Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2

Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10–20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the “Sentinel­2 for Agriculture” project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of “Sentinel-2 like” time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the “Joint Experiment for Crop Assessment and Monitoring” network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future “Sentinel­2 for Agriculture” system.Instituto de Clima y AguaFil: Bontemps, Sophie. Université Catholique de Louvain. Earth and Life Institute; BélgicaFil: Arias, Marcela. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Cara, Cosmin. CS Romania S.A.; RumaniaFil: Dedieu, Gérard. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Guzzonato, Eric. CS Systèmes d’Information; FranciaFil: Hagolle, Olivier. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Inglada, Jordi. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Matton, Nicolas. Université Catholique de Louvain. Earth and Life Institute; BélgicaFil: Morin, David. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Popescu, Ramona. CS Romania S.A.; RumaniaFil: Rabaute, Thierry. CS Systèmes d’Information; FranciaFil: Savinaud, Mickael. CS Systèmes d’Information; FranciaFil: Sepulcre, Guadalupe. Université Catholique de Louvain. Earth and Life Institute; BélgicaFil: Valero, Silvia. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Ahmad, Ijaz. Pakistan Space and Upper Atmosphere Research Commission. Space Applications Research Complex. National Agriculture Information Center Directorate; PakistánFil: Bégué, Agnès. Centre de Coopération Internationale en Recherche Agronomique pour le Développerment; FranciaFil: Wu, Bingfang. Chinese Academy of Sciences. Institute of Remote Sensing and Digital Earth; República de ChinaFil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Diarra, Alhousseine. Université Cadi Ayyad. Faculté des Sciences Semlalia; MarruecosFil: Dupuy, Stéphane. Centre de Coopération Internationale en Recherche Agronomique pour le Développerment; FranciaFil: French, Andrew. United States Department of Agriculture. Agricultural Research Service. Arid Land Agricultural Research Center; ArgentinaFil: Akhtar, Ibrar ul Hassan. Pakistan Space and Upper Atmosphere Research Commission. Space Applications Research Complex. National Agriculture Information Center Directorate; PakistánFil: Kussul, Nataliia. National Academy of Sciences of Ukraine. Space Research Institute and State Space Agency of Ukraine; UcraniaFil: Lebourgeois, Valentine. Centre de Coopération Internationale en Recherche Agronomique pour le Développerment; FranciaFil: Le Page, Michel. Université Cadi Ayyad. Faculté des Sciences Semlalia. Laboratoire Mixte International TREMA; Marruecos. Universite de Toulose - Le Mirail. Centre d’Etudes Spatiales de la BIOsphère; FranciaFil: Newby, Terrence. Agricultural Research Council; SudáfricaFil: Savin, Igor. V.V. Dokuchaev Soil Science Institute; RusiaFil: Verón, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Koetz, Benjamin. European Space Agency. European Space Research Institute; ItaliaFil: Defourny, Pierre. Université Catholique de Louvain. Earth and Life Institute; Bélgic

β-Catenin Signaling Increases during Melanoma Progression and Promotes Tumor Cell Survival and Chemoresistance

Beta-catenin plays an important role in embryogenesis and carcinogenesis by controlling either cadherin-mediated cell adhesion or transcriptional activation of target gene expression. In many types of cancers nuclear translocation of beta-catenin has been observed. Our data indicate that during melanoma progression an increased dependency on the transcriptional function of beta-catenin takes place. Blockade of beta-catenin in metastatic melanoma cell lines efficiently induces apoptosis, inhibits proliferation, migration and invasion in monolayer and 3-dimensional skin reconstructs and decreases chemoresistance. In addition, subcutaneous melanoma growth in SCID mice was almost completely inhibited by an inducible beta-catenin knockdown. In contrast, the survival of benign melanocytes and primary melanoma cell lines was less affected by beta-catenin depletion. However, enhanced expression of beta-catenin in primary melanoma cell lines increased invasive capacity in vitro and tumor growth in the SCID mouse model. These data suggest that beta-catenin is an essential survival factor for metastatic melanoma cells, whereas it is dispensable for the survival of benign melanocytes and primary, non-invasive melanoma cells. Furthermore, beta-catenin increases tumorigenicity of primary melanoma cell lines. The differential requirements for beta-catenin signaling in aggressive melanoma versus benign melanocytic cells make beta-catenin a possible new target in melanoma therapy

