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Abstract Background Salmonella is a major zoonotic food-borne pathogen that persists on poultry farms, and animals undergo reinfection with endemic strains. The present study aimed to investigate the characteristics and dissemination of antimicrobial-resistant Salmonella within and between broiler farms that used enrofloxacin and those that did not. Results Cloacal and environmental (litter, feed, and water) samples from two selected flocks in each of 12 farms owned by the same company were collected three times over a 30-day period of two production cycles during 2015–2016. The rate of Salmonella isolation was 7.8% (123/1584). Nine Salmonella serotypes (116 isolates) and seven untypable isolates were identified, and Salmonella Montevideo was the most prevalent serotype. Azithromycin-resistant (17.9%) and colistin-resistant (3.3%) isolates were detected, and multidrug-resistant isolates (43.1%) were also observed. No isolate was resistant to enrofloxacin or ciprofloxacin; however, intermediate resistance to enrofloxacin was significantly higher (P

Antimicrobial resistance and pulsed-field gel electrophoresis (PFGE) genotypes of collected S. enterica ser. Gallinarum isolates were investigated to examine the epidemiological relationship between field outbreak isolates of S. enterica ser. Gallinarum. Thirty S. enterica ser. Gallinarum isolates collected from poultry farms with FT outbreaks from 2013 to 2018 in South Korea were analyzed. All isolates were resistant to at least 3 of the 18 antimicrobials tested and exhibited an MDR phenotype. All isolates showed resistance to streptomycin, sulfisoxazole, and colistin. One isolate was resistant to 9 antimicrobials. The antimicrobial resistance profile, streptomycin-sulfisoxazole-colistin-nalidixic acid-ciprofloxacin-gentamicin (18/30, 60.0%), was the most prevalent. PFGE types were classified into 10 groups with a 100% correlation cutoff in dendrograms for 30 field isolates. The dominant PFGE types were 1 (8/30, 26.7%), 4 (7/30, 23.3%), and 9 (5/30, 16.7%). Interestingly some isolates collected from the same and different companies had the same PFGE type. We reported a high MDR rate in S. enterica ser. Gallinarum isolates. The present study highlights the occurrence of horizontal spread and cyclic contamination of MDR S. enterica ser. Gallinarum within the same company. Furthermore, we showed cross-contamination between different companies. The characterization of these isolates would be helpful in the development of prevention and control strategies for MDR S. enterica ser. Gallinarum infection in South Korea.

In this study, six satellite-based terrestrial latent heat flux (LE) products were evaluated in the vegetation dominated Haihe River basin of North China. These LE products include Global Land Surface Satellite (GLASS) LE product, FLUXCOM LE product, Penman-Monteith-Leuning V2 (PML_V2) LE product, Global Land Evaporation Amsterdam Model datasets (GLEAM) LE product, Breathing Earth System Simulator (BESS) LE product, and Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD16) LE product. Eddy covariance (EC) data collected from six flux tower sites and water balance method derived evapotranspiration (WBET) were used to evaluate these LE products at site and basin scales. The results indicated that all six LE products were able to capture the seasonal cycle of LE in comparison to EC observations. At site scale, GLASS LE product showed the highest coefficients of determination (R2) (0.58, p < 0.01) and lowest root mean square error (RMSE) (28.2 W/m2), followed by FLUXCOM and PML products. At basin scale, the LE estimates from GLASS product provided comparable performance (R2 = 0.79, RMSE = 18.8 mm) against WBET, compared with other LE products. Additionally, there was similar spatiotemporal variability of estimated LE from the six LE products. This study provides a vital basis for choosing LE datasets to assess regional water budget.

