No abstract

In ultra-rare bone diseases, information on growth during childhood is sparse. Juvenile Paget disease (JPD) is an ultra-rare disease, characterized by loss of function of osteoprotegerin (OPG). OPG inhibits osteoclast activation via the receptor activator of nuclear factor-κB (RANK) pathway. In JPD, overactive osteoclasts result in inflammatory-like bone disease due to grossly elevated bone resorption. Knowledge on the natural history of JPD, including final height and growth, is limited. Most affected children receive long-term antiresorptive treatment, mostly with bisphosphonates, to contain bone resorption, which may affect growth. In this study, we report the follow-up of height, growth velocity, and skeletal maturation in a 16-year-old female patient with JPD. The patient was treated with cyclic doses of pamidronate starting at 2.5 years of age and with 2 doses of denosumab at the age of 8 years, when pamidronate was paused. In the following years, a sustainable decline in a height <i>z</i>-score and a stunted pubertal growth spurt; despite appropriate maturation of the epiphyseal plates of the left hand, the proximal right humerus and both femora were observed. Whether this reflects the growth pattern in JPD or might be associated to the antiresorptive treatments is unclear, since there is very limited information available on the effect of bisphosphonates and denosumab on growth and the growth plate in pediatric patients. Studies are needed to understand the natural history of an ultra-rare bone disease and to assess the effects of antiresorptive treatment on the growing skeleton.

Time series analysis has high potential for the monitoring of agricultural management intensity and crop yield. The C-band synthetic aperture radar (SAR) data from Sentinel-1 provide a unique source to create area-wide dense time series of indicators that are sensitive to crop parameters. Here, time series of backscattering coefficient ratio from Sentinel-1 were established in individual winter wheat fields over three consecutive years. Phenology metrics were computed in order to indicate the length of the season, where the plant is substantially growing in height and building biomass. The average estimated lengths of season of winter wheat were 112 days in 2017, 77 days in 2018 and 91 days in 2019. The observed lengths of the season in the reference were 114 days in 2017, 73 days in 2018 and 88 days in 2019. The results for the individual winter wheat fields show that the length of the season was estimated with an RMSD (root mean squared deviation) of less than 2 weeks for all three years. The results confirmed that the VH/VV ratio has high potential for monitoring phenological features.

No abstract

Information on crop phenology is essential when aiming to better understand the impacts of climate and climate change, management practices, and environmental conditions on agricultural production. Today’s novel optical and radar satellite data with increasing spatial and temporal resolution provide great opportunities to provide such information. However, so far, we largely lack methods that leverage this data to provide detailed information on crop phenology at the field level. We here propose a method based on dense time series from Sentinel-1, Sentinel 2, and Landsat 8 to detect the start of seven phenological stages of winter wheat from seeding to harvest. We built different feature sets from these input data and compared their performance for training a one-dimensional temporal U-Net. The model was evaluated using a comprehensive reference data set from a national phenology network covering 16,000 field observations from 2017 to 2020 for winter wheat in Germany and compared against a baseline set by a Random Forest model. Our results show that optical and radar data are differently well suited for the detection of the different stages due to their unique characteristics in signal processing. The combination of both data types showed the best results with 50.1% to 65.5% of phenological stages being predicted with an absolute error of less than six days. Especially late stages can be predicted well with, e.g., a coefficient of determination (R²) between 0.51 and 0.62 for harvest, while earlier stages like stem elongation remain a challenge (R² between 0.06 and 0.28). Moreover, our results indicate that meteorological data have comparatively low explanatory potential for fine-scale phenological developments of winter wheat.Overall, our results demonstrate the potential of dense satellite image time series from Sentinel and Landsat sensor constellations in combination with the versatility of deep learning models for determining phenological timing.