Is there an optimal strategy for real-time continuous glucose monitoring in pediatrics? A 12-month French multi-center, prospective, controlled randomized trial (Start-In!)

AIM: To compare the efficacy of three strategies for real-time continuous glucose monitoring (RT-CGM) over 12 months in children and adolescents with type 1 diabetes. METHODS: A French multicenter trial (NCT00949221) with a randomized, controlled, prospective, open, and parallel-group design was conducted. After 3 months of RT-CGM, patients were allocated to one of three groups: return to self-monitoring of blood glucose, continuous CGM (80% of the time), or discontinuous CGM (40% of the time). The primary outcome was hemoglobin A1c (HbA1c) levels from 3 to 12 months. The secondary outcomes were acute metabolic events, hypoglycemia, satisfaction with CGM and cost. RESULTS: We included 151 subjects, aged 2 to 17 years, with a mean HbA1c level of 8.5% (SD0.7; 69 mmol/mol). The longitudinal change in HbA1c levels was similar in all three groups, at 3, 6, 9 and 12 months. The medical secondary endpoints did not differ between groups. The rate of severe hypoglycemia was significantly lower than that for the pretreatment year for the entire study population. Subjects reported consistent use and good tolerance of the device, regardless of age or insulin treatment. The use of full-time RT-CGM for 3 months costs the national medical insurance system €2629 per patient. CONCLUSION: None of the three long-term RT-CGM strategies evaluated in pediatric type 1 diabetes was superior to the others in terms of HbA1c levels. CGM-use for 3 months decreased rates of severe hypoglycemia. Our results confirm the feasibility of long-term RT-CGM-use and the need to improve educational support for patients and caregivers

Farm Area Segmentation in Satellite Images Using DeepLabv3+ Neural Networks

Farm detection using low resolution satellite images is an important part of digital agriculture applications such as crop yield monitoring. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. In this paper, semantic segmentation of farm areas is addressed using low resolution satellite images. The segmentation is performed in two stages; First, local patches or Regions of Interest (ROI) that include farm areas are detected. Next, deep semantic segmentation strategies are employed to detect the farm pixels. For patch classification, two previously developed local patch classification strategies are employed; a two-step semi-supervised methodology using hand-crafted features and Support Vector Machine (SVM) modelling and transfer learning using the pretrained Convolutional Neural Networks (CNNs). For the latter, the high-level features learnt from the massive filter banks of deep Visual Geometry Group Network (VGG-16) are utilized. After classifying the image patches that contain farm areas, the DeepLabv3+ model is used for semantic segmentation of farm pixels. Four different pretrained networks, resnet18, resnet50, resnet101 and mobilenetv2, are used to transfer their learnt features for the new farm segmentation problem. The first step results show the superiority of the transfer learning compared to hand-crafted features for classification of patches. The second step results show that the model trained based on resnet50 achieved the highest semantic segmentation accuracy.acceptedVersionPeer reviewe

Light regimes in Populus plantations using the Voxel-based Light Interception Model

Three-dimensional light interception by three uniform Populus canopies was studied using the Voxel-based Light Interception Model (VLIM) in combination with ground-based Light Detection and Ranging (LiDAR) measurements. As the VLIM was developed and validated in a virtual environment to ensure reference data availability, the objective was to test the consistency of the measurement and analysis protocol in real forest canopies. An automated pre-processing of raw LiDAR scans delivered high quality structure information, which was imported into a ray-tracing algorithm for modelling of light/canopy interactions. The low within-plot variability (mean standard deviatio

