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Background: Allogeneic hematopoietic stem cell transplantation (allo-HSCT) following chemotherapy is part of standard treatment protocol for patients with acute myeloid leukemia (AML). FUS-ERG+ AML is rare but has an extremely poor prognosis even with allo-HSCT in remission, possibly due to its a leukemia stem cell (LSC)-driven disease resulting in chemotherapy resistance and a novel therapy is urgently required. It has been reported that FUS-ERG-positive AML expresses CD123, a marker of LSC, in some cases. CD123-targeted CAR T cell (CART123) is promising immunotherapy, but how to improve the complete remission (CR) rate and rescue potential hematopoietic toxicity still need to explore. Case Presentation: We used donor-derived CART123 as part of conditioning regimen for haploidentical HSCT (haplo-HSCT) in a patient with FUS-ERG+ AML who relapsed after allogeneic transplantation within 3 months, resists to multi-agent chemotherapy and donor lymphocyte infusion (DLI) and remained non-remission, aiming to reduce these chemotherapy-resistant blasts and rescue potential hematopoietic toxicity. The blasts in BM were reduced within 2 weeks and coincided with CAR copies expansion after CART123 infusion. The patient achieved full donor chimerism, CR with incomplete blood count recovery, and myeloid implantation. Conclusion: Our results hints that CART123 reduces the chemotherapy-resistant AML blasts for FUS-ERG+ AML without affecting the full donor chimerism and myeloid implantation.

Leaf area index (LAI) is a crucial variable in all kinds of ecosystem, climate and crop yield models, describing the fluxes of energy, mass and momentum between the surface and the planetary boundary layer. To accurately determine the corn LAI, several methods of LAI, estimation have been evaluated in this investigation, including vegetation indices, principal component analysis (PCA), the neural network method (NN), the look-up table (LUT) inversion from PROSAIL model and the Hybrid model. Comparisons were conducted based on field-measured corn canopy hyperspectral reflectance and LAI data over northeastern China. In order to fairly compare the LAI estimation performance of different methods, the ground-measured data were separated into two sets (modeling data and validation data), except the LUT and hybrid methods of PROSAIL-based. The results indicated that the PCA method delivered the best performance for corn LAI estimation (with maximum R-2=0.814 and minimum RMSE=0.501) in this study. The hybrid model and EVI provided moderate results. Comparatively, the LUT and NN methods were less successful and NDVI provided the worst corn LAI estimation performance in this study. The PCA method shows great potential for performing well on corn LAI estimation from hyperspectral information. PCA can avoid the reflectance saturation defect of dense canopy in a certain extent, can utilize hyperspectral reflectance data much more effectively than other methods, and is not limited by the band numbers, it can also reduce noise and provide an great correlation with LAI from the hyper-bands or the multi-bands reflectance. (C) 2011 Elsevier B.V. All rights reserved.

International audience

International audience; Vegetation foliage clumping significantly alters its radiation environment and therefore affects vegetation growth as well as water and carbon cycles. The clumping index is useful in ecological and meteorological models because it provides new structural information in addition to the effective leaf area index (LAI) retrieved from mono-angle remote sensing and allows accurate separation of sunlit and shaded leaves in the canopy. Not accounting for the foliage clumping in LAI retrieval algorithms leads to substantial underestimation of actual LAI, especially for needleleaf forests. Normalized Difference between Hotspot and Darkspot (NDHD) index has been previously used to retrieve global clumping index maps from POLarization and Directionality of the Earth’s Reflectances (POLDER) data at ~6 km resolution, from Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) product at 500 m resolution. Most recently the algorithm was applied with Multi-angle Imaging SpectroRadiometer (MISR) data at 275 m resolution over selected areas. In this presentation we characterize and intercompare the three products over a set of sites representing diverse biomes and different canopy structures. The products are also directly validated with both in-situ vertical profiles and seasonal trajectories of clumping index. We illustrate that the vertical distribution of foliage and especially the effect of understory needs to be taken into account while validating foliage clumping products from remote sensing products with values measured in the field. Satellite measurements respond to the structural effects near the top of canopies, while ground measurements may be biased by the lower vegetation layers. Additionally, caution should be taken regarding the misclassification in land cover maps as their errors can be propagated into the foliage clumping maps. Our results indicate that MODIS data and MISR data with 275 m resolution in particular can provide good quality clumping index estimates at pertinent scales for modeling local carbon and energy fluxes.

