Rice, weeds and shifting cultivation in a tropical rain forest : a study of vegetation dynamics

The study deals with the rain forest area in south-west Côte d'lvoire (Taï National Park). Descriptions are given of the area's history, agricultural practices, geology, geomorphology, soils, flora and vegetation. The shifting cultivation system based on upland rice was studied as it is practiced without land shortage and under constraints. Possible adaptations of the system to the increasing population pressure have been tested on the fields of local farmers. Special attention was paid to the dynamics of the weed population and to the competition between rice and weeds. The classifications of primary forest, secondary forest and field vegetations are based on their complete floristic composition and was carried out by tabular comparison of plot-data

Adhesive Hard-Sphere Colloidal Dispersions. A Small-Angle Neutron-Scattering Study of Stickiness and the Structure Factor

Small-angle neutron-scattering structure factor measurements were made on sterically stabilized silica spheres dispersed in benzene up to volume fractions of 0.30. Benzene is only a marginal solvent for the stabilizing layer on the surface of the particles. The particles are made attractive by lowering temperature. This attraction is modeled by a square well potential, the depth of which varies with temperature. At the highest temperature studied, our experimental system behaved effectively as an assembly of hard spheres, whereas at the lowest temperature the system approaches a spinodal. Using Baxter's theory we were able to evaluate the interaction parameters and to calculate the structure factor. Experimental structure factors were satisfactorily reproduced over the entire temperature range studied

Single-center versus multi-center biparametric MRI radiomics approach for clinically significant peripheral zone prostate cancer

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A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics

OBJECTIVES: To determine the value of a deep learning masked (DLM) auto-fixed volume of interest (VOI) segmentation method as an alternative to manual segmentation for radiomics-based diagnosis of clinically significant (CS) prostate cancer (PCa) on biparametric magnetic resonance imaging (bpMRI). MATERIALS AND METHODS: This study included a retrospective multi-center dataset of 524 PCa lesions (of which 204 are CS PCa) on bpMRI. All lesions were both semi-automatically segmented with a DLM auto-fixed VOI method (averaging < 10 s per lesion) and manually segmented by an expert uroradiologist (averaging 5 min per lesion). The DLM auto-fixed VOI method uses a spherical VOI (with its center at the location of the lowest apparent diffusion coefficient of the prostate lesion as indicated with a single mouse click) from which non-prostate voxels are removed using a deep learning-based prostate segmentation algorithm. Thirteen different DLM auto-fixed VOI diameters (ranging from 6 to 30 mm) were explored. Extracted radiomics data were split into training and test sets (4:1 ratio). Performance was assessed with receiver operating characteristic (ROC) analysis. RESULTS: In the test set, the area under the ROC curve (AUCs) of the DLM auto-fixed VOI method with a VOI diameter of 18 mm (0.76 [95% CI: 0.66-0.85]) was significantly higher (p = 0.0198) than that of the manual segmentation method (0.62 [95% CI: 0.52-0.73]). CONCLUSIONS: A DLM auto-fixed VOI segmentation can provide a potentially more accurate radiomics diagnosis of CS PCa than expert manual segmentation while also reducing expert time investment by more than 97%. KEY POINTS: * Compared to traditional expert-based segmentation, a deep learning mask (DLM) auto-fixed VOI placement is more accurate at detecting CS PCa. * Compared to traditional expert-based segmentation, a DLM auto-fixed VOI placement is faster and can result in a 97% time reduction. * Applying deep learning to an auto-fixed VOI radiomics approach can be valuable

Soil Erosion under Land Use Change from Three Catchments in Laos, Thailand and Vietnam

Abstract: The systems often identified as &quot;traditional&quot; undergo rapid changes as a response to demographic, economic, political and cultural drivers. These transitional periods are often most critical for soil erosion. The on-site impacts of soil erosion reduce the soil chemical fertility through nutrient and organic depletion, and acid subsoil exposure. Erosion also damages the physical fertility by removing surface soil, reducing the soil depth and water holding capacity, and exposing gravel and rocks. These combined processes result in less productive soils, hence lower farm income. To obtain the initial crop yield prior to erosion, increased amounts of inputs are needed, which is most often beyond the economic capacity of the small holders. To study the impact of land use change upon erosion, concurrent case studies, as seen with a dynamic perspective, can compensate for long-term monitoring studies. This approach provides data, which can be used for prediction soil erosion based on global change scenarios. The main objective of this study was to assess the influence of the rapid change of cropping systems on water erosion from three small catchments in three countries of South-East Asia (Laos, Thailand, Vietnam), using a multidisciplinary approach. These three catchments were selected because of their similar biophysical components (very steep slopes on shales; Janeau et al., submitted) and their land use intensification gradient. This investigation was conducted under the auspices the Management of Soil Erosion Consortium (MSEC) started in 1998 (Amado et al., 2002). Water discharge and soil erosion were monitored during three years at the outlet of each catchment using weirs. These data were used to calibrate and validate the PCARES model (Predicting Catchment Runoff and Soil Erosion for Sustainability) in each cachment. This GISbased model was developed in the Philippines for very steep slope condition

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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