An in vivo model of antiangiogenic therapy allowed us to identify genes upregulated by bevacizumab treatment, including Fatty Acid Binding Protein 3 (FABP3) and FABP7, both of which are involved in ...fatty acid uptake. In vitro, both were induced by hypoxia in a hypoxia-inducible factor-1α (HIF-1α)-dependent manner. There was a significant lipid droplet (LD) accumulation in hypoxia that was time and O2 concentration dependent. Knockdown of endogenous expression of FABP3, FABP7, or Adipophilin (an essential LD structural component) significantly impaired LD formation under hypoxia. We showed that LD accumulation is due to FABP3/7-dependent fatty acid uptake while de novo fatty acid synthesis is repressed in hypoxia. We also showed that ATP production occurs via β-oxidation or glycogen degradation in a cell-type-dependent manner in hypoxia-reoxygenation. Finally, inhibition of lipid storage reduced protection against reactive oxygen species toxicity, decreased the survival of cells subjected to hypoxia-reoxygenation in vitro, and strongly impaired tumorigenesis in vivo.
The objective classification of sediment source groups is at present an under-investigated aspect of source tracing studies, which has the potential to statistically improve discrimination between ...sediment sources and reduce uncertainty. This paper investigates this potential using three different source group classification schemes.
The first classification scheme was simple surface and subsurface groupings (Scheme 1). The tracer signatures were then used in a two-step cluster analysis to identify the sediment source groupings naturally defined by the tracer signatures (Scheme 2). The cluster source groups were then modified by splitting each one into a surface and subsurface component to suit catchment management goals (Scheme 3). The schemes were tested using artificial mixtures of sediment source samples. Controlled corruptions were made to some of the mixtures to mimic the potential causes of tracer non-conservatism present when using tracers in natural fluvial environments. It was determined how accurately the known proportions of sediment sources in the mixtures were identified after unmixing modelling using the three classification schemes.
The cluster analysis derived source groups (2) significantly increased tracer variability ratios (inter-/intra-source group variability) (up to 2122%, median 194%) compared to the surface and subsurface groupings (1). As a result, the composition of the artificial mixtures was identified an average of 9.8% more accurately on the 0–100% contribution scale. It was found that the cluster groups could be reclassified into a surface and subsurface component (3) with no significant increase in composite uncertainty (a 0.1% increase over Scheme 2). The far smaller effects of simulated tracer non-conservatism for the cluster analysis based schemes (2 and 3) was primarily attributed to the increased inter-group variability producing a far larger sediment source signal that the non-conservatism noise (1). Modified cluster analysis based classification methods have the potential to reduce composite uncertainty significantly in future source tracing studies.
•Robust discrimination between sediment sources is essential for fingerprinting.•Source groups were classified according to management goals and tracer signatures.•Objective classification reduced intra- and increased inter-group variability.•Objective classification significantly reduced uncertainty in unmixing model outputs.•The impacts of tracer non-conservatism were reduced with objective classification.
Atmospheric dust has many negative impacts within different ecosystems and it is therefore beneficial to assemble reliable evidence on the key sources of the dust problem. In this study, for first ...time, two different source modelling approaches comprising generalized likelihood uncertainty estimation (GLUE) and Monte Carlo simulation were applied to map spatial source contributions to atmospheric dust samples collected in Ahvaz, Khuzestan province, Iran. A total of 264 surficial soil samples were collected from five potential spatial dust sources. Additionally, nine dust samples were collected in February 2015. The performance of both GLUE and Monte Carlo simulation for quantifying uncertainty associated with the source contributions predicted using an un-mixing model were assessed and compared using mean absolute fit (MAF) and goodness-of-fit (GOF) estimators as well as 14 virtual sediment mixtures (VSM). Finally, the erodible fraction (EF) of topsoils and HYSPLIT model were used as further tests for validating the results of the GLUE and Monte Caro simulation. Based on both uncertainty modelling approaches, the loamy sand soil texture was recognized as the main spatial source of the target dust samples. Silty clay soils were estimated to be the least important spatial source of the target dust samples using the two modelling approaches. Both GLUE and Monte Carlo simulation returned MAF and GOF estimates >80%, with Monte Carlo performing slightly better. Based on the virtual mixture tests, the RMSE and MAE of the Monte Carlo simulation (<13.5% and <11%, respectively) was better than for GLUE (<20% and <16.3%, respectively). Spatial source maps generated using both GLUE and Monte Carlo simulation were consistent with the EF map generated using multiple regression (MR) and with routes dust transportation detected by HYSPLIT. Therefore, we recommend that future research into to the sources of atmospheric dust pollution integrates modelling approaches, VSM, EF and HYSPLIT model to quantify and map dust provenance reliably.
