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Background: Hemorrhagic fever with renal syndrome (HFRS) is a zoonosis caused by hantavirus (belongs to Hantaviridae family). A large amount of HFRS cases occur in China, especially in the Heilongjiang Province, raising great concerns regarding public health. The distribution of these cases across space-time often exhibits highly heterogeneous characteristics. Hence, it is widely recognized that the improved mapping of heterogeneous HFRS distributions and the quantitative assessment of the space-time disease transition patterns can advance considerably the detection, prevention and control of epidemic outbreaks. Methods: A synthesis of space-time mapping and probabilistic logic is proposed to study the distribution of monthly HFRS population-standardized incidences in Heilongjiang province during the period 2005–2013. We introduce a class-dependent Bayesian maximum entropy (cd-BME) mapping method dividing the original dataset into discrete incidence classes that overcome data heterogeneity and skewness effects and can produce space-time HFRS incidence estimates together with their estimation accuracy. A ten-fold cross validation analysis is conducted to evaluate the performance of the proposed cd-BME implementation compared to the standard class-independent BME implementation. Incidence maps generated by cd-BME are used to study the spatiotemporal HFRS spread patterns. Further, the spatiotemporal dependence of HFRS incidences are measured in terms of probability logic indicators that link class-dependent HFRS incidences at different space-time points. These indicators convey useful complementary information regarding intraclass and interclass relationships, such as the change in HFRS transition probabilities between different incidence classes with increasing geographical distance and time separation. Results: Each HFRS class exhibited a distinct space-time variation structure in terms of its varying covariance parameters (shape, sill and correlation ranges). Given the heterogeneous features of the HFRS dataset, the cd-BME implementation demonstrated an improved ability to capture these features compared to the standard implementation (e.g., mean absolute error: 0.19 vs. 0.43 cases/105 capita) demonstrating a point outbreak character at high incidence levels and a non-point spread character at low levels. Intraclass HFRS variations were found to be considerably different than interclass HFRS variations. Certain incidence classes occurred frequently near one class but were rarely found adjacent to other classes. Different classes may share common boundaries or they may be surrounded completely by another class. The HFRS class 0–68.5% was the most dominant in the Heilongjiang province (covering more than 2/3 of the total area). The probabilities that certain incidence classes occur next to other classes were used to estimate the transitions between HFRS classes. Moreover, such probabilities described the dependency pattern of the space-time arrangement of HFRS patches occupied by the incidence classes. The HFRS transition probabilities also suggested the presence of both positive and negative relations among the main classes. The HFRS indicator plots offer complementary visualizations of the varying probabilities of transition between incidence classes, and so they describe the dependency pattern of the space-time arrangement of the HFRS patches occupied by the different classes. Conclusions: The cd-BME method combined with probabilistic logic indicators offer an accurate and informative quantitative representation of the heterogeneous HFRS incidences in the space-time domain, and the results thus obtained can be interpreted readily. The same methodological combination could also be used in the spatiotemporal modeling and prediction of other epidemics under similar circumstances. [ABSTRACT FROM AUTHOR]

Background: Hemorrhagic fever with renal syndrome (HFRS) is a rodent-associated zoonosis caused by hantavirus. The HFRS was initially detected in northeast China in 1931, and since 1955 it has been detected in many regions of the country. Global climate dynamics influences HFRS spread in a complex nonlinear way. The quantitative assessment of the spatiotemporal variation of the “HFRS infections-global climate dynamics” association at a large geographical scale and during a long time period is still lacking. Methods and findings: This work is the first study of a recently completed dataset of monthly HFRS cases in Eastern China during the period 2005–2016. A methodological synthesis that involves a time-frequency technique, a composite space-time model, hotspot analysis, and machine learning is implemented in the study of (a) the association between HFRS incidence spread and climate dynamics and (b) the geographic factors impacting this association over Eastern China during the period 2005–2016. The results showed that by assimilating core and city-specific knowledge bases the synthesis was able to depict quantitatively the space-time variation of periodic climate-HFRS associations at a large geographic scale and to assess numerically the strength of this association in the area and period of interest. It was found that the HFRS infections in Eastern China has a strong association with global climate dynamics, in particular, the 12, 18 and 36 mos periods were detected as the three main synchronous periods of climate dynamics and HFRS distribution. For the 36 mos period (which is the period with the strongest association), the space-time correlation pattern of the association strength indicated strong temporal but rather weak spatial dependencies. The generated space-time maps of association strength and association hotspots provided a clear picture of the geographic variation of the association strength that often-exhibited cluster characteristics (e.g., the south part of the study area displays a strong climate-HFRS association with non-point effects, whereas the middle-north part displays a weak climate-HFRS association). Another finding of this work is the upward climate-HFRS coherency trend for the past few years (2013–2015) indicating that the climate impacts on HFRS were becoming increasingly sensitive with time. Lastly, another finding of this work is that geographic factors affect the climate-HFRS association in an interrelated manner through local climate or by means of HFRS infections. In particular, location (latitude, distance to coastline and longitude), grassland and woodland are the geographic factors exerting the most noticeable effects on the climate-HFRS association (e.g., low latitude has a strong effect, whereas distance to coastline has a wave-like effect). Conclusions: The proposed synthetic quantitative approach revealed important aspects of the spatiotemporal variation of the climate-HFRS association in Eastern China during a long time period, and identified the geographic factors having a major impact on this association. Both findings could improve public health policy in an HFRS-torn country like China. Furthermore, the synthetic approach developed in this work can be used to map the space-time variation of different climate-disease associations in other parts of China and the World. [ABSTRACT FROM AUTHOR]