National audience; Clumping index, the measure of foliage grouping relative to a random distribution of leaves in space, is a key structural parameter of plant canopies that influences canopy radiation regimes and controls canopy photosynthesis and other land–atmosphere interactions. Normalized Difference between Hotspot and Darkspot (NDHD) index has been previously used to retrieve global clumping index maps from POLarization and Directionality of the Earth's Reflectances (POLDER) data at ~6 km resolution, the Bidirectional Reflectance Distribution Function (BRDF) product from Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution, and most recently the algorithm was applied with Multi-angle Imaging SpectroRadiometer (MISR) data at 275 m resolution over selected areas. In this presentation we characterize and intercompare the three products over a set of sites representing diverse biomes and different canopy structures. The products are also directly validated with both in-situ vertical profiles and seasonal trajectories of clumping index. We illustrate that the vertical distribution of foliage and especially the effect of understory needs to be taken into account while validating foliage clumping products from remote sensing data with values measured in the field. Satellite measurements respond to the structural effects near the top of canopies, while ground measurements may be biased by the lower vegetation layers. Additionally, caution should be taken regarding the misclassification in land cover maps as their errors can be propagated into the foliage clumping maps. Our results indicate that MODIS data and MISR data with 275 m in particular can provide good quality clumping index estimates at pertinent scales for modeling local carbon and energy fluxes.

International audience; [1] Leaf area index (LAI) is a critical variable for land surface and climate modeling studies. Several global LAI products exist, and it is important to know how these products perform and what their uncertainties are. Five major global LAI products—MODIS, GEOV1, GLASS, GLOBMAP, and JRC-TIP—were compared between 2003 and 2010 at a 0.01 spatial resolution and with a monthly time step. The daily Land-SAF product was used as a regional reference in order to evaluate the performance of other global products in Africa. Cross-sensor LAI conversion equations were derived for different biome types. Product uncertainties were assessed by looking into the product quantitative quality indicators (QQIs) attached to MODIS, GEOV1, and JRC-TIP. MODIS, GEOV1, GLASS, and GLOBMAP are generally consistent and show strong linear relationships between the products (R 2 > 0.74), with typical deviations of < 0.5 for nonforest and < 1.0 for forest biomes. JRC-TIP, the only effective LAI product, is about half the values of the other LAI products. The average uncertainties and relative uncertainties are in the following order: MODIS (0.17, 11.5%) < GEOV1 (0.24, 26.6%) < Land-SAF (0.36, 37.8%) < JRC-TIP (0.43, 114.3%). The highest relative uncertainties usually appear in ecological transition zones. More than 75% of MODIS, GEOV1, JRC-TIP, and Land-SAF pixels are within the absolute uncertainty requirements (AE 0.5) set by the Global Climate Observing System (GCOS), whereas more than 78.5% of MODIS and 44.6% of GEOV1 pixels are within the threshold for relative uncertainty (20%). This study reveals the discrepancies mainly due to differences between definitions, retrieval algorithms, and input data. Future product development and validation studies should focus on areas (e.g., sparsely vegetated and savanna areas) and periods (e.g., winter time) with higher uncertainties. Citation: Fang, H., et al. (2013), Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: Analysis of climatologies and theoretical uncertainties

International audience; Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation (EO) has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progress made in temporal compositing and prospects for future LAI estimation are discussed. Subsequently, the overview presents various LAI product validation schemes, uncertainties in global moderate resolution LAI products and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for under‐represented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, LiDAR technology, and unmanned aerial vehicles (UAV) be pursued to broaden the production and application of LAI.