Physiological interpretation of a hyperspectral time series in a citrus orchard

Hyperspectral remote sensing for monitoring horticultural production systems requires the understanding of how plant physiology, canopy structure, management and solar elevation affect the retrieved canopy reflectance during different stages of the phenological cycle. Hence, the objective of this study was to set up and to interpret a hyperspectral time series for a mature and healthy citrus orchard in the Western Cape province of South Africa considering these effects. Based on the remotely sensed data, biophysical parameters at the canopy level were derived and related to known observed physiological and phenological changes at the leaf level and to orchard management. Fractions of mature fruit, flowers, and sunburnt leaves were considered, and indices related to canopy structure chlorophyll content and canopy water status were calculated. Results revealed small cover fractions of mature fruit, flowers and sunburnt leaves of respectively 2.1%, 3.1% and 7.0%, but the high spectral contrast between flowers and leaves allowed a successful classification of flowering intensity. Furthermore, it was shown that canopy level time series of vegetation indices were sensitive to changes in solar elevation and soil reflectance which could be reduced by applying an empirical soil line correction for the most affected indices. Most trends in vegetation indices at the canopy level could be explained by a combination of changes at the leaf level (chlorophyll, carotenoids, dry matter), changes in canopy structure (leaf area index and leaf angle distribution) and changes in cover fractions of vegetative flushes, flowers and sunburnt leaves. The transformed chlorophyll absorption ratio index over the optimised soil adjusted vegetation index (MCARI/OSAVI) was best related to leaf level trends in chlorophyll content. Seasonal changes in the photochemical reflectance index (PRI) were linked to inverse changes in the carotenoid-to-chlorophyll ratio. Canopy structure indices (the modified triangular vegetation index or MTVI2 and the standardized leaf area index determining index or sLAIDI) were sensitive to changes in leaf area index, average leaf angle as well to management interactions (pruning and harvest). Canopy water status was highly impacted during the spring flush due to expanding leaves that concealed trends in the underlying mature leaves. Seasonal trends in soil and weeds reflectance were related to changes in volumetric soil water content and to the earlier and reduced growth period of non-irrigated weeds. © 2011 Elsevier B.V.Articl

Physiological interpretation of a hyperspectral time series in a citrus orchard

Hyperspectral remote sensing for monitoring horticultural production systems requires the understanding of how plant physiology, canopy structure, management and solar elevation affect the retrieved canopy reflectance during different stages of the phenological cycle. Hence, the objective of this study was to set up and to interpret a hyperspectral time series for a mature and healthy citrus orchard in the Western Cape province of South Africa considering these effects. Based on the remotely sensed data, biophysical parameters at the canopy level were derived and related to known observed physiological and phenological changes at the leaf level and to orchard management. Fractions of mature fruit, flowers, and sunburnt leaves were considered, and indices related to canopy structure chlorophyll content and canopy water status were calculated. Results revealed small cover fractions of mature fruit, flowers and sunburnt leaves of respectively 2.1%, 3.1% and 7.0%, but the high spectral contrast between flowers and leaves allowed a successful classification of flowering intensity. Furthermore, it was shown that canopy level time series of vegetation indices were sensitive to changes in solar elevation and soil reflectance which could be reduced by applying an empirical soil line correction for the most affected indices. Most trends in vegetation indices at the canopy level could be explained by a combination of changes at the leaf level (chlorophyll, carotenoids, dry matter), changes in canopy structure (leaf area index and leaf angle distribution) and changes in cover fractions of vegetative flushes, flowers and sunburnt leaves. The transformed chlorophyll absorption ratio index over the optimised soil adjusted vegetation index (MCARI/OSAVI) was best related to leaf level trends in chlorophyll content. Seasonal changes in the photochemical reflectance index (PRI) were linked to inverse changes in the carotenoid-to-chlorophyll ratio. Canopy structure indices (the modified triangular vegetation index or MTVI2 and the standardized leaf area index determining index or sLAIDI) were sensitive to changes in leaf area index, average leaf angle as well to management interactions (pruning and harvest). Canopy water status was highly impacted during the spring flush due to expanding leaves that concealed trends in the underlying mature leaves. Seasonal trends in soil and weeds reflectance were related to changes in volumetric soil water content and to the earlier and reduced growth period of non-irrigated weeds

Extracting physiological info from a hyperspectral time series of a citrus orchard

The collection and interpretation of a hyperspectral time series for a mature citrus orchard in the Western Cape province of South Africa - using a field spectroradiometer - are presented. As a first analysis of this spectral data time series covering the 350-2500 nm range, vegetation, indices were computed. Spectral data were collected from 30 representative trees on a monthly basis, supplemented with climatology, orchard management practices, leaf and soil nutrient data. The hypothesis was tested that biophysical parameters derived at the canopy level from spectral observations can be related to known observed physiological and phenological changes. Most trends in the vegetation indices derived at the canopy level can be linked to observed changes at the leaf level and in the canopy structure, as elicited by the leaf area index (LAI) and the leaf angle distribution (LAD). The Modified Chlorophyll Absorption Ration Index (MCARI/OSAVI) was the index most closely related to leaf level trends in chlorophyll content. Additionally, we could observe a noticeable influence of sunburn and vegetative flush. Seasonal changes in the photochemical reflectance index (PRI) could be inversely linked to changes in the carotenoid-to-chlorophyll ratio. Canopy structure indices such as the Standardized Leaf Area Index Determining Index (sLAIDI) were found sensitive to changes in LAI, average leaf angle as well as to orchard management (pruning and harvest)