Consensus scoring model for the molecular docking study of mTOR kinase inhibitor

The discovery of mammalian target of rapamycin (mTOR) kinase inhibitors has always been a research hotspot of antitumor drugs. Consensus scoring used in the docking study of mTOR kinase inhibitors usually improves hit rate of virtual screening. Herein, we attempt to build a series of consensus scoring models based on a set of the common scoring functions. In this paper, twenty-five kinds of mTOR inhibitors (16 clinical candidate compounds and 9 promising preclinical compounds) are carefully collected, and selected for the molecular docking study used by the Glide docking programs within the standard precise (SP) mode. The predicted poses of these ligands are saved, and revaluated by twenty-six available scoring functions, respectively. Subsequently, consensus scoring models are trained based on the obtained rescoring results by the partial least squares (PLS) method, and validated by Leave-one-out (LOO) method. In addition, three kinds of ligand efficiency indices (BEI, SEI, and LLE) instead of pIC₅₀ as the activity could greatly improve the statistical quality of build models. Two best calculated models 10 and 22 using the same BEI indice have following statistical parameters, respectively: for model 10, training set R² = 0.767, Q² = 0.647, RMSE = 0.024, and for test set R² = 0.932, RMSE = 0.026; for model 22, raining set R² = 0.790, Q² = 0.627, RMSE = 0.023, and for test set R² = 0.955, RMSE = 0.020. These two consensus scoring model would be used for the docking virtual screening of novel mTOR inhibitors.     

Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

This study investigated the effects of upstream stations’ flow records on the performance of artificial neural network (ANN) models for predicting daily watershed runoff. As a comparison, a multiple linear regression (MLR) analysis was also examined using various statistical indices. Five streamflow measuring stations on the Cahaba River, Alabama, were selected as case studies. Two different ANN models, multi layer feed forward neural network using Levenberg–Marquardt learning algorithm (LMFF) and radial basis function (RBF), were introduced in this paper. These models were then used to forecast one day ahead streamflows. The correlation analysis was applied for determining the architecture of each ANN model in terms of input variables. Several statistical criteria (RMSE, MAE and coefficient of correlation) were used to check the model accuracy in comparison with the observed data by means of K-fold cross validation method. Additionally, residual analysis was applied for the model results. The comparison results revealed that using upstream records could significantly increase the accuracy of ANN and MLR models in predicting daily stream flows (by around 30%). The comparison of the prediction accuracy of both ANN models (LMFF and RBF) and linear regression method indicated that the ANN approaches were more accurate than the MLR in predicting streamflow dynamics. The LMFF model was able to improve the average of root mean square error (RMSE_ave) and average of mean absolute percentage error (MAPE_ave) values of the multiple linear regression forecasts by about 18% and 21%, respectively. In spite of the fact that the RBF model acted better for predicting the highest range of flow rate (flood events, RMSE_ave/RBF = 26.8 m³/s vs. RMSE_ave/LMFF = 40.2 m³/s), in general, the results suggested that the LMFF method was somehow superior to the RBF method in predicting watershed runoff (RMSE/LMFF = 18.8 m³/s vs. RMSE/RBF = 19.2 m³/s). Eventually, statistical differences between measured and predicted medians were evaluated using Mann-Whitney test, and differences in variances were evaluated using the Levene's test.     

Discrete return lidar-based prediction of leaf area index in two conifer forests

Leaf area index (LAI) is a key forest structural characteristic that serves as a primary control for exchanges of mass and energy within a vegetated ecosystem. Most previous attempts to estimate LAI from remotely sensed data have relied on empirical relationships between field-measured observations and various spectral vegetation indices (SVIs) derived from optical imagery or the inversion of canopy radiative transfer models. However, as biomass within an ecosystem increases, accurate LAI estimates are difficult to quantify. Here we use lidar data in conjunction with SPOT5-derived spectral vegetation indices (SVIs) to examine the extent to which integration of both lidar and spectral datasets can estimate specific LAI quantities over a broad range of conifer forest stands in the northern Rocky Mountains. Our results show that SPOT5-derived SVIs performed poorly across our study areas, explaining less than 50% of variation in observed LAI, while lidar-only models account for a significant amount of variation across the two study areas located in northern Idaho; the St. Joe Woodlands (R² = 0.86; RMSE = 0.76) and the Nez Perce Reservation (R² = 0.69; RMSE = 0.61). Further, we found that LAI models derived from lidar metrics were only incrementally improved with the inclusion of SPOT 5-derived SVIs; increases in R² ranged from 0.02–0.04, though model RMSE values decreased for most models (0–11.76% decrease). Significant lidar-only models tended to utilize a common set of predictor variables such as canopy percentile heights and percentile height differences, percent canopy cover metrics, and covariates that described lidar height distributional parameters. All integrated lidar-SPOT 5 models included textural measures of the visible wavelengths (e.g. green and red reflectance). Due to the limited amount of LAI model improvement when adding SPOT 5 metrics to lidar data, we conclude that lidar data alone can provide superior estimates of LAI for our study areas.     

Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery

Light use efficiency (LUE) is an important variable characterizing plant eco-physiological functions and refers to the efficiency at which absorbed solar radiation is converted into photosynthates. The estimation of LUE at regional to global scales would be a significant advantage for global carbon cycle research. Traditional methods for canopy level LUE determination require meteorological inputs which cannot be easily obtained by remote sensing. Here we propose a new algorithm that incorporates the enhanced vegetation index (EVI) and a modified form of land surface temperature (T_m) for the estimation of monthly forest LUE based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Results demonstrate that a model based on EVI × T_m parameterized from ten forest sites can provide reasonable estimates of monthly LUE for temperate and boreal forest ecosystems in North America with an R² of 0.51 (p < 0.001) for the overall dataset. The regression coefficients (a, b) of the LUE–EVI × T_m correlation for these ten sites have been found to be closely correlated with the average EVI (EVI_ave, R² = 0.68, p = 0.003) and the minimum land surface temperature (LST_min, R² = 0.81, p = 0.009), providing a possible approach for model calibration. The calibrated model shows comparably good estimates of LUE for another ten independent forest ecosystems with an overall root mean square error (RMSE) of 0.055 g C per mol photosynthetically active radiation. These results are especially important for the evergreen species due to their limited variability in canopy greenness. The usefulness of this new LUE algorithm is further validated for the estimation of gross primary production (GPP) at these sites with an RMSE of 37.6 g C m^? 2 month^? 1 for all observations, which reflects a 28% improvement over the standard MODIS GPP products. These analyses should be helpful in the further development of ecosystem remote sensing methods and improving our understanding of the responses of various ecosystems to climate change.     

Remote sensing of the distribution and abundance of host species for spruce budworm in Northern Minnesota and Ontario

Insects and disease affect large areas of forest in the U.S. and Canada. Understanding ecosystem impacts of such disturbances requires knowledge of host species distribution patterns on the landscape. In this study, we mapped the distribution and abundance of host species for the spruce budworm (Choristoneura fumiferana) to facilitate landscape scale planning and modeling of outbreak dynamics. We used multi-temporal, multi-seasonal Landsat data and 128 ground truth plots (and 120 additional validation plots) to map basal area (BA), for 6.4 million hectares of forest in northern Minnesota and neighboring Ontario. Partial least-squares (PLS) regression was used to determine relationships between ground data and Landsat sensor data. Subsequently, BA was mapped for all forests, as well as for two specific host tree genera (Picea and Abies). These PLS regression analyses yielded estimates for overall forest BA with an R² of 0.62 and RMSE of 4.67 m² ha^? 1 (20% of measured BA), white spruce relative BA with an R² of 0.88 (RMSE = 12.57 m² ha^? 1 [20% of measured]), and balsam fir relative BA with an R² of 0.64 (RMSE = 6.08 m² ha^? 1 [33% of measured]). We also used this method to estimate the relative BA of deciduous and coniferous species, each with R² values of 0.86 and RMSE values of 9.89 m² ha^? 1 (23% of measured) and 9.78 m² ha^? 1 (16% of measured), respectively. Of note, winter imagery (with snow cover) and shortwave infrared-based indices – especially the shortwave infrared/visible ratio – strengthened the models we developed. Because ground measurements were made largely in forest stands containing spruce and fir, modeled results are not applicable to stands dominated by non-target conifers such as pines and cedar. PLS regression has proven to be an effective modeling tool for regional characterization of forest structure within spatially heterogeneous forests using multi-temporal Landsat sensor data.     

