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This paper develops two-stage inverse data envelopment analysis models with undesirable outputs to formulate resource plans for 16 Chinese listed commercial banks whose outputs are increased and overall efficiency is kept unchanged in the short term. We use these models to meet three different output targets, namely, increasing both the desirable and undesirable outputs by the same percentage, increasing these outputs by different percentages, and increasing only the desirable outputs while keeping the undesirable outputs unchanged. We find that operation cost and interest expense are more flexible than labor in the adjustment process and that deposits have no obvious law of change. The findings of this work provide some suggestions for bank managers. [ABSTRACT FROM AUTHOR]

The deterioration of water quality has become a primary environmental concern worldwide. Understanding the status of water quality and identifying the influencing factors are important for water resources management. However, reported analyses have mostly been conducted in small and focused areas. It is still unclear if factors driving spatial variation in water quality would be different in extended spatial scales. In this paper, we analyzed spatial pattern of inland surface water quality in China using a dataset with four water quality parameters (i.e., pH, DO, NH4+-N and CODMn) and the water quality level. We tested the effects of anthropogenic (i.e., land use and socio-economic) and natural (i.e., climatic and topographic) factors on spatial variation in water quality. The study concluded that the overall inland surface water quality in China was at level III (fair). Water quality level was strongly correlated with CODMn and NH4+-N concentration. In contrast to reported studies that suggested land use patterns were the determinants of inland surface water quality, this study revealed that both anthropogenic and natural factors played important roles in explaining spatial variation of inland surface water quality in China. Among the tested explanatory variables, mean elevation within watershed appeared as the best predictor for pH, while annual precipitation and mean air temperature were the most important explanatory variables for CODMn and DO, respectively. NH4+-N concentration and water quality level were most strongly correlated with the percent of forest cover in watershed. Compared to studies at smaller spatial scales, this study found different influencing factors of surface water quality, suggesting that factors may play different roles at different spatial scales of consideration. Therefore management policies and measures in water quality control must be established and implemented accordingly. Since currently adopted parameters for monitoring of inland surface water quality in China are largely influenced by natural variables, additional physicochemical and biological indicators are needed for a robust assessment of human impacts on water quality. [ABSTRACT FROM AUTHOR]

This paper combines a Granger causality test and a VAR model to investigate the relationships among oil price shocks, global economic policy uncertainty (GEPU), and China’s industrial economic growth. Based on monthly data from 2000 to 2017, we reveal that GEPU and world oil prices jointly Granger cause China's industrial economic growth; world oil prices have a positive effect on China's industrial economic growth, while GEPU has a negative effect. Further analyses investigate the asymmetry effect of oil prices and find that the negative component shows a more significant impact on China's industrial economic growth. The results are robust to different oil price and EPU proxies. [ABSTRACT FROM AUTHOR]

Background: In this research, an meta-analysis was performed for assessment of the associations between O6-methyguanine-DNA methyltransferase (MGMT) promoter hypermethylation possessing low-grade intraepithelial lesion (LSIL), high-grade intraepithelial lesion (HSIL), cervical cancer (CC), and clinicopathological characters of CC. Methods: Literature selection were conducted through searching PubMed, Web of science, EMBASE, China National Knowledge Infrastructure and Wanfang databases (up to November 2018). An assessment of associations between MGMT methylation and LSIL, HSIL, CC risk and clinicopathological characteristics was performed through pooled odds ratios (ORs) with relevant 95% confidence intervals (CIs). Subgroup analyses, meta-regressions and Galbraith plots were conducted to conduct an exploration on the possible sources of heterogeneity. The genome-wide DNA methylation array studies were extracted from Gene Expression Omnibus (GEO) databases for validation of these outcomes. Results: In this meta-analysis of 25 published articles, MGMT hypermethylation gradually elevated the rates among control group (12.16%), LSIL (20.92%), HSIL (36.33%) and CC (41.50%) specimens. MGMT promoter methylation was significant associated with the increased risk of LSIL by 1.74-fold (P<0.001), HSIL by 3.71-fold (P<0.001) and CC by 7.08-fold (P<0.001) compared with control. A significant association between MGMT promoter methylation with FIGO stage was also found (OR = 2.81, 95% CI: 1.79–4.41, p<0.001). The results of GEO datasets showed that 5 CpG sites in MGMT with a great diagnostic value for the screening of cervical cancer. Conclusion: The meta-analysis indicated the association between MGMT promoter hypermethylation and squamous intraepithelial lesion and cervical cancer. MGMT methylation detection might have a potential value to be an epigenetic marker for the clinical diagnosis of cervical cancer. [ABSTRACT FROM AUTHOR]

Background: Studies have shown that D-dimer levels are significantly correlated with the differential diagnosis and clinicopathological features of breast cancer. However, the results are currently limited and controversial. Therefore, we performed this meta-analysis to evaluate the relationship between D-dimer levels and breast cancer. Materials and methods: The PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Chinese Biomedical Literature, and Wanfang databases were searched to find studies that assessed the association of D-dimer with clinicopathological features of breast cancer and its usefulness in aiding with differential diagnosis. The standardized mean difference (SMD) was applied as the correlation measure. Results: A total of 1244 patients with breast cancer from 15 eligible studies were included in the meta-analysis. D-dimer levels were higher in the breast cancer group than in the benign (SMD = 1.02; 95% confidence interval [CI] = 0.53–1.52) and healthy (SMD = 1.27; 95% CI = 0.85–1.68) control groups. In addition, elevated D-dimer levels were associated with progesterone receptor-negative tumors (SMD = -0.25; 95% CI = -0.44–-0.05). Similarly, there was a significant correlation between D-dimer levels and tumor node metastasis staging (n = 11, SMD = 0.82; 95% CI = 0.57–1.06) and lymph node involvement (n = 8, SMD = 0.79; 95% CI = 0.50–1.09). In contrast, other clinicopathological factors, including estrogen receptor expression and human epidermal growth factor receptor 2 expression, were not associated with D-dimer levels. Conclusion: The results of this meta-analysis indicate that plasma D-dimer levels can be used as an important reference for the early identification and staging of breast cancer. [ABSTRACT FROM AUTHOR]

