Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes

The growth of mass populations of toxin-producing cyanobacteria is a serious concern for the ecological status of inland waterbodies and for human and animal health. In this study we examined the performance of four semi-analytical algorithms for the retrieval of chlorophyll a (Chl a) and phycocyanin (C-PC) from data acquired by the Compact Airborne Spectrographic Imager-2 (CASI-2) and the Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor. The retrieval accuracies of the semi-analytical models were compared to those returned by optimally calibrated empirical band-ratio algorithms. The best-performing algorithm for the retrieval of Chl a was an empirical band-ratio model based on a quadratic function of the ratio of re!ectance at 710 and 670 nm (R2=0.832; RMSE=29.8%). However, this model only provided a marginally better retrieval than the best semi-analytical algorithm. The best-performing model for the retrieval of C-PC was a semi-analytical nested band-ratio model (R2=0.984; RMSE=3.98 mg m−3). The concentrations of C-PC retrieved using the semi-analytical model were correlated with cyanobacterial cell numbers (R2=0.380) and the particulate and total (particulate plus dissolved) pools of microcystins (R2=0.858 and 0.896 respectively). Importantly, both the empirical and semi-analytical algorithms were able to retrieve the concentration of C-PC at cyanobacterial cell concentrations below current warning thresholds for cyanobacteria in waterbodies. This demonstrates the potential of remote sensing to contribute to early-warning detection and monitoring of cyanobacterial blooms for human health protection at regional and global scales

An operational analysis of Lake Surface Water Temperature

Operational analyses of Lake Surface Water Temperature (LSWT) have many potential uses including improvement of numerical weather prediction (NWP) models on regional scales. In November 2011, LSWT was included in the Met Office Operational Sea Surface Temperature and Ice Analysis (OSTIA) product, for 248 lakes globally. The OSTIA analysis procedure, which has been optimised for oceans, has also been used for the lakes in this first version of the product. Infra-red satellite observations of lakes and in situ measurements are assimilated. The satellite observations are based on retrievals optimised for Sea Surface Temperature (SST) which, although they may introduce inaccuracies into the LSWT data, are currently the only near-real-time information available. The LSWT analysis has a global root mean square difference of 1.31 K and a mean difference of 0.65 K (including a cool skin effect of 0.2 K) compared to independent data from the ESA ARC-Lake project for a 3-month period (June to August 2009). It is demonstrated that the OSTIA LSWT is an improvement over the use of climatology to capture the day-to-day variation in global lake surface temperatures

LYN Kinase in the Tumor Microenvironment Is Essential for the Progression of Chronic Lymphocytic Leukemia

Survival of chronic lymphocytic leukemia (CLL) cells strictly depends on the support of an appropriate tumor microenvironment. Here, we demonstrate that LYN kinase is essential for CLL progression. Lyn deficiency results in a significantly reduced CLL burden in vivo. Loss of Lyn within leukemic cells reduces B cell receptor (BCR) signaling including BTK phosphorylation, but surprisingly does not affect leukemic cell expansion. Instead, syngeneic CLL transplantation of CLL cells into Lyn- or Btk-deficient recipients results in a strongly delayed leukemic progression and prolonged survival. Moreover, Lyn deficiency in macrophages hinders nursing functions for CLL cells, which is mediated by direct contact rather than secretion of soluble factors. Taken together, LYN and BTK seem essential for the formation of a microenvironment supporting leukemic growth