Vasorelaxant effect of a phenylethylamine analogue based on schwarzinicine A an alkaloid isolated from the leaves of Ficus schwarzii

N-Phenethyl-1-phenyl-pentan-3-amine (1) is a new compound synthesised as a simplified analogue of schwarzinicine A (2), a natural compound extracted from Ficus schwarzii. Compound 1 differs from compound 2 due to its structural simplification, featuring two phenyl rings without methoxy substitution, as opposed to compound 2, which possesses three 3,4-dimethoxy aromatic rings. Our previous research findings highlighted the calcium-inhibitory effects of compound 2, but the mechanism of action for compound 1 remains unexplored, serving as the primary focus of this study. Building upon our earlier research, this study aimed to elucidate compound 1's calcium-modulating potential by using rat-isolated aortae in an organ bath set-up and HEK cells expressing hTRPC channels with the fluorometric assay to measure calcium influx. Compound 1 elicited a vasorelaxation response (Emax 111.4%) similar to its parent compound 2 (Emax 123.1%), and inhibited hTRPC3-, hTRPC4-, hTRPC5-, and hTRPC6-mediated calcium influx into HEK cells with IC50 values of 6, 2, 2, 5 µM, respectively. Compound 1 has a similar pharmacological profile as its parent compound 2, whereby it exerts a vasorelaxant effect by attenuating calcium influx and inhibits multiple TRPC channels

A statistical approach for the assessment of the saturated hydraulic conductivity applied to an Austrian region

Study region: This study refers to the Hydrological Open Air Laboratory (HOAL) watershed, located in South-West Austria. Study focus: The spatial variability of saturated hydraulic conductivity, Ks, is sometimes synthetized by geometric mean, K̃s, and coefficient of variation, while areal-average infiltration models rely upon the arithmetic mean, K¯sl, associated to log-transformed Ks, and relative coefficient of variation, CVal. Robust estimation of K¯sl and CVal, as well as of K̃s and associated coefficient of variation, CVg,would require a large number of Ks observations. The determination of the minimum number of Ks measurements, n*, for obtaining sufficiently accurate values of each aforementioned quantity over an area is an open issue addressed here. A statistical approach has been applied to Ks datasets on three grassy plots for an uncertainty analysis based on the non-parametric bootstrap method with replacement. The uncertainty of each quantity has been derived for different observation numbers and areas. New hydrological insights for the region: Considering different sub-regions in the largest plot the uncertainty is almost invariant with increasing the sub-region area beyond a threshold. Furthermore, for a given n*, the uncertainty of K¯sl and CVal is much smaller than that of K̃s and CVg. Our approach defines a methodology for determining over an area the n* associated to a fixed uncertainty level in the joint estimation of the selected quantities. Guidelines for investigations over different plots are also proposed

Introduction to hydrology

Hydrology deals with the occurrence, movement, and storage of water in the earth system. Hydrologic science comprises understanding the underlying physical and stochastic processes involved and estimating the quantity and quality of water in the various phases and stores. The study of hydrology also includes quantifying the effects of such human interventions on the natural system at watershed, river basin, regional, country, continental, and global scales. The process of water circulating from precipitation in the atmosphere falling to the ground, traveling through a river basin (or through the entire earth system), and then evaporating back to the atmosphere is known as the hydrologic cycle. This introductory chapter includes seven subjects, namely, hydroclimatology, surface water hydrology, soil hydrology, glacier hydrology, watershed and river basin modeling, risk and uncertainty analysis, and data acquisition and information systems. The emphasis is on recent developments particularly on the role that atmospheric and climatic processes play in hydrology, the advances in hydrologic modeling of watersheds, the experiences in applying statistical concepts and laws for dealing with risk and uncertainty and the challenges encountered in dealing with nonstationarity, and the use of newer technology (particularly spaceborne sensors) for detecting and estimating the various components of the hydrologic cycle such as precipitation, soil moisture, and evapotranspiration

Prediction of rainfall runoff‐induced sediment load from bare land surfaces by generalized regression neural network and empirical model

Based on three rainfall run-off-induced sediment transport data for bare surface experimental plots, the generalized regression neural network (GRNN) and empirical models were developed to predict sediment load. Rainfall intensity, slope, rainfall duration, soil particle median diameter, clay content of the soil, rill density and soil particle mass density constituted the input variables of the models while sediment load was the target output. the GRNN model was trained and tested. the GRNN model was found successful in predicting sediment load. Sensitivity analysis by the GRNN model revealed that slope and rainfall duration were the most sensitive parameters. in addition to the GRNN model, two empirical models were proposed: (1) in the first empirical model, all the input variables were related to the sediment load, and (2) in the second empirical model, only rainfall intensity, slope and rainfall duration were related to the sediment load. the empirical models were calibrated and validated. At the calibration stage, the coefficients and the exponents of the empirical models were obtained using the genetic algorithm optimization method. the validated empirical models were also applied to two more experimental data sets: (1) one data set was from a field experiment, and (2) one set was from a laboratory experiment. the results indicated the success of the empirical models in predicting sediment load from bare land surfaces