Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling

The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC (R-2 = 0.82 and R-2 = 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages

miRNA-126 Orchestrates an Oncogenic Program in B Cell Precursor Acute Lymphoblastic Leukemia

MicroRNA (miRNA)-126 is a known regulator of hematopoietic stem cell quiescence. We engineered murine hematopoiesis to express miRNA-126 across all differentiation stages. Thirty percent of mice developed monoclonal B cell leukemia, which was prevented or regressed when a tetracycline-repressible miRNA-126 cassette was switched off. Regression was accompanied by upregulation of cell-cycle regulators and B cell differentiation genes, and downregulation of oncogenic signaling pathways. Expression of dominant-negative p53 delayed blast clearance upon miRNA-126 switch-off, highlighting the relevance of p53 inhibition in miRNA-126 addiction. Forced miRNA-126 expression in mouse and human progenitors reduced p53 transcriptional activity through regulation of multiple p53-related targets. miRNA-126 is highly expressed in a subset of human B-ALL, and antagonizing miRNA-126 in ALL xenograft models triggered apoptosis and reduced disease burden

Planet care from space

Assessing crops health and status is becoming relevant to support farmers’ decisions and actions for a sustainable agriculture. The use of remote sensing techniques in agriculture has become widely popular during the past years. Earth Observing (EO) data can greatly contribute to constantly monitor crops phenology and to estimate important vegetation biophysical parameters. This work presents a hybrid approach, which exploits the PROSAIL-PRO model and Machine Learning (ML) algorithms, to estimate maize biophysical variables, such as Canopy Chlorophyll Content (CCC) and Leaf Area Index (LAI). The test site is represented by two maize fields located near Grosseto (Tuscany, IT), where two field campaigns were carried out in July 2018. During the same period, the airborne sensor Hyplant-DUAL acquired two images of the test site. These images were used to simulate PRISMA and Sentinel-2 data in order to investigate the difference of the retrieval performance between hyperspectral and multispectral EO data. Results show similar performance between Sentinel-2 and PRISMA. The ML algorithms, providing the best performance (GPR and NN) within the hybrid framework, were then applied to actual Sentinel-2 images. The retrieval results for LAI and CCC were compared to estimations assessed through the ESA S2Toolbox. The comparison showed that the proposed method provides better results than those achieved through S2Toolbox, for both LAI (R2 = 0.85 and MAE = 0.39; S2Toolbox: R2 = 0.35 and MAE = 0.87) and CCC (R2 = 0.73 and MAE = 0.20; S2Toolbox: R2 = 0.29 and MAE = 0.68)

Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data

Crop modeling and remote sensing are key tools to gain deeper understanding on cropping system dynamics and, ultimately, to increase the sustainability of agricultural productions. This study presents a system to estimate rice yields at sub-field scale based on the integration of a biophysical model and remotely sensed products. Leaf area index (LAI) data derived from decametric optical imageries (i.e., Landsat-8, Landsat-7 and Sentinel\u20132A) were assimilated into the WARM rice model via automatic recalibration of crop parameters at a fine spatial resolution (30 m 7 30 m), targeting the lowest error between simulated and remotely sensed LAI. The performance of the system was evaluated by comparing simulated yield using default and recalibrated parameters at sub-field scale with yield maps generated by a GPS-equipped harvester. The training dataset included 40 paddy fields in Northern Italy, which were sampled during three cropping seasons, from 2014 to 2016. The assimilation of remotely sensed LAI into model parameters increased the accuracy of the system: MAE and RRMSE were 0.66 t ha-1 [CI: 0.54 t ha-1\u20130.78 t ha-1] and 13.8% [CI: 11.7%\u201315.7%], respectively, whereas they were 0.82 t ha-1 [CI: 0.68 t ha-1\u20130.96 t ha-1) and 15.7% [CI: 14.1%,\u201317.4%] without assimilation. Moreover, the system allowed to properly reproduce the within-field yield variability, thus laying the basis for possible applications in precision agriculture advisory services