Forecasting groundwater level of karst aquifer in a large mining area using partial mutual information and NARX hybrid model.

Predicting the groundwater level of karst aquifers in North China Coalfield is essential for early warning of mine water hazards and regional water resources management. However, the dynamic changes of strata structure and hydrogeological parameters driven by coal mining activity cause challenges to the process-oriented groundwater model. In order to achieve accurate prediction of groundwater level in large mining areas, this study was the first to use the data-driven Nonlinear Autoregressive with External Input (NARX) model to predict the groundwater level of six karst aquifer observation wells in Pingshuo Mining Area. Three variable input scenarios were set up, solely considering meteorological factors, anthropogenic disturbance factors, and considering both meteorological and anthropogenic disturbance factors. The novel partial mutual information (PMI) screening algorithm was adopted to determine optimized input variables in each scenario. The input and feedback delay coefficients of NARX model were determined by using Seasonal-trend Decomposition Procedure Based on Loess (STL) algorithm and auto- and cross-correlation functions. The results showed that PMI algorithm can effectively screen out the optimal input variables for predicting groundwater level, the NSE coefficients of the PMI-NARX models under the three scenarios were 38.81%, 4.26% and 41.46% higher than those of the corresponding control experiments, respectively. In addition, the prediction performance of the PMI-NARX built on the basis of meteorological factors is poor (NSE <0.63). However, in scenarios which solely use anthropogenic disturbance factors and both use meteorological and anthropogenic disturbance factors, the PMI-NARX coupling models exhibit good prediction performance (NSE and R2 are all greater than 0.8). Especially under solely considering anthropogenic disturbance factors scenario, the model still exhibited good prediction accuracy with a negligible number of input variables. The results can provide technical and theoretical support for the prediction of groundwater level in other mining areas.

Comparison of the performance of the CRUSADE, ACUITY-HORIZONS, and ACTION bleeding scores in ACS patients undergoing PCI: insights from a cohort of 4939 patients in China.

BACKGROUND: The CRUSADE, ACTION and ACUITY-HORIZONS scores are commonly used for predicting in-hospital major bleeding events in patients with acute coronary syndrome (ACS), but the homogeneous nature of these models' population limits simple extrapolation to other local population. We aimed to compare the performance of the three risk models in Chinese patients. METHODS: We evaluated the performance of the three predicting scores for predicting in-hospital major bleeding events defined by thrombolysis in myocardial infarction (TIMI) serious (major and minor) episodes, in a cohort of Chinese ACS patients with either non-ST-elevation ACS (NSTE-ACS) or ST-elevation myocardial infarction (STEMI). Calibration and discrimination of the three risk models were evaluated by the Hosmer-Lemeshow test and C-statistic, respectively. We compared the predictive accuracy of the risk scores by the Delong non-parametric test. RESULTS: TIMI serious bleeding rate was 1.1% overall (1.9% and 0.86% for STEMI and NSTE-ACS, respectively). The CRUSADE, ACTION and ACUTIY-HORIZONS scores showed an adequate discriminatory capacity for major bleeding: in overall patients, the C-statistic was 0.80, 0.77, and 0.70, respectively; in NSTE-ACS patients, the C-statistic was 0.73, 0.72, and 0.64, respectively; in STEMI patients, the C-statistic was 0.91, 0.92, and 0.75, respectively. The C-statistic for the ACUITY-HORIZONS model was significantly lower than those of the CRUSADE and ACTION scores for the prediction of TIMI serious bleeding in overall patients (compared with CRUSADE, z = 3.83, P = 0.02; compared with ACTION, z = 3.51, P = 0.03); in NSTE-ACS patients (compared with CRUSADE, z = 2.37, P = 0.01; compared with ACTION, z = 2.11, P = 0.04), and in STEMI patients (compared with CRUSADE, z = 2.6.77, P = 0.02; compared with ACTION, z = 7.91, P = 0.002). No differences were observed when the CRUSADE and ACTION models were compared to each other, regardless of overall patients (z = 0.68, P = 0.31) and both of ACS types (NSTE-ACS, z = 0.52, P = 0.60), and STEMI patients (z = 0.36, P = 0.74). However, the three risk scores all overestimated the absolute major bleeding risk in each risk stratification in our study. For example, the predicted rate of CRUSADE score at high risk stratification was 11.9% vs. an actual rate of 5.3%. CONCLUSIONS: The CRUSADE and ACTION scores had a greater calibration and discrimination for in-hospital major bleeding compared with the ACUITY-HORIZONS score in Chinese patients with ACS undergoing PCI. However, they all overestimated the bleeding risk rate for Chinese populations. Calibration of these risk scores would be useful for the generalization in Chinese populations.

High-density genetic linkage map construction and QTL mapping of grain shape and size in the wheat population Yanda1817 × Beinong6.

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F 8 recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010-2014 by inclusive composite interval mapping (ICIM) (LOD ≥ 2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82-23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.