Basiliximab Induction and Postoperative Steroid-free Immunosuppression With Tacrolimus in Pediatric Liver Transplantation: A Randomized Clinical Trial.

BACKGROUND: Optimizing the immunosuppressive regimen is essential to improve the long-term outcomes of pediatric liver transplant recipients. METHODS: We conducted a prospective, randomized, open-label study to compare the safety and efficacy of 2 treatment approaches during pediatric liver transplantation: tacrolimus monotherapy following basiliximab induction (the study group) and a dual regimen of tacrolimus plus steroids (the control group). A total of 150 patients were enrolled, with 75 patients allocated to each group. RESULTS: In both groups, recipients achieved graft and recipient overall survival rates exceeding 93%, with no statistically significant differences between them. However, the study group exhibited a significantly lower incidence of acute cellular rejection (ACR), delayed occurrence of ACR, and an improved ACR-free survival rate at 2 y compared with the control group. Notably, the study group also showed a significant reduction in the incidence of de novo donor-specific antibodies at 3-mo and 2-y posttransplant. Furthermore, 6 mo after the transplant, the study group demonstrated significant improvements in weight-for-age Z score and height-for-age Z score. No notable differences were observed in postoperative complications or the incidence of liver fibrosis between the 2 groups. CONCLUSIONS: Basiliximab induction combine with tacrolimus (TAC) monotherapy is a safe and effective immunosuppressive regimen to reduce the episodes of ACR without influencing the development of liver fibrosis and graft and recipient survival rate after pediatric liver transplantation.

Multi-scale spatial analysis of satellite-retrieved surface evapotranspiration in Beijing, a rapidly urbanizing region under continental monsoon climate.

As one of the important components of hydrological cycle, evapotranspiration spatial distribution is of great significance to regional water resources planning and rational utilization. This research used Surface Energy Balance System model to estimate the daily evapotranspiration (ET) in Beijing based on Landsat 8 observations. Results showed that the daily ET in Beijing ranged from 3.469 to 5.474 mm/day. ET is known to decrease with the increase of land surface temperature (LST) and to increase with the increase of Normalized Difference Vegetation Index (NDVI). NDVI primarily decreased from the northwest to the southeast. When the NDVI value was 0.4-0.6, the average ET peaked at 4.88 mm/day, and then slightly decreased by 3.7%. The coefficient of determination of NDVI (0.95) was much greater than that of LST (0.30) upon linear fitting, showing LST was not the main factor controlling ET in Beijing. In contrast to the linear fitting results, the spatial correlation between LST and ET is more significant than that between NDVI and ET in the global bivariate spatial analysis, where the absolute value of global bivariate Moran's I of LST (0.51) was higher than that of NDVI (0.21) at a resolution of 150 m. And the univariate spatial autocorrelation indices of LST, ET, and NDVI equaled 0.84, 0.65, and 0.51, respectively. Furthermore, the complex spatial distribution pattern of variables could significantly affect the correlation analysis results. Local bivariate spatial analysis showed that over 60% of the Beijing area had a significant correlation, of which the negative correlation area of LST accounted for about 85%, and the positive correlation area of NDVI accounted for 74%. By improving the correlation analysis accuracy, the regional conditions for the establishment of correlation analysis results were clarified from the overall correlation analysis results.

Impact of particle sizes on health risks and source-specific health risks for heavy metals in road dust.

To analyze the impact of particle sizes on sources and related health risks for heavy metals, road dust samples in Beijing were collected and sifted into five particle sizes. The positive matrix factorization (PMF), human health risk assessment model (HHRA), and Monte Carlo simulation were used in the health risk assessment and source apportionment. Results showed that mass of particles < 74 µm occupied about 50% of the total particles, while only 8.48% of the particles were > 500 µm. Mass distribution and concentrations of heavy metals in each particle size changed in temporal. Over 85.00% of carcinogenic risks (CR) were from particles <74 µm, whereas CR from particles >250 µm were ignorable. Sources for health risks in each particle size were traffic exhaust, fuel combustion, construction, and use of pesticides and fertilizers. Proportions of sources to CR differed among particle sizes. Traffic exhaust and fuel combustion contributed over 90% to CR in particles <74 µm, whereas construction contributed the highest (31.68-54.14%) among all sources in particles 74-250 µm. Furthermore, the difference between health risks based on sifted road dust and that based on unsifted road dust was quantitatively analyzed. Source-specific health risk apportionment based on unsifted road dust was not presentative to all particle sizes, and true value of health risks could be over 2.5 times of the estimated value based on unsifted road dust, emphasized the importance of sifting of road dust.