Modelling the fate and transport of negatively buoyant storm–river water in small multi-basin lakes

The dynamics of negatively buoyant river plumes in a small multi-basin kettle lake with steep bathymetry (Toolik Lake, AK) are simulated using a Cartesian hydrodynamic model based on the solution of the three-dimensional shallow water equations. To validate the model, model predictions are compared with results from previous analytical and laboratory studies and with field observations. The grid resolution adopted for the Toolik Lake model is 0.5 m (= Δz) in the vertical and 20 m (= Δx) in the horizontal, so that the ratio of the bottom slope S₀ to Δz/Δx is lower than 4 in 99% of the computational domain. With that resolution, the model represents correctly the rate of mixing between lake and river water and the speed of propagation of downslope gravity currents. The model provides accurate predictions of the temperature structure (RMSE = 0.25 °C) and of eddy diffusivities at the depths of the intrusions of incoming water. Measurements and modelling show similar fractions and depth distribution of river water on a cross-basin transect, which suggests that the mixing dynamics of the plume as it transits between basins are well resolved. Thus, the stage is set to quantify the ecological consequences of storm events in small lakes with several interconnected basins using coupled biological measurements and 3D modelling.     

Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content,leaf mass per area,leaf area index and leaf canopy biomass

This article aims at finding efficient hyperspectral indices for the estimation of forest sun leaf chlorophyll content (CHL, µg cm_leaf^? 2), sun leaf mass per area (LMA, g_{dry matter} m_leaf^? 2), canopy leaf area index (LAI, m²_leaf m_soil^? 2) and leaf canopy biomass (B_leaf, g_{dry matter} m_soil^? 2). These parameters are useful inputs for forest ecosystem simulations at landscape scale. The method is based on the determination of the best vegetation indices (index form and wavelengths) using the radiative transfer model PROSAIL (formed by the newly-calibrated leaf reflectance model PROSPECT coupled with the multi-layer version of the canopy radiative transfer model SAIL). The results are tested on experimental measurements at both leaf and canopy scales. At the leaf scale, it is possible to estimate CHL with high precision using a two wavelength vegetation index after a simulation based calibration. At the leaf scale, the LMA is more difficult to estimate with indices. At the canopy scale, efficient indices were determined on a generic simulated database to estimate CHL, LMA, LAI and Bleaf in a general way. These indices were then applied to two Hyperion images (50 plots) on the Fontainebleau and Fougères forests and portable spectroradiometer measurements. They showed good results with an RMSE of 8.2 µg cm^? 2 for CHL, 9.1 g m^? 2 for LMA, 1.7 m² m^? 2 for LAI and 50.6 g m^? 2 for Bleaf. However, at the canopy scale, even if the wavelengths of the calibrated indices were accurately determined with the simulated database, the regressions between the indices and the biophysical characteristics still had to be calibrated on measurements. At the canopy scale, the best indices were: for leaf chlorophyll content: ND_chl = (ρ₉₂₅ ? ρ₇₁₀)/(ρ₉₂₅ + ρ₇₁₀), for leaf mass per area: ND_LMA = (ρ₂₂₆₀ ? ρ₁₄₉₀)/(ρ₂₂₆₀ + ρ₁₄₉₀), for leaf area index: D_LAI = ρ₁₇₂₅ ? ρ₉₇₀, and for canopy leaf biomass: ND_Bleaf = (ρ₂₁₆₀ ? ρ₁₅₄₀)/(ρ₂₁₆₀ + ρ₁₅₄₀).     

The possibility of using C20 fullerene and graphene as semiconductor segments for detection,and destruction of cyanogen-chloride chemical agent

Detection of hazardous chemical species by changing the electrical conductivity of a semiconductor matter is a proposed and applied way for decreasing their subsequent unpleasant effects. Recently, many examples of using inorganic or organic materials, polymeric, and also nano-sized species as sensors were reported in which, in some cases, those matters were strongly affective and suitable.In this project, we have made an assessment on whether the graphene segment or C₂₀ fullerene, able to sense the existence of cyanogen chloride NCCl? In order to gain trustable results, the possible reaction pathways along with the adsorption kinetics were investigated. Moreover, the electronic density of states DOS showed that C₂₀ fullerene senses the existence of cyanogen chloride agent with a clearer signal (ΔE_g = 0.0110 eV) compared to the graphene segment (ΔE_g = 0.0001 eV). Also the adsorption energy calculations showed that cyanogen chloride could be adsorbed by the fullerene in a multi-step process (E_ads1 = −0.852 kcal mol⁻¹; E_ads2 = −0.446 kcal mol⁻¹; E_ads3 = −2.330 kcal mol⁻¹).     