Coarse spatial resolution sensors play a major role in capturing temporal variation, as satellite images that capture fine spatial scales have a relatively long revisit cycle. The trade-off between the revisit cycle and spatial resolution hinders the access of terrestrial latent heat flux (LE) data with both fine spatial and temporal resolution. In this paper, we firstly investigated the capability of an Extremely Randomized Trees Fusion Model (ERTFM) to reconstruct high spatiotemporal resolution reflectance data from a fusion of the Chinese GaoFen-1 (GF-1) and the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Then, based on the merged reflectance data, we used a Modified-Satellite Priestley–Taylor (MS–PT) algorithm to generate LE products at high spatial and temporal resolutions. Our results illustrated that the ERTFM-based reflectance estimates showed close similarity with observed GF-1 images and the predicted NDVI agreed well with observed NDVI at two corresponding dates (r = 0.76 and 0.86, respectively). In comparison with other four fusion methods, including the widely used spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM, ERTFM had the best performance in terms of predicting reflectance (SSIM = 0.91; r = 0.77). Further analysis revealed that LE estimates using ERTFM-based data presented more detailed spatiotemporal characteristics and provided close agreement with site-level LE observations, with an R2 of 0.81 and an RMSE of 19.18 W/m2. Our findings suggest that the ERTFM can be used to improve LE estimation with high frequency and high spatial resolution, meaning that it has great potential to support agricultural monitoring and irrigation management.

ESC-resistant E. coli isolates were collected from broiler chickens, a slaughterhouse, and retail meat to assess their dispersion and their involvement in cross-contamination. ESBL-/AmpC-producing E. coli were isolated during the slaughter process of all six investigated chicken flocks from scalding, feather removal, first conveyor, evisceration, second washing, third conveyor, and third washing areas, and from handling workers in the slaughterhouse. ESC-resistant E. coli isolates with the same pulsed-field gel electrophoresis type were found in the same site (scalding) on different sampling days. ESBL/AmpC-producing E. coli isolates were absent in the lairage area in the slaughterhouse, but present in the retail markets in 36.8% (7/19) of the chicken flocks. The blaCTX-M genes and blaCMY-2 were conjugated to recipient E. coli J53 in 67.5% (27/40) and 56.1% (23/41) of ESBL-producing and AmpC-producing E. coli isolates, respectively. The presence of the same conjugative plasmids was found in genetic diversity ESC-resistant E. coli colonies collected on different sampling days. Our study emphasizes that cross-contamination of ESBL/AmpC-producing E. coli in slaughterhouse has a crucial impact on the occurrence of ESC resistance in retail chicken meat.

The long-term estimation of grassland aboveground biomass (AGB) is important for grassland resource management in the Three-River Headwaters Region (TRHR) of China. Due to the lack of reliable grassland AGB datasets since the 1980s, the long-term spatiotemporal variation in grassland AGB in the TRHR remains unclear. In this study, we estimated AGB in the grassland of 209,897 km2 using advanced very high resolution radiometer (AVHRR), MODerate-resolution Imaging Spectroradiometer (MODIS), meteorological, ancillary data during 1982–2018, and 75 AGB ground observations in the growth period of 2009 in the TRHR. To enhance the spatial representativeness of ground observations, we firstly upscaled the grassland AGB using a gradient boosting regression tree (GBRT) model from ground observations to a 1 km spatial resolution via MODIS normalized difference vegetation index (NDVI), meteorological and ancillary data, and the model produced validation results with a coefficient of determination (R2) equal to 0.76, a relative mean square error (RMSE) equal to 88.8 g C m−2, and a bias equal to −1.6 g C m−2 between the ground-observed and MODIS-derived upscaled AGB. Then, we upscaled grassland AGB using the same model from a 1 km to 5 km spatial resolution via AVHRR NDVI and the same data as previously mentioned with the validation accuracy (R2 = 0.74, RMSE = 57.8 g C m−2, and bias = −0.1 g C m−2) between the MODIS-derived reference and AVHRR-derived upscaled AGB. The annual trend of grassland AGB in the TRHR increased by 0.37 g C m−2 (p < 0.05) on average per year during 1982–2018, which was mainly caused by vegetation greening and increased precipitation. This study provided reliable long-term (1982–2018) grassland AGB datasets to monitor the spatiotemporal variation in grassland AGB in the TRHR.

