strapdown inertial navigation system as the reference system and the multi-global navigation satellite systems as the sub-systems. The unified processing method takes all the global navigation satellite systems as a whole as one sub-system while the distributed processing method takes each global navigation satellite system as one sub-system. The centralized filter and federated filter are utilized respectively to process the global navigation satellite system observation data in the unified processing method and the distributed processing method. The mathematical models of the unified processing method and the distributed processing method are given in detail. Through theoretical derivation and mathematical simulations, the performances of the unified processing method and the distributed processing method are investigated and compared, showing that while they have the same position (velocity) accuracy, the distributed processing method offers better efficiency than the unified processing method especially when the number of global navigation satellite systems is large (>3).

Four ionic liquid (IL) columns, SLB-IL59, SLB-IL60, SLB-IL65, and SLB-IL111, were evaluated for more rapid analysis or improved resolution of long-chain methyl and ethyl esters of omega-3, omega-6, and additional positional isomeric and stereoisomeric blends of fatty acids found in fish oil, flaxseed oil, and potentially more complicated compositions. The three structurally distinct IL columns provided shorter retention times and more symmetric peak shapes for the fatty acid methyl or ethyl esters than a conventional polyethylene glycol column (PEG), resolving cis- and trans-fatty acid isomers that coeluted on the PEG column. The potential for improved resolution of fatty acid esters is important for complex food and supplement applications, where different forms of fatty acid can be incorporated. Vacuum ultraviolet detection contributed to further resolution for intricate mixtures containing cis- and trans-isomers, as exemplified in a fatty acid blend of shorter chain C18:1 esters with longer chain polyunsaturated fatty acid (PUFA) esters.

Previous studies of land dissection density (D) often find contradictory results regarding factors controlling its spatial variation. We hypothesize that the dominant controlling factors (and the interactions between them) vary from region to region due to differences in each region's local characteristics and geologic history. We test this hypothesis by applying a geographical detector method to eight physiographic divisions of the conterminous United States and identify the dominant factor(s) in each. The geographical detector method computes the power of determinant (q) that quantitatively measures the affinity between the factor considered and D. Results show that the factor (or factor combination) with the largest q value is different for physiographic regions with different characteristics and geologic histories. For example, lithology dominates in mountainous regions, curvature dominates in plains, and glaciation dominates in previously glaciated areas. The geographical detector method offers an objective framework for revealing factors controlling Earth surface processes.

Hand, foot, and mouth disease (HFMD), predominantly occurs among infants and children. Previous studies have shown that suitable, stable temperatures favor HFMD virus reproduction; however, temperature fluctuations also affect virus transmission, and there are, so far, no studies concerning the association between such fluctuations and the incidence of HFMD. The objective of this study was to map the spatial-temporal distribution of HFMD incidence and quantify the long-term effects of temperature fluctuations on HFMD incidence in children. HFMD cases in children under five, from January 2009 to December 2013, in Beijing, Tianjin, and Hebei provinces of China, were used in this study. The GeoDetector and Bayesian space-time hierarchy models were employed to explore the spatial-temporal association between temperature fluctuations and HFMD incidence. The results indicate that HFMD incidence had significant spatial stratified heterogeneity (GeoDetector q-statistic = 0.83, p

Background Hand, foot and mouth disease (HFMD) has become a substantial threat recently. However few studies have quantified spatiotemporal heterogeneity of HFMD and detected spatiotemporal interactive effect of potential driving factors on this disease. Methods Using GeoDetector and Bayesian space-time hierarchy model, we characterized the epidemiology of HFMD in Henan, one of the largest population provinces in China, from 2012 to 2013, and quantified the impacts of potential driving factors. Results Notably, 21.43 and 24.60% counties were identified as hot and cold spots, respectively. Spatially, the hotspots were mainly clustered in regions where the economic level was high. Temporally, the highest incidence period of HFMD was discovered to be in late spring and early summer. The impact of meteorological and socio-economic factors on the disease are significant, and this study found that a 1 °C rise in temperature was related to an increase of 4.09% in the HFMD incidence, a 1% increment in relative humidity was associated with a 1.77% increase of the disease, and a 1% increment in ratio of urban to rural population was associated with a 0.16% increase of the disease. Conclusion Meteorological and socio-economic factors presented significantly association with HFMD incidence, high-risk mainly appeared in large cities and their adjacent regions in hot and humid season. These findings will be helpful for HFMD risk control and disease-prevention policies implementation. Electronic supplementary material The online version of this article (10.1186/s12879-018-3546-2) contains supplementary material, which is available to authorized users.