Monitoring land degradation risk using ASTER data: The non-evaporative fraction as an indicator of ecosystem function

There is a need to develop operational land degradation indicators for large regions to prevent losses of biological and economic productivity. Disturbance events press ecosystems beyond resilience and modify the associated hydrological and surface energy balance. Therefore, new indicators for water-limited ecosystems can be based on the partition of the surface energy into latent (λE) and sensible heat flux (H).In this study, a new methodology for monitoring land degradation risk for regional scale application is evaluated in a semiarid area of SE Spain. Input data include ASTER surface temperature and reflectance products, and other ancillary data. The methodology employs two land degradation indicators, one related to ecosystem water use derived from the non-evaporative fraction (NEF = H / (λE + H)), and another related to vegetation greenness derived from the NDVI. The surface energy modeling approach used to estimate the NEF showed errors within the range of similar studies (R² = 0.88; RMSE = 0.18 (22%)).To create quantitative indicators suitable for regional analysis, the NEF and NDVI were standardized between two possible extremes of ecosystem status: extremely disturbed and undisturbed in each climatic region to define the NEFS (NEF Standardized) and NDVIS (NDVI Standardized). The procedure was successful, as it statistically identified ecosystem status extremes for both indicators without supervision. Evaluation of the indicators at disturbed and undisturbed (control) sites, and intermediate surface variables such as albedo or surface temperature, provided insights on the main surface energy status controls following disturbance events. These results suggest that ecosystem functional indicators, such as the NEFS, can provide information related to the surface water deficit, including the role of soil properties.     

Computational assessment of several hydrogen-free high energy compounds

Tetrazino-tetrazine-tetraoxide (TTTO) is an attractive high energy compound, but unfortunately, it is not yet experimentally synthesized so far. Isomerization of TTTO leads to its five isomers, bond-separation energies were empolyed to compare the global stability of six compounds, it is found that isomer 1 has the highest bond-separation energy (1204.6 kJ/mol), compared with TTTO (1151.2 kJ/mol); thermodynamic properties of six compounds were theoretically calculated, including standard formation enthalpies (solid and gaseous), standard fusion enthalpies, standard vaporation enthalpies, standard sublimation enthalpies, lattice energies and normal melting points, normal boiling points; their detonation performances were also computed, including detonation heat (Q, cal/g), detonation velocity (D, km/s), detonation pressure (P, GPa) and impact sensitivity (h₅₀, cm), compared with TTTO (Q = 1311.01 J/g, D = 9.228 km/s, P = 40.556 GPa, h₅₀ = 12.7 cm), isomer 5 exhibites better detonation performances (Q = 1523.74 J/g, D = 9.389 km/s, P = 41.329 GPa, h₅₀ =  28.4 cm).     

On reliability of the folded hypercubes

In this paper, we explore the 2-extra connectivity and 2-extra-edge-connectivity of the folded hypercube FQ_n. We show that κ₂(FQ_n) = 3n − 2 for n ⩾ 8; and λ₂(FQ_n) = 3n − 1 for n ⩾ 5. That is, for n ⩾ 8 (resp. n ⩾ 5), at least 3n − 2 vertices (resp. 3n − 1 edges) of FQ_n are removed to get a disconnected graph that contains no isolated vertices (resp. edges). When the folded hypercube is used to model the topological structure of a large-scale parallel processing system, these results can provide more accurate measurements for reliability and fault tolerance of the system.     

Assessment of grape yield and composition using the reflectance based Water Index in Mediterranean rainfed vineyards

