Fine-scale population spatialization data of China in 2018 based on real location-based big data.

Accurate location-based big data has a high resolution and a direct interaction with human activities, allowing for fine-scale population spatial data to be realized. We take the average of Tencent user location big data as a measure of ambient population. The county-level statistical population data in 2018 was used as the assigned input data. The log linear spatially weighted regression model was used to establish the relationship between location data and statistical data to allocate the latter to a 0.01° grid, and the ambient population data of mainland China was obtained. Extracting street-level (lower than county-level) statistics for accuracy testing, we found that POP2018 has the best fit with the actual permanent population (R2 = 0.91), and the error is the smallest (MSEPOP2018 = 22.48

Activating miRNA-mRNA network in gemcitabine-resistant pancreatic cancer cell associates with alteration of memory CD4+ T cells.

BACKGROUND: To identify key microRNAs (miRNAs) and their target mRNAs related to gemcitabine-resistant pancreatic cancer (PC) and investigate the association between gemcitabine-resistant-related miRNAs and mRNAs and immune infiltration. METHODS: Expression profiles of miRNAs and mRNAs were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs and mRNAs (referred to as "DEmiRNAs" and "DEmRNAs", respectively) were distinguished between gemcitabine-resistant PC cells and its parental cells. The DEmRNAs targeted by the DEmiRNAs were retrieved using miRDB, microT, and Targetscan. Furthermore, GO and KEGG pathway enrichment analysis and GSEA were performed. The Kaplan-Meier plotter was used to analyze the prognosis of key DEmiRNAs and DEmRNAs on PC patients. The relationship between the key DEmRNAs and tumor-infiltrating immune cells in PC was investigated using CIBERSORT method using the LM22 signature as reference. Key infiltrating immune cells were further analyzed for the associations with prognosis of TCGA PAAD patients. RESULTS: Four DEmiRNAs, including hsa-miR-3178, hsa-miR-485-3p, hsa-miR-574-5p, and hsa-miR-584-5p, were identified to target seven DEmRNAs, including MSI2, TEAD1, GNPDA1, RND3, PRKACB, TRIM68, and YKT6, individually, in gemcitabine-resistant PC cells versus parental cells. Gemcitabine-resistant PC cells were enriched in proteasome-related, immune-related, and memory CD4+ T cell-related pathways, indicating a gemcitabine therapeutic effect on PC cells. All four DEmiRNAs and almost all DEmRNAs had an impact on the prognosis of PC patients. All seven DEmRNAs had remarkable effects on CD4+ memory T cells, which were affected by the gemcitabine therapeutic effect. Effector memory CD4+ T cells rather than central memory CD4+ T cells predicted a good prognosis according to the TCGA PAAD dataset. CONCLUSIONS: Gemcitabine resistance can alter the fraction of memory CD4+ T cells via hsa-miR-3178, hsa-miR-485-3p, hsa-miR-574-5p and hsa-miR-584-5p targeted MSI2, TEAD1, GNPDA1, RND3, PRKACB, TRIM68, and YKT6 network in PC.

miR-23a-5p inhibits cell proliferation and invasion in pancreatic ductal adenocarcinoma by suppressing ECM1 expression.

Pancreatic ductal adenocarcinoma (PDAC) is a genetic disease and a leading cause of cancer-related mortality. However, the molecular mechanism underlying PDAC progression remains unclear. In this study, we first confirmed that ECM1 is significantly upregulated in PDAC tissues and that its high levels of expression are closely associated with an advanced histologic grade and a poor prognosis using The Cancer Genome Atlas (TCGA) dataset and the Gene Expression Omnibus (GEO) database. We then found that miR-23a-5p binds directly to the ECM1 3'-untranslated region (3'-UTR), thereby inhibiting ECM1 expression. Functional studies revealed that the induced expression of ECM1 promoted oncogenic abilities and reversed the suppressive effects induced by miR-23a-5p. Collectively, our findings indicate that ECM1 is a proto-oncogene and show that targeting the miR-23a-5p/ECM1 axis may represent a promising therapeutic strategy for PDAC.

A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma.

