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Tillage practices influence physical, chemical, and biological soil properties, which also affect soil quality and consequently plant growth. In this study, the main objective was to evaluate the effect of different tillage systems on soil physical properties by using geophysical methods, namely, ground-penetrating radar (far-field and near-field GPR), capacitance probes (ThetaProbe and 5TE), electromagnetic induction (EMI) (Profiler and EM38), soil sampling, and by soil penetrometer. Since 2005, three contrasting tillage systems were applied on different plots of an agricultural field: i) conventional tillage (CT) with mouldboard ploughing to 27 cm depth, ii) deep loosening tillage (DL) with a heavy tine cultivator to 30 cm depth, and iii) reduced tillage (RT) with a spring tine cultivator to 10 cm depth. The geophysical and soil strength measurements were performed in April 2010. We observed that tillage influences the soil resistance (deeper tillage decreases soil resistance), which could be partly seen in the radar data. Soil water content reference measurements (capacitance probes and soil sampling) were in a relatively good agreement with the water content estimates from far-field GPR. We also observed that the tillage influences surface water content. Mean surface water content was significantly lower for CT than for DL and RT, which was partly explained by lower macropore connectivity between the topsoil and the deeper layers after conventional tillage. This study confirms the potential of GPR and EMI sensors for soil physical properties determination at the field scale and for the characterization of agricultural management practices.

Soil hydraulic properties are of main interest for estimating water and energy fluxes at the land surface. In general, estimation of the hydraulic properties relies on the measurement of the soil water status within the soil profile usually performed with in-situ sensors such as time-domain reflectometry (TDR) or capacitance probes. Unfortunately, these sensors do not account for the high spatial and temporal variability in the soil, especially over large areas. In the opposite, proximal sensors such as ground-based microwave radiometer and radar can be used to obtained information at a larger scale. However, these sensors solely provide shallow subsurface information on the moisture status, and the retrievals might be biased by confounding factors such as soil surface roughness. L-band radiometer and off-ground ground-penetrating radar (GPR) measurements were performed above a rectangular wooden box (2.00 x 2.00 m2 area and 1.00 m height) homogeneously filled with sand. The radiometer was fixed on an arc of 5 m radius with an observation angle of 35° while the GPR was fixed above the sand box at nadir position. In order to avoid the brightness temperatures measured being affected by radiance originating from areas outside the sand box, the surrounding of the sand box was covered with a reflector made up of a mesh grid with a mash-size much smaller than the wavelength of the radiometer. Ground-truth soil moisture data were obtained by using TDR and capacitance sensors installed at 7 depths ranging from 5 to 80 cm. Soil temperature and soil conductivity data were also collected at each depth. The objective of this study is to compare estimated soil hydraulic properties of the sand box from radiometer, radar, and in-situ measurements and analyze the uncertainty related to these estimations.

A joint analysis of full-waveform information content in ground penetrating radar (GPR) and electromagnetic induction (EMI) synthetic data was investigated to reconstruct the electrical properties of multilayered media. The GPR and EMI systems operate in zero-offset, off-ground mode and are designed using vector network analyser technology. The inverse problem is formulated in the least-squares sense. We compared four approaches for GPR and EMI data fusion. The two first techniques consisted of defining a single objective function, applying different weighting methods. As a first approach, we weighted the EMI and GPR data using the inverse of the data variance. The ideal point method was also employed as a second weighting scenario. The third approach is the naive Bayesian method and the fourth technique corresponds to GPR–EMI and EMI–GPR sequential inversions. Synthetic GPR and EMI data were generated for the particular case of a two-layered medium. Analysis of the objective function response surfaces from the two first approaches demonstrated the benefit of combining the two sources of information. However, due to the variations of the GPR and EMI model sensitivities with respect to the medium electrical properties, the formulation of an optimal objective function based on the weighting methods is not straightforward. While the Bayesian method relies on assumptions with respect to the statistical distribution of the parameters, it may constitute a relevant alternative for GPR and EMI data fusion. Sequential inversions of different configurations for a two layered medium show that in the case of high conductivity or permittivity for the first layer, the inversion scheme can not fully retrieve the soil hydrogeophysical parameters. But in the case of low permittivity and conductivity for the first layer, GPR–EMI inversion provides proper estimation of values compared to the EMI–GPR inversion.

We used integrated hydrogeophysical inversion of time-lapse, proximal ground penetrating radar (GPR) data to remotely infer the unsaturated soil hydraulic properties of a laboratory sand during an infiltration event. The inversion procedure involved full-waveform modeling of the radar signal and one-dimensional, vertical flow modeling. We combined the radar model with HYDRUS-1D. The radar system was set up using standard, handheld vector network analyzer technology. Significant effects of water dynamics were observed on the time-lapse radar data. The estimated hydraulic parameters were relatively consistent with direct characterization of undisturbed sand samples. Significant differences were particularly observed for the saturated hydraulic conductivity, which was underestimated by two orders of magnitude. Nevertheless, the use of soil hydraulic parameters derived from reference measurements failed to correctly predict water dynamics, whereas GPR-based predictions captured some of the major features of time domain reflectometry measurements and better agreed with visual observations. These results suggest that the proposed method is promising for noninvasive, effective hydraulic characterization of the shallow subsurface and hence, monitoring of water dynamics at the field scale. © Soil Science Society of America.