Multi-scenario simulation of ecological risk assessment based on ecosystem service values in the Beijing-Tianjin-Hebei region.

In this study, a framework for ecological risk assessment based on ecosystem service values and risk probability was established. Remote sensing was used to estimate the value of ecosystem services at the regional scale. Considering the natural and anthropogenic factors and using the entropy weight method to assign weights, probability index was constructed. In addition, multiple scenarios based on the ordered weighted averaging (OWA) method were simulated to reduce subjective uncertainty in the assessment. The results showed that the ecosystem service values generated by the gas regulation value accounted for the largest proportion, with a ratio of 46% in the Beijing-Tianjin-Hebei region. From 2005 to 2015, the value of ecosystem services decreased, falling by 2.5 × 107 Yuan. The level of ecological risk was relatively high, with a corresponding area ratio of 32.89%. Spatially, the areas with high risk were concentrated in the southeastern areas, and areas with relatively low risk were distributed in the western and northern areas. This high risk was probably caused by urbanization which was characterized by reduction of farmland and increase in impervious surface. Multi-scenario simulation showed that the areas of unstable ecological risk zones covered 30% and were mainly concentrated in the surroundings of developing cities. In areas of unstable risk distribution, the relationship between development and protection should be considered. This framework increases the reliability and practicability of ecological risk assessment results and has potential application value for regional risk control in the context of urbanization.

Significance of using dynamic land-use data and its threshold in hydrology and water quality simulation models.

Land-use changes have a significant impact on the hydrological cycle and non-point source (NPS) pollution discharge and transport. Thus, using dynamic land-use inputs in the simulation models is important. However, there is currently no clear standard for which situation the land-use data should be updated in the models. In this study, we quantified the impacts of land-use change on hydrological and NPS pollution simulation outputs, and analyzed the thresholds for land-use change level and time nodes. The results indicated that the error caused by land-use change had a linear relationship with the land-use change level. The total nitrogen (TN) output error was the most sensitive to land-use change, with a gradient of 0.73. The impact of land-use change on the model outputs was different at different temporal scales. Flow and TN had the highest output errors at a daily scale, while sediment had the highest output error at an annual scale. The threshold analysis results revealed that the land-use change thresholds for the flow, sediment, and TN simulations were 40%, 30%, and 10%, respectively. When the land-use change level exceeded the threshold, the model simulation error increased dramatically. The land-use change time node would also affect the simulation performance, especially for TN. This study initially explored the quantified standard for land-use data updates in the SWAT model. The results could be useful for improving the simulation accuracy of the SWAT model and may provide ideas for follow-up studies.

Source-specific risk apportionment and critical risk source identification of antibiotic resistance in Fenhe River basin, China.

A comprehensive understanding of the sources and distribution of antibiotic resistance risk is essential for controlling antibiotic pollution and resistance. Based on surface water samples collected from the Fenhe River basin in the flood season, using the positive matrix factorization (PMF) model, the risk quotient (RQ) method and the multiple attribute decision making (MADM) method, the resistance risk and source-specific resistance risk of antibiotics were analyzed in this study. The results showed that sulfonamides (SAs) were the dominant antibiotics with a mean concentration of 118.30 ng/L, whereas tetracyclines (TCs) and macrolides (MLs) had the highest detection frequencies (100%). The significant resistance risk rate of antibiotics in the entire river basin was 48%, but no high risk occurred. The significant resistance risk rate of quinolones (QNs) was the highest (100%), followed by that of MLs and TCs. Owing to human activities, the most serious resistance risk occurred in the midstream of the river basin. The resistance risk was the lowest upstream. The antibiotics were mainly contributed by six sources. Pharmaceutical wastewater was the main source, accounting for 30%, followed by livestock discharge (22%). The resistance risk from the six sources showed clear differences, but none of the sources caused a high risk of antibiotic resistance. Pharmaceutical wastewater poses the greatest risk of antibiotic resistance in the Fenhe River basin and is widely distributed. The second greatest source was livestock discharge, which was mainly concentrated in the upstream and midstream areas. The critical sources upstream, midstream, and downstream were all pharmaceutical wastewater, whereas the sequences of other sources were different because different areas were affected by different human activities. The proposed method might provide an important reference for the identification the key source of antibiotics and management of antibiotic pollution, as well as help for the management of antibiotics in Fenhe and Shanxi Province.

A four-way model (FEST) for source apportionment: Development, verification, and application.