A total of 136 Salmonella isolates from chicken feces and meat samples of the top 12 integrated chicken production companies throughout Korea were collected. Among the 17 ESC-resistant Salmonella; blaCTX-M-15 was the most prevalent gene and two strains carried blaTEM-1/blaCTX-M-15 and blaCMY-2, respectively. The transferable blaCTX-M-15 gene was carried by IncFII plasmid in three isolates and the blaCMY-2 gene carried by IncI1 plasmid in one isolate. blaCMY-2 gene-harboring strain was selected as the donor based on the high frequency of blaCMY-2 gene transfer in vitro and its transfer frequencies were determined at 10−3 transconjugants per recipient. The transfer of blaCMY-2 gene-harboring plasmid derived from chicken isolate into a human pathogen; enteroinvasive Escherichia coli (EIEC), presented in mouse intestine with about 10−1 transfer frequency without selective pressure. From the competition experiment; blaCMY-2 gene-harboring transconjugant showed variable fitness burden depends on the parent strains. Our study demonstrated direct evidence that the blaCMY-2 gene harboring Salmonella from chicken could frequently transfer its ESC-resistant gene to E. coli in a mouse intestine without antimicrobial pressure; resulting in the emergence of multidrug resistance in potentially virulent EIEC isolates of significance to human health; which can increase the risk of therapeutic inadequacy or failures.

Interleukin-33 (IL-33), a member of the IL-1 superfamily, has been shown to play a critical role in many diseases through regulating the immune cell responses, including myeloid-derived suppressor cells (MDSCs). Our previous study demonstrated that IL-33 might play a protective role in kidney injury in gout patients by regulating the lipid metabolism. However, the role of IL-33in the development of MSU-induced inflammation remains elusive. In this study, an increased IL-33 expression was observed in gout patients, which was positively correlated with inflammatory marker CRP. To explore the effects and mechanisms of the increased IL-33 expression in the gout patients, the anti-ST2 antibody and exogenous recombinant IL-33 were used in MSU-induced peritonitis animal model that mimics human gout. Compared with control group, mice with exogenous recombinant IL-33 significantly ameliorated the inflammatory cells infiltration, while blockage of IL-33 signaling by anti-ST2 had no effect on the development of MSU-induced peritonitis. Furthermore, the crucial inflammatory cytokine IL-1β was markedly decreased in IL-33-treated mice. Besides that, a large number of anti-inflammatory MDSCs with CD11b+Gr1intF4/80+ phenotype was observed in the IL-33-treated mice, and adoptive transfer of IL-33-induced MDSCs (CD11b+Gr1intF4/80+) markedly inhibited the IL-1β production in MSU-induced peritonitis. In conclusion, our data provide clear evidences that the increased expression of IL-33 in the gout patients might be due to a cause of self-negative regulation, which inhibits the development of MSU-induced inflammation through expanding MDSCs. Thus, IL-33 might serve as a promising therapeutic target for gout.

Gout is a common metabolic disease in humans, and it is due to persistently elevated levels of uric acid in the blood. At high levels, uric acid crystallizes and the crystals deposit in joints and surrounding tissues, resulting in an attack of gout. Interestingly, the gout attack can spontaneously resolve within a few days. However, the self-limited mechanism of gout remains elusive. It has been demonstrated that CD14 plays an important role in self-remission of gout. In this study, we found that the proportion of CD14-positive PBMCs was decreased in gout patients when compared with healthy controls and the serum sCD14 level was also considerably decreased in gout patients in comparison to healthy controls. In addition, sCD14 levels were positively correlated with CRP levels. Furthermore, the effect of MSU on the levels of CD14 in healthy volunteer’s PBMC was explored in in vitro experiment. The results showed that CD14 expression on macrophage and sCD14 levels in the culture supernatants were significantly decreased after MSU treatment. However, there was no significance in the levels of membrane CD14 and sCD14 in healthy volunteer’s PBMC stimulated by LPS. Taken together, these results suggest that CD14 might play an important role in self-remission of gout.