Quantitative Susceptibility Mapping Using a Multispectral Autoregressive Moving Average Model to Assess Hepatic Iron Overload

Background: R2*-MRI is clinically used to noninvasively assess hepatic iron content (HIC) to guide potential iron chelation therapy. However, coexisting pathologies, such as fibrosis and steatosis, affect R2* measurements and may thus confound HIC estimations. Purpose: To evaluate whether a multispectral auto regressive moving average (ARMA) model can be used in conjunction with quantitative susceptibility mapping (QSM) to measure magnetic susceptibility as a confounder-free predictor of HIC. Study Type: Phantom study and in vivo cohort. Subjects: Nine iron phantoms covering clinically relevant R2* range (20–1200/second) and 48 patients (22 male, 26 female, median age 18 years). Field Strength/Sequence: Three-dimensional (3D) and two-dimensional (2D) multi-echo gradient echo (GRE) at 1.5 T. Assessment: ARMA-QSM modeling was performed on the complex 3D GRE signal to estimate R2*, fat fraction (FF), and susceptibility measurements. R2*-based dry clinical HIC values were calculated from the 2D GRE acquisition using a published R2*-HIC calibration curve as reference standard. Statistical Tests: Linear regression analysis was performed to compare ARMA R2* and susceptibility-based estimates to iron concentrations and dry clinical HIC values in phantoms and patients, respectively. Results: In phantoms, the ARMA R2* and susceptibility values strongly correlated with iron concentrations (R2 ≥ 0.9). In patients, the ARMA R2* values highly correlated (R2 = 0.97) with clinical HIC values with slope = 0.026, and the susceptibility values showed good correlation (R2 = 0.82) with clinical dry HIC values with slope = 3.3 and produced a dry-to-wet HIC ratio of 4.8. Data Conclusion: This study shows the feasibility that ARMA-QSM can simultaneously estimate susceptibility-based wet HIC, R2*-based dry HIC and FFs from a single multi-echo GRE acquisition. Our results demonstrate that both, R2* and susceptibility-based wet HIC values estimated with ARMA-QSM showed good association with clinical dry HIC values with slopes similar to published R2*-biopsy HIC calibration and dry-to-wet tissue weight ratio, respectively. Hence, our study shows that ARMA-QSM can provide potentially confounder-free assessment of hepatic iron overload. Level of Evidence: 3. Technical Efficacy: Stage 2

Morphological characterization of hepatic steatosis and Monte Carlo modeling of MRI signal for accurate quantification of fat fraction and relaxivity

Chemical-shift-based fat-water MRI signal models with single- or dual-R2* correction have been proposed for quantification of fat fraction (FF) and assessment of hepatic steatosis. However, there is a void in our understanding of which model truly mimics the underlying biophysical mechanism of steatosis on MRI signal relaxation. The purpose of this study is to morphologically characterize and build realistic steatosis models from histology and synthesize MRI signal using Monte Carlo simulations to investigate the accuracy of single- and dual-R2* models in quantifying FF and R2*. Fat morphology was characterized by performing automatic segmentation on 16 mouse liver histology images and extracting the radius, nearest neighbor (NN) distance, and regional anisotropy of fat droplets. A gamma distribution function (GDF) was used to generalize extracted features, and regression analysis was performed to derive relationships between FF and GDF parameters. Virtual steatosis models were created based on derived morphological and statistical descriptors, and the MRI signal was synthesized at 1.5 T and 3 T. R2* and FF values were calculated using single- and dual-R2* models and compared with in vivo R2*-FF calibrations and simulated FFs. The steatosis models generated with regional anisotropy and NN distribution closely mimicked the true in vivo fat morphology. For both R2* models, predicted R2* values showed positive correlation with FFs, with slopes similar to those of the in vivo calibrations (P \u3e 0.05), and predicted FFs showed excellent agreement with true FFs (R2 \u3e 0.99), with slopes close to unity. Our study, hence, demonstrates the proof of concept for generating steatosis models from histologic data and synthesizing MRI signal to show the expected signal relaxation under conditions of steatosis. Our results suggest that a single R2* is sufficient to accurately estimate R2* and FF values for lower FFs, which agrees with in vivo studies. Future work involves characterizing and building steatosis models at higher FFs and testing single- and dual-R2* models for accurate assessment of steatosis