In studying the spatial, temporal, and particle size variations heavy metal sources, a source apportionment model for a four-way array of data is required. In this study, referencing two-way and three-way models, a four-way (particle fractions, elements, sites, and time) source apportionment model (FEST) was developed. Errors in the three-way models solving four-way problems verified the necessity of developing the FEST model. The results showed that the FEST model had a higher accuracy than the existing models, which was probably because of more constraints and input data in the FEST model. Based on the sampled data in Beijing, sources were apportioned for the four-way array of data using the FEST model, and the spatial, temporal, and particle size variations of sources were evaluated. The main sources of heavy metals were similar to those in our prior studies, whereas the contributions of sources to specific heavy metals differed. Traffic exhaust and fuel combustion contributed more to fine particles than coarse particles, indicating that the two should be controlled preferentially among all sources. The management of traffic exhaust should be focused on the central and northern areas in each season, and the control of fuel combustion should be strengthened in the southern area in winter.

A Declining Trend in China's Future Cropland-N2O Emissions Due to Reduced Cropland Area.

Croplands are the largest anthropogenic source of nitrous oxide (N2O), a powerful greenhouse gas that contributes to the growing atmospheric N2O burden. However, few studies provide a comprehensive depiction of future cropland-N2O emissions on a national scale due to a lack of accurate cropland prediction data. Herein, we present a newly developed distributed land-use change prediction model for the high-precision prediction of national-scale land-use change. The high-precision land-use data provide an opportunity to elucidate how the changes in cropland area will affect the magnitude and spatial distribution of N2O emissions from China's croplands during 2020-2070. The results showed a declining trend in China's total cropland-N2O emissions from 0.44 ± 0.03 Tg N/year in 2020 to 0.39 ± 0.07 Tg N/year in 2070, consistent with a cropland area reduction from (1.78 ± 0.02) × 108 ha to (1.40 ± 0.15) × 108 ha. However, approximately 31% of all calculated cities in China would emit more than the present level. Furthermore, different land use and climate change scenarios would have important impacts on cropland-N2O emissions. The Grain for Green Plan implemented in China would effectively control emissions by approximately 12%.

Study of uncertainty of satellite and reanalysis precipitation products and their impact on hydrological simulation.

Satellite and reanalysis precipitation products are potential alternatives in hydrological studies, and it is very important to evaluate their accuracy and potential use for reliable simulations. In this study, three precipitation products (Tropical Rainfall Measuring Mission 3B43 Version 7 (TRMM 3B43), spatial interpolation grid data based on 2472 national meteorological observation stations in China (GRID_0.5), and National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR)) were evaluated against gauge observations in the Xiangxi River watershed of Hubei Province. The performance results indicated that the results of the three precipitation products were correlated with those of the rain gauges; however, there were differences among the three products. TRMM 3B43 tended to overestimate precipitation with the highest correlation coefficient, while NCEP-CFSR tended to underestimate precipitation with the least satisfactory performance, and the performance of GRID_0.5 ranked between them. However, the annual and monthly mean errors differed, as the errors of most of the results driven by NCEP-CFSR were lowest. The errors varied at different time scales. During years with high precipitation, the results were often underestimated, while the results are often overestimated during years with low precipitation. According to the average monthly results, the GRID_0.5 results were closest to the gauge observations for most months. During the wet season, TRMM 3B43 performed better, while NCEP-CFSR precipitation performed better during the dry season. The errors from precipitation to streamflow, NPS pollution, and water environmental capacity (WEC) driven by the three precipitation products increased gradually, ranging from 10% for precipitation to over 20% for NPS pollution and almost 100% for WEC. The error increase for NCEP-CFSR was lower than that of the other two products. Although the simulation error from precipitation to the WEC results driven by the three precipitation products gradually increased, the degree of overestimation and underestimation became smaller.

Multiscale spatiotemporal characteristics of landscape patterns, hotspots, and influencing factors for soil erosion.