An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is fundamental and crucial for the rational utilization of water resources in the Haihe River Basin (HRB). However, the sparsity of flux observation sites hinders the accurate characterization of spatiotemporal LE patterns over the HRB. In this study, we estimated the daily LE across the HRB using the gradient boosting regression tree (GBRT) from global land surface satellite NDVI data, reanalysis data and eddy covariance data. Compared with the random forests (RF) and extra tree regressor (ETR) methods, the GBRT obtains the best results, with R2 = 0.86 and root mean square error (RMSE = 18.1 W/m2. Then, we applied the GBRT algorithm to map the average annual terrestrial LE of the HRB from 2016 to 2018 with a spatial resolution of 0.05°. When compared with the Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) LE products, the difference between the terrestrial LE estimated by the GBRT algorithm and the GLASS and MODIS products was less than 20 W/m2 in most areas; thus, the GBRT algorithm was reliable and reasonable for estimating the long-term LE estimation over the HRB.

An operational and accurate model for estimating global or regional terrestrial latent heat of evapotranspiration (ET) across different land-cover types from satellite data is crucial. Here, a simplified Priestley–Taylor (SPT) model was developed without surface net radiation (Rn) by combining incident shortwave radiation (Rs), satellite vegetation index, and air relative humidity (RH). Ground-measured ET for 2000–2009 collected by 100 global FLUXNET eddy covariance (EC) sites was used to calibrate and evaluate the SPT model. A series of cross-validations demonstrated the reasonable performance of the SPT model to estimate seasonal and spatial ET variability. The coefficients of determination (R2) of the estimated versus observed daily (monthly) ET ranged from 0.42 (0.58) (p < 0.01) at shrubland (SHR) flux sites to 0.81 (0.86) (p < 0.01) at evergreen broadleaf forest (EBF) flux sites. The SPT model was applied to estimate agricultural ET at high spatial resolution (16 m) from Chinese Gaofen (GF)-1 data and monitor long-term (1982–2018) ET variations in the Three-River Headwaters Region (TRHR) of mainland China using the Global LAnd-Surface Satellite (GLASS) normalized difference vegetation index (NDVI) product. The proposed SPT model without Rn provides an alternative model for estimating regional terrestrial ET across different land-cover types.

Positive identification rates of Salmonella enterica in hatcheries and upstream breeder farms were 16.4% (36/220) and 3.0% (6/200), respectively. Among the Salmonella serovars identified in the hatcheries, S. enterica ser. Albany (17/36, 47.2%) was the most prevalent, followed by the serovars S. enterica ser. Montevideo (11/36, 30.6%) and S. enterica ser. Senftenberg (5/36, 13.9%), which were also predominant. Thirty-six isolates showed resistance to at least one antimicrobial tested, of which 52.8% (n = 19) were multidrug resistant (MDR). Thirty-three isolates (enrofloxacin, MIC ≥ 0.25) showed point mutations in the gyrA and parC genes. One isolate, S. enterica ser. Virchow, carrying the blaCTX-M-15 gene from the breeder farm was ceftiofur resistant. Pulsed-field gel electrophoresis (PFGE) showed that 52.0% S. enterica ser. Montevideo and 29.6% S. enterica ser. Albany isolates sourced from the downstream of hatcheries along the broiler chicken supply chain carried the same PFGE types as those of the hatcheries. Thus, the hatcheries showed a high prevalence of Salmonella isolates with high antimicrobial resistance and no susceptible isolate. The AMR isolates from hatcheries originating from breeder farms could disseminate to the final retail market along the broiler chicken supply chain. The emergence of AMR Salmonella in hatcheries may be due to the horizontal spread of resistant isolates. Therefore, Salmonella control in hatcheries, particularly its horizontal transmission, is important.

The aim of this study was to analyze the prevalence, antimicrobial resistance, and genetic diversity of Campylobacter isolates that were obtained from whole chicken production stages in Korea. A total of 1348 samples were collected from 10 production lines. The prevalence of Campylobacter in breeder farm, broiler farm, slaughterhouse, and retail meat products was 50.0%, 3.3%, 13.4%, and 68.4%, respectively, and Campylobacter was not detected at the hatchery stage. Resistance to quinolones/fluoroquinolones was the most prevalent at all stages. Among the multidrug-resistant isolates, 16 isolates (19.8%) from breeder farm were resistant to both azithromycin and ciprofloxacin. A total of 182 isolates were subdivided into 82 pulsed-field gel electrophoresis (PFGE) genotypes with 100% similarity. Diverse genotypes were presented with discontinuous patterns along the whole production chain. Thirty percent of Campylobacter-free flocks became positive after slaughtering. An identical genotype was simultaneously detected from both breeder farm and retail meat, even from different production lines. This study reveals that antimicrobial-resistant Campylobacter contamination can occur at all stages of the chicken supply chain. In particular, the breeder farm and slaughterhouse should be the main control points, as they are the potential stages at which antimicrobial-resistant Campylobacter could spread to retail meat products by horizontal transmission.