Bosten Lake is an important region of Northwest China that has transformed from a freshwater lake to a saltwater lake since the 1970s. The water quality in the Bosten Lake basin is important for social and economic development, and nitrogen (N) and phosphorus (P) are the key indicators of water quality. The land use data, precipitation data and Digital Elevation Model (DEM) data with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were used to simulate the N and P exports of the Bosten Lake basin. The spatial and temporal dynamics of nitrogen and phosphorus exports, and the response of nitrogen and phosphorus exports to land use change and precipitation change were analyzed between 2000 and 2015. The results show that the amount of N and P exports increased during 2000–2015, and the N and P exports are mainly distributed around Bosten Lake. The N and P exports are greatly affected by cultivated land, built-up areas and grassland, while they are less affected by other land use types. The high precipitation areas with small exports of N and P are mainly distributed in mountain areas, while small precipitation areas with large exports of N and P are distributed in plains where the cultivated land and built-up areas are concentrated. The InVEST model can be used in Northwest China, and the statistical downscaling of reanalysis precipitation data can be used in the InVEST model to improve the simulation accuracy in the data scarce regions of Northwest China. [ABSTRACT FROM AUTHOR]

Objective: Many studies focused on reasons behind the increasing incidence and the spread of human brucellosis in mainland China, yet most of them lacked comprehensive consideration with quantitative evidence. Hence, this study aimed to further investigate the epidemic mechanism and associated factors of human brucellosis so as to provide thoughts for future countermeasures in China and the rest of the world. Methods: Data of human brucellosis incidence and some associated factors in economy, animal husbandry, transportation as well as health and hygiene were collected at provincial level from 2005–2016. Time series plots were first used to visualize the annual incidence and annual rate of change of human brucellosis for each province, then cluster analysis categorized all the 31 provinces of mainland China based on their incidence time series during the study period. In addition, according to the characteristics of data, the dynamic panel data model in combination with supervised principal component analysis was proposed to explore effects of associated factors on human brucellosis. Results: 1. The incidence rate of human brucellosis in mainland China increased three-fold from 1.41 per 100,000 people in 2005 to 4.22 per 100,000 people in 2014, though it went down a little in 2015 and 2016. Incidence rates in the north have always been higher than those in the south, but the latter also experienced an upward trend especially between 2012 and 2016. 2. The 31 provinces of mainland China were categorized into three clusters, and each cluster had its own characteristics of incidence time series. 3. The impact of health and hygiene situations on the prevention and control work of human brucellosis was still very limited and trivial (regression coefficient = -0.02). Therefore, it was plausible to presume that improving the personal average number of medical institutes and the proportion of rural medical expenditure might be helpful in preventing and controlling human brucellosis. Conclusions: The epidemic status of human brucellosis has changed in both spatial and temporal dimensions in recent years in mainland China. Apart from traditional control measures, more attention should be paid to the improvement of medical healthcare especially in rural areas in the hope of enhancing the control effect. [ABSTRACT FROM AUTHOR]

Accurately predicting reference evapotranspiration (ET0) with limited climatic data is crucial for irrigation scheduling design and agricultural water management. This study evaluated eight machine learning models in four categories, i.e. neuron-based (MLP, GRNN and ANFIS), kernel-based (SVM, KNEA), tree-based (M5Tree, XGBoost) and curve-based (MARS) models, for predicting daily ET0 with maximum/maximum temperature and precipitation data during 2001–2015 from 14 stations in various climatic regions of China, i.e., arid desert of northwest China (NWC), semi-arid steppe of Inner Mongolia (IM), Qinghai-Tibetan Plateau (QTP), (semi-)humid cold-temperate northeast China (NEC), semi-humid warm-temperate north China (NC), humid subtropical central China (CC) and humid tropical south China (SC). The results showed machine learning models using only temperature data obtained satisfactory daily ET0 estimates (on average R2 = 0.829, RMSE = 0.718 mm day−1, NRMSE = 0.250 and MAE = 0.508 mm day−1). The prediction accuracy was improved by 7.6% across China when information of precipitation was further considered, particularly in (sub)tropical humid regions (by 9.7% in CC and 12.4% in SC). The kernel-based SVM, KNEA and curve-based MARS models generally outperformed the others in terms of prediction accuracy, with the best performance by KNEA in NWC and IM, by SVM in QTP, CC and SC, and very similar performance by them in NEC and NC. SVM (1.9%), MLP (2.0%), MARS (2.6%) and KNEA (6.4%) showed relatively small average increases in RMSE during testing compared with training RMSE. SVM is highly recommended for predicting daily ET0 across China in light of best accuracy and stability, while KNEA and MARS are also promising powerful models. [ABSTRACT FROM AUTHOR]