Soil erosion is an increasingly serious eco-environmental problem, and effective control of soil erosion is an important part of soil resource protection and ecological restoration. In this study, the multi-scale characteristics and influencing factors of soil erosion were analyzed in the Beijing-Tianjin-Hebei (BTH) region from 2000 to 2015. The results showed that the average soil erosion in the study area was 3500 t/(km2·a), in which the severe erosion areas accounted for 10% of the total area. Although the total soil erosion rate decreased by 60% from 2000 to 2015, the rate of current soil erosion was higher than the soil loss tolerance. The severe erosion area had the highest aggregation index, making it the most suitable for centralized treatment. Meanwhile, the fractal dimension index of severe erosion showed a downward trend from 2000 to 2015. This decrease in complexity led to a more optimistic conservation situation. The hotspot areas overlapped with the relatively high erosion zones and were aggregated as three large patches in the northern, southwestern, and southern BTH regions. Soil erosion distribution depends on both anthropogenic activities and natural conditions. The slope factor, which reflects the impact of natural factors on soil erosion, was the most dominant factor on soil erosion from 2000 to 2010. Conversely, the land use factor, which is mainly controlled by humans, became the dominant factor in 2015. The distribution characteristics and influencing factors of soil erosion both had scale effects. As the scale decreased from city to town, the patches of high and severe erosion classes became more regular and aggregated, the hotspot area had the most concentrated and severe soil erosion rate at the town scale, and human impacts became dominant. Conservation targeting hotspot areas measured at the town scale, which was 20% of the total area, could reduce the total soil loss by 38%. For a region with a complex structure, the main influencing factors showed strong spatial dependence.

Spatial-temporal characteristics, source-specific variation and uncertainty analysis of health risks associated with heavy metals in road dust in Beijing, China.

Based on the concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in 144 road dust samples collected from 36 sites across 4 seasons from 2016 to 2017 in Beijing, this study systematically analyzed the levels and main sources of health risks in terms of their temporal and spatial variations. A combination of receptor models (positive matrix factorization and multilinear engine-2), human health risk assessment models, and Monte Carlo simulations were used to apportion the seasonal variation of the health risks associated with these heavy metals. While non-carcinogenic risks were generally acceptable, Cr and Ni induced cautionary carcinogenic risks (CR) to children (confidence levels was approximately 80% and 95%, respectively).. Additionally, fuel combustion posed cautionary CR to children in all seasons, while the level of CR from other sources varied, depending on the seasons. Heavy metal concentrations were the most influential variables for uncertainties, followed by ingestion rate and skin adherence factor. The values and spatial patterns of health risks were influenced by the spatial pattern of risks from each source.

Temporal variations of levels and sources of health risk associated with heavy metals in road dust in Beijing from May 2016 to April 2018.

To analyze the temporal variations of heavy metals, health risk, and source-specific health risk, 24 road dust samples were collected from Beijing in each month in two years. The temporal variations of Hg, Pb, and Ni were higher than other heavy metals. Most heavy metals reached their highest concentrations either in winter or in spring, then the concentrations decreased and reached the lowest values in autumn. Human health risk assessment (HHRA) model showed that As, Cr, and Ni might pose cautionary carcinogenic risk (CR) to children (CR > 10-6). CR for adults were only 0.15 to 0.19 times of that for children. Four sources were identified based on positive matrix factorization model and HHRA model, they were traffic exhaust, fuel combustion, construction, and use of pesticides and fertilizers. Influenced by the difference of carcinogenicity of heavy metals, traffic exhaust contributed the largest to heavy metals (36.02%, over 42.24% higher than other sources), while contributions of fuel combustion to CR (36.95%) was similar to traffic exhaust (37.17%). Monte-Carlo simulation showed that the 95th percentile of probability density functions of CR posed by Cr and Ni from each source were 9.90 × 10-5 to 2.64 × 10-4, posing cautionary carcinogenic risk to children. The seasonal change of CR varied among different sources. CR from use of pesticides and fertilizers in spring was 35.06 times of that in winter, and that from fuel combustion in winter was 1.15-2.40 times of that in other seasons. CR from each source was sensitive to ingestion rate and skin adherence factor.

Identification and uncertainty analysis of high-risk areas of heavy metals in sediments of the Yangtze River estuary, China.

In this study, ordinary kriging (OK) and indicator kriging (IK) were used to analyze the uncertainty associated with high-risk areas of seven heavy metals (As, Cd, Cr, Cu, Hg, Pb, and Zn) in sediments of the Yangtze River estuary during four seasons. The OK results showed that the high-risk areas of Cd, Cr, Cu, Hg, and Pb had a high proportion, with the highest corresponding to Cr pollution (up to 60%). Predictions based on IK revealed that the proportion of high-risk areas of Cr, Cd, and Hg pollution were high, especially that of Cr was higher than 90%. However, there were uncertainties between the OK and IK results. The uncertainty results revealed that the uncertainty areas of Cr pollution were relatively large, accounting for about 30%, while those of Cd, Cu, and Hg pollution were lower than 10%.