Soil sealing (loss of soil resources due to extensive land covering for the purpose of house building, road construction etc.) and subsequent soil landscape pattern changes constitute typical environmental problems in many places worldwide. Previous studies concentrated on soil sealing in urbanized regions, whereas rural areas have not been given sufficient attention. Accordingly, this paper studies soil landscape pattern dynamics (i.e., landscape pattern changes in response to rural anthropogenic activities) in the Tiaoxi watershed (Zhejiang province, eastern China), in which surface sealing is by far the predominant component of human forcing with respect to environmental change. A novel approach of quantifying the impacts of rural anthropogenic activities on soil resources is presented. Specifically, quantitative relationships were derived between five soil landscape pattern metrics (patch density, edge density, shape index, Shannon’s diversity index and aggregation index) and three rural anthropogenic activity indicators (anthropogenic activity intensity, distance to towns, and distance to roads) at two landscape block scales (3 and 5 km) between 1985 and 2010. The results showed that the Tiaoxi watershed experienced extensive rural settlement expansion and high rates of soil sealing. Soil landscapes became more fragmented, more irregular, more isolated, and less diverse. Relationships between soil landscape pattern changes and rural anthropogenic activities differed with the scale (spatial and temporal) and variable considered. In particular, the anthropogenic activity intensity was found to be the most important indicator explaining social development intensity, whereas the other two proximity indicators had a significant impact at certain temporal interval. In combination with scale effects, spatial dependency (correlation) was shown to play a key role that should be carefully taken into consideration in any relevant environmental study. Overall, the findings of this work suggest that soil sealing can be a critical human forcing issue with considerable consequences deserving serious attention by the experts, the public and the government alike. [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BC) is the most common female malignant tumor. Previous studies have suggested a big incidence disparity among different cities in China. The present work selected a typical city, Hangzhou, to study BC incidence disparity within the city. Methods: Totally, 8784 female breast cancer cases were obtained from the Hangzhou Center for Disease Control and Prevention during the period 2008–2012. Analysis of Variance and Poisson Regression were the statistical tools implemented to compare incidence disparity in the space-time domain (reference group: township residents during 2008, area: subdistrict, town, and township, time frame: 2008–2012), space-time scan statistics was employed to detect significant spatiotemporal clusters of BC compared to the null hypothesis that the probability of cases diagnosed at a particular location was equal to the probability of cases diagnosed in the whole study area. Geographical Information System (GIS) was used to generate BC spatial distribution and cluster maps at the township level. Results: The subdistrict populations were found to have the highest and most stable BC incidence. Although town and township populations had a relatively low incidence, it displayed a significant increasing trend from 2008 to 2012. The BC incidence distribution was spatially heterogeneous and clustered with a trend-surface from the southwest low area to the northeast high area. High clusters were located in the northeastern Hangzhou area, whereas low clusters were observed in the southwestern area during the time considered. Conclusions: Better healthcare service and lifestyle changes may be responsible for the increasing BC incidence observed in towns and townships. One high incidence cluster (Linping subdistrict) and two low incidence clusters (middle Hangzhou) were detected. The low clusters may be attributable mainly to developmental level disparity, whereas the high cluster could be associated with other risk factors, such as environmental pollution. [ABSTRACT FROM AUTHOR]