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•First attempt to map uncertainty associated with dust source contributions•GLUE and Monte Carlo simulation applied to quantify source contributions to dust•Both models validated using virtual sediment mixtures and an erodible fraction map•Monte Carlo performs slightly better than GLUE.•Integration of modelling, mixture tests and erodible fraction calculation recommended
•Fingerprinting was used to assess the contribution of recreational roads to sediment yield.•Three statistical approaches were used to select composite fingerprints.•The Modified MixSIR Bayesian ...model was used for source apportionment.•The source apportionment estimates were sensitive to the tracers.•Recreational roads were the dominant source of suspended sediment.
Road construction associated with land development generally increases erosion and sediment yields. Construction of unpaved roads has the potential to alter hydro-sedimentological behavior and catchment sediment source dynamics and, to date, this has largely been investigated in forested environments. The objective of this study, therefore, was to assess the relative importance of unpaved recreational roads as a sediment source alongside hillslope surface soils and stream channel banks in a non-forested mountainous catchment in northern Tehran, Iran, using a fingerprinting procedure. Eleven geochemical tracers were measured on 27 samples collected to characterise the sediment sources and five suspended sediment samples collected at the study catchment outlet. The statistical analysis employed to select three different composite fingerprints for discriminating the sediment sources comprised: (1) the Kruskal–Wallis H test (KW-H), (2) a combination of KW-H and discriminant function analysis (DFA), and (3) a combination of KW-H and principal components & classification analysis (PCCA). A Bayesian un-mixing model was used to ascribe sediment source contributions using the three composite fingerprints. Using the KW-H composite signature, the respective relative contributions (with uncertainty ranges) from recreational roads, hillslope surface soils and channel banks were estimated as 64.5% (57.7–73.1), 1.1% (0.1–4.9), and 33.9% (24.9–41.0), compared to 55.3% (45.5–68.5), 1.9% (0.1–7.9) and 42.1% (27.8–52.4) using a composite signature selected using a combination of KW-H and DFA, or 82.0% (69.7–93.8), 8.2% (0.7–22.7) and 7.3% (0.7–21.0) using a fingerprint selected using KW-H and PCCA. The root mean square difference between the apportionment results using the fingerprints identified on the basis of the three different statistical approaches ranged from 5.5% to 25.7%, highlighting the sensitivity of source estimates to the tracers used. Regardless, the different composite signatures all suggested that unpaved recreational roads were the dominant source of the suspended sediment samples, underscoring the need for mitigation measures targeting these anthropogenic features of the catchment system, including closure to permit re-vegetation, surface ripping and/or mulching to improve infiltration or gravel re-surfacing to reduce exposure of bare surfaces to sediment mobilisation.
Land susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex ...penalized quantile regression (NCPQR), Neural network with feature extraction (NNFE), Monotone multi-layer perception neural network (MMLPNN), Ridge regression (RR), Boosting generalized linear model (BGLM), Negative binomial generalized linear model (NBGLM), Boosting generalized additive model (BGAM), Spline generalized additive model (SGAM), Spike and slab regression (SSR), Stochastic gradient boosting (SGB), support vector machine (SVM), Relevance vector machine (RVM) and the Cubist and Adaptive network-based fuzzy inference system (ANFIS). Thirteen factors controlling wind erosion were mapped, and multicollinearity among these factors was quantified using the tolerance coefficient (TC) and variance inflation factor (VIF). Model performance was assessed by RMSE, MAE, MBE, and a Taylor diagram using both training and validation datasets. The result showed that five models (MMLPNN, SGAM, Cforest, BGAM and SGB) are capable of delivering a high prediction accuracy for land susceptibility to wind erosion hazard. DEM, precipitation, and vegetation (NDVI) are the most critical factors controlling wind erosion in the study area. Overall, regression-based machine learning models are efficient techniques for mapping land susceptibility to wind erosion hazards.