OBJECTIVES: To develop and validate a radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC). METHODS: One hundred twelve consecutive HCC patients who underwent hepatectomy were included in the study pool (training cohort: n = 80, validation cohort: n = 32), and another 13 patients were included in a pilot prospective analysis. A total of 713 radiomics features were extracted from portal-phase computed tomography (CT) images. A logistic regression was used to construct a radiomics score (Rad-score). Then a nomogram, including Rad-score and other risk factors, was built with a multivariate logistic regression model. The discrimination, calibration and clinical utility of nomogram were evaluated. RESULTS: The Rad-score could predict PHLF with an AUC of 0.822 (95% CI, 0.726-0.917) in the training cohort and of 0.762 (95% CI, 0.576-0.948) in the validation cohort; however, the approach could not completely outmatch the existing methods (CP [Child-Pugh], MELD [Model of End Stage Liver Disease], ALBI [albumin-bilirubin]). The individual predictive nomogram that included the Rad-score, MELD and performance status (PS) showed better discrimination with an AUC of 0.864 (95% CI, 0.786-0.942), which was higher than the AUCs of the conventional methods (nomogram vs CP, MELD, and ALBI at P < 0.001, P < 0.005, and P < 0.005, respectively). In the validation cohort, the nomogram discrimination was also superior to those of the other three methods (AUC: 0.896; 95% CI, 0.774-1.000). The calibration curves showed good agreement in both cohorts, and the decision curve analysis of the entire cohort revealed that the nomogram was clinically useful. A pilot prospective analysis showed that the radiomics nomogram could predict PHLF with an AUC of 0.833 (95% CI, 0.591-1.000). CONCLUSIONS: A nomogram based on the Rad-score, MELD, and PS can predict PHLF.

Drought analysis of the Haihe river basin based on GRACE terrestrial water storage.

The Haihe river basin (HRB) in the North China has been experiencing prolonged, severe droughts in recent years that are accompanied by precipitation deficits and vegetation wilting. This paper analyzed the water deficits related to spatiotemporal variability of three variables of the gravity recovery and climate experiment (GRACE) derived terrestrial water storage (TWS) data, precipitation, and EVI in the HRB from January 2003 to January 2013. The corresponding drought indices of TWS anomaly index (TWSI), precipitation anomaly index (PAI), and vegetation anomaly index (AVI) were also compared for drought analysis. Our observations showed that the GRACE-TWS was more suitable for detecting prolonged and severe droughts in the HRB because it can represent loss of deep soil water and ground water. The multiyear droughts, of which the HRB has sustained for more than 5 years, began in mid-2007. Extreme drought events were detected in four periods at the end of 2007, the end of 2009, the end of 2010, and in the middle of 2012. Spatial analysis of drought risk from the end of 2011 to the beginning of 2012 showed that human activities played an important role in the extent of drought hazards in the HRB.

Assessment of the GHG reduction potential from energy crops using a combined LCA and biogeochemical process models: a review.

The main purpose for developing biofuel is to reduce GHG (greenhouse gas) emissions, but the comprehensive environmental impact of such fuels is not clear. Life cycle analysis (LCA), as a complete comprehensive analysis method, has been widely used in bioenergy assessment studies. Great efforts have been directed toward establishing an efficient method for comprehensively estimating the greenhouse gas (GHG) emission reduction potential from the large-scale cultivation of energy plants by combining LCA with ecosystem/biogeochemical process models. LCA presents a general framework for evaluating the energy consumption and GHG emission from energy crop planting, yield acquisition, production, product use, and postprocessing. Meanwhile, ecosystem/biogeochemical process models are adopted to simulate the fluxes and storage of energy, water, carbon, and nitrogen in the soil-plant (energy crops) soil continuum. Although clear progress has been made in recent years, some problems still exist in current studies and should be addressed. This paper reviews the state-of-the-art method for estimating GHG emission reduction through developing energy crops and introduces in detail a new approach for assessing GHG emission reduction by combining LCA with biogeochemical process models. The main achievements of this study along with the problems in current studies are described and discussed.

Spatial-temporal variation of marginal land suitable for energy plants from 1990 to 2010 in China.

Energy plants are the main source of bioenergy which will play an increasingly important role in future energy supplies. With limited cultivated land resources in China, the development of energy plants may primarily rely on the marginal land. In this study, based on the land use data from 1990 to 2010(every 5 years is a period) and other auxiliary data, the distribution of marginal land suitable for energy plants was determined using multi-factors integrated assessment method. The variation of land use type and spatial distribution of marginal land suitable for energy plants of different decades were analyzed. The results indicate that the total amount of marginal land suitable for energy plants decreased from 136.501 million ha to 114.225 million ha from 1990 to 2010. The reduced land use types are primarily shrub land, sparse forest land, moderate dense grassland and sparse grassland, and large variation areas are located in Guangxi, Tibet, Heilongjiang, Xinjiang and Inner Mongolia. The results of this study will provide more effective data reference and decision making support for the long-term planning of bioenergy resources.