The aim of the present study was to investigate variation in antimicrobial resistance in Clostridium perfringens (C. perfringens) isolated from chickens after withdrawal of antimicrobial growth promoters (AGPs); and to investigate the correlation between the presence of toxin genes (cpb2, netB, and tpeL) and antimicrobial resistance. Altogether, 162 isolates of C. perfringens were obtained from chickens displaying clinical signs of necrotic enteritis (n = 65) and from healthy chickens (n = 97) in Korea during 2010–2016. Compared to before AGP withdrawal, increased antimicrobial resistance or MIC50/MIC90 value was observed for nine antimicrobials including penicillin, tetracycline, tylosin, erythromycin, florfenicol, enrofloxacin, monensin, salinomycin, and maduramycin. Significantly (p < 0.05) higher resistance to gentamicin, clindamycin, and virginiamycin was found in isolates from chickens with necrotic enteritis compared to those from healthy chickens. tpeL gene was not detected in C. perfringens isolates from healthy chickens. A correlation between toxin gene prevalence and antibiotic resistance was found in the C. perfringens isolates. Because the usage of antimicrobials may contribute to the selection of both resistance and toxin genes, these can potentially make it challenging to control antimicrobial resistance in pathogenic colonies. Therefore, a more complete understanding of the interplay between resistance and virulence genes is required.

Contour detection has been extensively investigated as a fundamental problem in computer vision. In this study, a biologically-inspired candidate weighting framework is proposed for the challenging task of detecting meaningful contours. In contrast to previous models that detect contours from pixels, a modified superpixel generation processing is proposed to generate a contour candidate set and then weigh the candidates by extracting hierarchical visual cues. We extract the low-level visual local cues to weigh the contour intrinsic property and mid-level visual cues on the basis of Gestalt principles for weighting the contour grouping constraint. Experimental results tested on the BSDS benchmark show that the proposed framework exhibits promising performances to capture meaningful contours in complex scenes.

Surface all-wave net radiation (Rn) is a crucial variable driving many terrestrial latent heat (LE) models that estimate global LE. However, the differences between different Rn products and their impact on global LE estimates still remain unclear. In this study, we evaluated two Rn products, Global LAnd Surface Satellite (GLASS) beta version Rn and Modern-Era Retrospective Analysis for Research and Applications-version 2 (MERRA-2) Rn, from 2007–2017 using ground-measured data from 240 globally distributed in-situ radiation measurements provided by FLUXNET projects. The GLASS Rn product had higher accuracy (R2 increased by 0.04–0.26, and RMSE decreased by 2–13.3 W/m2) than the MERRA-2 Rn product for all land cover types on a daily scale, and the two Rn products differed greatly in spatial distribution and variations. We then determined the resulting discrepancies in simulated annual global LE using a simple averaging model by merging five diagnostic LE models: RS-PM model, SW model, PT-JPL model, MS-PT model, and SIM model. The validation results showed that the estimated LE from the GLASS Rn had higher accuracy (R2 increased by 0.04–0.14, and RMSE decreased by 3–8.4 W/m2) than that from the MERRA-2 Rn for different land cover types at daily scale. Importantly, the mean annual global terrestrial LE from GLASS Rn was 2.1% lower than that from the MERRA-2 Rn. Our study showed that large differences in satellite and reanalysis Rn products could lead to substantial uncertainties in estimating global terrestrial LE.