Protein kinases represent promising anticancer drug targets. We describe here the meriolins, a new family of inhibitors of cyclin-dependent kinases (CDK). Meriolins represent a chemical structural hybrid between meridianins and variolins, two families of kinase inhibitors extracted from various marine invertebrates. Variolin B is currently in preclinical evaluation as an antitumor agent. A selectivity study done on 32 kinases showed that, compared with variolin B, meriolins display enhanced specificity toward CDKs, with marked potency on CDK2 and CDK9. The structures of pCDK2/cyclin A/variolin B and pCDK2/cyclin A/meriolin 3 complexes reveal that the two inhibitors bind within the ATP binding site of the kinase, but in different orientations. Meriolins display better antiproliferative and proapoptotic properties in human tumor cell cultures than their parent molecules, meridianins and variolins. Phosphorylation at CDK1, CDK4, and CDK9 sites on, respectively, protein phosphatase 1alpha, retinoblastoma protein, and RNA polymerase II is inhibited in neuroblastoma SH-SY5Y cells exposed to meriolins. Apoptosis triggered by meriolins is accompanied by rapid Mcl-1 down-regulation, cytochrome c release, and activation of caspases. Meriolin 3 potently inhibits tumor growth in two mouse xenograft cancer models, namely, Ewing's sarcoma and LS174T colorectal carcinoma. Meriolins thus constitute a new CDK inhibitory scaffold, with promising antitumor activity, derived from molecules initially isolated from marine organisms.

We analyze the common surface reflection and full-wave inversion methods to retrieve the soil surface dielectric permittivity and correlated water content from air-launched ground-penetrating radar (GPR) measurements. In the full-wave approach, antenna effects are filtered out from the raw radar data in the frequency domain, and full-wave inversion is performed in the time domain, on a time window focused on the surface reflection. Synthetic experiments are performed to investigate the most critical hypotheses on which both techniques rely, namely, the negligible effects of the soil electric conductivity (s) and layering. In the frequency range 1-2 GHz we show that for sigma>0.1 Sm-1, significant errors are made on the estimated parameters, e. g., an absolute error of 0.10 in water content may be observed for sigma=1 Sm-1. This threshold is more stringent with decreasing frequency. Contrasting surface layering may proportionally lead to significant errors when the thickness of the surface layer is close to one fourth the wavelength in the medium, which corresponds to the depth resolution. Absolute errors may be >0.10 in water content for large contrasts. Yet we show that full-wave inversion presents valuable advantages compared to the common surface reflection method. First, filtering antenna effects may prevent absolute errors >0.04 in water content, depending of the antenna height. Second, the critical reference measurements above a perfect electric conductor (PEC) are not required, and the height of the antenna does not need to be known a priori. This averts absolute errors of 0.02-0.09 in water content when antenna height differences of 1-5 cm occur between the soil and the PEC. A laboratory experiment is finally presented to analyze the stability of the estimates with respect to actual measurement and modeling errors. While the conditions were particularly well suited for applying the common reflection method, better results were obtained using full-wave inversion.

[1] We report on a laboratory experiment that investigates the effect of soil surface roughness on the identification of the soil electromagnetic properties from full-wave inversion of ground-penetrating radar (GPR) data in the frequency domain. The GPR system consists of an ultrawide band stepped-frequency continuous-wave radar combined with an off-ground monostatic horn antenna. Radar measurements were performed above a rectangular container filled with a loose sandy soil subject to seven water contents and four random surface roughnesses, including a smooth surface as reference. Compared to previous studies, we have reduced the modeling error of the GPR signal for the smooth surface case thanks to improved antenna transfer functions by solving an overdetermined system of equations based on six model configurations instead of only three. Then, the continuously increasing effect of surface roughness on the radar signal with respect to frequency is clearly observed. In close accordance with Rayleigh's criterion, both the radar signal and the inversely estimated parameters are not significantly affected if the surface protuberances are smaller than one eighth of a wavelength. In addition, when this criterion is not respected, errors are made in the estimated parameters, but the inverse solution remains stable. This demonstrates the promising perspectives for application of GPR for noninvasive water content estimation in agricultural and environmental field applications.

In this paper we have tested the feasibility of the inverse modeling approach to derive root water uptake parameters (RWUP) from soil water content data using numerical experiments for three differently textured soils and for an optimal drying period. The RWUP of interest are the rooting depth and the bottom root length density. In a first step, a thorough sensitivity analysis was performed. This showed that soil water content dynamics is relatively insensitive to RWUP and that the sensitivity depends on the texture of the considered soil. For medium-fine textured soil, the sensitivity is particularly low due to relatively high unsaturated hydraulic conductivity values. These ones allow a “compensating effect” to occur, i.e., vertical unsaturated water fluxes overshadowing in some way the root water uptake. In a second step, we analyzed the well-posedness of the solution (stability and nonuniqueness) when only RWUP are optimized. For this case, the inverse problem is clearly ill-posed except for the estimation of the rooting depth parameter for coarse and the very fine textured soils. In a third step, we addressed the case where RWUP are estimated simultaneously with additional parameters of the system (i.e., with soil hydraulic parameters). For this case, our study showed that the inverse problem is well-posed for the coarse and very fine textured soils, allowing for the estimation of both RWUP of interest provided that a powerful global optimization algorithm is used. On the contrary, the estimation of RWUP is unfeasible for medium-fine textured soil due to the “compensating effect” of the vertical unsaturated water flows. In conclusion, we can state that the inverse modeling approach can be applied to derive RWUP for some soils (coarse and very fine textured) and that the feasibility is strongly improved if the RWUP are simultaneously optimized with additional parameters. Nevertheless, more detailed research is needed to apply the inverse modeling approach to real cases for which additional issues are likely to be encountered such as soil heterogeneity and root dynamics.