In rainfed vineyards water deficits play a major role in determining berry yield and composition. Therefore, reliable indicators of vine water status might be of great value for the optimization of grape yield and quality. In the present study the feasibility of using hyperspectral reflectance indices related to plant biophysical properties at predicting berry yield and quality attributes in rainfed vineyards is assessed. The study was conducted on Vitis vinifera cv. Chardonnay in commercial vineyards in the D.O. Penedès region (Catalonia, Spain) over two consecutive years (2007–2008). Field measurements of fractional intercepted Photosynthetic Active Radiation (fIPAR), canopy reflectance, predawn water potential (Ψ_p) and the canopy to air temperature difference at midday (ΔT_midday) were conducted at the stage of veraison. Yield, Total Soluble Solids (TSS), Titratable Acidity (TA) and the ratio TSS/TA (maturation index, IMAD) were determined at harvest. Contrasted water availability among vineyards prompted considerable variation in berry yield and quality attributes. Across years, higher yield was accompanied by higher TA (r = 0.59, p < 0.01) and lower IMAD (r = ? 0.63, p < 0.01) while no significant relationship was observed between yield and TSS. Yield was related to canopy vigor (fIPAR) in a variable extend: in 2007, yield was positively related to fIPAR (r = 0.71, p < 0.05) while yield was found to decrease along with increasing fIPAR in 2008 (r = ? 0.62, p < 0.05). Contrastingly, NDVI provided consistent estimates of yield across years (r = 0.57, p < 0.05). These results suggest that NDVI might be more appropriate to characterize the effects of varying water availability on yield than fIPAR. In addition, yield was found to be related to ΔT_midday (r = ? 0.63 and r = ? 0.66, in 2007 and 2008, respectively). Accordingly, the Water Index (WI), an indicator of vine water status, provided robust estimates of yield across years (r = 0.61, p < 0.01). The strength of the correlation between NDVI and WI vs. yield suggests that yield was influenced by changes in both leaf area (intercepted light) and photosynthesis (stomatal aperture) in a variable extent according to the timing and severity of water deficits in the years of study. Berry quality attributes did not show significant relationships against fIPAR but were related to ΔT_midday. Accordingly, NDVI did not show significant correlation with berry quality attributes, while WI was found to be consistently related to TA (r = 0.70, p < 0.01) and IMAD (r = ? 0.71, p < 0.01) across years. The results obtained suggest that the WI might provide reliable estimates of berry quality attributes in vineyards experiencing moderate to severe water deficits with potential application in precision viticulture activities such as selective harvesting according to grape quality attributes as well as for ripening assessment.     

Interpretation and topographic compensation of conifer canopy self-shadowing

The self-shadowing of conifer canopies results from the size and arrangement of trees within a stand and is a first-order term controlling radiance from forested terrain at common pixel scales of tens of meters. Although self-shadowing is a useful attribute for forest remote-sensing classification, compensation for the topographic effects of self-shadowing has proven problematic. This study used airborne canopy LiDAR measurements of 80 Pacific Northwest, USA conifer stands ranging in development stage from pre-canopy closure to old-growth in order to model canopy self-shadowing for four solar zenith angles (SZA). The shadow data were compared to physical measurements used to characterize forest stands, and were also used to test and improve terrain compensation models for remotely sensed images of forested terrain. Canopy self-shadowing on flat terrain strongly correlates with the canopy's geometric complexity as measured by the rumple index (canopy surface area/ground surface area) (R² = 0.94–0.87 depending on SZA), but is less correlated with other stand measurements: 95th percentile canopy height (R² = 0.68), mean diameter at breast height (dbh) (R² = 0.65), basal area ha^? 1 (R² = 0.18), and canopy stem count ha^? 1 (R² = 0.18). The results in this paper support interpretation of self-shadowing as a function of canopy complexity, which is an important ecological characteristic in its own right. Modeling of canopy self-shadowing was used to assess the accuracy of the Sun-Canopy-Sensor (SCS) topographic correction, and to develop a new empirical Adaptive Shade Compensation (ASC) topographic compensation model. ASC used measured shadow (as an estimate of canopy complexity) and the SCS term (to describe the illumination geometry) as independent variables in multiple regressions to determine the topographic correction. The ASC model provided more accurate radiance corrections with limited variation in results across the full range of canopy complexities and incidence angles.     

Evaluation of three methodologies to estimate the VO2max in people of different ages

Aging and gender are factors that affect the variation of physical work capacity. The present paper highlights the importance of the metabolism used by ergonomics to establish the appropriate limits of loads at work. This study compares the aerobic capacity of people from 20 to 71 years old split in 5 different groups. The laboratory experiment tested 33 volunteers (19 women and 14 men). A submaximal step test was used to measure the VO₂ using a portable breath by breath metabolic system and a telemetric heart rate monitor. Three methods to estimate the VO_2max were compared: 1) a direct measurement of VO₂, 2) estimation by heart rate, and 3) a step test method using predetermined charts. Significant difference was encountered among the estimation methods as well as among the age ranges (F_2,92 = 6.43, p < 0.05 y F_4,92 = 7.18, p < 0.05 respectively). The method of direct measurement and the method of predetermined charts were different for the estimation of the VO_2max with a confidence level of 95%. The method of predetermined charts is better adapted for males and people younger than 30 years. The estimation through non invasive heart rate apparatus was a good appraiser of the maximal oxygen consumption considering both genders and all the age groups.     

