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Bile acid synthesis defects (BASDs) comprise a group of rare diseases that can be severely disabling. Bile acid supplementation with 5 to 15 mg/kg cholic acid (CA) has been hypothesized to decrease endogenous bile acid production, stimulate bile secretion, and improve bile flow and micellar solubilization, thereby improving the biochemical profile and potentially slowing down disease progression. Currently, CA treatment is unavailable in the Netherlands, and CA capsules were compounded by the Amsterdam UMC Pharmacy from CA raw material. This study aims to determine the pharmaceutical quality and stability of the pharmacy compounded CA capsules. Pharmaceutical quality tests were performed on 25 mg and 250 mg CA capsules according to general monographs of the European Pharmacopoeia 10th ed. For the stability study, the capsules were stored under long-term conditions (25 °C ± 2 °C/60% ± 5% RH) and accelerated conditions (40 °C ± 2 °C/75% ± 5% RH). Samples were analyzed at 0, 3, 6, 9 and 12 months. The findings demonstrate that the pharmacy compounded CA capsules within a range of 25–250 mg that complied with the European regulations in regard to product quality and safety. The pharmacy compounded CA capsules are suitable for use in patients with BASD, as clinically indicated. With its simple formulation, pharmacies are provided a guidance on product validation and stability testing when commercial CA capsules are unavailable. [ABSTRACT FROM AUTHOR]

Taking photographs with a commercially available digital camera is an efficient and objective method for determining the green fractional vegetation cover (FVC) for field validation of satellite products. However, classifying leaves under shadows in processing digital images remains challenging and results in classification errors. To address this problem, an automatic shadow-resistant algorithm in the Commission Internationale d'Eclairage L*a*b* color space (SHAR-LABFVC) based on a documented FVC estimation algorithm (LABFVC) is proposed in this paper. The hue saturation intensity (HSI) is introduced in SHAR-LABFVC to enhance the brightness of shaded parts of the image. The lognormal distribution is used to fit the frequency of vegetation greenness and to classify vegetation and the background. Real and synthesized images are used for evaluation, and the results are in good agreement with the visual interpretation, particularly when the FVC is high and the shadows are deep, indicating that SHAR-LABFVC is shadow resistant. Without specific improvements to reduce the shadow effect, the underestimation of FVC can be up to 0.2 in the flourishing period of vegetation at a scale of 10 m. Therefore, the proposed algorithm is expected to improve the validation accuracy of remote sensing products. [ABSTRACT FROM AUTHOR]

The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies. [ABSTRACT FROM AUTHOR]