Dengue fever (DF) has been a growing public-health concern in China since its emergence in Guangdong Province in 1978. Of all the regions that have experienced dengue outbreaks in mainland China, the city of Guangzhou is the most affected. This study aims to investigate the potential risk factors for dengue virus (DENV) transmission in Guangzhou, China, from 2006 to 2014. The impact of risk factors on DENV transmission was qualified by the q-values calculated using a novel spatial-temporal method, the GeoDetector model. Both climatic and socioeconomic factors were considered. The impacts on DF incidence of each single factor and the interaction of two factors were analysed. The results show that the number of days with rainfall of the month before last has the highest determinant power, with a q-value of 0.898 (P < 0.01); the q-values of the other factors related to temperature and precipitation were around 0.38–0.50. Integrating a Pearson correlation analysis, nonlinear associations were found between the DF incidence in Guangzhou and the climatic factors considered. The coupled impact of the different variables considered was enhanced compared with their individual effects. In addition, an increased number of tourists in the city were associated with a high incidence of DF. This study demonstrates that the number of rain days in a month has great influence on the DF incidence of the month after next; the temperature and precipitation have nonlinear impacts on the DF incidence in Guangzhou; both the domestic and overseas tourists coming to the city increase the risk of DENV transmission. These findings are useful in the risk assessment of DENV transmission, to predict DF outbreaks and to implement preventive DF reduction strategies.

Rationale The chromatographic analysis of topiramate and its degradation products is challenging due to the absence of chromophoric moieties in their structures, the wide polarity range of the compounds and their ionization differences. This work proposes two new mass spectrometry approaches for evaluating these analytes. Methods Based on the calculated experimental limit of detection (LOD), a highly sensitive high-performance liquid chromatography (HPLC) paired-ion electrospray ionization mass spectrometry (PIESI-MS) method was developed for the determination of topiramate inorganic degradation products. The influence of different solvent systems on the LODs for topiramate and its main degradation products was determined in both positive/negative ionization modes. In addition, a HPLC method to analyze both organic and inorganic degradation products was proposed by mass spectrometry with positive/negative ion switching electrospray ionization. Results A sensitive HPLC/PIESI-MS method was achieved for the efficient separation of topiramate inorganic degradation products. Both sulfate and sulfamate were detected in the positive selected ion monitoring (SIM) mode with an increased sensitivity compared with the negative SIM mode. The HPLC/ESI-MS analysis with positive/negative ion switching allowed the simultaneous separation and detection of the major degradation products of topiramate in a 10-min run using a single column and a single detector. Conclusions Two new alternative MS approaches for analyzing the main degradation products of topiramate were developed. The proposed methods are considered advantageous over the existing methods and can be applied to quality control studies of topiramate.

Integration of Landsat images and multisource data using spatial statistical analysis and geographical detector models can reveal the individual and interactive influences of anthropogenic activities and ecological factors on concentrations of atmospheric particulate matter less than 2.5 microns in diameter (PM2.5). This approach has been used in many studies to estimate biomass and forest disturbance patterns and to monitor carbon sinks. However, the approach has rarely been used to comprehensively analyze the individual and interactive influences of anthropogenic factors (e.g., population density, impervious surface percentage) and ecological factors (e.g., canopy density, stand age, and elevation) on PM2.5 concentrations. To do this, we used Landsat-8 images and meteorological data to retrieve quantitative data on the concentrations of particulates (PM2.5), then integrated a forest management planning inventory (FMPI), population density distribution data, meteorological data, and topographic data in a Geographic Information System database, and applied a spatial statistical analysis model to identify aggregated areas (hot spots and cold spots) of particulates in the urban area of Jinjiang city, China. A geographical detector model was used to analyze the individual and interactive influences of anthropogenic and ecological factors on PM2.5 concentrations. We found that particulate concentration hot spots are mainly distributed in urban centers and suburbs, while cold spots are mainly distributed in the suburbs and exurban region. Elevation was the dominant individual factor affecting PM2.5 concentrations, followed by dominant tree species and meteorological factors. A combination of human activities (e.g., population density, impervious surface percentage) and multiple ecological factors caused the dominant interactive effects, resulting in increased PM2.5 concentrations. Our study suggests that human activities and multiple ecological factors effect PM2.5 concentrations both individually and interactively. We conclude that in order to reveal the direct and indirect effects of human activities and multiple factors on PM2.5 concentrations in urban forests, quantification of fusion satellite data and spatial statistical methods should be conducted in urban areas.

