45 resultados para Fresh Cheese


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The algorithms designed to estimate snow water equivalent (SWE) using passive microwave measurements falter in lake-rich high-latitude environments due to the emission properties of ice covered lakes on low frequency measurements. Microwave emission models have been used to simulate brightness temperatures (Tbs) for snowpack characteristics in terrestrial environments but cannot be applied to snow on lakes because of the differing subsurface emissivities and scattering matrices present in ice. This paper examines the performance of a modified version of the Helsinki University of Technology (HUT) snow emission model that incorporates microwave emission from lake ice and sub-ice water. Inputs to the HUT model include measurements collected over brackish and freshwater lakes north of Inuvik, Northwest Territories, Canada in April 2008, consisting of snowpack (depth, density, and snow water equivalent) and lake ice (thickness and ice type). Coincident airborne radiometer measurements at a resolution of 80x100 m were used as ground-truth to evaluate the simulations. The results indicate that subsurface media are simulated best when utilizing a modeled effective grain size and a 1 mm RMS surface roughness at the ice/water interface compared to using measured grain size and a flat Fresnel reflective surface as input. Simulations at 37 GHz (vertical polarization) produce the best results compared to airborne Tbs, with a Root Mean Square Error (RMSE) of 6.2 K and 7.9 K, as well as Mean Bias Errors (MBEs) of -8.4 K and -8.8 K for brackish and freshwater sites respectively. Freshwater simulations at 6.9 and 19 GHz H exhibited low RMSE (10.53 and 6.15 K respectively) and MBE (-5.37 and 8.36 K respectively) but did not accurately simulate Tb variability (R= -0.15 and 0.01 respectively). Over brackish water, 6.9 GHz simulations had poor agreement with airborne Tbs, while 19 GHz V exhibited a low RMSE (6.15 K), MBE (-4.52 K) and improved relative agreement to airborne measurements (R = 0.47). Salinity considerations reduced 6.9 GHz errors substantially, with a drop in RMSE from 51.48 K and 57.18 K for H and V polarizations respectively, to 26.2 K and 31.6 K, although Tb variability was not well simulated. With best results at 37 GHz, HUT simulations exhibit the potential to track Tb evolution, and therefore SWE through the winter season.

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Fluorescence in situ hybridization (FISH) with rRNA-targeted oligonucleotide probes was used to investigate the phylogenetic composition of bacterioplankton communities in several freshwater and marine samples. An average of about 50% of the cells were detected by probes for the domains Bacteria and Archaea. Cells were concentrated from water samples (1 to 100 ml) on white polycarbonate filters (diameter, 47 mm; pore size, 0.2 mm; type GTTP 4700 [Millipore, Eschborn, Germany]) by applying a vacuum of <25 kPa. They were subsequently fixed by covering the filter with 3 ml of a freshly prepared, phosphate-buffered saline (pH 7.2)-4% paraformaldehyde (Sigma, Deisenhofen, Germany) solution for 30 min at room temperature. Airdried filters are ready for hybridization and can be stored at 220°C or room temperature for several months without showing apparent changes. Probes BET42a, GAM42a, and PLA886 were used with competitor oligonucleotides as described previously amongst others in Manz et al., (1992; doi:10.1016/S0723-2020(11)80121-9). The filters were transferred to a vial containing 50 ml of prewarmed (48°C) washing solution (70 mM NaCl, 20 mM Tris-HCl [pH 7.4], 5 mM EDTA, 0.01% sodium dodecyl sulfate) and incubated freely floating without shaking at 48°C for 15 min. The filter sections were dried on Whatman 3M paper (Whatman Ltd., Maidstone, United Kingdom) and covered with 50 ml of DAPI solution (1 mg/ml in distilled water filtered through at 0.2-mm filter) for 5 min at room temperature in the dark. For each sample and probe, more than 500 cells were enumerated; for the DAPI examination, more than 1,500 cells were counted per sample. All probe-specific cell counts are presented as the percentage of cells visualized by DAPI. The mean abundances and standard deviations were calculated from the counts of 10 to 20 randomly chosen fields on each filter section. All counts were corrected by subtracting the counts obtained with the negative control NON338. Mean and standard deviation were calculated from the counts of 10 to 20 randomly chosen fields on each filter section.