Since the launch of the Three Gorges Dam on the Yangtze River, a distinctive reservoir fluctuation zone has been created and significantly modified by regular dam operations. Sediment redistribution ...within this artificial landscape differs substantially from that in natural fluvial riparian zones, due to a specific hydrological regime comprising steps of water impoundment with increasing magnitudes and seasonal water level fluctuation holding a range of sediment fluxes. This study reinterpreted post-dam sedimentary dynamics in the reservoir fluctuation zone by stratigraphy determination of a 345-cm long sediment core, and related it to impact of the hydrological regime. Seasonality in absolute grain-size composition of suspended sediment was applied as a methodological basis for stratigraphic differentiation. Sedimentary laminations with relatively higher proportions of sandy fractions were ascribed to sedimentation during the dry season when proximal subsurface bank erosion dominates source contributions, while stratigraphy with a lower proportion of sandy fractions is possibly contributed by sedimentation during the wet season when distal upstream surface erosion prevails. Chronology determination revealed non-linear and high annual sedimentation rates ranging from 21.7 to 152.1cm/yr. Although channel geomorphology may primarily determine the spatial extent of sedimentation, seasonal sedimentary dynamics was predominantly governed by the frequency, magnitude, and duration of flooding. Summer inundation by natural floods with enhanced sediment loads produced from upstream basins induced higher sedimentation rates than water impoundment during the dry season when distal sediment supply was limited. We thus conclude that flow regulation manipulates contemporary seasonal sedimentary dynamics in the reservoir fluctuation zone, though little impact on total sediment retention rate was detected. Ongoing reductions in flow and sediment supply under human disturbance may have profound implications in affecting sedimentary equilibrium in the reservoir fluctuation zone. The results herein provide insights of how big dams have disrupted the sediment conveyance processes of large scale fluvial systems.
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•Hydrological regime in the reservoir fluctuation zone has been significantly altered.•Grain-size variations are diagnostic for sedimentary stratigraphy differentiation.•Post-dam sedimentation processes were reproduced by chronology determination.•Regular flow regulation controls contemporary seasonal sedimentary dynamics.
Pd–Ga bimetallic nanoparticles are formed under CO2 hydrogenation to methanol (523K, 3MPa) and under pure hydrogen reduction (523K, 0.1MPa) on a Pd (1wt.%)/Ga2O3 catalyst. The main roles of the ...bimetallic particles in the reaction mechanism are to hydrogenate the carbonaceous species adsorbed on the Ga2O3 support and to inhibit CO production. Display omitted
► Pd–Ga bimetallic nanoparticles are formed upon hydrogen reduction. ► Pd–Ga bimetallic nanoparticles enhance the selectivity to methanol. ► In situ infrared experiments show that Pd–Ga/Ga2O3 behaves as truly bifunctional. ► Encapsulation of Pd by Ga2O3 is observed by electron microscopy after air exposure.
The effect of palladium–gallia interaction in Pd(1wt.%)/β-Ga2O3 during selective methanol synthesis by CO2 hydrogenation was studied. A detailed quasi-in situ transmission electron microscopy analysis of the as-prepared H2-reduced catalyst, without exposing it to air, showed that Ga–Pd bimetallic (nano)particles were formed under a reductive atmosphere at or above 523K. However, these particles were unstable; upon air exposure, a dramatic and extensive encapsulation of the metallic crystallites by Ga2O3 occurred. In addition, the function of the bimetallic particles in the mechanism of methanol synthesis was investigated by in situ infrared spectroscopy at 0.7MPa. The results confirmed those of previous studies in which the stepwise hydrogenation of (bi)carbonate to formate and then to methoxy groups on the Ga2O3 surface took place via a bifunctional pathway. In this pathway, the role of the Ga–Pd bimetallic crystallites was to provide atomic hydrogen, via spillover, to the oxidic surface and to hamper both CH3OH decomposition and CO production.
