Accuracy-enhanced solar resource maps of South Africa

Paper presented to the 3rd Southern African Solar Energy Conference, South Africa, 11-13 May, 2015.SolarGIS is a global database of solar resource and meteorological parameters, developed and operated by GeoModel Solar. This database is updated daily by real-time satellite, atmospheric and meteorological data inputs. The aim of presented work was accuracy enhancement of solar resource data for South Africa, Lesotho and Swaziland. This was achieved by regional adaptation of SolarGIS solar model with data measured at fourteen high-standard solar measuring stations sourced by Eskom, GeoSUN Africa, SAURAN, STERG and Ripasso Energy. The accuracy-enhancement procedure is based on correlation of the ground measurements with the satellite-based SolarGIS model and determination of correction coefficients for model inputs. Use of these coefficients reduced systematic deviation of the input aerosol data, which is key factor determining the model accuracy in Southern Africa. The user uncertainty of the longterm estimate based on adapted data is in the range of ±5% to ±7.5% for DNI, and ±3% to ±4% for GHI. The model now delivers more accurate high-resolution solar resource time series, which helps reducing financial risk and improving engineering quality of the solar power plants. The presented maps show longterm yearly averages of Direct Normal Irradiation (DNI) and Global Horizontal Irradiation (GHI) with 1-km spatial resolution. They are calculated by aggregation of sub-hourly modeled time series, representing a period 1994 to 2013. The maps are accessible from http://www.sauran.net/. High resolution data can be accessed from http://solargis.info.cf201

A Density-Dependent Switch Drives Stochastic Clustering and Polarization of Signaling Molecules

Positive feedback plays a key role in the ability of signaling molecules to form highly localized clusters in the membrane or cytosol of cells. Such clustering can occur in the absence of localizing mechanisms such as pre-existing spatial cues, diffusional barriers, or molecular cross-linking. What prevents positive feedback from amplifying inevitable biological noise when an un-clustered “off” state is desired? And, what limits the spread of clusters when an “on” state is desired? Here, we show that a minimal positive feedback circuit provides the general principle for both suppressing and amplifying noise: below a critical density of signaling molecules, clustering switches off; above this threshold, highly localized clusters are recurrently generated. Clustering occurs only in the stochastic regime, suggesting that finite sizes of molecular populations cannot be ignored in signal transduction networks. The emergence of a dominant cluster for finite numbers of molecules is partly a phenomenon of random sampling, analogous to the fixation or loss of neutral mutations in finite populations. We refer to our model as the “neutral drift polarity model.” Regulating the density of signaling molecules provides a simple mechanism for a positive feedback circuit to robustly switch between clustered and un-clustered states. The intrinsic ability of positive feedback both to create and suppress clustering is a general mechanism that could operate within diverse biological networks to create dynamic spatial organization

Cartographic aspects of land cover change detection (over- and underestimation in the I&CORINE Land Cover 2000 project)

This paper presents the results of analysis of the data obtained by the method of computer-aided visual interpretation of satellite images used for identification of changes in land cover within the framework of the Image and CORINE Land Cover 2000 (I&CLC2000) Project (jointly managed by the European Environment Agency in Copenhagen, Denmark and the Joint Research Centre of the European Commission in Ispra, Italy). These data are also relevant in cartography. Land cover changes identified by the method mentioned may contain mistakes caused by over- or underestimation. The paper describes these mistakes. Overestimation (technical change) of the extent of land cover change is caused by adding the residual polygons (smaller than 25 ha) to neighbouring polygons. Underestimation is caused by the fact that discernible changes concerning areas larger than 5 ha which showed up in objects with areas smaller than 25 ha were not identified and, consequently, not included in either CLC90 or CLC2000 data layers; e.g. Dutch CLC_change database users' accuracy indicates an overestimation of 8.8% whereas the comparison of net change indicates a small, insignificant underestimation. In spite of the problems referred to, caused by overestimation or underestimation, the datasets on land cover changes in Europe for the 1990s and the year 2000 ( +/- one year) can also be used for the compilation of land cover change maps at the regional, national and European levels

Specific antinuclear antibodies are associated with clinical features in systemic lupus erythematosus

Methods: A consecutive cohort of 289 patients with SLE was included; 235 fulfilled ACR criteria for SLE and were further analysed. ANA profiles were determined by line immunoassay and by indirect immunofluorescence on Crithidia luciliae. An extensive list of signs/symptoms was evaluated. Results: Five clusters of antibodies were defined by cluster analysis: 1—antibodies to SmB, SmD, RNP-A, RNP-C, and RNP-70k; 2—antibodies to Ro52, Ro60, and SSB; 3, 4, and 5—antibodies to ribosomal P, histones and dsDNA, respectively. Significant associations (p⩽0.01) were found between anti-RNP-70k, anti-RNP-A, anti-RNP-C and Raynaud's phenomenon, between anti-RNP-A, anti-RNP-70k and leucopenia, and between anti-RNP-A, anti-RNP-C and a lower prevalence of urine cellular casts. Anti-SSA, anti-SSB were associated with xerostomia, and anti-SSB with pericarditis. Antibodies to ribosomal P were associated with haemolytic anaemia, leucopenia, and alopecia. Patients with anti-dsDNA antibodies had a higher risk for cellular casts and a lower risk for photosensitivity. Antihistone antibodies were associated with arthritis. Conclusions: In a large and consecutive cohort of patients with SLE, clusters of antibodies were identified. Previously reported associations of antibodies with symptoms were confirmed and new associations found