Calculating the burden of disease of avian-origin H7N9 infections in China.

OBJECTIVE: A total of 131 cases of avian-originated H7N9 infection have been confirmed in China mainland from February 2013 to May 2013. We calculated the overall burden of H7N9 cases in China as of 31 May 2013 to provide an example of comprehensive burden of disease in the 21st century from an acute animal-borne emerging infectious disease. DESIGN: We present an accurate and operable method for estimating the burden of H7N9 cases in China. The main drivers of economic loss were identified. Costs were broken down into direct (outpatient and inpatient examination and treatment) and indirect costs (cost of disability-adjusted life years (DALYs) and losses in the poultry industry), which were estimated based on field surveys and China statistical year book. SETTING: Models were applied to estimate the overall burden of H7N9 cases in China. PARTICIPANTS: 131 laboratory-confirmed H7N9 cases by 31 May 2013. OUTCOME MEASURE: Burden of H7N9 cases including direct and indirect losses. RESULTS: The total direct medical cost was ¥16 422 535 (US$2 627 606). The mean cost for each patient was ¥10 117 (US$1619) for mild patients, ¥139 323 (US$22 292) for severe cases without death and ¥205 976 (US$32 956) for severe cases with death. The total cost of DALYs was ¥17 356 561 (US$2 777 050). The poultry industry losses amounted to ¥7.75 billion (US$1.24 billion) in 10 affected provinces and ¥3.68 billion (USD$0.59 billion) in eight non-affected adjacent provinces. CONCLUSIONS: The huge poultry industry losses followed live poultry markets closing down and poultry slaughtering in some areas. Though the proportion of direct medical losses and DALYs losses in the estimate of H7N9 burden was small, the medical costs per case were extremely high (particularly for addressing the use of modern medical devices). A cost-effectiveness assessment for the intervention should be conducted in a future study.

Spatio-temporal variation of PM2.5 concentrations and their relationship with geographic and socioeconomic factors in China.

The air quality in China, particularly the PM2.5 (particles less than 2.5 µm in aerodynamic diameter) level, has become an increasing public concern because of its relation to health risks. The distribution of PM2.5 concentrations has a close relationship with multiple geographic and socioeconomic factors, but the lack of reliable data has been the main obstacle to studying this topic. Based on the newly published Annual Average PM2.5 gridded data, together with land use data, gridded population data and Gross Domestic Product (GDP) data, this paper explored the spatial-temporal characteristics of PM2.5 concentrations and the factors impacting those concentrations in China for the years of 2001-2010. The contributions of urban areas, high population and economic development to PM2.5 concentrations were analyzed using the Geographically Weighted Regression (GWR) model. The results indicated that the spatial pattern of PM2.5 concentrations in China remained stable during the period 2001-2010; high concentrations of PM2.5 are mostly found in regions with high populations and rapid urban expansion, including the Beijing-Tianjin-Hebei region in North China, East China (including the Shandong, Anhui and Jiangsu provinces) and Henan province. Increasing populations, local economic growth and urban expansion are the three main driving forces impacting PM2.5 concentrations.

Effective key parameter determination for an automatic approach to land cover classification based on multispectral remote sensing imagery.

The classification of land cover based on satellite data is important for many areas of scientific research. Unfortunately, some traditional land cover classification methods (e.g. known as supervised classification) are very labor-intensive and subjective because of the required human involvement. Jiang et al. proposed a simple but robust method for land cover classification using a prior classification map and a current multispectral remote sensing image. This new method has proven to be a suitable classification method; however, its drawback is that it is a semi-automatic method because the key parameters cannot be selected automatically. In this study, we propose an approach in which the two key parameters are chosen automatically. The proposed method consists primarily of the following three interdependent parts: the selection procedure for the pure-pixel training-sample dataset, the method to determine the key parameters, and the optimal combination model. In this study, the proposed approach employs both overall accuracy and their Kappa Coefficients (KC), and Time-Consumings (TC, unit: second) in order to select the two key parameters automatically instead of using a test-decision, which avoids subjective bias. A case study of Weichang District of Hebei Province, China, using Landsat-5/TM data of 2010 with 30 m spatial resolution and prior classification map of 2005 recognised as relatively precise data, was conducted to test the performance of this method. The experimental results show that the methodology determining the key parameters uses the portfolio optimisation model and increases the degree of automation of Jiang et al.'s classification method, which may have a wide scope of scientific application.

Study on association between spatial distribution of metal mines and disease mortality: a case study in Suxian District, South China.