PM

Table S1. contains descriptive characteristics for meteorological and socio-economic variables selected in this study. Tables S2â S4. contain the estimated the posterior means and RR of BSTHM coefficients in three strata. (DOCX 23â kb)

Based on the fact that persistent mutation of vinous color was not conducive to the stabilization of vinous quality during the aging, research advance on the stable mechanism of endophytic fungi to colour of red wine during the aging, including investigative status and developmental dynamic at home and abroad, endophytes and pigment of materials in wine, including effect of endophyte on vinaceous color, and even the application of tracer method in wine was summarized, respectively. The relationship between diversity of community the endophytic fungi and the main pigment material in wine was existent objectively, also included the response mechanism on colony dynamic of endophytic fungi to the various pigment as well as substance related to color, which laid the foundation for exploring the relationships between endophytic fungi and wine color, and the variational mechanism of the color under endophytic fungi during the aging period of wine. Color as an important reference index of wine quality influenced not only the sensory evaluation of consumer, but also the quality of wine because of the self-aggregation or combination of phenolic composition with other substances under the endophytic fungi during the storage. Only steady wine in the color could guarantee the security of quality.

Appendix 2. Spatial effect modeling. (DOCX 18 kb)

This article was based on the climate data of 125 meteorological stations from 1982 to 2011 in Yunnan Province, the daily basis ET0 was calculated with Penman-Monteith equation recommended by FAO in 1998. Then the Kriging interpolation method and related methods of climate statistical diagnosis analysis were used to analyze the features. The main results showed that the highest value of annual ET0 of Yunnan Province was the joint parts of Dali, Lijiang and Chuxiong, and also the north of Chuxiong and Kunming, while the least value was the northeast and northwest of Yunnan Province. The ET0 was showed rising trend, the speed was about 5.9mm/10a, but the rising trend was not significant. The ET0 of spring was highest, and the ET0 of summer and autumn was taken the second place, the ET0 of winter was last. The ET0 of spring, summer, autumn, winter was account for 33.35 per cent, 28.91 per cent, 20.36, per cent, 17.39 per cent of annual ET0 respectively. The ET0 of May was highest and December was least in annual. ET0 was closely related to wind speed, relative humidity, hours of sunshine and the daily maximum temperature. The correlation between wind speed, hours of sunshine, the daily maximum temperature and ET0 was very significantly positive, while the correlation between relative humidity and ET0 was very significantly negative.

Appendix 1. Non-linear modeling. (DOCX 14 kb)

The detection window for drugs of abuse, including performance-enhancing drugs, is limited by the sensitivity of analytical methodologies. Herein, paired ion electrospray ionization mass spectrometry (PIESI-MS) was employed for sensitive analysis of performance-enhancing drugs and drugs of abuse by detecting their glucuronide and sulfate conjugates. The proposed approach provides enhanced sensitivity for these drug metabolites, and overcomes the drawbacks of the less sensitive negative ion mode ESI-MS by detecting the anionic metabolites in the positive ion mode at higher m/z where the background noise is less. Absolute LODs down to sub-pg levels were obtained with the use of the optimal symmetrical or unsymmetrical ion pairing reagents. One to three orders of magnitude improvement were obtained compared to other reported methods performed in the negative ion mode. Structurally similar steroid conjugates were chromatographically separated and detected by HPLC coupled with PIESI-MS. Finally, an off-line solid phase extraction (SPE) protocol was successfully developed to eliminate any matrix effects in the analysis of human urine samples.

At the current stage, the service performance analysis of BeiDou satellite navigation system (BDS) can provide important references for BDS applications. Accuracy, integrity, availability, and continuity are important indicators of the service capability of the satellite navigation system. In this paper, the relationships among them are analyzed firstly. Secondly, for describing accuracy, position dilution of precision (PDOP) is selected and the quantitative calculation formulas of its availability and continuity are established. Then PDOP of BDS in Asia-Pacific region is simulated and analyzed, the results show that the average availability and continuity of PDOP in the service area during 24 h are up to 99.60% and 0.9947/h, respectively. Thirdly, this paper has a brief introduction to weighted least square algorithm of receiver autonomous integrity monitoring (RAIM) and analyzes the relationships among the probability of false alert, the probability of missed detection, threshold, the minimum detectable error and the number of visible satellites. The threshold and the minimum detectable error will increase with the increasing number of visible satellites when given the false alert probability and missed detection probability, respectively. Finally, the quantitative calculation formula of RAIM availability and continuity is presented and RAIM availability and continuity are simulated and analyzed as well, the results show that the average availability and continuity in the service area are up to 99.95% and 0.9984/h, respectively.