Improved Sézary cell detection and novel insights into immunophenotypic and molecular heterogeneity in Sézary syndrome

Sézary syndrome (SS) is an aggressive leukemic form of cutaneous T-cell lymphoma with neoplastic CD4+ T cells present in skin, lymph nodes, and blood. Despite advances in therapy, prognosis remains poor, with a 5-year overall survival of 30%. The immunophenotype of Sézary cells is diverse, which hampers efficient diagnosis, sensitive disease monitoring, and accurate assessment of treatment response. Comprehensive immunophenotypic profiling of Sézary cells with an in-depth analysis of maturation and functional subsets has not been performed thus far. We immunophenotypically profiled 24 patients with SS using standardized and sensitive EuroFlow-based multiparameter flow cytometry. We accurately identified and quantified Sézary cells in blood and performed an in-depth assessment of their phenotypic characteristics in comparison with their normal counterparts in the blood CD4+ T-cell compartment. We observed inter- and intrapatient heterogeneity and phenotypic changes over time. Sézary cells exhibited phenotypes corresponding with classical and nonclassical T helper subsets with different maturation phenotypes. We combined multiparameter flow cytometry analyses with fluorescence-activated cell sorting and performed RNA sequencing studies on purified subsets of malignant Sézary cells and normal CD4+ T cells of the same patients. We confirmed pure monoclonality in Sézary subsets, compared transcriptomes of phenotypically distinct Sézary subsets, and identified novel downregulated genes, most remarkably THEMIS and LAIR1, which discriminate Sézary cells from normal residual CD4+ T cells. Together, these findings further unravel the heterogeneity of Sézary cell subpopulations within and between patients. These new data will support improved blood staging and more accurate disease monitoring

Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

In Norway, 30 % of the annual precipitation falls as snow. Knowledge of the snow reservoir is therefore important for energy production and water resource management. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of 1 km over an area in southern Norway for 2 years (1 September 2014–31 August 2016). Experiments were carried out using two different forcing data sets: (1) hourly forecasts from the operational weather forecast model AROME MetCoOp (2.5 km grid spacing) including post-processed temperature (500 m grid spacing) and wind, and (2) gridded hourly observations of temperature and precipitation (1 km grid spacing) combined with meteorological forecasts from AROME MetCoOp for the remaining weather variables required by SURFEX/Crocus. We present an evaluation of the modelled snow depth and snow cover in comparison to 30 point observations of snow depth and MODIS satellite images of the snow-covered area. The evaluation focuses on snow accumulation and snowmelt. Both experiments are capable of simulating the snowpack over the two winter seasons, but there is an overestimation of snow depth when using meteorological forecasts from AROME MetCoOp (bias of 20 cm and RMSE of 56 cm), although the snow-covered area in the melt season is better represented by this experiment. The errors, when using AROME MetCoOp as forcing, accumulate over the snow season. When using gridded observations, the simulation of snow depth is significantly improved (the bias for this experiment is 7 cm and RMSE 28 cm), but the spatial snow cover distribution is not well captured during the melting season. Underestimation of snow depth at high elevations (due to the low elevation bias in the gridded observation data set) likely causes the snow cover to decrease too soon during the melt season, leading to unrealistically little snow by the end of the season. Our results show that forcing data consisting of post-processed NWP data (observations assimilated into the raw NWP weather predictions) are most promising for snow simulations, when larger regions are evaluated. Post-processed NWP data provide a more representative spatial representation for both high mountains and lowlands, compared to interpolated observations. There is, however, an underestimation of snow ablation in both experiments. This is generally due to the absence of wind-induced erosion of snow in the SURFEX/Crocus model, underestimated snowmelt and biases in the forcing data

Searching for a new game life-saver (Op zoek naar een nieuwe Wildredder)

In recent years the numbers of several grassland birds have decreased. Especially the number of black-tailed godwits has decreased rapidly. Few -days-old black-tailed godwit chicks are killed during mowing, this is because they are not able to escape from the mowing machine. They do not flee but press themselves on the ground to hide from possible predators. In this report we have tried to evaluate the possibilities or find new solutions to prevent the chick from being killed by mowing. We have tried to find technological and non-technological solutions to detect the chicks or to scare them away

Amsterdam 1742: Project in het kader van de ESF-opleiding Historische Informatieverwerking: machineleesbaar maken van het 'Kohier van de Personeele Quotisatie te Amsterdam over het jaar 1742'

Dit bestand is de bijna volledige digitale weergave (90%) van het register van de “Personele Quotisatie” van de stad Amsterdam in 1742. De grondslag voor de aanleg van dit register was de resolutie van de Staten van Holland op 7 maart 1742 om een belastingkohier voor de inkomstenbelasting aan te leggen. Hierin moesten alle ingezetenen met een jaarlijks inkomen van 600 gulden of meer worden opgenomen. Het kohier bevat voor de stad Amsterdam 12.655 aangeslagenen en is in 1945 door mr W.F.H. Oldewelt in een uitgave van het Genootschap Amstelodamum gepubliceerd. Door de aard van de gegevens is het een belangrijke bron voor de economisch-, sociaal- en demografisch-historische studie van Amsterdam in het midden van de 18de eeuw. Gedurende vier jaar, van 1991-1995, is in het kader van de ESF-opleiding Historische Informatieverwerking gewerkt aan de digitalisering van dit Kohier op basis van de bronnenuitgave van Oldewelt (1945, zie verder onder Source). In 1991 is er een start gemaakt met het machineleesbaar maken van het Kohier. Ten behoeve van de invoer van de gegevens uit het Kohier is toen door Marc Luijting de AM-1742 database ontwikkeld. Ongeveer 90% van de gegevens over in totaal 52 stadswijken (350 pagina’s) is via dit project digitaal beschikbaar

Use of atmospheric radiation measurement program data from Barrow, Alaska for evaluation and development of snow-albedo parameterizations

Snow albedo is determined from the ratio of out-going to incoming solar radiation using three years of broadband shortwave radiometer data obtained from the Barrow, Alaska, Atmospheric Radiation Measurement (ARM) site. These data are used for the evaluation of various types of snow-albedo parameterizations applied in numerical weather prediction or climate models. These snow-albedo parameterizations are based on environmental conditions (e.g., air or snow temperature), snow related characteristics (e.g., snow depth, snow age), or combinations of both. The ARM data proved to be well suited for snow-albedo evaluation purposes for a low-precipitation tundra environment. The evaluation confirms that snow-age dependent parameterizations of snow albedo work well during snowmelt, while parameterizations considering meteorological conditions often perform better during snow accumulation. Current difficulties in parameterizing snow albedo occur for long episodes of snow-event free conditions and episodes with a high frequency of snow events or strong snowfall. In a further step, the first two years of the ARM albedo dataset is used to develop a snow-albedo parameterization, and the third year’s data serves for its evaluation. This parameterization considers snow depth, wind speed, and air temperature which are found to be significant parameters for snow-albedo modeling under various conditions. Comparison of all evaluated snow-albedo parameterizations with this new parameterization shows improved snow-albedo prediction