Metal mines release toxic substances into the environment and can therefore negatively impact the health of residents in nearby regions. This paper sought to investigate whether there was excess disease mortality in populations in the vicinity of the mining area in Suxian District, South China. The spatial distribution of metal mining and related activities from 1985 to 2012, which was derived from remote sensing imagery, was overlapped with disease mortality data. Three hotspot areas with high disease mortality were identified around the Shizhuyuan mine sites, i.e., the Dengjiatang metal smelting sites, and the Xianxichong mine sites. Disease mortality decreased with the distance to the mining and smelting areas. Population exposure to pollution was estimated on the basis of distance from town of residence to pollution source. The risk of dying according to disease mortality rates was analyzed within 7-25 km buffers. The results suggested that there was a close relationship between the risk of disease mortality and proximity to the Suxian District mining industries. These associations were dependent on the type and scale of mining activities, the area influenced by mining and so on.

Spatiotemporal variation of surface water quality for decades: a case study of Huai River System, China.

Characterization of spatiotemporal variation of water quality is a basic environmental issue with implications for public health in China. Trends in the temporal and spatial distribution of water quality in the Huai River System (HRS) were analyzed using yearly surface water quality data collected from 1982 to 2009. Results showed that the water quality of the main stream deteriorated in the 1990s and early 2000s but has been ameliorated since 2005. The sections that were classified as severely polluted from the monitoring data were located largely in the middle reach. The water quality of HRS fluctuated during the period 1997-2009; it has improved and stabilized since 2005. In terms of spatialized frequency of serious pollution, heavily polluted regions were mostly concentrated in the area along several tributaries of the Ying, Guo and New Sui Rivers as well as the area north of Nansi Lake. These regions decreased from 1997 to 2009, especially after 2005. Our analysis indicated that water pollution in HRS had a close relation with population and primary industry during the period 1997-2009, and implied that spatiotemporal variation of surface water quality could provide a scientific foundation for human health risk assessment of the Huai River Basin.

A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery.

Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization) with convenience.

Evaluation of relative water use efficiency (RWUE) at a regional scale: a case study of Tuhai-Majia Basin, China.

Enhancing water use efficiency (WUE) is the key approach to maintain sustainable water resource supply. Due to the complexity of the water cycle, accurate estimation of WUE at the regional scale is a challenging task. Here we presented a framework of relative water use efficiency (RWUE). According to the linkage between RWUE and land use types, assessment of WUE at a regional scale could be performed operationally. This approach was evaluated in a study area, Tuhai-Majia Basin, North China. Based on remote sensing-derived evapotranspiration (ET) and land use data, regional WUE were assessed accordingly. The mean RWUE of agriculture, ecosystem and total basin in 2005 was 60.12, 30.07 and 62.5%, respectively. Spatial analysis showed that the agricultural WUE played the dominant role in water-saving of the study area; water management of unused land (RWUE of 2005 was 5.46%), especially wetland protection and other unused land development, will contribute significantly to ecological RWUE improvement. Temporal analysis indicated that there was considerable inter-annual variability in RWUE time series profiles. The agricultural interlude period might be important for enhancing WUE in the Tuhai-Majia Basin. In general, the results indicated that the RWUE-based method was an efficient and simple method to evaluate WUE at regional scale.

Evaluation of hyperspectral indices for chlorophyll-a concentration estimation in Tangxun Lake (Wuhan, China).

Chlorophyll-a (Chl-a) concentration is a major indicator of water quality which is harmful to human health. A growing number of studies have focused on the derivation of Chl-a concentration information from hyperspectral sensor data and the identification of best indices for Chl-a monitoring. The objective of this study is to assess the potential of hyperspectral indices to detect Chl-a concentrations in Tangxun Lake, which is the second largest lake in Wuhan, Central China. Hyperspectral reflectance and Chl-a concentration were measured at ten sample sites in Tangxun Lake. Three types of hyperspectral methods, including single-band reflectance, first derivative of reflectance, and reflectance ratio, were extracted from the spectral profiles of all bands of the hyperspectral sensor. The most appropriate bands for algorithms mentioned above were selected based on the correlation analysis. Evaluation results indicated that two methods, the first derivative of reflectance and reflectance ratio, were highly correlated (R(2) > 0.8) with the measured Chl-a concentrations. Thus, the spatial and temporal variations of Chl-a concentration could be conveniently monitored with these hyperspectral methods.

An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies.

The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS) have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS) is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B) data integrated with a Pattern Decomposition Method (PDM) and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM). The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable.

Advances in multi-sensor data fusion: algorithms and applications.

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of "algorithm fusion" methods; (3) Establishment of an automatic quality assessment scheme.