Ocean latent heat flux (LHF) is an essential variable for air–sea interactions, which establishes the link between energy balance, water and carbon cycle. The low-latitude ocean is the main heat source of the global ocean and has a great influence on global climate change and energy transmission. Thus, an accuracy estimation of high-resolution ocean LHF over low-latitude area is vital to the understanding of energy and water cycle, and it remains a challenge. To reduce the uncertainties of individual LHF products over low-latitude areas, four machine learning (ML) methods (Artificial Neutral Network (ANN), Random forest (RF), Bayesian Ridge regression and Random Sample Consensus (RANSAC) regression) were applied to estimate low-latitude monthly ocean LHF by using two satellite products (JOFURO-3 and GSSTF-3) and two reanalysis products (MERRA-2 and ERA-I). We validated the estimated ocean LHF using 115 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA and RAMA). The validation results demonstrate that the performance of LHF estimations derived from the ML methods (including ANN, RF, BR and RANSAC) were significantly better than individual LHF products, indicated by R2 increasing by 3.7–46.4%. Among them, the LHF estimation using the ANN method increased the R2 of the four-individual ocean LHF products (ranging from 0.56 to 0.79) to 0.88 and decreased the RMSE (ranging from 19.1 to 37.5) to 11 W m−2. Compared to three other ML methods (RF, BR and RANSAC), ANN method exhibited the best performance according to the validation results. The results of relative uncertainty analysis using the triangle cornered hat (TCH) method show that the ensemble LHF product using ML methods has lower relative uncertainty than individual LHF product in most area. The ANN was employed to implement the mapping of annual average ocean LHF over low-latitude at a spatial resolution of 0.25° during 2003–2007. The ocean LHF fusion products estimated from ANN methods were 10–30 W m−2 lower than those of the four original ocean products (MERRA-2, JOFURO-3, ERA-I and GSSTF-3) and were more similar to observations.

The methods for accurately fusing medium- and high-spatial-resolution satellite reflectance are vital for monitoring vegetation biomass, agricultural irrigation, ecological processes and climate change. However, the currently existing fusion methods cannot accurately capture the temporal variation in reflectance for heterogeneous landscapes. In this study, we proposed a new method, the spatial and temporal reflectance fusion method based on the unmixing theory and a fuzzy C-clustering model (FCMSTRFM), to generate Landsat-like time-series surface reflectance. Unlike other data fusion models, the FCMSTRFM improved the similarity of pixels grouped together by combining land cover maps and time-series data cluster algorithms to define endmembers. The proposed method was tested over a 2000 km2 study area in Heilongjiang Provence, China, in 2017 and 2018 using ten images. The results show that the accuracy of the FCMSTRFM is better than that of the popular enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) (correlation coefficient (R): 0.8413 vs. 0.7589; root mean square error (RMSE): 0.0267 vs. 0.0401) and the spatial-temporal data fusion approach (STDFA) (R: 0.8413 vs. 0.7666; RMSE: 0.0267 vs. 0.0307). Importantly, the FCMSTRFM was able to maintain the details of temporal variations in complicated landscapes. The proposed method provides an alternative method to monitor the dynamics of land surface variables over complicated heterogeneous regions.

Reliable estimates of terrestrial latent heat flux (LE) at high spatial and temporal resolutions are of vital importance for energy balance and water resource management. However, currently available LE products derived from satellite data generally have high revisit frequency or fine spatial resolution. In this study, we explored the feasibility of the high spatiotemporal resolution LE fusion framework to take advantage of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Chinese GaoFen-1 Wide Field View (GF-1 WFV) data. In particular, three-fold fusion schemes based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were employed, including fusion of surface reflectance (Scheme 1), vegetation indices (Scheme 2) and high order LE products (Scheme 3). Our results showed that the fusion of vegetation indices and further computing LE (Scheme 2) achieved better accuracy and captured more detailed information of terrestrial LE, where the determination coefficient (R2) varies from 0.86 to 0.98, the root-mean-square error (RMSE) ranges from 1.25 to 9.77 W/m2 and the relative RSME (rRMSE) varies from 2% to 23%. The time series of merged LE in 2017 using the optimal Scheme 2 also showed a relatively good agreement with eddy covariance (EC) measurements and MODIS LE products. The fusion approach provides spatiotemporal continuous LE estimates and also reduces the uncertainties in LE estimation, with an increment in R2 by 0.06 and a decrease in RMSE by 23.4% on average. The proposed high spatiotemporal resolution LE estimation framework using multi-source data showed great promise in monitoring LE variation at field scale, and may have value in planning irrigation schemes and providing water management decisions over agroecosystems.