This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), ...stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.
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•Assessment of 10 widely used machine learning algorithms (MLA) for GESM in Iran.•Comparison of MLA using a training dataset for gully erosion and statistical measures such as RMSE, MAE, and R-squared.•The RF algorithm was selected as the best model.
Fertilizer applications can enhance soil fertility, pasture growth and thereby increase production. Nitrogen fertilizer has, however, been identified as a significant source of nitrous oxide (N2O) ...emissions from agriculture if not used correctly and can thereby increase the environmental damage costs associated with agricultural production. The optimum use of organic fertilizers requires an improved understanding of nutrient cycles and their controls. Against this context, the objective of this research was to evaluate the scope for reducing N2O emissions from grassland using a number of manure management practices including more frequent applications of smaller doses and different methods of application. We used a modified UK-DNDC model and N2O emissions from grasslands at Pwllpeiran (PW), UK during the calibration period in autumn, were 1.35 kg N/ha/y (cattle slurry) and 0.95 kg N/ha/y (farmyard manure), and 2.31 kg N/ha/y (cattle slurry) and 1.08 kg N/ha/y (farmyard manure) during validation period in spring, compared to 1.43 kg N/ha/y (cattle slurry) and 0.29 kg N/ha/y (farmyard manure) during spring at North Wyke (NW), UK. The modelling results suggested that the time period between fertilizing and sampling (TPFA), rainfall and the daily average air temperature are key factors for N2O emissions. Also, the emission factor (EF) varies spatio-temporally (0–2%) compared to uniform 1% EF assumption of IPCC. Predicted N2O emissions were positively and linearly (R2 ≈ 1) related with N loadings under all scenarios. During the scenario analysis, the use of high frequency, low dose fertilizer applications compared to a single one off application was predicted to reduce N2O peak fluxes and overall emissions for cattle slurry during the autumn and spring seasons at the PW and NW experimental sites by 17% and 15%, respectively. These results demonstrated that an optimized application regime using outputs from the modelling approach is a promising tool for supporting environmentally-friendly precision agriculture.
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•UK-DNDC model used to simulated N2O fluxes for two organic fertilizers.•Time periods between fertilizing and sampling are key factors for N2O emissions.•Optimized organic fertilizer applications can reduce peak N2O fluxes.•Predicted N2O emissions are positively and linearly related to N loadings.•Emission factor depends on soil, vegetation, climate, and fertilizer properties.
Understanding catchment hydrological response to intensive land use/cover change (LUCC) and climate change provides a basis for taking effective measures for the future. Runoff is a critical ...indicator of catchment hydrological processes that reflects the combined effects of climate changes and local human activities. In this study, three main tributary sub-catchments underlain by soft sandstone in the Yellow River basin, China, were chosen to attribute runoff variations to climatic change and human activities through improving the Budyko elasticity model. The results suggested that: (1) annual runoff exhibited a significant decreasing trend during the past 30 years (1981–2016, p < 0.01),with an average decline rate of 1.07 mm a−1; (2) the precipitation elasticity of runoff (εP) and that of potential evapotranspiration (εEo) varied from 2.42 to 2.96 and from −1.96 to −1.42, respectively, indicating that runoff is more sensitive to changes in P than those in Eo in the context of climate change; (3) the attribution analysis demonstrated that, on average, vegetation change (mainly anthropogenic vegetation coverage increase) accounted for 92% of the decline in runoff whereas climate change (including precipitation and potential evapotranspiration variations and consequent vegetation change) accounted for the rest 8%.
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•Climatic and anthropogenic impacts on vegetation changes are distinguished•Annual runoff displayed a significant downward trend during 1981–2016•The annual mean NDVI presented an obvious upward trend•Runoff decline is mainly caused by anthropogenic LUCC
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