The exotic plant Spartina alterniflora was introduced to Yueqing Bay more than 20 years ago for tidal land reclamation and as a defense against typhoons, but it has rapidly expanded and caused enormous ecological consequences. Mapping the spread and distribution of S. alterniflora is the first step toward understanding the factors that determine the population expansion patterns. Remote sensing is a promising tool to monitor the expansion of S. alterniflora. Twelve Landsat TM images and Support Vector Machine (SVM) were used to delineate the invasion of S. alterniflora from 1993 to 2009, and SPOT 6 images and Object-Based Image Analysis (OBIA) were used to map the distribution of S. alterniflora in 2014. In situ data and Unmanned Aerial Vehicle (UAV) images were used as supplementary data. S. alterniflora spread rapidly in Yueqing Bay over the past 21 years. Between 1993 and 2009, the area of S. alterniflora increased by 608 times (from 4 to 2432 ha). The rapid expansion of S. alterniflora covered almost all of the bare mudflats around the mangrove forests and the cultivated mudflats. However, from 2009 to 2014, the rate of expansion of S. alterniflora began to slow down in Yueqing Bay, and the total area of S. alterniflora in Yantian decreased by 275 ha. These phenomena can be explained by the landscape changes and ecological niches. Through the expansion of S. alterniflora, it was found that the ecological significance and environmental impact of S. alterniflora was different in different regions in Yueqing Bay. The conservation plans for Yueqing Bay should consider both the positive and negative effects of S. alterniflora, and the governmental policy should be based on the different circumstances of the regions. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most commonly diagnosed cancers worldwide. The primary aim of this work is the study of breast cancer disparity among Chinese women in urban vs. rural regions and its associations with socioeconomic factors. Data on breast cancer incidence were obtained from the Chinese cancer registry annual report (2005–2009). The ten socioeconomic factors considered in this study were obtained from the national population 2000 census and the Chinese city/county statistical yearbooks. Student’s T test was used to assess disparities of female breast cancer and socioeconomic factors in urban vs. rural regions. Pearson correlation and ordinary least squares (OLS) models were employed to analyze the relationships between socioeconomic factors and cancer incidence. It was found that the breast cancer incidence was significantly higher in urban than in rural regions. Moreover, in urban regions, breast cancer incidence remained relatively stable, whereas in rural regions it displayed an annual percentage change (APC) of 8.55. Among the various socioeconomic factors considered, breast cancer incidence exhibited higher positive correlations with population density, percentage of non-agriculture population, and second industry output. On the other hand, the incidence was negatively correlated with the percentage of population employed in primary industry. Overall, it was observed that higher socioeconomic status would lead to a higher breast cancer incidence in China. When studying breast cancer etiology, special attention should be paid to environmental pollutants, especially endocrine disruptors produced during industrial activities. Lastly, the present work’s findings strongly recommend giving high priority to the development of a systematic nationwide breast cancer screening program for women in China; with sufficient participation, mammography screening can considerably reduce mortality among women. [ABSTRACT FROM AUTHOR]

Accurately quantifying soil organic carbon (SOC) is considered fundamental to studying soil quality, modeling the global carbon cycle, and assessing global climate change. This study evaluated the uncertainties caused by up-scaling of soil properties from the county scale to the provincial scale and from lower-level classification of Soil Species to Soil Group, using four methods: the mean, median, Soil Profile Statistics (SPS), and pedological professional knowledge based (PKB) methods. For the SPS method, SOC stock is calculated at the county scale by multiplying the mean SOC density value of each soil type in a county by its corresponding area. For the mean or median method, SOC density value of each soil type is calculated using provincial arithmetic mean or median. For the PKB method, SOC density value of each soil type is calculated at the county scale considering soil parent materials and spatial locations of all soil profiles. A newly constructed 1∶50,000 soil survey geographic database of Zhejiang Province, China, was used for evaluation. Results indicated that with soil classification levels up-scaling from Soil Species to Soil Group, the variation of estimated SOC stocks among different soil classification levels was obviously lower than that among different methods. The difference in the estimated SOC stocks among the four methods was lowest at the Soil Species level. The differences in SOC stocks among the mean, median, and PKB methods for different Soil Groups resulted from the differences in the procedure of aggregating soil profile properties to represent the attributes of one soil type. Compared with the other three estimation methods (i.e., the SPS, mean and median methods), the PKB method holds significant promise for characterizing spatial differences in SOC distribution because spatial locations of all soil profiles are considered during the aggregation procedure. [ABSTRACT FROM AUTHOR]

Related Transcriptional Enhancer Factor-1 (RTEF-1) has been suggested to induce angiogenesis through regulating target genes. Whether RTEF-1 has a direct role in angiogenesis and what specific genes are involved in RTEF-1 driven angiogenisis have not been elucidated. We found that over-expressing RTEF-1 in Human dermal microvascular endothelial cells-1 (HMEC-1) significantly increased endothelial cell aggregation, growth and migration while the processes were inhibited by siRNA of RTEF-1. In addition, we observed that Endothelial differentiation gene-1 (Edg-1) expression was up-regulated by RTEF-1 at the transcriptional level. RTEF-1 could bind to Edg-1 promoter and subsequently induce its activity. Edg-1 siRNA significantly blocked RTEF-1-driven increases in endothelial cell aggregation in a Matrigel assay and retarded RTEF-1-induced endothelial cell growth and migration. Pertussis Toxin (PTX), a Gi/Go protein sensitive inhibitor, was found to inhibit RTEF-1 driven endothelial cell aggregation and migration. Our data demonstrates that Edg-1 is a potential target gene of RTEF-1 and is involved in RTEF-1-induced angiogenesis in endothelial cells. Gi/Go protein coupled receptor pathway plays a role in RTEF-1 driven angiogenesis in endothelial cells. [ABSTRACT FROM AUTHOR]