An accurate estimation of spatially and temporally continuous latent heat flux (LE) is essential in the assessment of surface water and energy balance. Various satellite-derived LE products have been generated to enhance the simulation of terrestrial LE, yet each individual LE product shows large discrepancies and uncertainties. Our study used Extremely Randomized Trees (ETR) to fuse five satellite-derived terrestrial LE products to reduce uncertainties from the individual products and improve terrestrial LE estimations over Europe. The validation results demonstrated that the estimation using the ETR fusion method increased the R2 of five individual LE products (ranging from 0.53 to 0.61) to 0.97 and decreased the RMSE (ranging from 26.37 to 33.17 W/m2) to 5.85 W/m2. Compared with three other machine learning fusion models, Gradient Boosting Regression Tree (GBRT), Random Forest (RF), and Gaussian Process Regression (GPR), ETR exhibited the best performance in terms of both training and validation accuracy. We also applied the ETR fusion method to implement the mapping of average annual terrestrial LE over Europe at a resolution of 0.05 ◦ in the period from 2002 to 2005. When compared with global LE products such as the Global Land Surface Satellite (GLASS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), the fusion LE using ETR exhibited a relatively small gap, which confirmed that it is reasonable and reliable for the estimation of the terrestrial LE over Europe.

Abstract.: Warfarin is the most frequently prescribed anticoagulant for the long-term treatment in the clinic. Recent studies have shown that polymorphic alleles within the CYP2C9, VKORC1, and CYP4F2 genes are related to the warfarin dosage requirement. In this study, a novel nonsynonymous mutation (1009C>A) in CYP2C9 was detected in a warfarin-hypersensitive patient, while the other two candidate genes were both found to be homozygous for the wild-type alleles. The newly identified point mutation results in an amino acid substitution at position 337 of the CYP2C9 protein (P337T) and has been designated as the novel allele CYP2C9*58. When expressed in insect cell microsomes, the relative intrinsic clearance values of the CYP2C9.58 variant for tolbutamide and losartan were quite similar to those of the typical defective variant CYP2C9.3, whereas the clearance value of CYP2C9.58 for diclofenac was slightly higher than that of another typical defective variant CYP2C9.2. These data suggested that when compared with wild-type CYP2C9.1, the enzymatic activity of the novel allelic variant has been greatly reduced by the 1009C>A mutation. If patients carrying this allele take drugs metabolized by CYP2C9, their metabolic rate might be slower than that of wild-type allele carriers and thus much more attention should be paid to their clinical care. [Supplementary methods and Figure: available only at http://dx.doi.org/10.1254/jphs.13189FP] Keywords:: CYP2C9, allelic variant, insect cell microsome, functional analysis in vitro

Many existing satellite evapotranspiration (ET)-based drought indices have characterized regional drought condition successfully, but the relatively short time span of ET products limits their use in long-term climatological drought assessment. In this study, we assess Evaporative Drought Index (EDI) as a drought monitoring indicator over Northeast China through a retrospective comparison with drought-related indicators. After verifying its utility for detecting documented regional drought events and impacts of drought on crop production, we apply it to improve our understanding of the variation in dryness over Northeast China from 1982 to 2015. Our results illustrate that EDI is generally effective for characterizing terrestrial moisture condition and its standardized formula, namely, Standardized Evaporative Drought Index (sEDI) corresponds well with historical drought events and inter-annual grain crop yields over Northeast China. Although the calculation of sEDI does not directly incorporate precipitation and soil moisture, statistical analyses indicate sEDI can detect drought in accordance with the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI), with the highest correlations found in the west part of Northeast China (R < −0.7). Further analysis illustrates sEDI is more related to commonly-used drought metrics over areas with short canopy vegetation (R < −0.5) than woodland (R < −0.2), which suggests precipitation may not be a good representative of drought condition over areas with deep-rooted vegetation. Then, we find 56.5% of Northeast China shows an upward dry trend from 1982 to 2015, which mainly concentrates in the west part of the study area. Conversely, 14.4% of Northeast China shows a significant wetted trend and most of them locate at cropland areas, due to the improved water management. This study suggests that EDI is a feasible method to monitor spatially distributed drought condition and can provide unique drought information not reflected by rainfall deficits, which also can be used to evaluate traditional precipitation-based indicators.