Evaluation of response characteristics of resistive oxygen sensors based on porous cerium oxide thick film using pressure modulation method

In order to reduce the response time of resistive oxygen sensors using porous cerium oxide thick film, it is important to ascertain the factors controlling response. Pressure modulation method (PMM) was used to find the rate-limiting step of sensor response. This useful method measures the amplitude of sensor output (H(f)) for the sine wave modulation of oxygen partial pressure at constant frequency (f). In PMM, “break” response time, which is minimum period in which the sensor responds precisely, can be measured. Three points were examined: (1) simulated calculations of PMM were carried out using a model of porous thick film in which spherical particles are connected in a three-dimensional network; (2) sensor response speed was experimentally measured using PMM; and (3) the diffusion coefficient and surface reaction coefficient were estimated by comparison between experiment and calculation. The plot of log f versus log H(f) in the high f region was found to have a slope of approximately −0.5 for both porous thick film and non-porous thin film, when the rate-limiting step was diffusion. Calculations showed the response time of porous thick film was 1/20 that of non-porous thin film when the grain diameter of the porous thick film was the same as the thickness of non-porous thin film. At 973 K, “break” response time (t_b) of the resistive oxygen sensor was found by experiment to be 109 ms. It was concluded that the response of the resistive oxygen sensor prepared in this study was strongly controlled by diffusion at 923–1023 K, since the experiment revealed that the slope of plot of log f versus log H(f) in the high f region was approximately −0.5. At 923–1023 K, the diffusion coefficient of oxygen vacancy in porous ceria (D_V) was expressed as follows: D_V (m²s⁻¹) = 5.78 × 10⁻⁴ exp(−1.94 eV/kT). At 1023 K, the surface reaction coefficient (K) was found to exceed 10⁻⁴ m/s.     

Bounds for the Roots of Lacunary Polynomials

A polynomial P(X)  = X^d + a_d − 1X^d − 1 + ⋯ is called lacunary when a_d − 1 =  0. We give bounds for the roots of such polynomials with complex coefficients. These bounds are much smaller than for general polynomials.     

Facile synthesis of lanthanide vanadates and their luminescent properties

GdVO₄:Eu³⁺, Bi³⁺ with tetragonal phase has been successfully synthesized by employing efficient irradiations. The assembly of composites with fine grains based on acoustic energy and microwave radiation requires low temperature (90 °C) and short reaction time (60 min). All the compounds exhibited red emissions and they can be sensitized through the doped Bi³⁺ ions. The dependence of pH changes and doping concentration on the fluorescence features has been discussed. The photoluminescence measurements show that the optical properties achieved the best results at pH = 9 for GdVO₄:Eu³⁺(5 mol%), Bi³⁺(1 mol%) or pH = 7 for GdVO₄:Eu³⁺.     

Comparison of interpolation methods for depth to groundwater and its temporal and spatial variations in the Minqin oasis of northwest China

Severe water shortages and dramatic declines in groundwater levels have resulted in environmental deterioration in the Minqin oasis, an arid region of northwest China. Understanding temporal and spatial variations in the depth to groundwater in the region is important for developing management strategies. Depth to groundwater records for 48 observation wells in the Minqin oasis were available for 22 years from 1981 to 2003, allowing us to compare three different interpolation methods based on three selected years (1981, 1990, 2002) as starting points. The three methods were inverse distance weighting (IDW), radial basis function (RBF), and kriging (including ordinary kriging (OK), simple kriging (SK), and universal kriging (UK)). Cross-validation was applied to evaluate the accuracy of the various methods, and two indices – the correlation coefficient (R²) and the root mean squared error (RMSE) – were used to compare the interpolation methods. Another two indices – deviation of estimation errors (σ) and 95% prediction interval (95 PPI) – were used to assess prediction errors. Comparison of interpolated values with observed values indicates that simple kriging is the optimal method for interpolating depth to groundwater in this region: it had the lowest standard deviation of estimation errors and smallest 95% prediction interval (95 PPI). By using the simple kriging method and an autoregressive model for depth to groundwater based on the data from 1981 to 2003, this work revealed systematic temporal and spatial variations in the depth to groundwater in the Minqin oasis. The water table has declined rapidly over the past 22 years, with the average depth to groundwater increasing from 4.95 m in 1981 to 14.07 m in 2002. We attribute the decline in the water table to excessive extraction and to decreases in irrigation channel leakage.