An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is crucial to the management and planning of water resources for arid and semi-arid areas, for which LE estimations from different satellite sensors unfortunately often contain data gaps and are inconsistent. Many integration approaches have been implemented to overcome these limitations; however, most suffer from either the persistent bias of relying on datasets at only one resolution or the spatiotemporal inconsistency of LE products. In this study, we exhibit an integration case in the midstream of the Heihe River Basin of northwest China by using a multi-resolution Kalman filter (MKF) method to develop continuous and consistent LE maps from satellite LE datasets across different resolutions. The Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16), the Landsat-based LE product derived from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor, and ground observations of eddy covariance flux tower from June to September 2012 are used. The integrated results illustrate that data gaps of MOD16 dropped to less than 0.4% from the original 27–52%, and the root-mean-square error (RMSE) between the LE products decreased by 50.7% on average. Our findings indicate that the MKF method has excellent capacity to fill data gaps, reduce uncertainty, and improve the consistency of multiple LE datasets at different resolutions.

Terrestrial biophysical variables play an essential role in quantifying the amount of energy budget, water cycle, and carbon sink over the Three-River Headwaters Region of China (TRHR). However, direct field observations are missing in this region, and few studies have focused on the long-term spatiotemporal variations of terrestrial biophysical variables. In this study, we evaluated the spatiotemporal dynamics of biophysical variables including meteorological variables, vegetation, and evapotranspiration (ET) over the TRHR, and analyzed the response of vegetation and ET to climate change in the period from 1982 to 2015. The main input gridded datasets included meteorological reanalysis data, a satellite-based vegetation index dataset, and the ET product developed by a process-based Priestley−Taylor algorithm. Our results illustrate that: (1) The air temperature and precipitation over the TRHR increased by 0.597 °C and 41.1 mm per decade, respectively, while the relative humidity and surface downward shortwave radiation declined at a rate of 0.9% and 1.8 W/m2 per decade during the period 1982−2015, respectively. We also found that a ‘dryer warming’ tendency and a ‘wetter warming’ tendency existed in different areas of the TRHR. (2) Due to the predominant ‘wetter warming’ tendency characterized by the increasing temperature and precipitation, more than 56.8% of areas in the TRHR presented a significant increment in vegetation (0.0051/decade, p < 0.05), particularly in the northern and western meadow areas. When energy was the limiting factor for vegetation growth, temperature was a considerably more important driving factor than precipitation. (3) The annual ET of the TRHR increased by 3.34 mm/decade (p < 0.05) with an annual mean of 230.23 mm/year. More importantly, our analysis noted that ET was governed by terrestrial water supply, e.g., soil moisture and precipitation in the arid region of the western TRHR. By contrast, atmospheric evaporative demand derived by temperature and relative humidity was the primary controlling factor over the humid region of the southeastern TRHR. It was noted that land management activities, e.g., irrigation, also had a nonnegligible impact on the temporal and spatial variation of ET.

Assembly accuracy and accuracy stability prediction are significant research directions for improving the reliability and efficiency of precision assembly. In this study, an improved method for assembly accuracy stability prediction, based on the contact-pressure distribution entropy, is presented. By using the contact-pressure distribution as the evaluation parameter instead of the strain-energy distribution, the improved method can not only predict the assembly accuracy of precision assembly more efficiently, but also predict the stability of the assembly accuracy with variations in the ambient temperature. The contact pressure has a clearer mechanical significance than strain energy density in the assembly process, which can be used to distinguish the actual contact area from the contact surface. Hence, the improved method is more efficient and accurate than the original. This study utilizes the same case used in the original method and an additional case from the actual production process to verify the improved method. The correctness and validity of the improved method are proved by these case studies.